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White Paper

Overcoming Research Challenges in Depression

Depression clinical studies are rife with roadblocks. Learn how digital and remote methods can help to circumvent them.

CNS
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Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

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Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
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The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
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Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

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Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

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  7. Furukawa TA, Cipriani A, Atkinson LZ, et al. Placebo response rates in antidepressant trials: a systematic review of published and unpublished double-blind randomised controlled studies. Lancet Psychiat. 2016 Nov;3(11):1059-1066. doi: 10.1016/S2215-0366(16)30307-8
  8. Fried EI, Nesse RM. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 2015 Apr 6;13:72.
  9. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960 Feb;23(1):56-62. doi: 10.1136/jnnp.23.1.56
  10. Fried EI. Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Front Psychol. 2015 Mar 23;6:309. doi: 10.3389/fpsyg.2015.00309
  11. Grahek I, Shenhav A, Musslick S, Krebs RM, Koster EHW. Motivation and cognitive control in depression. Neurosci Biobehav Rev. 2019 Jul;102:371-381. doi: 10.1016/j.neubiorev.2019.04.011
  12. Scheurich A, Fellgiebel A, Schermuly I, Bauer S, Wölfges R, Müller MJ. Experimental evidence for a motivational origin of cognitive impairment in major depression. Psychol Med. 2008 Feb;38(2):237-246. doi: 10.1017/S0033291707002206
  13. Moritz S, Stöckert K, Hauschildt M, et al. Are we exaggerating neuropsychological impairment in depression? Reopening a closed chapter. Expert Rev Neurother. 2017 Aug;17(8):839-846. doi: 10.1080/14737175.2017.1347040
  14. Crocker LD, Heller W, Warren SL, O'Hare AJ, Infantolino ZP, Miller GA. Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front Hum Neurosci. 2013 Jun 11;7:261.
  15. Fervaha G, Foussias G, Takeuchi H, Agid O, Remington G. Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Compr Psychiatry. 2016 Apr;66:31-38. doi: 10.1016/j.comppsych.2015.12.004
  16. Bi R, Dong W, Zheng Z, Li S, Zhang D. Altered motivation of effortful decision-making for self and others in subthreshold depression. Depress Anxiety. 2022 Aug;39(8-9):633-645. doi: 10.1002/da.23267
  17. Parkinson B, Meacock R, Sutton M, et al. Designing and using incentives to support recruitment and retention in clinical trials: a scoping review and a checklist for design. Trials. 2019 Nov 9;20(1):624.
  18. Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. 2020 Apr-Jun;11(2):64-69. doi: 10.4103/picr.PICR_206_19
  19. Obegi JH. How Common is Recent Denial of Suicidal Ideation among Ideators, Attempters, and Suicide Decedents? A Literature Review. Gen Hosp Psychiatry. 2021 Sep-Oct;72:92-95. doi: 10.1016/j.genhosppsych.2021.07.009
  20. D’Arrigo T. Half of patients with suicidal thoughts deny it. Psychiatric News. November 29, 2021. Accessed March 27, 2023. https://doi.org/10.1176/appi.pn.2021.10.9
  21. Ballard ED, Snider SL, Nugent AC, Luckenbaugh DA, Park L, Zarate CA Jr. Active suicidal ideation during clinical antidepressant trials. Psychiatry Res. 2017 Nov;257:303-308. doi: 10.1016/j.psychres.2017.07.065
  22. Patrick F, Young AH, Williams SC, Perkins AM. Prescreening clinical trial volunteers using an online personality questionnaire. Neuropsychiatr Dis Treat. 2018 Sep 5;14:2297-2303. doi: 10.2147/NDT.S169469
  23. Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020 Nov 19;18(1):352.
  24. Kendrick T, Moore M, Leydon G, et al. Patient-reported outcome measures for monitoring primary care patients with depression (PROMDEP): study protocol for a randomised controlled trial. Trials. 2020 May 29;21(1):441. doi: 10.1186/s13063-020-04344-9.
  25. IsHak WW, Mirocha J, Pi S, et al. Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues Clin Neurosci. 2014 Jun;16(2):171-183. doi: 10.31887/DCNS.2014.16.2/rcohen
  26. Center for Drug Evaluation and Research. Major Depressive Disorder: Developing Drugs for Treatment Guidance for Industry. FDA. June 2018. Accessed April 7, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/major-depressive-disorder-developing-drugs-treatment
  27. Guideline on clinical investigation of medicinal products in the treatment of depression. European Medicines Agency. May 30, 2013. Accessed April 7, 2023. EMA/CHMP/185423/2010 Rev 2.
  28. De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022 Jan 11;5(1):3.
  29. Fowler JC, Skubiak T, Engelhard K, et al. Feasibility of a Noninterventional Decentralized Clinical Trial Model in Adults with Major Depressive Disorder. J Sci Innov Med. 2021 Jan 19;4(1):1. doi: 10.29024/jsim.84
  30. Miralles I, Granell C, Díaz-Sanahuja L, et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR Mhealth Uhealth. 2020 Apr 2;8(4):e14897. doi: 10.2196/14897
  31. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1
  32. Rathbone AL, Prescott J. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740
  33. Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021 Apr 1;78(4):361-371. doi: 10.1001/jamapsychiatry.2020.4364
  34. Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiat. 2021 Jun;8(6):500-511. doi: 10.1016/S2215-0366(21)00077-8
  35. Topooco N, Berg M, Johansson S, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. 2018 Jun 26;4(4):199-207. doi: 10.1192/bjo.2018.18
  36. Simon GE, Ralston JD, Savarino J, Pabiniak C, Wentzel C, Operskalski BH. Randomized trial of depression follow-up care by online messaging. J Gen Intern Med. 2011 Jul;26(7):698-704. doi: 10.1007/s11606-011-1679-8
  37. Bailey E, Mühlmann C, Rice S, et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med Ethics. 2020 May 13;21(1):37.
  38. Yamamoto RT, Sadler M, Dallabrida S. Patients May Be Concerned with The Consequences of Their Answers When Reporting on Symptoms of Depression. Oral presentation at: ISPOR Europe; November 2019; Copenhagen, Denmark.
  39. Kobak KA, Leuchter A, DeBrota D, et al. Site versus centralized raters in a clinical depression trial: impact on patient selection and placebo response. J Clin Psychopharmacol. 2010 Apr;30(2):193-197. doi: 10.1097/JCP.0b013e3181d20912
White Paper

Overcoming Research Challenges in Depression

CNS
Download the PDF

Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

‍

‍

Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
‍

The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
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Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

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Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

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  2. Santomauro DF, Mantilla Herrera AM, Shadid J, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021 Nov 6;398(10312):1700-1712.
  3. IMHE. New Global Burden of Disease analyses show depression and anxiety among the top causes of health loss worldwide, and a significant increase due to the COVID-19 pandemic. October 8, 2021. Accessed April 7, 2023. https://www.healthdata.org/acting-data/new-ihme-analyses-show-depression-and-anxiety-among-top-causes-health-burden-worldwide
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  7. Furukawa TA, Cipriani A, Atkinson LZ, et al. Placebo response rates in antidepressant trials: a systematic review of published and unpublished double-blind randomised controlled studies. Lancet Psychiat. 2016 Nov;3(11):1059-1066. doi: 10.1016/S2215-0366(16)30307-8
  8. Fried EI, Nesse RM. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 2015 Apr 6;13:72.
  9. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960 Feb;23(1):56-62. doi: 10.1136/jnnp.23.1.56
  10. Fried EI. Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Front Psychol. 2015 Mar 23;6:309. doi: 10.3389/fpsyg.2015.00309
  11. Grahek I, Shenhav A, Musslick S, Krebs RM, Koster EHW. Motivation and cognitive control in depression. Neurosci Biobehav Rev. 2019 Jul;102:371-381. doi: 10.1016/j.neubiorev.2019.04.011
  12. Scheurich A, Fellgiebel A, Schermuly I, Bauer S, Wölfges R, Müller MJ. Experimental evidence for a motivational origin of cognitive impairment in major depression. Psychol Med. 2008 Feb;38(2):237-246. doi: 10.1017/S0033291707002206
  13. Moritz S, Stöckert K, Hauschildt M, et al. Are we exaggerating neuropsychological impairment in depression? Reopening a closed chapter. Expert Rev Neurother. 2017 Aug;17(8):839-846. doi: 10.1080/14737175.2017.1347040
  14. Crocker LD, Heller W, Warren SL, O'Hare AJ, Infantolino ZP, Miller GA. Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front Hum Neurosci. 2013 Jun 11;7:261.
  15. Fervaha G, Foussias G, Takeuchi H, Agid O, Remington G. Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Compr Psychiatry. 2016 Apr;66:31-38. doi: 10.1016/j.comppsych.2015.12.004
  16. Bi R, Dong W, Zheng Z, Li S, Zhang D. Altered motivation of effortful decision-making for self and others in subthreshold depression. Depress Anxiety. 2022 Aug;39(8-9):633-645. doi: 10.1002/da.23267
  17. Parkinson B, Meacock R, Sutton M, et al. Designing and using incentives to support recruitment and retention in clinical trials: a scoping review and a checklist for design. Trials. 2019 Nov 9;20(1):624.
  18. Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. 2020 Apr-Jun;11(2):64-69. doi: 10.4103/picr.PICR_206_19
  19. Obegi JH. How Common is Recent Denial of Suicidal Ideation among Ideators, Attempters, and Suicide Decedents? A Literature Review. Gen Hosp Psychiatry. 2021 Sep-Oct;72:92-95. doi: 10.1016/j.genhosppsych.2021.07.009
  20. D’Arrigo T. Half of patients with suicidal thoughts deny it. Psychiatric News. November 29, 2021. Accessed March 27, 2023. https://doi.org/10.1176/appi.pn.2021.10.9
  21. Ballard ED, Snider SL, Nugent AC, Luckenbaugh DA, Park L, Zarate CA Jr. Active suicidal ideation during clinical antidepressant trials. Psychiatry Res. 2017 Nov;257:303-308. doi: 10.1016/j.psychres.2017.07.065
  22. Patrick F, Young AH, Williams SC, Perkins AM. Prescreening clinical trial volunteers using an online personality questionnaire. Neuropsychiatr Dis Treat. 2018 Sep 5;14:2297-2303. doi: 10.2147/NDT.S169469
  23. Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020 Nov 19;18(1):352.
  24. Kendrick T, Moore M, Leydon G, et al. Patient-reported outcome measures for monitoring primary care patients with depression (PROMDEP): study protocol for a randomised controlled trial. Trials. 2020 May 29;21(1):441. doi: 10.1186/s13063-020-04344-9.
  25. IsHak WW, Mirocha J, Pi S, et al. Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues Clin Neurosci. 2014 Jun;16(2):171-183. doi: 10.31887/DCNS.2014.16.2/rcohen
  26. Center for Drug Evaluation and Research. Major Depressive Disorder: Developing Drugs for Treatment Guidance for Industry. FDA. June 2018. Accessed April 7, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/major-depressive-disorder-developing-drugs-treatment
  27. Guideline on clinical investigation of medicinal products in the treatment of depression. European Medicines Agency. May 30, 2013. Accessed April 7, 2023. EMA/CHMP/185423/2010 Rev 2.
  28. De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022 Jan 11;5(1):3.
  29. Fowler JC, Skubiak T, Engelhard K, et al. Feasibility of a Noninterventional Decentralized Clinical Trial Model in Adults with Major Depressive Disorder. J Sci Innov Med. 2021 Jan 19;4(1):1. doi: 10.29024/jsim.84
  30. Miralles I, Granell C, Díaz-Sanahuja L, et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR Mhealth Uhealth. 2020 Apr 2;8(4):e14897. doi: 10.2196/14897
  31. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1
  32. Rathbone AL, Prescott J. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740
  33. Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021 Apr 1;78(4):361-371. doi: 10.1001/jamapsychiatry.2020.4364
  34. Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiat. 2021 Jun;8(6):500-511. doi: 10.1016/S2215-0366(21)00077-8
  35. Topooco N, Berg M, Johansson S, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. 2018 Jun 26;4(4):199-207. doi: 10.1192/bjo.2018.18
  36. Simon GE, Ralston JD, Savarino J, Pabiniak C, Wentzel C, Operskalski BH. Randomized trial of depression follow-up care by online messaging. J Gen Intern Med. 2011 Jul;26(7):698-704. doi: 10.1007/s11606-011-1679-8
  37. Bailey E, Mühlmann C, Rice S, et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med Ethics. 2020 May 13;21(1):37.
  38. Yamamoto RT, Sadler M, Dallabrida S. Patients May Be Concerned with The Consequences of Their Answers When Reporting on Symptoms of Depression. Oral presentation at: ISPOR Europe; November 2019; Copenhagen, Denmark.
  39. Kobak KA, Leuchter A, DeBrota D, et al. Site versus centralized raters in a clinical depression trial: impact on patient selection and placebo response. J Clin Psychopharmacol. 2010 Apr;30(2):193-197. doi: 10.1097/JCP.0b013e3181d20912
White Paper

Overcoming Research Challenges in Depression

Depression clinical studies are rife with roadblocks. Learn how digital and remote methods can help to circumvent them.

CNS
Download the PDF

Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

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Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
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The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
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Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

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Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

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  12. Scheurich A, Fellgiebel A, Schermuly I, Bauer S, Wölfges R, Müller MJ. Experimental evidence for a motivational origin of cognitive impairment in major depression. Psychol Med. 2008 Feb;38(2):237-246. doi: 10.1017/S0033291707002206
  13. Moritz S, Stöckert K, Hauschildt M, et al. Are we exaggerating neuropsychological impairment in depression? Reopening a closed chapter. Expert Rev Neurother. 2017 Aug;17(8):839-846. doi: 10.1080/14737175.2017.1347040
  14. Crocker LD, Heller W, Warren SL, O'Hare AJ, Infantolino ZP, Miller GA. Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front Hum Neurosci. 2013 Jun 11;7:261.
  15. Fervaha G, Foussias G, Takeuchi H, Agid O, Remington G. Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Compr Psychiatry. 2016 Apr;66:31-38. doi: 10.1016/j.comppsych.2015.12.004
  16. Bi R, Dong W, Zheng Z, Li S, Zhang D. Altered motivation of effortful decision-making for self and others in subthreshold depression. Depress Anxiety. 2022 Aug;39(8-9):633-645. doi: 10.1002/da.23267
  17. Parkinson B, Meacock R, Sutton M, et al. Designing and using incentives to support recruitment and retention in clinical trials: a scoping review and a checklist for design. Trials. 2019 Nov 9;20(1):624.
  18. Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. 2020 Apr-Jun;11(2):64-69. doi: 10.4103/picr.PICR_206_19
  19. Obegi JH. How Common is Recent Denial of Suicidal Ideation among Ideators, Attempters, and Suicide Decedents? A Literature Review. Gen Hosp Psychiatry. 2021 Sep-Oct;72:92-95. doi: 10.1016/j.genhosppsych.2021.07.009
  20. D’Arrigo T. Half of patients with suicidal thoughts deny it. Psychiatric News. November 29, 2021. Accessed March 27, 2023. https://doi.org/10.1176/appi.pn.2021.10.9
  21. Ballard ED, Snider SL, Nugent AC, Luckenbaugh DA, Park L, Zarate CA Jr. Active suicidal ideation during clinical antidepressant trials. Psychiatry Res. 2017 Nov;257:303-308. doi: 10.1016/j.psychres.2017.07.065
  22. Patrick F, Young AH, Williams SC, Perkins AM. Prescreening clinical trial volunteers using an online personality questionnaire. Neuropsychiatr Dis Treat. 2018 Sep 5;14:2297-2303. doi: 10.2147/NDT.S169469
  23. Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020 Nov 19;18(1):352.
  24. Kendrick T, Moore M, Leydon G, et al. Patient-reported outcome measures for monitoring primary care patients with depression (PROMDEP): study protocol for a randomised controlled trial. Trials. 2020 May 29;21(1):441. doi: 10.1186/s13063-020-04344-9.
  25. IsHak WW, Mirocha J, Pi S, et al. Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues Clin Neurosci. 2014 Jun;16(2):171-183. doi: 10.31887/DCNS.2014.16.2/rcohen
  26. Center for Drug Evaluation and Research. Major Depressive Disorder: Developing Drugs for Treatment Guidance for Industry. FDA. June 2018. Accessed April 7, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/major-depressive-disorder-developing-drugs-treatment
  27. Guideline on clinical investigation of medicinal products in the treatment of depression. European Medicines Agency. May 30, 2013. Accessed April 7, 2023. EMA/CHMP/185423/2010 Rev 2.
  28. De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022 Jan 11;5(1):3.
  29. Fowler JC, Skubiak T, Engelhard K, et al. Feasibility of a Noninterventional Decentralized Clinical Trial Model in Adults with Major Depressive Disorder. J Sci Innov Med. 2021 Jan 19;4(1):1. doi: 10.29024/jsim.84
  30. Miralles I, Granell C, Díaz-Sanahuja L, et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR Mhealth Uhealth. 2020 Apr 2;8(4):e14897. doi: 10.2196/14897
  31. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1
  32. Rathbone AL, Prescott J. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740
  33. Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021 Apr 1;78(4):361-371. doi: 10.1001/jamapsychiatry.2020.4364
  34. Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiat. 2021 Jun;8(6):500-511. doi: 10.1016/S2215-0366(21)00077-8
  35. Topooco N, Berg M, Johansson S, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. 2018 Jun 26;4(4):199-207. doi: 10.1192/bjo.2018.18
  36. Simon GE, Ralston JD, Savarino J, Pabiniak C, Wentzel C, Operskalski BH. Randomized trial of depression follow-up care by online messaging. J Gen Intern Med. 2011 Jul;26(7):698-704. doi: 10.1007/s11606-011-1679-8
  37. Bailey E, Mühlmann C, Rice S, et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med Ethics. 2020 May 13;21(1):37.
  38. Yamamoto RT, Sadler M, Dallabrida S. Patients May Be Concerned with The Consequences of Their Answers When Reporting on Symptoms of Depression. Oral presentation at: ISPOR Europe; November 2019; Copenhagen, Denmark.
  39. Kobak KA, Leuchter A, DeBrota D, et al. Site versus centralized raters in a clinical depression trial: impact on patient selection and placebo response. J Clin Psychopharmacol. 2010 Apr;30(2):193-197. doi: 10.1097/JCP.0b013e3181d20912
White Paper

Overcoming Research Challenges in Depression

CNS
Download the PDF

Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

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Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
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The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
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Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

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Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

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  2. Santomauro DF, Mantilla Herrera AM, Shadid J, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021 Nov 6;398(10312):1700-1712.
  3. IMHE. New Global Burden of Disease analyses show depression and anxiety among the top causes of health loss worldwide, and a significant increase due to the COVID-19 pandemic. October 8, 2021. Accessed April 7, 2023. https://www.healthdata.org/acting-data/new-ihme-analyses-show-depression-and-anxiety-among-top-causes-health-burden-worldwide
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  8. Fried EI, Nesse RM. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 2015 Apr 6;13:72.
  9. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960 Feb;23(1):56-62. doi: 10.1136/jnnp.23.1.56
  10. Fried EI. Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Front Psychol. 2015 Mar 23;6:309. doi: 10.3389/fpsyg.2015.00309
  11. Grahek I, Shenhav A, Musslick S, Krebs RM, Koster EHW. Motivation and cognitive control in depression. Neurosci Biobehav Rev. 2019 Jul;102:371-381. doi: 10.1016/j.neubiorev.2019.04.011
  12. Scheurich A, Fellgiebel A, Schermuly I, Bauer S, Wölfges R, Müller MJ. Experimental evidence for a motivational origin of cognitive impairment in major depression. Psychol Med. 2008 Feb;38(2):237-246. doi: 10.1017/S0033291707002206
  13. Moritz S, Stöckert K, Hauschildt M, et al. Are we exaggerating neuropsychological impairment in depression? Reopening a closed chapter. Expert Rev Neurother. 2017 Aug;17(8):839-846. doi: 10.1080/14737175.2017.1347040
  14. Crocker LD, Heller W, Warren SL, O'Hare AJ, Infantolino ZP, Miller GA. Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front Hum Neurosci. 2013 Jun 11;7:261.
  15. Fervaha G, Foussias G, Takeuchi H, Agid O, Remington G. Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Compr Psychiatry. 2016 Apr;66:31-38. doi: 10.1016/j.comppsych.2015.12.004
  16. Bi R, Dong W, Zheng Z, Li S, Zhang D. Altered motivation of effortful decision-making for self and others in subthreshold depression. Depress Anxiety. 2022 Aug;39(8-9):633-645. doi: 10.1002/da.23267
  17. Parkinson B, Meacock R, Sutton M, et al. Designing and using incentives to support recruitment and retention in clinical trials: a scoping review and a checklist for design. Trials. 2019 Nov 9;20(1):624.
  18. Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. 2020 Apr-Jun;11(2):64-69. doi: 10.4103/picr.PICR_206_19
  19. Obegi JH. How Common is Recent Denial of Suicidal Ideation among Ideators, Attempters, and Suicide Decedents? A Literature Review. Gen Hosp Psychiatry. 2021 Sep-Oct;72:92-95. doi: 10.1016/j.genhosppsych.2021.07.009
  20. D’Arrigo T. Half of patients with suicidal thoughts deny it. Psychiatric News. November 29, 2021. Accessed March 27, 2023. https://doi.org/10.1176/appi.pn.2021.10.9
  21. Ballard ED, Snider SL, Nugent AC, Luckenbaugh DA, Park L, Zarate CA Jr. Active suicidal ideation during clinical antidepressant trials. Psychiatry Res. 2017 Nov;257:303-308. doi: 10.1016/j.psychres.2017.07.065
  22. Patrick F, Young AH, Williams SC, Perkins AM. Prescreening clinical trial volunteers using an online personality questionnaire. Neuropsychiatr Dis Treat. 2018 Sep 5;14:2297-2303. doi: 10.2147/NDT.S169469
  23. Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020 Nov 19;18(1):352.
  24. Kendrick T, Moore M, Leydon G, et al. Patient-reported outcome measures for monitoring primary care patients with depression (PROMDEP): study protocol for a randomised controlled trial. Trials. 2020 May 29;21(1):441. doi: 10.1186/s13063-020-04344-9.
  25. IsHak WW, Mirocha J, Pi S, et al. Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues Clin Neurosci. 2014 Jun;16(2):171-183. doi: 10.31887/DCNS.2014.16.2/rcohen
  26. Center for Drug Evaluation and Research. Major Depressive Disorder: Developing Drugs for Treatment Guidance for Industry. FDA. June 2018. Accessed April 7, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/major-depressive-disorder-developing-drugs-treatment
  27. Guideline on clinical investigation of medicinal products in the treatment of depression. European Medicines Agency. May 30, 2013. Accessed April 7, 2023. EMA/CHMP/185423/2010 Rev 2.
  28. De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022 Jan 11;5(1):3.
  29. Fowler JC, Skubiak T, Engelhard K, et al. Feasibility of a Noninterventional Decentralized Clinical Trial Model in Adults with Major Depressive Disorder. J Sci Innov Med. 2021 Jan 19;4(1):1. doi: 10.29024/jsim.84
  30. Miralles I, Granell C, Díaz-Sanahuja L, et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR Mhealth Uhealth. 2020 Apr 2;8(4):e14897. doi: 10.2196/14897
  31. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1
  32. Rathbone AL, Prescott J. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740
  33. Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021 Apr 1;78(4):361-371. doi: 10.1001/jamapsychiatry.2020.4364
  34. Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiat. 2021 Jun;8(6):500-511. doi: 10.1016/S2215-0366(21)00077-8
  35. Topooco N, Berg M, Johansson S, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. 2018 Jun 26;4(4):199-207. doi: 10.1192/bjo.2018.18
  36. Simon GE, Ralston JD, Savarino J, Pabiniak C, Wentzel C, Operskalski BH. Randomized trial of depression follow-up care by online messaging. J Gen Intern Med. 2011 Jul;26(7):698-704. doi: 10.1007/s11606-011-1679-8
  37. Bailey E, Mühlmann C, Rice S, et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med Ethics. 2020 May 13;21(1):37.
  38. Yamamoto RT, Sadler M, Dallabrida S. Patients May Be Concerned with The Consequences of Their Answers When Reporting on Symptoms of Depression. Oral presentation at: ISPOR Europe; November 2019; Copenhagen, Denmark.
  39. Kobak KA, Leuchter A, DeBrota D, et al. Site versus centralized raters in a clinical depression trial: impact on patient selection and placebo response. J Clin Psychopharmacol. 2010 Apr;30(2):193-197. doi: 10.1097/JCP.0b013e3181d20912
White Paper

Overcoming Research Challenges in Depression

CNS
Download the PDF

Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

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Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
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The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
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Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

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Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

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  2. Santomauro DF, Mantilla Herrera AM, Shadid J, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021 Nov 6;398(10312):1700-1712.
  3. IMHE. New Global Burden of Disease analyses show depression and anxiety among the top causes of health loss worldwide, and a significant increase due to the COVID-19 pandemic. October 8, 2021. Accessed April 7, 2023. https://www.healthdata.org/acting-data/new-ihme-analyses-show-depression-and-anxiety-among-top-causes-health-burden-worldwide
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  8. Fried EI, Nesse RM. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 2015 Apr 6;13:72.
  9. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960 Feb;23(1):56-62. doi: 10.1136/jnnp.23.1.56
  10. Fried EI. Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Front Psychol. 2015 Mar 23;6:309. doi: 10.3389/fpsyg.2015.00309
  11. Grahek I, Shenhav A, Musslick S, Krebs RM, Koster EHW. Motivation and cognitive control in depression. Neurosci Biobehav Rev. 2019 Jul;102:371-381. doi: 10.1016/j.neubiorev.2019.04.011
  12. Scheurich A, Fellgiebel A, Schermuly I, Bauer S, Wölfges R, Müller MJ. Experimental evidence for a motivational origin of cognitive impairment in major depression. Psychol Med. 2008 Feb;38(2):237-246. doi: 10.1017/S0033291707002206
  13. Moritz S, Stöckert K, Hauschildt M, et al. Are we exaggerating neuropsychological impairment in depression? Reopening a closed chapter. Expert Rev Neurother. 2017 Aug;17(8):839-846. doi: 10.1080/14737175.2017.1347040
  14. Crocker LD, Heller W, Warren SL, O'Hare AJ, Infantolino ZP, Miller GA. Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front Hum Neurosci. 2013 Jun 11;7:261.
  15. Fervaha G, Foussias G, Takeuchi H, Agid O, Remington G. Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Compr Psychiatry. 2016 Apr;66:31-38. doi: 10.1016/j.comppsych.2015.12.004
  16. Bi R, Dong W, Zheng Z, Li S, Zhang D. Altered motivation of effortful decision-making for self and others in subthreshold depression. Depress Anxiety. 2022 Aug;39(8-9):633-645. doi: 10.1002/da.23267
  17. Parkinson B, Meacock R, Sutton M, et al. Designing and using incentives to support recruitment and retention in clinical trials: a scoping review and a checklist for design. Trials. 2019 Nov 9;20(1):624.
  18. Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. 2020 Apr-Jun;11(2):64-69. doi: 10.4103/picr.PICR_206_19
  19. Obegi JH. How Common is Recent Denial of Suicidal Ideation among Ideators, Attempters, and Suicide Decedents? A Literature Review. Gen Hosp Psychiatry. 2021 Sep-Oct;72:92-95. doi: 10.1016/j.genhosppsych.2021.07.009
  20. D’Arrigo T. Half of patients with suicidal thoughts deny it. Psychiatric News. November 29, 2021. Accessed March 27, 2023. https://doi.org/10.1176/appi.pn.2021.10.9
  21. Ballard ED, Snider SL, Nugent AC, Luckenbaugh DA, Park L, Zarate CA Jr. Active suicidal ideation during clinical antidepressant trials. Psychiatry Res. 2017 Nov;257:303-308. doi: 10.1016/j.psychres.2017.07.065
  22. Patrick F, Young AH, Williams SC, Perkins AM. Prescreening clinical trial volunteers using an online personality questionnaire. Neuropsychiatr Dis Treat. 2018 Sep 5;14:2297-2303. doi: 10.2147/NDT.S169469
  23. Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020 Nov 19;18(1):352.
  24. Kendrick T, Moore M, Leydon G, et al. Patient-reported outcome measures for monitoring primary care patients with depression (PROMDEP): study protocol for a randomised controlled trial. Trials. 2020 May 29;21(1):441. doi: 10.1186/s13063-020-04344-9.
  25. IsHak WW, Mirocha J, Pi S, et al. Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues Clin Neurosci. 2014 Jun;16(2):171-183. doi: 10.31887/DCNS.2014.16.2/rcohen
  26. Center for Drug Evaluation and Research. Major Depressive Disorder: Developing Drugs for Treatment Guidance for Industry. FDA. June 2018. Accessed April 7, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/major-depressive-disorder-developing-drugs-treatment
  27. Guideline on clinical investigation of medicinal products in the treatment of depression. European Medicines Agency. May 30, 2013. Accessed April 7, 2023. EMA/CHMP/185423/2010 Rev 2.
  28. De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022 Jan 11;5(1):3.
  29. Fowler JC, Skubiak T, Engelhard K, et al. Feasibility of a Noninterventional Decentralized Clinical Trial Model in Adults with Major Depressive Disorder. J Sci Innov Med. 2021 Jan 19;4(1):1. doi: 10.29024/jsim.84
  30. Miralles I, Granell C, Díaz-Sanahuja L, et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR Mhealth Uhealth. 2020 Apr 2;8(4):e14897. doi: 10.2196/14897
  31. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1
  32. Rathbone AL, Prescott J. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740
  33. Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021 Apr 1;78(4):361-371. doi: 10.1001/jamapsychiatry.2020.4364
  34. Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiat. 2021 Jun;8(6):500-511. doi: 10.1016/S2215-0366(21)00077-8
  35. Topooco N, Berg M, Johansson S, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. 2018 Jun 26;4(4):199-207. doi: 10.1192/bjo.2018.18
  36. Simon GE, Ralston JD, Savarino J, Pabiniak C, Wentzel C, Operskalski BH. Randomized trial of depression follow-up care by online messaging. J Gen Intern Med. 2011 Jul;26(7):698-704. doi: 10.1007/s11606-011-1679-8
  37. Bailey E, Mühlmann C, Rice S, et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med Ethics. 2020 May 13;21(1):37.
  38. Yamamoto RT, Sadler M, Dallabrida S. Patients May Be Concerned with The Consequences of Their Answers When Reporting on Symptoms of Depression. Oral presentation at: ISPOR Europe; November 2019; Copenhagen, Denmark.
  39. Kobak KA, Leuchter A, DeBrota D, et al. Site versus centralized raters in a clinical depression trial: impact on patient selection and placebo response. J Clin Psychopharmacol. 2010 Apr;30(2):193-197. doi: 10.1097/JCP.0b013e3181d20912
White Paper

Overcoming Research Challenges in Depression

Depression clinical studies are rife with roadblocks. Learn how digital and remote methods can help to circumvent them.

Download the PDF
CNS

Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

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Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
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The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
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Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

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Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

  1. Ferrari AJ, Santomauro DF, Mantilla Herrera AM, et al. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiat. 2022 Feb;9(2):137-150. doi: 10.1016/S2215-0366(21)00457-0
  2. Santomauro DF, Mantilla Herrera AM, Shadid J, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021 Nov 6;398(10312):1700-1712.
  3. IMHE. New Global Burden of Disease analyses show depression and anxiety among the top causes of health loss worldwide, and a significant increase due to the COVID-19 pandemic. October 8, 2021. Accessed April 7, 2023. https://www.healthdata.org/acting-data/new-ihme-analyses-show-depression-and-anxiety-among-top-causes-health-burden-worldwide
  4. Goodwin RD, Dierker LC, Wu M, Galea S, Hoven CW, Weinberger AH. Trends in U.S. Depression Prevalence From 2015 to 2020: The Widening Treatment Gap. Am J Prev Med. 2022 Nov;63(5):726-733. doi: 10.1016/j.amepre.2022.05.014
  5. Fried EI. Moving forward: how depression heterogeneity hinders progress in treatment and research. Expert Rev Neurother. 2017 May;17(5):423-425. doi: 10.1080/14737175.2017.1307737
  6. Santor DA, Gregus M, Welch A. Focus Article: Eight Decades of Measurement in Depression. Measurement: Interdisciplinary Research and Perspectives. 2006;4(3):135–155. doi: 10.1207/s15366359mea0403_1
  7. Furukawa TA, Cipriani A, Atkinson LZ, et al. Placebo response rates in antidepressant trials: a systematic review of published and unpublished double-blind randomised controlled studies. Lancet Psychiat. 2016 Nov;3(11):1059-1066. doi: 10.1016/S2215-0366(16)30307-8
  8. Fried EI, Nesse RM. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 2015 Apr 6;13:72.
  9. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960 Feb;23(1):56-62. doi: 10.1136/jnnp.23.1.56
  10. Fried EI. Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Front Psychol. 2015 Mar 23;6:309. doi: 10.3389/fpsyg.2015.00309
  11. Grahek I, Shenhav A, Musslick S, Krebs RM, Koster EHW. Motivation and cognitive control in depression. Neurosci Biobehav Rev. 2019 Jul;102:371-381. doi: 10.1016/j.neubiorev.2019.04.011
  12. Scheurich A, Fellgiebel A, Schermuly I, Bauer S, Wölfges R, Müller MJ. Experimental evidence for a motivational origin of cognitive impairment in major depression. Psychol Med. 2008 Feb;38(2):237-246. doi: 10.1017/S0033291707002206
  13. Moritz S, Stöckert K, Hauschildt M, et al. Are we exaggerating neuropsychological impairment in depression? Reopening a closed chapter. Expert Rev Neurother. 2017 Aug;17(8):839-846. doi: 10.1080/14737175.2017.1347040
  14. Crocker LD, Heller W, Warren SL, O'Hare AJ, Infantolino ZP, Miller GA. Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front Hum Neurosci. 2013 Jun 11;7:261.
  15. Fervaha G, Foussias G, Takeuchi H, Agid O, Remington G. Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Compr Psychiatry. 2016 Apr;66:31-38. doi: 10.1016/j.comppsych.2015.12.004
  16. Bi R, Dong W, Zheng Z, Li S, Zhang D. Altered motivation of effortful decision-making for self and others in subthreshold depression. Depress Anxiety. 2022 Aug;39(8-9):633-645. doi: 10.1002/da.23267
  17. Parkinson B, Meacock R, Sutton M, et al. Designing and using incentives to support recruitment and retention in clinical trials: a scoping review and a checklist for design. Trials. 2019 Nov 9;20(1):624.
  18. Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. 2020 Apr-Jun;11(2):64-69. doi: 10.4103/picr.PICR_206_19
  19. Obegi JH. How Common is Recent Denial of Suicidal Ideation among Ideators, Attempters, and Suicide Decedents? A Literature Review. Gen Hosp Psychiatry. 2021 Sep-Oct;72:92-95. doi: 10.1016/j.genhosppsych.2021.07.009
  20. D’Arrigo T. Half of patients with suicidal thoughts deny it. Psychiatric News. November 29, 2021. Accessed March 27, 2023. https://doi.org/10.1176/appi.pn.2021.10.9
  21. Ballard ED, Snider SL, Nugent AC, Luckenbaugh DA, Park L, Zarate CA Jr. Active suicidal ideation during clinical antidepressant trials. Psychiatry Res. 2017 Nov;257:303-308. doi: 10.1016/j.psychres.2017.07.065
  22. Patrick F, Young AH, Williams SC, Perkins AM. Prescreening clinical trial volunteers using an online personality questionnaire. Neuropsychiatr Dis Treat. 2018 Sep 5;14:2297-2303. doi: 10.2147/NDT.S169469
  23. Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020 Nov 19;18(1):352.
  24. Kendrick T, Moore M, Leydon G, et al. Patient-reported outcome measures for monitoring primary care patients with depression (PROMDEP): study protocol for a randomised controlled trial. Trials. 2020 May 29;21(1):441. doi: 10.1186/s13063-020-04344-9.
  25. IsHak WW, Mirocha J, Pi S, et al. Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues Clin Neurosci. 2014 Jun;16(2):171-183. doi: 10.31887/DCNS.2014.16.2/rcohen
  26. Center for Drug Evaluation and Research. Major Depressive Disorder: Developing Drugs for Treatment Guidance for Industry. FDA. June 2018. Accessed April 7, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/major-depressive-disorder-developing-drugs-treatment
  27. Guideline on clinical investigation of medicinal products in the treatment of depression. European Medicines Agency. May 30, 2013. Accessed April 7, 2023. EMA/CHMP/185423/2010 Rev 2.
  28. De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022 Jan 11;5(1):3.
  29. Fowler JC, Skubiak T, Engelhard K, et al. Feasibility of a Noninterventional Decentralized Clinical Trial Model in Adults with Major Depressive Disorder. J Sci Innov Med. 2021 Jan 19;4(1):1. doi: 10.29024/jsim.84
  30. Miralles I, Granell C, Díaz-Sanahuja L, et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR Mhealth Uhealth. 2020 Apr 2;8(4):e14897. doi: 10.2196/14897
  31. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1
  32. Rathbone AL, Prescott J. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740
  33. Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021 Apr 1;78(4):361-371. doi: 10.1001/jamapsychiatry.2020.4364
  34. Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiat. 2021 Jun;8(6):500-511. doi: 10.1016/S2215-0366(21)00077-8
  35. Topooco N, Berg M, Johansson S, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. 2018 Jun 26;4(4):199-207. doi: 10.1192/bjo.2018.18
  36. Simon GE, Ralston JD, Savarino J, Pabiniak C, Wentzel C, Operskalski BH. Randomized trial of depression follow-up care by online messaging. J Gen Intern Med. 2011 Jul;26(7):698-704. doi: 10.1007/s11606-011-1679-8
  37. Bailey E, Mühlmann C, Rice S, et al. Ethical issues and practical barriers in internet-based suicide prevention research: a review and investigator survey. BMC Med Ethics. 2020 May 13;21(1):37.
  38. Yamamoto RT, Sadler M, Dallabrida S. Patients May Be Concerned with The Consequences of Their Answers When Reporting on Symptoms of Depression. Oral presentation at: ISPOR Europe; November 2019; Copenhagen, Denmark.
  39. Kobak KA, Leuchter A, DeBrota D, et al. Site versus centralized raters in a clinical depression trial: impact on patient selection and placebo response. J Clin Psychopharmacol. 2010 Apr;30(2):193-197. doi: 10.1097/JCP.0b013e3181d20912
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White Paper

Overcoming Research Challenges in Depression

Depression clinical studies are rife with roadblocks. Learn how digital and remote methods can help to circumvent them.

CNS
Download the PDF

Introduction

Depression was ranked as the world’s second-most frequent mental health disorder leading to disability in 2019.1 Since then, global cases of depressive disorder have increased dramatically, affecting more than 200 million people.2,3 Unfortunately, this growing crisis has come without commensurate increases in treatment options.4

‍While there have been some successes in clinical care for patients, there have been few advancements in therapeutic innovation. Most therapeutics prescribed today for major depressive disorder (MDD) were developed in the 1980s and 1990s.5 Evidence shows most of them only slightly outperform placebos.5,6

This lack of successful therapeutic development has been hampered by numerous challenges in clinical trial design and delivery. Principle investigators often struggle to balance the demands of clinical practice with those of rigorous research; they are hindered by increasingly complex protocol designs as well as an insufficient number of patients willing to participate in trials.

Placebo response rates ranging from 35 to 40 percent for depression trials set a very high bar for proving efficacy of new therapies.7 What’s more, the tools used in depression trials have failed to keep up with innovation for other conditions, further impeding progress in this indication. Together, these obstacles point to a need to explore new approaches to mood disorder research.

Fortunately, opportunities abound to make trial participation more accessible for patients and investigators alike. Decentralized methods put the patient at the center of clinical trial design, incorporating features to facilitate remote engagement, near real-time reporting, automation, and seamless data integration—all of which are potentially transformative for depression trials. Not only can these methods improve participation rates, but they can also lead to more accurate data collection and improved trial success.

This white paper presents three unique issues associated with depression clinical trials: evaluation, engagement, and safety. It then reviews ways decentralized and hybrid models can help address these challenges to facilitate the task of bringing stronger evidence of efficacy and safety for new treatments.

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Challenges in Depression Clinical Research that Hinder Therapeutic Innovation
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The Challenge of Evaluating Depression

Depression is an extraordinarily broad condition routinely categorized as a unitary disorder. This paradox can impact the reliability and validity of interventional research.

Depression is often studied as a homogenous disorder when, in fact, it is characterized by a constellation of multiple and distinct subgroups. Major depressive disorder is characterized by nine symptoms in the Diagnostic and Statistical Manual of Mental Health (DSM)-5. Yet, two patients with a DSM-5 diagnosis of MDD often do not share a single symptom.5,8 A study of 3,703 depressed people identified 1,030 unique symptom profiles, and the most common symptom profile was exhibited at a frequency of only 1.8 percent.8

HAM-D, currently considered the standard for depression assessments, was developed in 19609 and does not include anhedonia, cognition, or the painful physical symptoms that are important in depression.8,10 Interestingly, in a publication reporting the use of the Hamilton Rating Scale, Hamilton himself omits any reference to what he describes as the “total crude score” and focuses his discussion on the more important clinical factors of depression.9,10

Since 1918, more than 280 measures of depression severity have been developed and published with differences in content, response format, and objectives.6 Yet, trials investigating treatments for depression have primarily relied on only 6 of these measures, most of which were developed more than thirty years ago.6

Perhaps it should not come as a surprise, then, to learn that the most commonly prescribed treatments for MDD only slightly outperformed placebos.5,6 As Dr. Zia Choudhry, MD, PhD, MBA, explains, “The lack of success in depression trials can be attributed in part to the fact that patient populations are simply too heterogeneous. There is a body of evidence which supports that these subgroups are unique. Unfortunately, in the clinical world, they have all been lumped together as MDD.”

In a 2015 study, Eiko Fried questions whether depression should even be considered a distinct disease category.10 He argues that by lumping so many patients together, the industry has not been able to target the specific symptoms troubling the distinct patients, leading to poor therapeutic progress by drug companies.

Fried’s research also found that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that have an impact on impairment and risk factors. Not only do these symptoms differ from each other in their impact on functioning, they also differ in their response to specific life events and their relationship with biological markers and risk factors.8,10

For example, certain depression patients may have weight gain as a predictor, whereas others may have symptoms associated with sleep disorder or alcohol use disorder. In some cases, depression may express through lack of pleasure-seeking, whereas, in others, anxiety symptoms may be predominant. In each case, these anhedonic symptoms may implicate different neurobiological pathways and, therefore, require different treatments.

Some of the challenges associated with evaluating therapeutic efficacy within this heterogenous population can be tackled through a more symptom-based approach to cohort recruitment and endpoint design, leveraging decentralized methods. This will be further discussed below.
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Amotivation, Engagement, and Adherence Challenges

Depression is often associated with a lack of motivation to exert effort for rewards.

Depression is characterized by impairments in attention, memory, and cognitive control.11 This profoundly influences how an individual thinks about themselves, others, and the world around them.

Depression also influences how information is processed, often making it difficult for people with depression to disengage from negative emotions, suppress irrelevant thoughts, or shift their attention from one task to another. Evidence12-15 shows these cognitive impairments are closely linked to a person’s emotional and motivational processes. This means patients with depression can display less enthusiasm towards testing, which in turn can lead to inadequate task completion.13

Amotivation is also a typical feature in major depressive disorder and refers to individuals exhibiting reduced willingness to exert effort, even for rewards.16 Although evidence17 has shown that pay-for-performance programs can be relatively successful in improving participation and adherence in certain therapeutic areas, individuals with depression have been reported to forego valuable care options to avoid risk or lack the motivation to engage regardless of the reward.16,18

These issues can be exacerbated when patients consent to participate in a trial without having a full and clear understanding of their responsibilities. Depression patients, like all patients, will often not have the patience to read a long consent form and will sign nonetheless. They can then often be surprised to discover their expected role and required tasks in the trial.

So, if these patients are less motivated to do even the most basic tasks in their lives, how can we expect to motivate them to participate in a clinical trial which adds more burden to their already heavy load? How can we engage people with depression during a clinical trial to retain their participation while maintaining compliance? We’ll discuss the answers to these questions below.
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Suicidality and Safety

Data suggests that about 50 percent of patients with suicidal thoughts will deny this if a healthcare professional or researcher asks them about it.19 Furthermore, in many cases, people who disclosed suicidal thoughts in apps and on paper then denied it when questioned directly in face-to-face assessments or interviews.

In one study, nearly 60 percent of those who reported their suicidal ideation on an app denied their suicidal ideation in a telephone interview less than 24 hours later.19 This highlights a potential disconnect between patients and their healthcare professionals, and it calls for a hard look at how we can improve these interactions and patient experiences.19

Dr. Joseph Obegi, author of a review titled “How common is recent denial of suicidal ideation among ideators, attempters, and suicide decedents,” explains that patients with mental health disorders harbor a fear of being shamed, are afraid of the stigma associated with mental health conditions, and—most importantly—are afraid of being hospitalized. This, coupled with the poor perception of the mental healthcare system, contributes to people denying suicidal thoughts.20

Patients deemed suicidal are often excluded from clinical trials when investigating psychiatric medication or neurobiological techniques. However, Ballard et al. (2018)21 reviewed results from 14 clinical antidepressant trials conducted in individuals with mood disorders and found that active suicidal ideation was relatively common and occurs in almost a quarter of patients. When active suicidal thoughts occurred, participants were either withdrawn for more proactive treatment or allowed to complete neurobiological procedures where no significant impact on study completion or patient safety was observed. Ballard et al. (2018)21 also noted that suicidal ideation decreased in most patients when asked to undergo research procedures. The decision to exclude these patients from trials is neither realistic nor ethical, given that this population is one that may be most likely to use and benefit from treatment.

Regardless of whether these patients are ultimately withdrawn from a study, careful suicide risk monitoring is an integral part of clinical trials in patients with depression. About half of decedents in depression trials denied suicide ideation in the previous week or month before suicide.18 Not only does this illustrate the importance of monitoring, but sponsors and researchers must also consider how the information is collected.

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Decentralized and Hybrid Clinical Trials: A Way Forward

Digital and remote technologies offer multiple opportunities to address each of the three previously highlighted challenges associated with depression trials.
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Sub-Segmentation of Depression Patients

As previously mentioned, analyzing specific symptoms and their causal associations can be an important step toward developing more personalized treatments that better recognize and address the heterogeneity of depressive disorders.

Although tracking a single severity score may be an important baseline and will continue to be key to regulatory approval, categorization of patients into more specific subgroups based on anhedonic symptoms may provide additional insights that increase the likelihood of identifying therapeutic solutions to more effectively treat patients.

Decentralized clinical trials can help to advance this more symptom-based approach by making it easier to recruit subgroups of cohorts and by facilitating the collection, monitoring, and analysis of additional symptom-related endpoints.

Subgroup cohort recruitment

One of the reasons it’s harder to recruit more specific populations is the difficulty of finding enough participants within proximity of certain sites. Decentralization can facilitate this process by expanding the geographic radius of recruitment. While there may still be a need for a face-to-face evaluation at the outset of a study, reducing the number of site visits needed can encourage more people to participate.

Remote prescreening can save time for both the trial coordinators and potential participants. This was the case, for example, in an anxiety disorder trial22 where participants were prescreened for anxious personality traits via an online platform. Those who were eligible were then invited to a telephone screening and, if still eligible, travelled to a study site for a medical check. In this way, 6,293 people initially screened were winnowed down to 24 people, who completed the trial on time.

The adoption of technology can also facilitate more adaptive trial designs, leading to more efficient and potentially more ethical protocols. DCT platforms can allow for more agile functionalities that enable the clinical trial team to build in or build out certain tools or parameters to identify and capture data. For example, a population enrichment approach can design in the selection of subpopulations for recruitment based on interim analyses of the groups that appear to be most benefiting from the treatment.23

While adaptive designs require more complex decision rules and operational planning at the outset, they may provide substantial benefits to both sponsors and patients—exposing as few patients as possible to ineffective treatments while boosting evidence amongst populations who are seeing positive treatment effects.23

An alternative to more targeted recruitment is to design for a more heterogenous population and then leverage analytics to identify subgroups. “Depression trials are years behind many other conditions in terms of identifying the best way to treat patients based on a phenotypic pattern of symptoms,” says Faith Matcham, PhD, CPsychol, Health Psychologist and Lecturer in Clinical Psychology at the University of Sussex. “What makes decentralized clinical trials so exciting is that you can collect incredible amounts of data at scale and then let that data show you where the relevant subpopulations are.“

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Additional endpoint collection

In order to properly understand and interpret the broad symptomology associated with depression, patients need to be able to easily report their symptoms in near real time.

The increasing access to, and usage of, smartphones presents an essential avenue to meeting this objective. Decentralized clinical trials enable researchers to collect data more frequently at higher volumes, opening the door to secondary endpoints that could tell us whether a therapeutic is more effective in depressive patients with sleep disorders, for example, as opposed to patients with alcohol addiction issues.

Of course, these conditions will sit on a continuum, but any progress that can be made to more effectively address symptoms may have an outsized impact on patient quality of life.

The section below will dive more deeply into the ways in which DCTs are empowering more patient-centered study design, which in turn facilitates better and richer endpoint collection.
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Boosting Engagement in Depression Trial Patients

Recent research suggests that participant engagement in depression trials can be positively influenced in two important ways.18

Patient-centered, tech-enabled study designs

Traditional clinical trials have been built around site visits and clinical assessments. Yet, as highlighted previously, the reporting of behaviors and symptoms is increasingly important, and these outcomes are best captured in the day-to-day lives of patients outside the clinic.

Research shows that patients value the use of questionnaires to confirm their diagnosis and monitor their progress.24 PROs for assessing patients’ well-being, quality of life, functioning, symptom severity, and treatment satisfaction are important in assessing the burden of illness and in evaluating the impact of treatment25, with both the U.S. FDA26 and EMA27 encouraging the use of patient experience data, including PROs, in clinical trials.

Technology often provides an easier way to capture these signals. Most PROs can be captured directly by patients from home via mobile phone applications. These apps include easier and more intuitive ways to ensure patients understand how to complete tasks such as consent, diaries, and reporting symptoms and changes in health status. In addition to their ease of use, remote technologies can provide patients with the space they need to carefully review the consent forms. Certain studies are now implementing quizzes to ensure that patients fully understand the protocol. Increased understanding leads to higher engagement. ePRO also makes it easier for patients to communicate potentially sensitive information about feelings, symptoms, or self-care that they might be embarrassed to talk about face to face.

One important confounding factor in clinical trials is study contamination. This occurs when people in a trial, unbeknownst to the study team, self-medicate in ways that might impact outcomes—whether that be through taking supplements like St. John’s Wort, melatonin for insomnia, marijuana, or alcohol. When these “concomitant medications” are not recorded, they can skew study results. Traditional trials do their best to put safeguards in place to collect this information at visits.

But, allowing patients to report in their own time and space—using the smartphones in their pockets while being gently nudged through alerts and reminders—can deliver more accurate, and thus higher quality, data.

Technologies, such as sensors or wearables, can also be used to passively track certain activities or behaviors central to psychiatric assessment, including sociability, sleep/wake cycles, cognition, activity, and movement.28 These devices capture data in ways that can provide objective information to complement other more subjective or self-reported assessments and can serve as useful exploratory endpoints.

Telemedicine, introduced into clinical trials through real-time video or virtual communication, is another enabler of more patient-centric studies. The American Psychiatric Association rates the strength of evidence for using telemedicine to remotely interview, assess, and perform cognitive testing as outstanding, with high levels of feasibility, validity, reliability, and subject satisfaction.29 These findings also apply to depression trials, where data shows that conducting psychometric assessments in patients with MDD had equivalent outcomes when collected via clinical trial sites versus collection by telemedicine.29

Centralized raters are not a new phenomenon. But, their use in decentralized clinical trials represents an important opportunity to reduce patient burden while improving data reliability and quality. Jenny Ly, PhD, Senior Clinical Scientist at SPRIM, explains that the use of central raters separates the clinician from the researcher and, therefore, reduces the placebo effect. This is because participants being recruited by their own providers may fear the trial will impact the care that they would receive. Centralized raters can remove the pressure on patients who prioritize being “good participants” at the risk of jeopardizing their healthcare.

While the choice of tools and the outcomes measured will vary across protocols, the technologies mentioned above can reduce the effort and emotional discomfort associated with participation in a trial, leading to increased compliance and completion.

Improved communication

Ongoing communication between a patient and the study team can foster improved adherence. But, it is important to recognize that patients have different preferences in communication styles. Depression patients in particular often prefer alternatives to face-to-face communication modes.

Smartphone-based applications are becoming increasingly common for delivering psychological interventions to patients suffering from mental health disorders.30

For instance, a survey assessing preferences of adults for in-person versus digital psychotherapy options for depression revealed that 54 percent of adults preferred either self-guided, expert-guided, or peer-supported digital treatment.31

Inputting information into mobile-based applications is not foreign to this group of patients, with chat, SMS, and video increasingly embraced for personalized digital care.32-35 These same mobile app features can be put to use in clinical research. In fact, data shows dropout rates are lower in studies offering in-app mood monitoring and human feedback.33

Patient optionality should be built in whenever possible. Faith Matcham cites an example from a depression trial run in the UK: “Some people preferred to communicate exclusively via text, and we communicated with them uniquely through text, unless we had reason to believe they had a substantial relapse. For others, communication was via emails.” The key is, of course, to ensure that the quality and consistency of data are maintained, regardless of the communication channel.

Remote study teams available via text, email, or phone can often feel more accessible to patients than site-based teams. Patients know the study coordinators are there when needed but in a way that feels less intrusive. This can result in a better patient-study team connection that drives greater engagement. For example, findings from a randomized trial of depression follow-up via online messaging resulted in 20 percent higher adherence and lower symptom checklist depression scores after 5 months.36.
‍

Leveraging Technology to Improve Patient Safety

The use of the internet to treat mental health issues has more recently been extended to include the management of suicidal ideation and behavior, where discretion and anonymity play a key role.37

Research suggests adults are more likely to be honest about suicide ideation or suicidal thoughts when the information is collected electronically.38 There is growing evidence that digital interventions, including smartphone applications, can combat stigma-related issues.37 While fully anonymous participation may not be appropriate for certain studies, conditional anonymity—wherein contact details are only accessed in case of emergency—may be worth exploring.37

The use of smartphone apps for remote data capture also has the unique ability to enable real-time monitoring and detection of suicide risk. The importance of being able to capture real-time data to improve patient safety cannot be overstated. As explained by Taylor Major, MBA, MPH, Senior Implementation Manager for a decentralized depression trial currently under way, “A study design that incorporates remote reporting can actually increase safety, because you're more likely to capture events in near real time, rather than two weeks later when the patient comes into the clinic and it may be too late to do anything about it.”

It is interesting to note that privacy is commonly raised as a concern when information is collected electronically. Yet, a recent investigator survey37 found that the key concern for investigators was the adequate management of patients who present with suicidal thoughts or behaviors; issues related to safe storage of data or participant competency to consent did not emerge as prevalent themes.

‍

‍

Conclusions

Depression trials seek to evaluate a complex syndrome across a heterogenous population often suffering from fatigue, low motivation, and avoidance of face-to-face interactions. This cohort also experiences a broad diversity of symptoms. The traditional approach to these trials, which privileges regular and often lengthy on-site clinical assessments using standardized and somewhat archaic tools, is clearly not a recipe for successful evidence collection to support new therapies. If trial sponsors hope to recruit and motivate patients to participate in trials that can collect richer evidence on their symptoms and thus lead to more effective treatments, they will want to give strong consideration to remote technologies.

The DCT model is an evolving, but powerful, solution to address many of the prevailing challenges associated with depression trials. Remote and technology-enabled reporting can facilitate the real-time capture of depression severity as well as specific symptomology. More anonymized communications can increase patient comfort levels and honesty of reporting. Device use can complement ePRO to capture complementary data on sleep, activity, facial features, as well as other behaviors, with the potential to serve as future novel outcomes.

Despite this, tremendous resistance persists amongst trial sponsors and practitioners who do not yet feel comfortable with the risk-reward ratio associated with adopting new technologies. No doubt, the best way to gain confidence for these sponsors is through an incremental approach. In fact, this paper does not advocate for all-or-nothing decentralization.

Hybridizing elements of trials with a mix of remote and on-site methodologies may improve trial efficiency and expand patient access while maintaining the benefits of on-site equipment and staff procedural expertise.39

But, there is no doubt that we will continue to see clinician-rated outcomes giving way to more patient-reported outcomes. This will require study designs to evolve with a focus on how to make the trial process more accessible, convenient, and engaging for patients.

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References

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