Conducting a fully mobile and randomised clinical trial for depression: access, engagement and expense

Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments

Mental health treatment advances - including neuropsychiatric medications and devices, psychotherapies, and cognitive treatments - lag behind other fields of clinical medicine such as cardiovascular care. One reason for this gap is the traditional techniques used in mental health clinical trials, which slow the pace of progress, produce inequities in care, and undermine precision medicine goals. Newer techniques and methodologies, which we term digital and precision trials, offer solutions. These techniques consist of (1) decentralized (i.e., fully-remote) trials which improve the speed and quality of clinical trials and increase equity of access to research, (2) precision measurement which improves success rate and is essential for precision medicine, and (3) digital interventions, which offer increased reach of, and equity of access to, evidence-based treatments. These techniques and their rationales are described in detail, along with challenges and solutions for their utilization. We conclude with a vignette of a depression clinical trial using these techniques.

Text messaging intervention to support patients with chronic pain during prescription opioid tapering: protocol for a double-blind randomised controlled trial

INTRODUCTION

Increases in pain and interference with quality of life is a common concern among people with chronic non-cancer pain (CNCP) who are tapering opioid medications. Research indicates that access to social and psychological support for pain self-management may help people to reduce their opioid dose without increasing pain and interference. This study evaluates the efficacy of a text messaging intervention designed to provide people with CNCP with social and psychological support for pain self-management while tapering long-term opioid therapy (LTOT) under the guidance of their prescriber.

METHODS AND ANALYSIS

A double-blind randomised controlled trial will be conducted. Patients with CNCP (n=74) who are tapering LTOT will be enrolled from across Australia. Participants will continue with their usual care while tapering LTOT under the supervision of their prescribing physician. They will randomly receive either a psychoeducational video and supportive text messaging (two Short Message Service (SMS) per day) for 12 weeks or the video only. The primary outcome is the pain intensity and interference assessed by the Pain, Enjoyment of Life and General Activity scale. Secondary outcomes include mood, self-efficacy, pain cognitions, opioid dose reduction, withdrawal symptoms, and acceptability, feasibility, and safety of the intervention. Participants will complete questionnaires at baseline and then every 4 weeks for 12 weeks and will be interviewed at week 12. This trial will provide evidence for the efficacy of a text messaging intervention to support patients with CNCP who are tapering LTOT. If proven to be efficacious and safe, this low-cost intervention can be implemented at scale.

ETHICS AND DISSEMINATION

The study protocol was reviewed and approved by the Northern Sydney Local Health District (Australia). Study results will be published in peer-reviewed journals and presented at scientific and professional meetings.

TRIAL REGISTRATION NUMBER

ACTRN12622001423707.

Assessing Patient Adherence to and Engagement With Digital Interventions for Depression in Clinical Trials: Systematic Literature Review

BACKGROUND

New approaches to the treatment of depression are necessary for patients who do not respond to current treatments or lack access to them because of barriers such as cost, stigma, and provider shortage. Digital interventions for depression are promising; however, low patient engagement could limit their effectiveness.

OBJECTIVE

This systematic literature review (SLR) assessed how participant adherence to and engagement with digital interventions for depression have been measured in the published literature, what levels of adherence and engagement have been reported, and whether higher adherence and increased engagement are linked to increased efficacy.

METHODS

We focused on a participant population of adults (aged ≥18 years) with depression or major depressive disorder as the primary diagnosis and included clinical trials, feasibility studies, and pilot studies of digital interventions for treating depression, such as digital therapeutics. We screened 756 unique records from Ovid MEDLINE, Embase, and Cochrane published between January 1, 2000, and April 15, 2022; extracted data from and appraised the 94 studies meeting the inclusion criteria; and performed a primarily descriptive analysis. Otsuka Pharmaceutical Development & Commercialization, Inc (Princeton, New Jersey, United States) funded this study.

RESULTS

This SLR encompassed results from 20,111 participants in studies using 47 unique web-based interventions (an additional 10 web-based interventions were not described by name), 15 mobile app interventions, 5 app-based interventions that are also accessible via the web, and 1 CD-ROM. Adherence was most often measured as the percentage of participants who completed all available modules. Less than half (44.2%) of the participants completed all the modules; however, the average dose received was 60.7% of the available modules. Although engagement with digital interventions was measured differently in different studies, it was most commonly measured as the number of modules completed, the mean of which was 6.4 (means ranged from 1.0 to 19.7) modules. The mean amount of time participants engaged with the interventions was 3.9 (means ranged from 0.7 to 8.4) hours. Most studies of web-based (34/45, 76%) and app-based (8/9, 89%) interventions found that the intervention group had substantially greater improvement for at least 1 outcome than the control group (eg, care as usual, waitlist, or active control). Of the 14 studies that investigated the relationship between engagement and efficacy, 9 (64%) found that increased engagement with digital interventions was significantly associated with improved participant outcomes. The limitations of this SLR include publication bias, which may overstate engagement and efficacy, and low participant diversity, which reduces the generalizability.

CONCLUSIONS

Patient adherence to and engagement with digital interventions for depression have been reported in the literature using various metrics. Arriving at more standardized ways of reporting adherence and engagement would enable more effective comparisons across different digital interventions, studies, and populations.

Better together: novel methods for measuring and modeling development of executive function diversity while accounting for unity

INTRODUCTION

Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented precise understanding of the developmental trajectory of their organization.

METHODS

We introduce novel methods to address challenges for both measuring and modeling EFs using an accelerated longitudinal design with a large, diverse sample of students in middle childhood (N = 1,286; ages 8 to 14). We used eight adaptive assessments hypothesized to measure three EFs, working memory, context monitoring, and interference resolution. We deployed adaptive assessments to equate EF challenge across ages and a data-driven, network analytic approach to reveal the evolving diversity of EFs while simultaneously accounting for their unity.

RESULTS AND DISCUSSION

Using this methodological paradigm shift brought new precision and clarity to the development of these EFs, showing these eight tasks are organized into three stable components by age 10, but refinement of composition of these components continues through at least age 14.

Integrating the "Quit and Stay Quit Monday" Model into Smoking Cessation Services for Smokers with Mental Health Conditions: A Pilot Randomized Controlled Trial

Introduction

People with mental health conditions (MHCs) are less likely to achieve long-term abstinence than people without MHCs. The Quit and Stay Quit Monday (QSQM) model offers a long-term approach to treating tobacco use by encouraging people to quit, requit, or recommit to quit smoking every Monday.

Aim

To evaluate the efficacy, patient satisfaction, and patient engagement with an intervention that integrated the QSQM model into multicomponent smoking cessation services among people with an MHC.

Methods

This was a randomized controlled pilot trial. Eligibility criteria were as follows: (1) ≥18 years old, (2) smoked a cigarette in the past 30 days, (3) diagnosis of an ICD-10 MHC, (4) interest in quitting smoking, (5) able to receive services in English, and (5) had an active email and a cell phone. The intervention group (n = 33) received QSQM-focused telephone coaching, a weekly QSQM email newsletter, a SmokefreeTXT anchored around a Monday quit date, and 4 weeks of nicotine replacement therapy (NRT). The control group (n = 36) received information about contacting their state Quitline for usual services. Primary outcomes were self-reported quit attempts, 7-day abstinence, and intervention satisfaction at 3 months.

Results

Twenty-four participants (73%) in the intervention group began telephone coaching, 26 (79%) enrolled in the QSQM email newsletter, 19 (58%) enrolled in SmokefreeTXT, and 15 (46%) used NRT. Using a penalized intent-to-treat approach, quit attempts in the intervention and control groups were 63.6% and 38.9% (OR 2.75, 95% CI 1.03-7.30), respectively. Seven-day abstinence in the two groups was 12.1% and 5.6% (OR 2.35, 95% CI 0.40-13.74), respectively. Of the 15 intervention group participants who set a quit date during the intervention, 13 (86.7%) selected a Monday quit day. Qualitative interviews revealed positive participant experiences with picking a Monday quit day. On follow-up surveys, 89.5%, 69.3%, and 64.3% of intervention participants reported that the counseling, QSQM email, and text messaging, respectively, were very or somewhat helpful.

Conclusions

The QSQM model was acceptable and potentially efficacious among people with MHCs, but intervention engagement and satisfaction were modest. Future research should adapt or develop new QSQM delivery approaches to improve patient engagement and potential efficacy of the model. This trial is registered with clinicaltrials.gov (NCT04512248).

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Introduction

Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has.

Methods

We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals.

Results

Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention.

Discussion

We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed.

Systematic Review Registration

Identifier: CRD42022357408.

Contribution of sustained attention abilities to real-world academic skills in children

Sustained attention is a critical cognitive ability that improves over the course of development and predicts important real-world outcomes, such as academic achievement. However, the majority of work demonstrating links between sustained attention and academic skills has been conducted in lab-based settings that lack the ecological validity of a more naturalistic environment, like school. Further, most studies focus on targeted academic measures of specific sub-skills and have not fully examined whether this relationship generalizes to broad measures of academic achievement that are used for important, real-world, academic advancement decisions, such as standardized test scores. To address this gap, we examined the role of sustained attention in predicting targeted and broad assessments of academic abilities, where all skills were assessed in group-based environments in schools. In a sample of over 700 students aged 9-14, we showed that attention was positively related to performance on targeted assessments (math fluency and reading comprehension), as well as broad academic measures (statewide standardized test scores). Moreover, we found that attention was more predictive of targeted math sub-skills compared to assessments of broad math abilities, but was equally predictive of reading for both types of measures. Our findings add to our understanding of how sustained attention is linked to academic skills assessed in more 'real-world', naturalistic school environments and have important implications for designing tools to support student's academic success.

Recruiting for a Randomized Clinical Trial for Late-Life Depression During COVID-19: Outcomes of Provider Referrals Versus Facebook Self-Referrals

OBJECTIVE

To evaluate the effectiveness of online recruitment for a clinical trial of pharmacotherapy for late-life depression during COVID-19.

METHODS

The authors calculated the yield, defined as recruitment leading to randomization (enrollment), from provider referrals versus Facebook self-referrals; compared characteristics and drop-out rates of participants from each source; and analyzed correlations between stringency of public health restrictions and referrals from each source over time.

RESULTS

Provider referrals had a significantly higher yield (10 of 33 referrals; 30.3%) versus Facebook self-referrals (14 of 323; 4.3%) (p <0.00001). Participants self-referred from Facebook had significantly more education; otherwise, both groups had similar characteristics and drop-out rates. While public health stringency was negatively correlated with provider referrals (ρ = -0.32) and positively correlated with Facebook self-referrals (ρ = 0.39), neither association reached statistical significance.

CONCLUSION

Online recruitment may improve access to clinical research for older depressed adults. Future studies should evaluate cost-effectiveness and potential barriers such as computer literacy.

Promoting participation in remote digital health studies: An expert interview study

Background

Remote digital health studies are on the rise and promise to reduce the operational inefficiencies of in-person research. However, they encounter specific challenges in maintaining participation (enrollment and retention) due to their exclusive reliance on technology across all study phases.

Objective

The goal of this study was to collect experts' opinions on how to facilitate participation in remote digital health studies.

Method

We conducted 13 semi-structured interviews with principal investigators, researchers, and software developers who had recent experiences with remote digital health studies. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, we performed a thematic analysis and mapped various approaches to successful study participation.

Results

Our analyses revealed four themes: (1) study planning to increase participation, where experts suggest that remote digital health studies should be planned based on adequate knowledge of what motivates, engages, and disengages a target population; (2) participant enrollment, highlighting that enrollment strategies should be selected carefully, attached to adequate support, and focused on inclusivity; (3) participant retention, with strategies that minimize the effort and complexity of study tasks and ensure that technology is adapted and responsive to participant needs, and (4) requirements for study planning focused on the development of relevant guidelines to foster participation in future studies.

Conclusions

Our findings highlight the significant requirements for seamless technology and researcher involvement in enabling high remote digital health study participation. Future studies can benefit from collected experiences and the development of guidelines to inform planning that balances participant and scientific requirements.

Participant retention in a fully remote trial of digital psychotherapy: Comparison of incentive types

Numerous studies have found that long term retention is very low in remote clinical studies (&gt;4 weeks) and to date there is limited information on the best methods to ensure retention. The ability to retain participants in the completion of key assessments periods is critical to all clinical research, and to date little is known as to what methods are best to encourage participant retention. To study incentive-based retention methods we randomized 215 US adults (18+ years) who agreed to participate in a sequential, multiple assignment randomized trial to either high monetary incentive (HMI, $125 USD) and combined low monetary incentive ($75 USD) plus alternative incentive (LMAI). Participants were asked to complete daily and weekly surveys for a total of 12 weeks, which included a tailoring assessment around week 5 to determine who should be stepped up and rerandomized to one of two augmentation conditions. Key assessment points were weeks 5 and 12. There was no difference in participant retention at week 5 (tailoring event), with approximately 75% of the sample completing the week-5 survey. By week 10, the HMI condition retained approximately 70% of the sample, compared to 60% of the LMAI group. By week 12, all differences were attenuated. Differences in completed measures were not significant between groups. At the end of the study, participants were asked the impressions of the incentive condition they were assigned and asked for suggestions for improving engagement. There were no significant differences between conditions on ratings of the fairness of compensation, study satisfaction, or study burden, but study burden, intrinsic motivation and incentive fairness did influence participation. Men were also more likely to drop out of the study than women. Qualitative analysis from both groups found the following engagement suggestions: desire for feedback on survey responses and an interest in automated sharing of individual survey responses with study therapists to assist in treatment. Participants in the LMAI arm indicated that the alternative incentives were engaging and motivating. In sum, while we were able to increase engagement above what is typical for such study, more research is needed to truly improve long term retention in remote trials.

Understanding users’ perspectives on mobile apps for anxiety management

Anxiety disorders are the most common type of mental health problem. The potential of apps to improve mental health has led to an increase in the number of anxiety apps available. Even though anxiety apps hold the potential to enhance mental health care for individuals, there is relatively little knowledge concerning users’ perspectives. This mixed-methods study aims to understand the nature of user burden and engagement with mental health apps (MHapps) targeting anxiety management, in order to identify ways to improve the design of these apps. Users’ perspectives on these apps were gathered by analyzing 600 reviews from 5 apps on the app stores (Study 1), and conducting 15 interviews with app users (Study 2). The results shed light on several barriers to adoption and sustained use. Users appreciate apps that offer content variation, customizability, and good interface design, and often requested an enhanced, personalized experience to improve engagement. We propose addressing the specific app quality issues identified through human-centered design, more personalized content delivery, and by improving features for social and therapeutic support.

Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression

Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a timely and long-term data resource to evaluate analytical approaches for developing digital behavioral markers and understand the effectiveness of mental health care delivered continuously and remotely.

Recruitment & Retention in Remote Research: Learnings from a Large Decentralized Real-World Study (Preprint)

BACKGROUND

Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner.

OBJECTIVE

We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated.

METHODS

We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression.

RESULTS

We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants' sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence.

CONCLUSIONS

Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.

Digital Intervention for Cognitive Deficits in Major Depression: A Randomized Controlled Trial to Assess Efficacy and Safety in Adults

OBJECTIVE

The authors evaluated AKL-T03, an investigational digital intervention delivered through a video game-based interface, designed to target the fronto-parietal network to enhance functional domains for attentional control. AKL-T03 was tested in adult patients with major depressive disorder and a demonstrated cognitive impairment at baseline.

METHODS

Adults ages 25-55 years on a stable antidepressant medication regimen with residual mild to moderate depression and an objective impairment in cognition (as measured using the symbol coding test) were enrolled in a double-blind randomized controlled study. Participants were randomized either to AKL-T03 or to an expectation-matched digital control intervention. Participants were assessed at baseline and after completion of their 6-week at-home intervention. The primary outcome measure was improvement in sustained attention, as measured by the Test of Variables of Attention (TOVA).

RESULTS

AKL-T03 (N=37) showed a statistically significant medium-effect-size improvement in sustained attention compared with the control intervention on the TOVA primary outcome (N=37) (partial eta-squared=0.11). Additionally, a composite score derived from all cognitive measures demonstrated significant improvement with AKL-T03 over the control intervention. Individual secondary and exploratory endpoints did not demonstrate statistically significant between-group differences. No serious adverse events were reported, and two patients (5.5%) in the AKL-T03 group reported an intervention-related adverse event (headache).

CONCLUSIONS

Treatment with AKL-T03 resulted in significant improvement in sustained attention, as well as in cognitive functioning as a whole, compared with a control intervention. AKL-T03 is a safe digital intervention that is effective in the treatment of cognitive impairment associated with major depression. Further research will be needed to understand the clinical consequences of this treatment-induced change.

Validation of At-Home Application of a Digital Cognitive Screener for Older Adults

Standardized neuropsychological assessments of older adults are important for both clinical diagnosis and biobehavioral research. Over decades, in-person testing has been the basis for population normative values that rank cognitive performance by demographic status. Most recently, digital tools have enabled remote data collection for cognitive measures, which offers the significant promise to extend the basis for normative values to be more inclusive of a larger cross section of the older population. We developed a Remote Characterization Module (RCM), using a speech-to-text interface, as a novel digital tool to administer an at-home, 25-min cognitive screener that mimics eight standardized neuropsychological measures. Forty cognitively healthy participants were recruited from a longitudinal aging research cohort, and they performed the same measures of memory, attention, verbal fluency and set-shifting in both in-clinic paper-and-pencil (PAP) and at-home RCM versions. The results showed small differences, if any, for how participants performed on in-person and remote versions in five of eight tasks. Critically, robust correlations between their PAP and RCM scores across participants support the finding that remote, digital testing can provide a reliable assessment tool for rapid and remote screening of healthy older adults’ cognitive performance in several key domains. The implications for digital cognitive screeners are discussed.

A systematic review of engagement reporting in remote measurement studies for health symptom tracking

Remote Measurement Technologies (RMTs) could revolutionise management of chronic health conditions by providing real-time symptom tracking. However, the promise of RMTs relies on user engagement, which at present is variably reported in the field. This review aimed to synthesise the RMT literature to identify how and to what extent engagement is defined, measured, and reported, and to present recommendations for the standardisation of future work. Seven databases (Embase, MEDLINE and PsycINFO (via Ovid), PubMed, IEEE Xplore, Web of Science, and Cochrane Central Register of Controlled Trials) were searched in July 2020 for papers using RMT apps for symptom monitoring in adults with a health condition, prompting users to track at least three times during the study period. Data were synthesised using critical interpretive synthesis. A total of 76 papers met the inclusion criteria. Sixty five percent of papers did not include a definition of engagement. Thirty five percent included both a definition and measurement of engagement. Four synthetic constructs were developed for measuring engagement: (i) engagement with the research protocol, (ii) objective RMT engagement, (iii) subjective RMT engagement, and (iv) interactions between objective and subjective RMT engagement. The field is currently impeded by incoherent measures and a lack of consideration for engagement definitions. A process for implementing the reporting of engagement in study design is presented, alongside a framework for definition and measurement options available. Future work should consider engagement with RMTs as distinct from the wider eHealth literature, and measure objective versus subjective RMT engagement.Registration: This review has been registered on PROSPERO [CRD42020192652].

Leveraging technology to personalize cognitive enhancement methods in aging

As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.

Enrollment and retention of participants in remote digital health studies: a scoping review and framework proposal (Preprint)

Background

Objective

Methods

Results

Conclusions

The Impact of Personalized Human Support on Engagement With Behavioral Intervention Technologies for Employee Mental Health: An Exploratory Retrospective Study

Digital healthcare has grown in popularity in recent years as a scalable solution to address increasing rates of mental illness among employees, but its clinical potential is limited by low engagement and adherence, particularly in open access interventions. Personalized guidance, involving structuring an intervention and tailoring it to the user to increase accountability and social support, is one way to increase engagement with digital health programs. This exploratory retrospective study therefore sought to examine the impact of guidance in the form of personalized prompts from a lay-person (i.e., non-health professional) on user's (N = 88) engagement with a 16-week Behavioral Intervention Technology targeting employee mental health and delivered through a mobile application. Chi-squared tests and Mann-Whitney tests were used to examine differences in retention and engagement between individuals who received personalized prompts throughout their 4-month program and individuals for whom personalized prompts were introduced in the seventh week of their program. There were no significant differences between the groups in the number of weeks they remained active in the app (personalized messages group Mdn = 3.5, IQR = 3; control group Mdn = 2.5, IQR = 4.5; p = 0.472). In the first 3 weeks of the intervention program, the proportion of individuals who explored the educational modules feature and the messaging with health coaches feature was also not significantly associated with group (ps = 1.000). The number of modules completed and number of messages sent to health coaches in the first 3 weeks did not differ significantly between the two groups (ps ≥ 0.311). These results suggest that guidance from a non-health professional is limited in its ability to increase engagement with an open access Behavioral Intervention Technology for employees. Moreover, the findings suggest that the formation of a relationship between the individual and the agent providing the guidance may be necessary in order for personalized guidance to increase engagement.

Lessons Learned Recruiting and Retaining Pregnant and Postpartum Individuals in Digital Trials: Viewpoint

In an increasingly connected world and in the midst of a global pandemic, digital trials offer numerous advantages over traditional trials that rely on physical study sites. Digital trials have the potential to improve access to research and clinical treatments for the most vulnerable and minoritized, including pregnant and postpartum individuals. However, digital trials are underutilized in maternal and child health research, and there is limited evidence to inform the design and conduct of digital trials. Our research collaborative, consisting of 5 research teams in the U.S. and Australia, aimed to address this gap. We collaborated to share lessons learned from our experiences recruiting and retaining pregnant and postpartum individuals in digital trials of social and behavioral interventions. We first discuss the promise of digital trials in improving participation in research during the perinatal period, as well as the unique challenges they pose. Second, we present lessons learned from 12 completed and ongoing digital trials that have used platforms such as Ovia, Facebook, and Instagram for recruitment. Our trials evaluated interventions for breastfeeding, prenatal and postpartum depression, insomnia, decision making, and chronic pain. We focus on challenges and lessons learned in 3 key areas: (1) rapid recruitment of large samples with a diversity of minoritized identities, (2) retention of study participants in longitudinal studies, and (3) prevention of fraudulent enrollment. We offer concrete strategies that we pilot-tested to address these challenges. Strategies presented in this commentary can be incorporated, as well as formally evaluated, in future studies.

Remote smartphone monitoring of Parkinson's disease and individual response to therapy

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10^-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.

Recruitment and engagement in disease self-management programs: Special concerns for rural residents reporting depression and/or anxiety

•

Poorer health outcomes are correlated with depression/anxiety in a rural population.

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Electronic modes of recruitment engage those reporting depression/anxiety into CDSME.

•

CDSME increases patient activation regardless of history of depression/anxiety.

Chronic disease self-management education (CDSME) programs benefit individuals with chronic diseases, including mental health conditions, by improving health-related outcomes and increasing engagement with the health care system. Recruiting individuals with a history of mental health conditions to participate in CDSME is challenging, particularly in rural, underserved areas. Hence, it is important to understand factors associated with the presence of mental health conditions, and impacts of CDSME on patient engagement. This project identifies individual and program-level characteristics, as well as recruitment characteristics, associated with reporting a history of depression and/or anxiety. It also assesses factors related to program engagement and the relationship between completing CDSME and patient activation. Data were collected during CDSME workshops offered in 2019 in a rural region of New York. Of the 421 enrollees who completed survey instruments, 162 reported a history of depression and/or anxiety. Univariate analyses indicated that those reporting a history of depression and/or anxiety were younger, female, in poorer health, had more comorbidities, were Medicaid beneficiaries, and had lower patient activation scores. They also heard about and signed up for the workshop through the internet at higher rates than those not reporting a history of depression and/or anxiety. Multivariable logistic regression modeling indicated age, self-rated health, and number of comorbidities were independent predictors of reporting a history of depression and/or anxiety. Among CDSME completers, patient activation significantly improved regardless of history of depression and/or anxiety. Engaging individuals with mental health conditions in CDSME requires a multimodal recruitment strategy incorporating electronic marketing and registration.

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Background

Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.

Objective

This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.

Methods

We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.

Results

A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.

Conclusions

This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

Adolescent dropout from brief digital mental health interventions within and beyond randomized trials

OBJECTIVE

Many adolescents struggle to access appropriate mental health care due to structural or psychological barriers. Although traditional barriers to participation (e.g., location, cost) are hypothetically reduced or removed in internet interventions, low retention reduces the likelihood that adolescents will receive the intervention dosage intended to produce beneficial effects. It is therefore key to determine what factors are associated with dropout in digital mental health interventions with adolescents both within and beyond the context of research studies.

METHODS

We compare completion rates from two projects evaluating self-guided, online single-session mental health interventions (SSIs) for adolescents. One was a randomized controlled trial (RCT) in which participants were paid for participation. The other was a program evaluation project in which participants were not paid for participation. We additionally compare SSI completion rates across various demographic groups and across baseline hopelessness levels.

RESULTS

There was a statistically significant difference in SSI completion status between the RCT (84.75% full-completers) and the program evaluation (36.86% full-completers), X ² (2, N = 2436) = 583.5, p < 0.05. There were no significant differences in the baseline hopelessness scores across completion statuses in either study. There were no significant differences in full-completion rates across demographic groups in either project.

CONCLUSION

Adolescents may be more likely to complete a brief digital mental health intervention in a paid, research-based context than in an unpaid, naturalistic context. Additionally, it is possible that the brevity of SSIs reduces demographic disparities related to retention by minimizing the time required to complete an intervention.

Impact of telelactation services on breastfeeding outcomes among Black and Latinx parents: protocol for the Tele-MILC randomized controlled trial

BACKGROUND

Breastfeeding offers many medical and neurodevelopmental advantages for birthing parents and infants; however, the majority of parents stop breastfeeding before it is recommended. Professional lactation support by the International Board Certified Lactation Consultants (IBCLCs) increases breastfeeding rates; however, many communities lack access to IBCLCs. Black and Latinx parents have lower breastfeeding rates, and limited access to professional lactation support may contribute to this disparity. Virtual "telelactation" consults that use two-way video have the potential to increase access to IBCLCs among disadvantaged populations. We present a protocol for the digital Tele-MILC trial, which uses mixed methods to evaluate the impact of telelactation services on breastfeeding outcomes. The objective of this pragmatic, parallel design randomized controlled trial is to assess the impact of telelactation on breastfeeding duration and exclusivity and explore how acceptability of and experiences with telelactation vary across Latinx, Black, and non-Black and non-Latinx parents to guide future improvement of these services.

METHODS

2400 primiparous, pregnant individuals age > 18 who intend to breastfeed and live in the USA underserved by IBCLCs will be recruited. Recruitment will occur via Ovia, a pregnancy tracker mobile phone application (app) used by over one million pregnant individuals in the USA annually. Participants will be randomized to (1) on-demand telelactation video calls on personal devices or (2) ebook on infant care/usual care. Breastfeeding outcomes will be captured via surveys and interviews and compared across racial and ethnic groups. This study will track participants for 8 months (including 6 months postpartum). Primary outcomes include breastfeeding duration and breastfeeding exclusivity. We will quantify differences in these outcomes across racial and ethnic groups. Both intention-to-treat and as-treated (using instrumental variable methods) analyses will be performed. This study will also generate qualitative data on the experiences of different subgroups of parents with the telelactation intervention, including barriers to use, satisfaction, and strengths and limitations of this delivery model.

DISCUSSION

This is the first randomized study evaluating the impact of telelactation on breastfeeding outcomes. It will inform the design and implementation of future digital trials among pregnant and postpartum people, including Black and Latinx populations which are historically underrepresented in clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04856163. Registered on April 23, 2021.

Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study

Background

Cognitive impairment (CI) is one of the most prevalent symptoms of multiple sclerosis (MS). However, it is difficult to include cognitive assessment as part of MS standard care since the comprehensive neuropsychological examinations are usually time-consuming and extensive.

Objective

To improve access to CI assessment, we evaluated the feasibility and potential assessment sensitivity of a tablet-based cognitive battery in patients with MS.

Methods

In total, 53 participants with MS (24 [45%] with CI and 29 [55%] without CI) and 24 non-MS participants were assessed with a tablet-based cognitive battery (Adaptive Cognitive Evaluation [ACE]) and standard cognitive measures, including the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). Associations between performance in ACE and the SDMT/PASAT were explored, with group comparisons to evaluate whether ACE modules can capture group-level differences.

Results

Correlations between performance in ACE and the SDMT (R=–0.57, P<.001), as well as PASAT (R=–0.39, P=.01), were observed. Compared to non-MS and non-CI MS groups, the CI MS group showed a slower reaction time (CI MS vs non-MS: P<.001; CI MS vs non-CI MS: P=.004) and a higher attention cost (CI MS vs non-MS: P=.02; CI MS vs non-CI MS: P<.001).

Conclusions

These results provide preliminary evidence that ACE, a tablet-based cognitive assessment battery, provides modules that could potentially serve as a digital cognitive assessment for people with MS.

Trial Registration

ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618

Exploring the Effects of In-App Components on Engagement With a Symptom-Tracking Platform Among Participants With Major Depressive Disorder (RADAR-Engage): Protocol for a 2-Armed Randomized Controlled Trial

BACKGROUND

Multi-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice.

OBJECTIVE

The aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)-base.

METHODS

We aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement).

RESULTS

Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022.

CONCLUSIONS

This study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/32653.

A Conversational Artificial Intelligence Agent for a Mental Health Care App: Evaluation Study of Its Participatory Design

BACKGROUND

Mobile apps for mental health are available on the market. Although they seem to be promising for improving the accessibility of mental health care, little is known about their acceptance, design methodology, evaluation, and integration into psychotherapy protocols. This makes it difficult for health care professionals to judge whether these apps may help them and their patients.

OBJECTIVE

Our aim is to describe and evaluate a protocol for the participatory design of mobile apps for mental health. In this study, participants and psychotherapists are engaged in the early phases of the design and development of the app empowered by conversational artificial intelligence (AI). The app supports interventions for stress management training based on cognitive behavioral theory.

METHODS

A total of 21 participants aged 33-61 years with mild to moderate levels of stress, anxiety, and depression (assessed by administering the Italian versions of the Symptom Checklist-90-Revised, Occupational Stress Indicator, and Perceived Stress Scale) were assigned randomly to 2 groups, A and B. Both groups received stress management training sessions along with cognitive behavioral treatment, but only participants assigned to group A received support through a mobile personal health care agent, designed for mental care and empowered by AI techniques. Psychopathological outcomes were assessed at baseline (T1), after 8 weeks of treatment (T2), and 3 months after treatment (T3). Focus groups with psychotherapists who administered the therapy were held after treatment to collect their impressions and suggestions.

RESULTS

Although the intergroup statistical analysis showed that group B participants could rely on better coping strategies, group A participants reported significant improvements in obsessivity and compulsivity and positive distress symptom assessment. The psychotherapists' acceptance of the protocol was good. In particular, they were in favor of integrating an AI-based mental health app into their practice because they could appreciate the increased engagement of patients in pursuing their therapy goals.

CONCLUSIONS

The integration into practice of an AI-based mobile app for mental health was shown to be acceptable to both mental health professionals and users. Although it was not possible in this experiment to show that the integration of AI-based conversational technologies into traditional remote psychotherapy significantly decreased the participants' levels of stress and anxiety, the experimental results showed significant trends of reduction of symptoms in group A and their persistence over time. The mental health professionals involved in the experiment reported interest in, and acceptance of, the proposed technology as a promising tool to be included in a blended model of psychotherapy.

A Smartphone Intervention for People With Serious Mental Illness: Fully Remote Randomized Controlled Trial of CORE

Background

People with serious mental illness (SMI) have significant unmet mental health needs. Development and testing of digital interventions that can alleviate the suffering of people with SMI is a public health priority.

Objective

The aim of this study is to conduct a fully remote randomized waitlist-controlled trial of CORE, a smartphone intervention that comprises daily exercises designed to promote reassessment of dysfunctional beliefs in multiple domains.

Methods

Individuals were recruited via the web using Google and Facebook advertisements. Enrolled participants were randomized into either active intervention or waitlist control groups. Participants completed the Beck Depression Inventory-Second Edition (BDI-II), Generalized Anxiety Disorder-7 (GAD-7), Hamilton Program for Schizophrenia Voices, Green Paranoid Thought Scale, Recovery Assessment Scale (RAS), Rosenberg Self-Esteem Scale (RSES), Friendship Scale, and Sheehan Disability Scale (SDS) at baseline (T1), 30-day (T2), and 60-day (T3) assessment points. Participants in the active group used CORE from T1 to T2, and participants in the waitlist group used CORE from T2 to T3. Both groups completed usability and accessibility measures after they concluded their intervention periods.

Results

Overall, 315 individuals from 45 states participated in this study. The sample comprised individuals with self-reported bipolar disorder (111/315, 35.2%), major depressive disorder (136/315, 43.2%), and schizophrenia or schizoaffective disorder (68/315, 21.6%) who displayed moderate to severe symptoms and disability levels at baseline. Participants rated CORE as highly usable and acceptable. Intent-to-treat analyses showed significant treatment×time interactions for the BDI-II (F_1,313=13.38; P<.001), GAD-7 (F_1,313=5.87; P=.01), RAS (F_1,313=23.42; P<.001), RSES (F_1,313=19.28; P<.001), and SDS (F_1,313=10.73; P=.001). Large effects were observed for the BDI-II (d=0.58), RAS (d=0.61), and RSES (d=0.64); a moderate effect size was observed for the SDS (d=0.44), and a small effect size was observed for the GAD-7 (d=0.20). Similar changes in outcome measures were later observed in the waitlist control group participants following crossover after they received CORE (T2 to T3). Approximately 41.5% (64/154) of participants in the active group and 60.2% (97/161) of participants in the waitlist group were retained at T2, and 33.1% (51/154) of participants in the active group and 40.3% (65/161) of participants in the waitlist group were retained at T3.

Conclusions

We successfully recruited, screened, randomized, treated, and assessed a geographically dispersed sample of participants with SMI entirely via the web, demonstrating that fully remote clinical trials are feasible in this population; however, study retention remains challenging. CORE showed promise as a usable, acceptable, and effective tool for reducing the severity of psychiatric symptoms and disability while improving recovery and self-esteem. Rapid adoption and real-world dissemination of evidence-based mobile health interventions such as CORE are needed if we are to shorten the science-to-service gap and address the significant unmet mental health needs of people with SMI during the COVID-19 pandemic and beyond.

Trial Registration

ClinicalTrials.gov NCT04068467; https://clinicaltrials.gov/ct2/show/NCT04068467

Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study

Background

Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health.

Objective

This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations.

Methods

We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform’s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study.

Results

Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users’ data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser–based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings.

Conclusions

We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health–related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

What Works Best to Engage Participants in Mobile App Interventions and e-Health: A Scoping Review

Background:

Despite the growing popularity of mobile app interventions, specific engagement components of mobile apps have not been well studied.

Methods:

The objectives of this scoping review are to determine which components of mobile health intervention apps encouraged or hindered engagement, and examine how studies measured engagement.

Results:

A PubMed search on March 5, 2020 yielded 239 articles that featured the terms engagement, mobile app/mobile health, and adult. After applying exclusion criteria, only 54 studies were included in the final analysis.

Discussion:

Common app components associated with increased engagement included: personalized content/feedback, data visualization, reminders/push notifications, educational information/material, logging/self-monitoring functions, and goal-setting features. On the other hand, social media integration, social forums, poor app navigation, and technical difficulties appeared to contribute to lower engagement rates or decreased usage. Notably, the review revealed a great variability in how engagement with mobile health apps is measured due to lack of established processes.

Conclusion:

There is a critical need for controlled studies to provide guidelines and standards to help facilitate engagement and its measurement in research and clinical trial work using mobile health intervention apps.

Technological prescription: evaluation of the effectiveness of mobile applications to improve depression and anxiety. Systematic review

Several studies have shown that, due to their features, mobile applications have a great potential to address mental health in depression and anxiety. We carried out a systematic review of publications from the last 10 years: from 1 January 2010 until 31 March 2020. Systematic reviews and meta-analyses related to the research question were also selected to identify other potentially eligible studies. The literature search in selected databases returned a total of 3,011 records from which a total of 22 articles were finally selected. The main conclusion of the study is that most of the scientific evidence found supports the hypothesis that mobile applications significantly improve the symptoms associated with depression and anxiety. Therefore, their effectiveness as a digital tool in the treatment of such health problems is proven. However, further studies and further evaluations of mobile applications are required (also in other languages) to incorporate this resource into the healthcare context. In addition, since mobile applications allow reinforcing concepts such as patient empowerment, shared decision-making and health literacy, their use would be highly positive for depression and anxiety, where there is a strong element of self-managing the disease.

Understanding People's Use of and Perspectives on Mood-Tracking Apps: Interview Study

BACKGROUND

Supporting mental health and wellness is of increasing interest due to a growing recognition of the prevalence and burden of mental health issues. Mood is a central aspect of mental health, and several technologies, especially mobile apps, have helped people track and understand it. However, despite formative work on and dissemination of mood-tracking apps, it is not well understood how mood-tracking apps used in real-world contexts might benefit people and what people hope to gain from them.

OBJECTIVE

To address this gap, the purpose of this study was to understand motivations for and experiences in using mood-tracking apps from people who used them in real-world contexts.

METHODS

We interviewed 22 participants who had used mood-tracking apps using a semistructured interview and card sorting task. The interview focused on their experiences using a mood-tracking app. We then conducted a card sorting task using screenshots of various data entry and data review features from mood-tracking apps. We used thematic analysis to identify themes around why people use mood-tracking apps, what they found useful about them, and where people felt these apps fell short.

RESULTS

Users of mood-tracking apps were primarily motivated by negative life events or shifts in their own mental health that prompted them to engage in tracking and improve their situation. In general, participants felt that using a mood-tracking app facilitated self-awareness and helped them to look back on a previous emotion or mood experience to understand what was happening. Interestingly, some users reported less inclination to document their negative mood states and preferred to document their positive moods. There was a range of preferences for personalization and simplicity of tracking. Overall, users also liked features in which their previous tracked emotions and moods were visualized in figures or calendar form to understand trends. One gap in available mood-tracking apps was the lack of app-facilitated recommendations or suggestions for how to interpret their own data or improve their mood.

CONCLUSIONS

Although people find various features of mood-tracking apps helpful, the way people use mood-tracking apps, such as avoiding entering negative moods, tracking infrequently, or wanting support to understand or change their moods, demonstrate opportunities for improvement. Understanding why and how people are using current technologies can provide insights to guide future designs and implementations.

Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling

BACKGROUND

Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care.

OBJECTIVE

This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity.

METHODS

A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9).

RESULTS

A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079).

CONCLUSIONS

This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

A Direct-to-Public Peer Support Program (Big White Wall) Versus Web-Based Information to Aid the Self-management of Depression and Anxiety: Results and Challenges of an Automated Randomized Controlled Trial

Background

Effective help for depression and anxiety reaches a small proportion of people who might benefit from it. The scale of the problem suggests the need for effective, safe web-based public health services delivered directly to the public. One model, the Big White Wall (BWW), offers peer support at low cost. As these interventions are delivered digitally, we tested whether a randomized controlled trial (RCT) intervention could also be fully delivered and evaluated digitally.

Objective

This study aims to determine the reach, feasibility, acceptability, baseline costs, and outcomes of a public health campaign for an automated RCT of the BWW, providing digital peer support and information, compared with a standard website used by the National Health Service Moodzone (MZ), to people with probable mild-to-moderate depression and anxiety disorder. The primary outcome was the change in self-rated well-being at 6 weeks, measured using the Warwick-Edinburgh Mental Well-Being Scale.

Methods

An 18-month campaign was conducted across Nottinghamshire, the United Kingdom (target population 914,000) to advertise the trial directly to the public through general marketing, web-based and social media sources, health services, other public services, and third-sector groups. The population reach of this campaign was examined by the number of people accessing the study website and self-registering to the study. A pragmatic, parallel-group, single-blind RCT was then conducted using a fully automated trial website in which eligible participants were randomized to receive either 6 months of access to BWW or signposted to MZ. Those eligible for participation were aged >16 years with probable mild-to-moderate depression or anxiety disorders.

Results

Of 6483 visitors to the study website, 1510 (23.29%) were eligible. Overall, 790 of 1510 (52.32%) visitors participated. Of 790 visitors, 397 (50.3%) were randomized to BWW and 393 (49.7%) to MZ. Their mean age was 38 (SD 13.8) years, 81.0% (640/790) were female, 93.4% (738/790) were White, and 47.4% (271/572) had no contact with health services in the previous 3 months. We estimated 3-month productivity losses of £1001.01 (95% CI 868.75-1133.27; US $1380.79; 95% CI 1198.35-1563.23) per person for those employed. Only 16.6% (131/790) participants completed the primary outcome assessment. There were no differences in the primary or secondary outcomes between the 2 groups.

Conclusions

Most participants reached and those eligible for this trial of digital interventions were White women not in recent contact with health services and whose productivity losses represent a significant annual societal burden. A fully automated RCT recruiting directly from the public failed to recruit and retain sufficient participants to test the clinical effectiveness of this digital intervention, primarily because it did not personally engage participants and explain how these unfamiliar interventions might benefit them.

Trial Registration

International Standard Randomized Controlled Trial Number (ISRCTN) 12673428; https://www.isrctn.com/ISRCTN12673428

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.8061

An Ecological Momentary Intervention for people with social anxiety: A descriptive case study

This study describes the development and pilot evaluation of a smartphone- delivered Ecological Momentary Intervention (EMI) for people with social anxiety symptoms. Using a software engineering framework (agile modeling, model-driven development, bottom-up development), mental health experts and software developers collaborated to develop a 4-module EMI app designed to reduce social anxiety in real-time. Fifty-five participants with social anxiety were randomly allocated to the EMI or a wait-list control arm. App downloads, usage and user satisfaction data were collected and mental health outcomes assessed at baseline and post-intervention. Software development practices allowed mental health experts to distil core elements of a psychological intervention into discrete software components but there were challenges in engaging mental health experts in the process. Relative to control there was no significant reduction in social anxiety among the EMI participants in the pilot trial. However, post-test data were available for only 4 intervention and 10 control participants and only 2 (4.0%) of the EMI participants downloaded the app. The two participants who both accessed the app and completed the post-test reported being satisfied with the intervention. Future research should address managing resources and providing additional training to support ongoing engagement with key stakeholders.

Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study

Background

Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care.

Objective

We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits.

Methods

Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log.

Results

All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future.

Conclusions

We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely.

Trial Registration

ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.

Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement

Meta-analyses have shown that digital mental health apps can be efficacious in reducing symptoms of depression and anxiety. However, real-world usage of apps is typically not sustained over time, and no studies systematically examine which features increase sustained engagement with apps or the relationship between engagement features and clinical efficacy. We conducted a systematic search of the literature to identify empirical studies that (1) investigate standalone apps for depression and/or anxiety in symptomatic participants and (2) report at least one measure of engagement. Features intended to increase engagement were categorized using the persuasive system design (PSD) framework and principles of behavioral economics. Twenty-five studies with 4159 participants were included in the analysis. PSD features were commonly used, whereas behavioral economics techniques were not. Smartphone apps were efficacious in treating symptoms of anxiety and depression in randomized controlled trials, with overall small-to-medium effects (g = 0.2888, SE = 0.0999, z(15) = 2.89, p = 0.0119, Q(df = 14) = 41.93, p < 0.0001, I2 = 66.6%), and apps that employed a greater number of engagement features as compared to the control condition had larger effect sizes (β = 0.0450, SE = 0.0164, t(15) = 2.7344, p = 0.0161). We observed an unexpected negative association between PSD features and engagement, as measured by completion rate (β = -0.0293, SE = 0.0121, t(17) = 02.4142, p = 0.0281). Overall, PSD features show promise for augmenting app efficacy, though engagement, as reflected in study completion, may not be the primary factor driving this association. The results suggest that expanding the use of PSD features in mental health apps may increase clinical benefits and that other techniques, such as those informed by behavioral economics, are employed infrequently.

Effectiveness of Smartphone-Based Cognitive Behavioral Therapy Among Patients With Major Depression: Systematic Review of Health Implications

BACKGROUND

Depression is often associated with rapid changes in mood and quality of life that persist for a period of 2 weeks. Despite medical innovations, there are problems in the provision of care. Long waiting times for treatment and high recurrence rates of depression cause enormous costs for health care systems. At the same time, comprehensive limitations in physical, psychological, and social dimensions are observed for patients with depression, which significantly reduce their quality of life. In addition to patient-specific limitations, undersupply and inappropriate health care have been determined. For this reason, new forms of care are discussed. Smartphone-based therapy is considered to have great potential due to its reach and easy accessibility. Low socioeconomic groups, which are always difficult to reach for public health interventions, can now be accessed due to the high dispersion of smartphones. There is still little information about the impact and mechanisms of smartphone-based therapy on depression. In a systematic literature review, the health implications of smartphone-based therapy were presented in comparison with standard care.

OBJECTIVE

The objective of this review was to identify and summarize the existing evidence regarding smartphone-based cognitive behavioral therapy for patients with depression and to present the health implications of smartphone-based cognitive behavioral therapy of considered endpoints.

METHODS

A systematic literature review was conducted to identify relevant studies by means of inclusion and exclusion criteria. For this purpose, the PubMed and Psyndex databases were systematically searched using a search syntax. The endpoints of depressive symptoms, depression-related anxiety, self-efficacy or self-esteem, and quality of life were analyzed. Identified studies were evaluated for study quality and risk of bias. After applying the inclusion and exclusion criteria, 8 studies were identified.

RESULTS

The studies examined in this review reported contradictory results regarding the investigated endpoints. In addition, due to clinical and methodological heterogeneity, it was difficult to derive evident results. All included studies reported effects on depressive symptoms. The other investigated endpoints were only reported by isolated studies. Only 50% (4/8) of the studies reported effects on depression-related anxiety, self-efficacy or self-esteem, and quality of life.

CONCLUSIONS

No clear implications of smartphone-based cognitive behavioral therapy could be established. Evidence for the treatment of depression using smartphone-based cognitive behavioral therapy is limited. Additional research projects are needed to demonstrate the effects of smartphone-based cognitive behavioral therapy in the context of evidence-based medicine and to enable its translation into standard care. Participatory technology development might help to address current problems in mobile health intervention studies.

Patient and physician perspectives of a smartphone application for depression: a qualitative study

BACKGROUND

Despite an increasing number of smartphone apps, such therapeutic tools have not yet consistently demonstrated their efficacy and many suffer from low retention rates. To ensure the development of efficient apps associated with high adherence, we aimed to identify, through a user-centred design approach, patient and physician expectations of a hypothetical app dedicated to depression.

METHODS

We conducted semi-structured interviews with physicians (psychiatrists and general practitioners) and patients who had experienced a major depressive episode during the last 12 months using the focus group method. The interviews were audio recorded, transcribed and analysed using qualitative content analysis to define codes, categories and emergent themes.

RESULTS

A total of 26 physicians and 24 patients were included in the study. The focus groups showed balanced sex and age distributions. Most participants owned a smartphone (83.3% of patients, 96.1% of physicians) and were app users (79.2% of patients and 96.1% of physicians). The qualitative content analysis revealed 3 main themes: content, operating characteristics and barriers to the use of the app. Expected content included the data collected by the app, aiming to provide information about the patient, data provided by the app, gathering psychoeducation elements, therapeutic tools and functionalities to help with the management of daily life and features expected for this tool. The "operating characteristics" theme gathered aims considered for the app, its potential target users, considered modalities of use and considerations around its accessibility and security of use. Finally, barriers to the use of the app included concerns about potential app users, its accessibility, safety, side-effects, utility and functioning. All themes and categories were the same for patients and physicians.

CONCLUSIONS

Physician and patient expectations of a hypothetical smartphone app dedicated to depression are high and confirmed the important role it could play in depression care. The key points expected by the users for such a tool are an easy and intuitive use and a personalised content. They are also waiting for an app that gives information about depression, offers a self-monitoring functionality and helps them in case of emergency.

Application of an Adaptive, Digital, Game-Based Approach for Cognitive Assessment in Multiple Sclerosis: Observational Study

Background

Cognitive impairment is one of the most debilitating manifestations of multiple sclerosis. Currently, the assessment of cognition relies on a time-consuming and extensive neuropsychological examination, which is only available in some centers.

Objective

To enable simpler, more accessible cognitive screening, we sought to determine the feasibility and potential assessment sensitivity of an unsupervised, adaptive, video game–based digital therapeutic to assess cognition in multiple sclerosis.

Methods

A total of 100 people with multiple sclerosis (33 with cognitive impairment and 67 without cognitive impairment) and 24 adults without multiple sclerosis were tested with the tablet game (EVO Monitor) and standard measures, including the Brief International Cognitive Assessment for Multiple Sclerosis (which included the Symbol Digit Modalities Test [SDMT]) and Multiple Sclerosis Functional Composite 4 (which included the Timed 25-Foot Walk test). Patients with multiple sclerosis also underwent neurological evaluations and contributed recent structural magnetic resonance imaging scans. Group differences in EVO Monitor performance and the association between EVO Monitor performance and standard measures were investigated.

Results

Participants with multiple sclerosis and cognitive impairment showed worse performance in EVO Monitor compared with participants without multiple sclerosis (P=.01) and participants with multiple sclerosis without cognitive impairment (all P<.002). Regression analyses indicated that participants with a lower SDMT score showed lower performance in EVO Monitor (r=0.52, P<.001). Further exploratory analyses revealed associations between performance in EVO Monitor and walking speed (r=–0.45, P<.001) as well as brain volumetric data (left thalamic volume: r=0.47, P<.001; right thalamic volume: r=0.39, P=.002; left rostral middle frontal volume: r=0.28, P=.03; right rostral middle frontal volume: r=0.27, P=.03).

Conclusions

These findings suggest that EVO Monitor, an unsupervised, video game–based digital program integrated with adaptive mechanics, is a clinically valuable approach to measuring cognitive performance in patients with multiple sclerosis.

Trial Registration

ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618

Toward dynamic phenotypes and the scalable measurement of human behavior

Precision psychiatry demands the rapid, efficient, and temporally dense collection of large scale and multi-omic data across diverse samples, for better diagnosis and treatment of dynamic clinical phenomena. To achieve this, we need approaches for measuring behavior that are readily scalable, both across participants and over time. Efforts to quantify behavior at scale are impeded by the fact that our methods for measuring human behavior are typically developed and validated for single time-point assessment, in highly controlled settings, and with relatively homogeneous samples. As a result, when taken to scale, these measures often suffer from poor reliability, generalizability, and participant engagement. In this review, we attempt to bridge the gap between gold standard behavioral measurements in the lab or clinic and the large-scale, high frequency assessments needed for precision psychiatry. To do this, we introduce and integrate two frameworks for the translation and validation of behavioral measurements. First, borrowing principles from computer science, we lay out an approach for iterative task development that can optimize behavioral measures based on psychometric, accessibility, and engagement criteria. Second, we advocate for a participatory research framework (e.g., citizen science) that can accelerate task development as well as make large-scale behavioral research more equitable and feasible. Finally, we suggest opportunities enabled by scalable behavioral research to move beyond single time-point assessment and toward dynamic models of behavior that more closely match clinical phenomena.

Standalone Smartphone Cognitive Behavioral Therapy-Based Ecological Momentary Interventions to Increase Mental Health: Narrative Review

BACKGROUND

A growing number of psychological interventions are delivered via smartphones with the aim of increasing the efficacy and effectiveness of these treatments and providing scalable access to interventions for improving mental health. Most of the scientifically tested apps are based on cognitive behavioral therapy (CBT) principles, which are considered the gold standard for the treatment of most mental health problems.

OBJECTIVE

This review investigates standalone smartphone-based ecological momentary interventions (EMIs) built on principles derived from CBT that aim to improve mental health.

METHODS

We searched the MEDLINE, PsycINFO, EMBASE, and PubMed databases for peer-reviewed studies published between January 1, 2007, and January 15, 2020. We included studies focusing on standalone app-based approaches to improve mental health and their feasibility, efficacy, or effectiveness. Both within- and between-group designs and studies with both healthy and clinical samples were included. Blended interventions, for example, app-based treatments in combination with psychotherapy, were not included. Selected studies were evaluated in terms of their design, that is, choice of the control condition, sample characteristics, EMI content, EMI delivery characteristics, feasibility, efficacy, and effectiveness. The latter was defined in terms of improvement in the primary outcomes used in the studies.

RESULTS

A total of 26 studies were selected. The results show that EMIs based on CBT principles can be successfully delivered, significantly increase well-being among users, and reduce mental health symptoms. Standalone EMIs were rated as helpful (mean 70.8%, SD 15.3; n=4 studies) and satisfying for users (mean 72.6%, SD 17.2; n=7 studies).

CONCLUSIONS

Study quality was heterogeneous, and feasibility was often not reported in the reviewed studies, thus limiting the conclusions that can be drawn from the existing data. Together, the studies show that EMIs may help increase mental health and thus support individuals in their daily lives. Such EMIs provide readily available, scalable, and evidence-based mental health support. These characteristics appear crucial in the context of a global crisis such as the COVID-19 pandemic but may also help reduce personal and economic costs of mental health impairment beyond this situation or in the context of potential future pandemics.

Mobile RDoC: Using Smartphones to Understand the Relationship Between Auditory Verbal Hallucinations and Need for Care

Objective

Auditory verbal hallucinations (AVH) are common in multiple clinical populations but also occur in individuals who are otherwise considered healthy. Adopting the National Institute of Mental Health's Research Domain Criteria (RDoC) framework, the aim of the current study was to integrate a variety of measures to evaluate whether AVH experience varies across clinical and nonclinical individuals.

Methods

A total of 384 people with AVH from 41 US states participated in the study; 295 participants (77%) who received inpatient, outpatient, or combination treatments for AVH and 89 participants (23%) who never received care. Participants used a multi-modal smartphone data collection system to report on their AVH experiences and co-occurring psychological states multiple times daily, over 30 days. In parallel, smartphone sensors recorded their physical activity, geolocation, and calling and texting behavior continuously.

Results

The clinical sample experienced AVH more frequently than the nonclinical group and rated their AVH as significantly louder and more powerful. They experienced more co-occurring negative affect and were more socially withdrawn, spending significantly more time at home and significantly less time near other people. Participants with a history of inpatient care also rated their AVH as infused with significantly more negative content. The groups did not differ in their physical activity or use of their smartphones for digital communication.

Conclusion

Smartphone-assisted remote data collection revealed real-time/real-place phenomenological, affective, and behavioral differences between clinical and nonclinical samples of people who experience AVH. The study provided strong support for the application of RDoC-informed approaches in psychosis research.

Online Patient Recruitment in Clinical Trials: Systematic Review and Meta-Analysis

Background

Recruitment for clinical trials continues to be a challenge, as patient recruitment is the single biggest cause of trial delays. Around 80% of trials fail to meet the initial enrollment target and timeline, and these delays can result in lost revenue of as much as US $8 million per day for drug developing companies.

Objective

This study aimed to conduct a systematic review and meta-analysis examining the effectiveness of online recruitment of participants for clinical trials compared with traditional in-clinic/offline recruitment methods.

Methods

Data on recruitment rates (the average number of patients enrolled in the study per month and per day of active recruitment) and conversion rates (the percentage of participants screened who proceed to enroll into the clinical trial), as well as study characteristics and patient demographics were collected from the included studies. Differences in online and offline recruitment rates and conversion rates were examined using random effects models. Further, a nonparametric paired Wilcoxon test was used for additional analysis on the cost-effectiveness of online patient recruitment. All data analyses were conducted in R language, and P<.05 was considered significant.

Results

In total, 3861 articles were screened for inclusion. Of these, 61 studies were included in the review, and 23 of these were further included in the meta-analysis. We found online recruitment to be significantly more effective with respect to the recruitment rate for active days of recruitment, where 100% (7/7) of the studies included had a better online recruitment rate compared with offline recruitment (incidence rate ratio [IRR] 4.17, P=.04). When examining the entire recruitment period in months we found that 52% (12/23) of the studies had a better online recruitment rate compared with the offline recruitment rate (IRR 1.11, P=.71). For cost-effectiveness, we found that online recruitment had a significantly lower cost per enrollee compared with offline recruitment (US $72 vs US $199, P=.04). Finally, we found that 69% (9/13) of studies had significantly better offline conversion rates compared with online conversion rates (risk ratio 0.8, P=.02).

Conclusions

Targeting potential participants using online remedies is an effective approach for patient recruitment for clinical research. Online recruitment was both superior in regard to time efficiency and cost-effectiveness compared with offline recruitment. In contrast, offline recruitment outperformed online recruitment with respect to conversion rate.

Social media recruitment for mental health research: A systematic review

BACKGROUND

Social media holds exciting promise for advancing mental health research recruitment, however, the extent and efficacy to which these platforms are currently in use are underexplored.

OBJECTIVE

A systematic review was conducted to characterize the current use and efficacy of social media in recruiting participants for mental health research.

METHOD

A literature review was performed using MEDLINE, EMBASE, and PsychINFO. Only non-duplicative manuscripts written in the English language and published between 1/1/2004-3/31/2019 were selected for further screening. Data extracted included study type and design, participant inclusion criteria, social media platform, advertising strategy, final recruited sample size, recruitment location, year, monetary incentives, comparison to other recruitment methods if performed, and final cost per participant.

RESULTS

A total of 176 unique studies that used social media for mental health research recruitment were reviewed. The majority of studies were cross-sectional (62.5%) in design and recruited adults. Facebook was overwhelmingly the recruitment platform of choice (92.6%), with the use of paid advertisements being the predominant strategy (60.8%). Of the reviewed studies, substance abuse (43.8%) and mood disorders (15.3%) were the primary subjects of investigation. In 68.3% of studies, social media recruitment performed as well as or better than traditional recruitment methods in the number and cost of final enrolled participants. The majority of studies used Facebook for recruitment at a median cost per final recruited study participant of $19.47. In 55.6% of the studies, social media recruitment was the more cost-effective recruitment method when compared to traditional methods (e.g., referrals, mailing).

CONCLUSION

Social media appears to be an effective and economical recruitment tool for mental health research. The platform raises methodological and privacy concerns not covered in current research regulations that warrant additional consideration.

Clinical Research in Hepatology in the COVID-19 Pandemic and Post-Pandemic Era: Challenges and the Need for Innovation

The severe acute respiratory syndrome coronavirus 2 pandemic has drastically altered all facets of clinical care and research. Clinical research in hepatology has had a rich tradition in several domains, including the discovery and therapeutic development for diseases such as hepatitis B and C and studying the natural history of many forms of chronic liver disease. National Institutes of Health, foundation, and industry funding have provided important opportunities to advance the academic careers of young investigators while they strived to make contributions to the field. Instantaneously, however, all nonessential research activities were halted when the pandemic started, forcing those involved in clinical research to rethink their research strategy, including a shift to coronavirus disease 2019 research while endeavoring to maintain their preexisting agenda. Strategies to maintain the integrity of ongoing studies, including patient follow-up, safety assessments, and continuation of investigational products, have included a shift to telemedicine, remote safety laboratory monitoring, and shipping of investigational products to study subjects. As a revamp of research is being planned, unique issues that face the research community include maintenance of infrastructure, funding, completion of studies in the predetermined time frame, and the need to reprogram career path timelines. Real-world databases, biomarker and long-term follow up studies, and research involving special groups (children, the homeless, and other marginalized populations) are likely to face unique challenges. The implementation of telemedicine has been dramatically accelerated and will serve as a backbone for the future of clinical research. As we move forward, innovation in clinical trial design will be essential for conducting optimized clinical research.

Psych-APP-Therapie: Smartphonebasierte Interventionen in der Psychotherapie – Eine systematische Übersichtsarbeit

<b><i>Hintergrund:</i></b> Technologiebasierte Interventionen haben in der Psychotherapie an Bedeutung gewonnen. Ein versorgungsrelevanter Ansatz sind Mental Health Apps. Ziel dieser systematischen Übersichtsarbeit ist es (1) international eva­luierte Apps zu den Störungsbildern Depressionen, Angststörungen und Essstörungen zu identifizieren, (2) deren Verfügbarkeit in deutschen App-Stores sowie (3) in deutscher Sprache zu prüfen und (4) ihre Effektivitätsergebnisse aus randomisiert-kontrollierten (Pilot-)Studien (RCTs) darzustellen. <b><i>Methode:</i></b> Die Übersichtsarbeit wurde in Anlehnung an das PRISMA Statement durchgeführt und ausgewertet. Eine systematische Recherche (2007–2018) der Datenbanken PubMed, PsychINFO sowie PSYNDEX wurde durchgeführt. Zudem fand eine Beurteilung der methodischen Qualität sowie der Effektivitätsergebnisse der (Pilot-)RCTs statt. <b><i>Ergebnisse:</i></b> Es wurden 2’571 Abstracts identifiziert und 47 Publikationen eingeschlossen (<i>N</i> = 32 unterschiedliche Apps; <i>N</i> = 24 [Pilot-]RCTs). Die Qualität der (Pilot-)RCTs ist überwiegend als gut bis moderat einzustufen. Die Ergebnisse waren heterogen (keine bis große Effekte [Cohens <i>d</i>] zwischen den Gruppen: –0,01; KI [–0,36; 0,34] bis 1,49; KI [1,00; 1,99]). Vier Apps mit einer Evaluation durch (Pilot-)RCTs sind in den deutschen App-Stores verfügbar – eine in deutscher Sprache. <b><i>Schlussfolgerungen:</i></b> Es liegt international eine Vielzahl von ersten App-Evaluationen zu den Störungsbildern vor, die Verfügbarkeit ausreichend evalu­ierter deutschsprachiger Apps in den deutschen App-Stores ist aber extrem limitiert. Englischsprachige, bereits evalu­ierte Apps könnten in das Deutsche übersetzt und – genauso wie schon verfügbare, aber nicht evaluierte deutschsprachige Apps – in RCTs inkl. Katamnesedaten evaluiert werden. So könnten smartphonebasierte Interventionen im Rahmen des “Digitalen-Versorgungs-Gesetzes” als ergänzendes Element in der Psychotherapie an Bedeutung gewinnen.