Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment

Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled Trial

BACKGROUND

Adolescence and early adulthood are pivotal stages for the onset of mental health disorders and the development of health behaviors. Digital behavioral activation interventions, with or without coaching support, hold promise for addressing risk factors for both mental and physical health problems by offering scalable approaches to expand access to evidence-based mental health support.

OBJECTIVE

This 2-arm pilot randomized controlled trial evaluated 2 versions of a digital behavioral health product, Vira (Ksana Health Inc), for their feasibility, acceptability, and preliminary effectiveness in improving mental health in young adults with depressive symptoms and obesity risk factors.

METHODS

A total of 73 participants recruited throughout the United States were randomly assigned to use Vira either as a self-guided product (Vira Self-Care) or with support from a health coach (Vira+Coaching) for 12 weeks. The Vira smartphone app used passive sensing of behavioral data related to mental health and obesity risk factors (ie, activity, sleep, mobility, and language patterns) and offered users personalized insights into patterns of behavior associated with their daily mood. Participants completed self-reported outcome measures at baseline and follow-up (12 weeks). All study procedures were completed via digital communications.

RESULTS

Both versions of Vira showed strong user engagement, acceptability, and evidence of effectiveness in improving mental health and stress. However, users receiving coaching exhibited more sustained engagement with the platform and reported greater reductions in depression (Cohen d=0.45, 95% CI 0.10-0.82) and anxiety (Cohen d=0.50, 95% CI 0.13-0.86) compared to self-care users. Both interventions also resulted in reduced stress (Vira+Coaching: Cohen d=-1.05, 95% CI -1.57 to --0.50; Vira Self-Care: Cohen d=-0.78, 95% CI -1.33 to -0.23) and were perceived as useful and easy to use. Coached users also reported reductions in sleep-related impairment (Cohen d=-0.51, 95% CI -1.00 to -0.01). Moreover, participants increased their motivation for and confidence in making behavioral changes, with greater improvements in confidence among coached users.

CONCLUSIONS

An app-based intervention using passive mobile sensing to track behavior and deliver personalized insights into behavior-mood associations demonstrated feasibility, acceptability, and preliminary effectiveness for reducing depressive symptoms and other mental health problems in young adults. Future directions include (1) optimizing the interventions, (2) conducting a fully powered trial that includes an active control condition, and (3) testing mediators and moderators of outcome effects.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05638516; https://clinicaltrials.gov/study/NCT05638516.

Evaluation of completely online psychotherapy with app-support versus therapy as usual for clients with depression or anxiety disorder: A retrospective matched cohort study investigating the effectiveness, efficiency, client satisfaction, and costs

INTRODUCTION

Depressive and anxiety disorders are common mental disorders ranking among the leading causes of global disease burden. Not all clients currently benefit from therapy and clients are looking for modern ways of therapy. Online psychotherapy is a promising option for better meeting clients' needs. Recently, a new psychotherapy concept has emerged that combines videoconferencing sessions with support through a mobile application. The latter allows for ecological momentary assessments and interventions, facilitates communication between patients and therapists in between sessions through chat, and allows for incorporating feedback-informed treatment principles.

MATERIAL AND METHODS

The study was a retrospective observational matched cohort study, comparing online psychotherapy with Therapy As Usual (TAU) for clients with depressive or anxiety disorders. Data were obtained via questionnaires, which are part of standard clinical care. Primary outcomes included general mental functioning, and symptoms of depression and anxiety. Secondary outcomes were efficiency, client satisfaction, and therapy costs. Primary endpoints were analyzed using linear mixed models analysis, with an interaction term between time and group. Secondary outcomes were analyzed using linear regression.

RESULTS

Larger improvements were observed in the online compared to the TAU group for general mental functioning and depressive disorder (i.e., General mental functioning: B = -8.50, 95 CI: -15.01 - -1.97, p = 0.011; Depressive disorder: B = - 3.66, 95 % CI: -5.79 - -1.54p < 0.01). No significant differences in change over time between the two groups were observed for anxiety disorder (B = -3.64, 95 % CI: (-13.10 - 5.82) p = 0.447). The total number of sessions was significantly higher in the online psychotherapy group than in TAU (B = 3.71, p < 0.01), although clients were matched on treatment time in weeks. Treatment session duration in minutes was comparable across the groups.

DISCUSSION

Online psychotherapy with app support showed to be a promising alternative to TAU for depressive and anxiety disorders. More research is needed to evaluate the effectiveness, cost-effectiveness and client satisfaction of online psychotherapy compared to TAU, such as randomized controlled trials or studies multiple baseline series designs, and in-depth qualitative research.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Circadian lifestyle determinants of immune checkpoint inhibitor efficacy

Immune Checkpoint Inhibitors (ICI) have revolutionised cancer care in recent years. Despite a global improvement in the efficacy and tolerability of systemic anticancer treatments, a sizeable proportion of patients still do not benefit maximally from ICI. Extensive research has been undertaken to reveal the immune- and cancer-related mechanisms underlying resistance and response to ICI, yet more limited investigations have explored potentially modifiable lifestyle host factors and their impact on ICI efficacy and tolerability. Moreover, multiple trials have reported a marked and coherent effect of time-of-day ICI administration and patients' outcomes. The biological circadian clock indeed temporally controls multiple aspects of the immune system, both directly and through mediation of timing of lifestyle actions, including food intake, physical exercise, exposure to bright light and sleep. These factors potentially modulate the immune response also through the microbiome, emerging as an important mediator of a patient's immune system. Thus, this review will look at critically amalgamating the existing clinical and experimental evidence to postulate how modifiable lifestyle factors could be used to improve the outcomes of cancer patients on immunotherapy through appropriate and individualised entrainment of the circadian timing system and temporal orchestration of the immune system functions.

Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study

BACKGROUND

Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used.

OBJECTIVE

We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD.

METHODS

Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes.

RESULTS

Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use.

CONCLUSIONS

Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.

Medical Telemonitoring for the Management of Hypertension in Older Patients in Japan

Hypertension is the most frequent modifiable risk factor associated with cardiovascular disease (CVD) morbidity and mortality. Even in older people, strict blood pressure (BP) control has been recommended to reduce CVD event risks. However, caution should be exercised since older hypertensive patients have increased physical vulnerability due to frailty and multimorbidity, and older patients eligible for clinical trials may not represent the general population. Medical telemonitoring systems, which enable us to monitor a patient's medical condition remotely through digital communication, have become much more prevalent since the coronavirus pandemic. Among various physiological parameters, BP monitoring is well-suited to the use of such systems, which enable healthcare providers to deliver accurate and safe BP management, even in the presence of frailty and/or living in geographically remote areas. Furthermore, medical telemonitoring systems could help reduce nonadherence to antihypertensive medications and clinical inertia, and also enable multi-professional team-based management of hypertension. However, the implementation of medical telemonitoring systems in clinical practice is not easy, and substantial barriers, including the development of user-friendly devices, integration with existing clinical systems, data security, and cost of implementation and maintenance, need to be overcome. In this review, we focus on the potential of medical telemonitoring for the management of hypertension in older people in Japan.

Long day's journey into sleep

My long day's journey into sleep began as an adolescent trying to manage my evening chronotype. The relief, I felt when my undergraduate finals were scheduled at night and as a medical student being able to select psychiatry over surgery deepened my interest in sleep and chronobiology. That interest was allowed to flourish at the National Institute of Mental Health and then at Yale Medical School in setting up a sleep laboratory. The decision to move to the University of Pittsburgh in 1973 led to a 42-year adventure in which we were able to initiate research efforts on the psychobiology of depression. Our interest in social zeitgebers (daily routines) led directly to the development and testing of a treatment intervention for mood disorders, interpersonal, and social rhythm therapy. Our continued emphasis on sleep and circadian rhythms convinced us that sleep and circadian factors were central to all of health, based on the importance of connectivity between sleep and major metabolic and cell functions. This ongoing research motivated our strong desire to study the developmental aspects of sleep. Our success was influenced immensely by the presence of young scientists and a strong subsequent interest in career mentoring. Finally, as we left Pittsburgh in 2015, we became involved in the field of continuous objective monitoring using the commercial smartphone's behavioral sensing capabilities. Our journey is not over. We hope to explore the potential of these remarkable devices to improve our understanding of sleep/wake and circadian factors across all of health.