Long-Term Outcomes of a Therapist-Supported, Smartphone-Based Intervention for Elevated Symptoms of Depression and Anxiety: Quasiexperimental, Pre-Postintervention Study

Evidence-Based Therapist-Supported Digital Mental Health Intervention for Patients Experiencing Medical Multimorbidity: A Retrospective Cohort Intent-to-Treat Study

OBJECTIVE

Multimorbidity or the co-occurrence of multiple health conditions is increasing globally and is associated with significant psychological complications. It is unclear whether digital mental health (DMH) interventions for patients experiencing multimorbidity are effective, particularly given that this patient population faces more treatment resistance. The goal of the current study was to examine the impact of smartphone-delivered DMH interventions for patients presenting with elevated internalizing symptoms that have reported multiple lifetime medical conditions.

METHODS

This preregistered (see https://osf.io/vh2et/ ) retrospective cohort intent-to-treat study with 2819 patients enrolled in a therapist-supported DMH intervention examined the associations between medical multimorbidity (MMB) and mental health outcomes.

RESULTS

Results indicated that more MMB was significantly associated with greater presenting mental health symptom severity. MMB did not have a deleterious influence on depressive symptom trajectories across treatment, although having one medical condition was associated with a steeper decrease in anxiety symptoms compared to patients with no medical conditions. Finally, MMB was not associated with time to dropout, but was associated with higher dropout and was differentially associated with fewer beneficial treatment outcomes, although this is likely attributable to higher presenting symptom severity, rather than lesser symptom reductions during treatment.

CONCLUSIONS

Overall, the Meru Health Program was associated with large effect size decreases in depressive and anxiety symptoms regardless of the number of MMB. Future DMH treatments and research might investigate tailored barrier reduction and extended treatment lengths for patients experiencing MMB to allow for greater treatment dose to reduce symptoms below clinical outcome thresholds.

Long-Term Efficacy of a Mobile Mental Wellness Program: Prospective Single-Arm Study

BACKGROUND

Rising rates of psychological distress (symptoms of depression, anxiety, and stress) among adults in the United States necessitate effective mental wellness interventions. Despite the prevalence of smartphone app-based programs, research on their efficacy is limited, with only 14% showing clinically validated evidence. Our study evaluates Noom Mood, a commercially available smartphone-based app that uses cognitive behavioral therapy and mindfulness-based programming. In this study, we address gaps in the existing literature by examining postintervention outcomes and the broader impact on mental wellness.

OBJECTIVE

Noom Mood is a smartphone-based mental wellness program designed to be used by the general population. This prospective study evaluates the efficacy and postintervention outcomes of Noom Mood. We aim to address the rising psychological distress among adults in the United States.

METHODS

A 1-arm study design was used, with participants having access to the Noom Mood program for 16 weeks (N=273). Surveys were conducted at baseline, week 4, week 8, week 12, week 16, and week 32 (16 weeks' postprogram follow-up). This study assessed a range of mental health outcomes, including anxiety symptoms, depressive symptoms, perceived stress, well-being, quality of life, coping, emotion regulation, sleep, and workplace productivity (absenteeism or presenteeism).

RESULTS

The mean age of participants was 40.5 (SD 11.7) years. Statistically significant improvements in anxiety symptoms, depressive symptoms, and perceived stress were observed by week 4 and maintained through the 16-week intervention and the 32-week follow-up. The largest changes were observed in the first 4 weeks (29% lower, 25% lower, and 15% lower for anxiety symptoms, depressive symptoms, and perceived stress, respectively), and only small improvements were observed afterward. Reductions in clinically relevant anxiety (7-item generalized anxiety disorder scale) and depression (8-item Patient Health Questionnaire depression scale) criteria were also maintained from program initiation through the 16-week intervention and the 32-week follow-up. Work productivity also showed statistically significant results, with participants gaining 2.57 productive work days from baseline at 16 weeks, and remaining relatively stable (2.23 productive work days gained) at follow-up (32 weeks). Additionally, effects across all coping, sleep disturbance (23% lower at 32 weeks), and emotion dysregulation variables exhibited positive and significant trends at all time points (15% higher, 23% lower, and 25% higher respectively at 32 weeks).

CONCLUSIONS

This study contributes insights into the promising positive impact of Noom Mood on mental health and well-being outcomes, extending beyond the intervention phase. Though more rigorous studies are necessary to understand the mechanism of action at play, this exploratory study addresses critical gaps in the literature, highlighting the potential of smartphone-based mental wellness programs to lessen barriers to mental health support and improve diverse dimensions of well-being. Future research should explore the scalability, feasibility, and long-term adherence of such interventions across diverse populations.

Real-time heart rate variability biofeedback amplitude during a large-scale digital mental health intervention differed by age, gender, and mental and physical health

Heart rate variability biofeedback (HRVB) is an efficacious treatment for depression and anxiety. However, translation to digital mental health interventions (DMHI) requires computing and providing real-time HRVB metrics in a personalized and user-friendly fashion. To address these gaps, this study validates a real-time HRVB feedback algorithm and characterizes the association of the main algorithmic summary metric-HRVB amplitude-with demographic, psychological, and health factors. We analyzed HRVB data from 5158 participants in a therapist-supported DMHI incorporating slow-paced breathing to treat depression or anxiety symptoms. A real-time feedback metric of HRVB amplitude and a gold-standard research metric of low-frequency (LF) power were computed for each session and then averaged within-participants over 2 weeks. We provide HRVB amplitude values, stratified by age and gender, and we characterize the multivariate associations of HRVB amplitude with demographic, psychological, and health factors. Real-time HRVB amplitude correlated strongly (r = .93, p < .001) with the LF power around the respiratory frequency (~0.1 Hz). Age was associated with a significant decline in HRVB (β = -0.46, p < .001), which was steeper among men than women, adjusting for demographic, psychological, and health factors. Resting high- and low-frequency power, body mass index, hypertension, Asian race, depression symptoms, and trauma history were significantly associated with HRVB amplitude in multivariate analyses (p's < .01). Real-time HRVB amplitude correlates highly with a research gold-standard spectral metric, enabling automated biofeedback delivery as a potential treatment component of DMHIs. Moreover, we identify demographic, psychological, and health factors relevant to building an equitable, accurate, and personalized biofeedback user experience.

The exercise-app Axia for axial spondyloarthritis enhances the home-based exercise frequency in axial spondyloarthritis patients - A cross-sectional survey

BACKGROUND

Patients with axial spondyloarthritis (axSpA) benefit from regular home-based exercise (HbE). In spite of recommendations, a relevant proportion of German axSpA patients does not adhere to recommended HbE practices. To enhance HbE care, we developed the novel digital therapeutic (DTx) "Axia" compliant with the European medical device regulation (MDR). Axia offers a modern app-based HbE solution with patient educative content and further integrated features.

OBJECTIVE

We aimed to assess Axia's efficacy, attractiveness, and functionality through a survey among axSpA-patients involved in the first user tests.

METHODS

A mixed-method online questionnaire with 38 items was administered to 37 axSpA volunteers after using Axia. Numeric rating scales (NRS) and likelihood scales were primarily used.

RESULTS

HbE frequency significantly increased from a median of 1 day/week to 6 days/week (p < 0.001) by using Axia. Existing HbE practitioners also increased their frequency (median of 4 days/week before, 6 days/week with Axia, p < 0.05). Axia received a median rating of 5 out of 5 stars. On NRS scales, Axia scored a median of 9 for intuitiveness and design, and a median of 8 for entertainment. 64.9% reported improved range of motion, 43.2% reported reduced pain, and 93.6% enhanced disease-specific knowledge. All users recommended Axia to other patients.

CONCLUSION

Axia increases axSpA patients HbE frequency, possibly due to its good intuitiveness and design, leading to reduction in pain and subjective improvement of range of motion. This warrants further investigation in large randomized controlled interventional trials to establish its efficacy conclusively and patients adherence to HbE.

Digital mental health interventions as stand-alone vs. augmented treatment as usual

BACKGROUND

Smartphone-based digital mental health interventions (DMHI) have been described as a purported solution to meet growing healthcare demands and lack of providers, but studies often don't account for whether patients are concurrently in another treatment modality.

METHODS

This preregistered quasi-experimental intent-to-treat study with 354 patients enrolled in a therapist-supported DMHI examined the treatment effectiveness of the Meru Health Program (MHP) as a stand-alone treatment as compared to the MHP in combination with any other form of treatment, including (1) in-person therapy, (2) psychotropic medication use, and (3) in-person therapy and psychotropic medication use.

RESULTS

Patients with higher baseline depressive and anxiety symptoms were more likely to self-select into multiple forms of treatment, an effect driven by patients in the MHP as adjunctive treatment to in-person therapy and psychotropic medication. Patients in combined treatments had significantly higher depressive and anxiety symptoms across treatment, but all treatment groups had similar decreasing depressive and anxiety symptom trajectories. Exploratory analyses revealed differential treatment outcomes across treatment combinations. Patients in the MHP in combination with another treatment had higher rates of major depressive episodes, psychiatric hospitalization, and attempted death by suicide at baseline.

CONCLUSIONS

Patients with higher depressive and anxiety symptoms tend to self-select into using DMHI in addition to more traditional types of treatment, rather than as a stand-alone intervention, and have more severe clinical characteristics. The use the MHP alone was associated with improvement at a similar rate to those with higher baseline symptoms who are in traditional treatments and use MHP adjunctively.

Therapist-supported digital mental health intervention for depressive symptoms: A randomized clinical trial

Depression is a chronic and debilitating mental disorder. Despite the existence of several evidence-based treatments, many individuals suffering from depression face myriad structural barriers to accessing timely care which may be alleviated by digital mental health interventions (DMHI). Accordingly, this randomized clinical trial (ClinicalTrials.gov: NCT04738084) investigated the efficacy of a newer version of the therapist-supported and guided DMHI, the Meru Health Program (MHP), which was recently enhanced with heart rate variability biofeedback and lengthened from 8- to 12-weeks duration, among people with elevated depression symptoms (N = 100, mean age 37). Recruited participants were randomized to the MHP (n = 54) or a waitlist control (n = 46) condition for 12 weeks. The MHP group had greater decreases in depression symptoms compared to the waitlist control (d = -0.8). A larger proportion of participants in the MHP group reported a minimal clinically important difference (MCID) in depression symptoms than participants in the waitlist control group (39.1 % vs. 9.8 %, χ2(1) = 9.90, p = .002). Similar effects were demonstrated for anxiety symptoms, quality of life, insomnia, and resilience. The results confirm the utility of the enhanced MHP in reducing depression symptoms and associated health burdens.

Determinants and outcome correlates of engagement with a mobile mental health intervention for depression and anxiety in middle-aged and older adults

OBJECTIVES

To examine baseline factors (i.e., age, gender, mobile device proficiency, sensory impairment) associated with app engagement in a 12-week mental health app intervention and to explore whether app engagement predicts changes in depression and anxiety symptoms among middle-aged and older adults.

METHOD

Mobile device proficiency, sensory impairment, depression, and anxiety symptoms were measured using questionnaires. App engagement was defined by metrics characterizing the core intervention features (i.e., messages sent to therapist, mindfulness meditation minutes, action tasks completed). Multiple regressions and multilevel models were conducted.

RESULTS

Forty-nine participants (M age = 57.40, SD = 11.09 years) enrolled. Women (β = .35, p < .05) and participants with less sensory impairment completed more action tasks (β = -.40, p < .05). Depressive and anxiety symptoms measured within the app declined significantly across treatment. Clinical significant improvements were observed for depression in 48.9% and for anxiety in 40% of participants. App engagement metrics were not predictive of depression or anxiety symptoms, either incrementally in time-lagged models or cumulatively in hierarchical linear regression analyses.

CONCLUSION

App engagement is multifaceted; participants engaged differently by gender and ability. Participation in this digital mental health intervention reduced depression and anxiety symptoms, but these findings should be interpreted with caution as the study did not include a control condition. Our findings underscore the importance of considering individual factors that may influence use of a digital mental health intervention.

Longitudinal outcomes of a therapist-supported digital mental health intervention for depression and anxiety symptoms: A retrospective cohort study

PURPOSE

This study examined treatment outcomes (depression and anxiety symptoms) up to 24 months after completion of a therapist-supported digital mental health intervention (DMHI).

METHODS

The sample consisted of 380 participants who participated in an eight-week DMHI from February 6, 2017 to May 20, 2019. Participants reported depression and anxiety symptoms at eight timepoints from baseline to 24 months. Mixed-effects modelling was used to investigate symptom changes over time. The proportion of participants meeting criteria for treatment response, clinically significant change, and remission of depression and anxiety symptoms were calculated, including proportions demonstrating each outcome sustained up to each timepoint.

RESULTS

Multivariate analyses yielded statistically significant reductions in depression (β = -5.40) and anxiety (β = -3.31) symptoms from baseline to end of treatment (8 weeks). Symptom levels remained significantly reduced from baseline through 24 months. The proportion of participants meeting criteria for clinical treatment outcomes remained constant over 24 months, although there were linear decreases in the proportions experiencing sustained clinical outcomes.

CONCLUSIONS

Treatment gains were made for depression and anxiety symptoms at the end of treatment and up to 24 months. Future studies should determine the feasibility of integrating post-treatment programmes into DMHIs to address symptom deterioration.

The effectiveness of collaborative care delivered via telehealth in a pediatric primary care population

Introduction

The prevalence of mental health conditions among children and adolescents in the United States has become a pressing concern, exacerbated by the COVID-19 pandemic. Collaborative care is an evidence-based model for identifying and treating depression and anxiety in healthcare settings, with additional promise for remote healthcare delivery. This study aims to evaluate the impact of a telehealth collaborative care model for adolescents with depression and anxiety in pediatric and primary care settings.

Methods

Secondary analysis was conducted using de-identified national data from Concert Health, a behavioral health medical group offering remote collaborative care across 17 states. Baseline, 90-day, and 120-day assessments of the PHQ-9 and GAD-7 were collected, along with baseline covariates. Stepwise regression analysis was performed to determine the contribution of select covariates to improvement rates.

Results

Among the analyzed data, 263 participants had complete PHQ-9 data, and 230 had complete GAD-7 data. In both the PHQ-9 and GAD-7 groups, over 50% of patients experienced treatment success based on success at discharge, as well as 90- and 120-day improvement rates. Predictors of success at discharge for the GAD-7 group included age at enrollment (OR 1.2258, 95% CI 1.01-1.496), clinical touchpoints (OR 1.1469, 95% CI 1.086-1.218), and lower baseline GAD-7 score (OR 0.9319, 95% CI 0.874-0.992). For the PHQ-9 group, Medicaid was significantly associated with not achieving a 50% reduction in PHQ-9 score at 120 days (OR 0.5874, 95% CI 0.349-0.979).

Discussion

Collaborative care has demonstrated its effectiveness in treating adolescent populations, providing an opportunity to expand access to evidence-based behavioral health treatment for young individuals. Notably, collaborative care is already integrated into the Medicaid fee schedule for 22 states and accepted by all commercial payers. Given that individuals often turn to their trusted primary care providers for behavioral health care, offering collaborative care to adolescents can play a crucial role in addressing the ongoing mental health crisis.

Efficacy, Safety, and Evaluation Criteria of mHealth Interventions for Depression: Systematic Review

BACKGROUND

Depression is a significant public health issue that can lead to considerable disability and reduced quality of life. With the rise of technology, mobile health (mHealth) interventions, particularly smartphone apps, are emerging as a promising approach for addressing depression. However, the lack of standardized evaluation tools and evidence-based principles for these interventions remains a concern.

OBJECTIVE

In this systematic review and meta-analysis, we aimed to evaluate the efficacy and safety of mHealth interventions for depression and identify the criteria and evaluation tools used for their assessment.

METHODS

A systematic review and meta-analysis of the literature was carried out following the recommendations of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. Studies that recruited adult patients exhibiting elevated depressive symptoms or those diagnosed with depressive disorders and aimed to assess the effectiveness or safety of mHealth interventions were eligible for consideration. The primary outcome of interest was the reduction of depressive symptoms, and only randomized controlled trials (RCTs) were included in the analysis. The risk of bias in the original RCTs was assessed using version 2 of the Cochrane risk-of-bias tool for randomized trials.

RESULTS

A total of 29 RCTs were included in the analysis after a comprehensive search of electronic databases and manual searches. The efficacy of mHealth interventions in reducing depressive symptoms was assessed using a random effects meta-analysis. In total, 20 RCTs had an unclear risk of bias and 9 were assessed as having a high risk of bias. The most common element in mHealth interventions was psychoeducation, followed by goal setting and gamification strategies. The meta-analysis revealed a significant effect for mHealth interventions in reducing depressive symptoms compared with nonactive control (Hedges g=-0.62, 95% CI -0.87 to -0.37, I2=87%). Hybrid interventions that combined mHealth with face-to-face sessions were found to be the most effective. Three studies compared mHealth interventions with active controls and reported overall positive results. Safety analyses showed that most studies did not report any study-related adverse events.

CONCLUSIONS

This review suggests that mHealth interventions can be effective in reducing depressive symptoms, with hybrid interventions achieving the best results. However, the high level of heterogeneity in the characteristics and components of mHealth interventions indicates the need for personalized approaches that consider individual differences, preferences, and needs. It is also important to prioritize evidence-based principles and standardized evaluation tools for mHealth interventions to ensure their efficacy and safety in the treatment of depression. Overall, the findings of this study support the use of mHealth interventions as a viable method for delivering mental health care.

TRIAL REGISTRATION

PROSPERO CRD42022304684; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304684.

Trajectories of depression symptoms in a therapist-supported digital mental health intervention: a repeated measures latent profile analysis

PURPOSE

Major depression affects 10% of the US adult population annually, contributing to significant burden and impairment. Research indicates treatment response is a non-linear process characterized by combinations of gradual changes and abrupt shifts in depression symptoms, although less is known about differential trajectories of depression symptoms in therapist-supported digital mental health interventions (DMHI).

METHODS

Repeated measures latent profile analysis was used to empirically identify differential trajectories based upon biweekly depression scores on the Patient Health Questionnaire-9 (PHQ-9) among patients engaging in a therapist-supported DMHI from January 2020 to July 2021. Multivariate associations between symptom trajectories with sociodemographics and clinical characteristics were examined with multinomial logistic regression. Minimal clinically important differences (MCID) were defined as a five-point change on the PHQ-9 from baseline to week 12.

RESULTS

The final sample included 2192 patients aged 18 to 82 (mean = 39.1). Four distinct trajectories emerged that differed by symptom severity and trajectory of depression symptoms over 12 weeks. All trajectories demonstrated reductions in symptoms. Despite meeting MCID criteria, evidence of treatment resistance was found among the trajectory with the highest symptom severity. Chronicity of major depressive episodes and lifetime trauma exposures were ubiquitous across the trajectories in a multinomial logistic regression model.

CONCLUSIONS

These data indicate that changes in depression symptoms during DMHI are heterogenous and non-linear, suggesting a need for precision care strategies to address treatment resistance and increase engagement. Future efforts should examine the effectiveness of trauma-informed treatment modules for DMHIs as well as protocols for continuation treatment and relapse prevention.

Mobile Acceptance and Commitment Therapy With Distressed First-Generation College Students: Microrandomized Trial

BACKGROUND

Extant gaps in mental health services are intensified among first-generation college students. Improving access to empirically based interventions is critical, and mobile health (mHealth) interventions are growing in support. Acceptance and commitment therapy (ACT) is an empirically supported intervention that has been applied to college students, via mobile app, and in brief intervals.

OBJECTIVE

This study evaluated the safety, feasibility, and effectiveness of an ACT-based mHealth intervention using a microrandomized trial (MRT) design.

METHODS

Participants (N=34) were 18- to 19-year-old first-generation college students reporting distress, who participated in a 6-week intervention period of twice-daily assessments and randomization to intervention. Participants logged symptoms, moods, and behaviors on the mobile app Lorevimo. After the assessment, participants were randomized to an ACT-based intervention or no intervention. Analyses examined proximal change after randomization using a weighted and centered least squares approach. Outcomes included values-based and avoidance behavior, as well as depressive symptoms and perceived stress.

RESULTS

The findings indicated the intervention was safe and feasible. The intervention increased values-based behavior but did not decrease avoidance behavior. The intervention reduced depressive symptoms but not perceived stress.

CONCLUSIONS

An MRT of an mHealth ACT-based intervention among distressed first-generation college students suggests that a larger MRT is warranted. Future investigations may tailor interventions to contexts where intervention is most impactful.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04081662; https://clinicaltrials.gov/show/NCT04081662.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/17086.

Treating depression with a smartphone-delivered self-help cognitive behavioral therapy for insomnia: a parallel-group randomized controlled trial

BACKGROUND

Despite its efficacy in treating comorbid insomnia and depression, cognitive behavioral therapy for insomnia (CBT-I) is limited in its accessibility and, in many countries, cultural compatibility. Smartphone-based treatment is a low-cost, convenient alternative modality. This study evaluated a self-help smartphone-based CBT-I in alleviating major depression and insomnia.

METHODS

A parallel-group randomized, waitlist-controlled trial was conducted with 320 adults with major depression and insomnia. Participants were randomized to receive either a 6-week CBT-I via a smartphone application, proACT-S, or waitlist condition. The primary outcomes included depression severity, insomnia severity, and sleep quality. The secondary outcomes included anxiety severity, subjective health, and acceptability of treatment. Assessments were administered at baseline, post-intervention (week 6) follow-up, and week 12 follow-up. The waitlist group received treatment after the week 6 follow-up.

RESULTS

Intention to treat analysis was conducted with multilevel modeling. In all but one model, the interaction between treatment condition and time at week 6 follow-up was significant. Compared with the waitlist group, the treatment group had lower levels of depression [Center for Epidemiologic Studies Depression Scale (CES-D): Cohen's d = 0.86, 95% CI (-10.11 to -5.37)], insomnia [Insomnia Severity Index (ISI): Cohen's d = 1.00, 95% CI (-5.93 to -3.53)], and anxiety [Hospital Anxiety and Depression Scale - Anxiety subscale (HADS-A): Cohen's d = 0.83, 95% CI (-3.75 to -1.96)]. They also had better sleep quality [Pittsburgh Sleep Quality Index (PSQI): Cohen's d = 0.91, 95% CI (-3.34 to -1.83)]. No differences across any measures were found at week 12, after the waitlist control group received the treatment.

CONCLUSION

proACT-S is an efficacious sleep-focused self-help treatment for major depression and insomnia.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04228146. Retrospectively registered on 14 January 2020. http://www.w3.org/1999/xlink">https://clinicaltrials.gov/ct2/show/NCT04228146.

A Systematic Review of Mobile Apps as an Adjunct to Psychological Interventions for Emotion Dysregulation

BACKGROUND

Mental health care has been enriched with the progressive use of technology during the last ten years, in particular after the COVID-19 pandemic. Mobile applications (apps) and smartphones have become the most widespread access point for many people who look for self-help in the psychological domain.

OBJECTIVE

We focused on a systematic review of mobile apps for mental health, focusing on the blending of apps with psychotherapy contexts, with a specific focus on emotional dysregulation.

METHODS

A comprehensive literature search (January 2017 to August 2022) in PubMed, PsycInfo, Web of Science, and the Cochrane Library was conducted. Abstracts were included if they described mental health mobile apps targeting emotional dysregulation and their use during ongoing psychological or psychotherapy treatment for adults and adolescents.

RESULTS

In total, 397 abstracts were identified; of these, 19 publications describing apps targeting borderline personality disorder, depression, anxiety, suicidal behaviors, and post-traumatic stress disorders met the inclusion criteria.

CONCLUSIONS

App-enhanced psychotherapy might be a winning combination in many scenarios, but at the same time, many issues must still be faced in this yet emerging scientific field. In conclusion, we tried to put together some major guidelines for mental health mobile app development in the context of psychological treatments.

Examining the temporal dynamics of anxiety and depressive symptoms during a therapist-supported, smartphone-based intervention for depression: Longitudinal observational study

OBJECTIVE

This study examined the temporal dynamics of anxiety and depressive symptoms during a 12-week therapist-supported, smartphone-delivered digital health intervention for symptoms of depression and anxiety.

METHODS

A total of 290 participants were included in the present analyses (age Mean = 39.64, SD = 10.25 years; 79% female; 54% self-reported psychotropic medication use). Linear mixed models were used to examine the concurrent anxiety-depression association and (2) the lead-lag anxiety-depression relationship, with greater anxiety predicted to precede an increase in depression.

RESULTS

In support of Hypothesis 1, greater anxiety during the current biweekly assessment was associated with greater depressive symptoms during the current biweekly assessment. In support of Hypothesis 2, greater anxiety during the prior biweekly assessment was associated with greater depressive symptoms during the current biweekly assessment but not vice-versa.

CONCLUSION

These findings demonstrate that anxiety and depressive symptoms may overlap and fluctuate in concert, with anxiety symptoms predicting subsequent depressive symptoms but not vice-versa. With sensitivity to study limitations, implications for future intervention designs are discussed.

Initial assessment of the feasibility and efficacy of a scalable digital CBT for generalized anxiety and associated health behaviors in a cardiovascular disease population

Generalized anxiety disorder (GAD) is a significant yet modifiable risk factor for worse cardiovascular disease (CVD) outcomes. The treatment of GAD in an accessible manner represents an unmet need in CVD, given that patients with CVD experience numerous barriers to in-person treatment engagement. This paper presents the rationale and design for an investigation of a strategy to enhance care for patients with CVD by introducing a scalable, affordable, and system-friendly digital intervention that targets a prominent modifiable risk factor (generalized anxiety and associated worry) for negative health behaviors in CVD. In the context of a randomized clinical trial design, we describe an experimental medicine approach for evaluating the degree to which a digital cognitive behavior therapy (dCBT), relative to a waitlist control group, engages anxiety and worry outcomes in a sample of 90 adults who have experienced an acute CVD event and who have comorbid GAD symptoms. We also investigate the degree to which dCBT leads to greater changes in GAD symptoms compared to the control condition and whether reductions in these symptoms are associated with corresponding reductions in cardiac anxiety and cardiac health behaviors (including smoking, physical activity, heart-healthy diet, and medication adherence). We propose that by targeting GAD symptoms in CVD in a way that does not tax ongoing medical care provision, we have the potential to improve the uptake of effective care and address both GAD and associated health behaviors.

Recruitment and retention in mobile application-based intervention studies: a critical synopsis of challenges and opportunities

Use of mobile health applications (mHealth apps) is becoming increasingly popular for the management of chronic illnesses, but mHealth-based intervention studies often have limitations associated with subject recruitment and retention. In this synopsis, we focus on targeted aspects of mHealth-based intervention studies, specifically: (i) subject recruitment, (ii) cohort sizes, and (iii) retention rates. We used the Google Scholar (meta-search) and Galileo search engines to identify sample articles focusing on mHealth apps and interventions published between 2010 and 2020 and selected 21 papers for detailed review. Most studies recruited relatively small cohorts (minimum: 20, maximum: 510). Retention rates had high variance with only five studies managing >80% subject retention throughout the study duration, 10.4% being the lowest. Eighty-five percent of the studies expressed concerns regarding study duration, app usage, and lack of proper implementation. The use of mHealth interventions generally yielded positive outcomes, but most studies discussed facing challenges associated with recruitment and retention. There is a clear need to identify strategies for recruiting larger cohorts and improving retention rates, and ultimately increasing the reliability of mHealth app-based intervention studies. We advise that potential underutilized opportunities lie at the intersection of mHealth and social media to address the limitations identified in the synopsis.

A Mixed-Methods Analysis of Mobile ACT Responses From Two Cohorts

Background

Mobile transdiagnostic therapies offer a solution to the challenges of limited access to psychological care. However, it is unclear if individuals can actively synthesize and adopt concepts and skills via an app without clinician support.

Aims

The present study measured comprehension of and engagement with a mobile acceptance and commitment therapy (ACT) intervention in two independent cohorts. Authors hypothesized that participants would recognize that behaviors can be flexible in form and function and respond in an ACT process-aligned manner.

Methods

Mixed-methods analyses were performed on open-ended responses collected from initial participants (n = 49) in two parallel micro-randomized trials with: 1) first-generation college students (FGCSs) (n = 25) from a four-year public research university and 2) individuals diagnosed with bipolar disorder (BP) (n = 24). Twice each day over six weeks, participants responded to questions about mood and behavior, after which they had a 50-50 chance of receiving an ACT-based intervention. Participants identified current behavior and categorized behavior as values-based or avoidant. Interventions were selected randomly from 84 possible prompts, each targeting one ACT process: engagement with values, openness to internal experiences, or self-awareness. Participants were randomly assigned to either exploratory (10 FGCS, 9 BP) or confirmatory (15 FGCS, 15 BP) groups for analyses. Responses from the exploratory group were used to inductively derive a qualitative coding system. This system was used to code responses in the confirmatory group. Coded confirmatory data were used for final analyses.

Results

Over 50% of participants in both cohorts submitted a non-blank response 100% of the time. For over 50% of participants, intervention responses aligned with the target ACT process for at least 96% of the time (FGCS) and 91% of the time (BP), and current behavior was labeled as values-based 70% (FGCS) and 85% (BP) of the time. Participants labeled similar behaviors flexibly as either values-based or avoidant in different contexts. Dominant themes were needs-based behaviors, interpersonal and family relationships, education, and time as a cost.

Conclusions

Both cohorts were engaged with the app, as demonstrated by responses that aligned with ACT processes. This suggests that participants had some level of understanding that behavior can be flexible in form and function.

Human Support in App-Based Cognitive Behavioral Therapies for Emotional Disorders: Scoping Review

BACKGROUND

Smartphone app-based therapies offer clear promise for reducing the gap in available mental health care for people at risk for or people with mental illness. To this end, as smartphone ownership has become widespread, app-based therapies have become increasingly common. However, the research on app-based therapies is lagging behind. In particular, although experts suggest that human support may be critical for increasing engagement and effectiveness, we have little systematic knowledge about the role that human support plays in app-based therapy. It is critical to address these open questions to optimally design and scale these interventions.

OBJECTIVE

The purpose of this study is to provide a scoping review of the use of human support or coaching in app-based cognitive behavioral therapy for emotional disorders, identify critical knowledge gaps, and offer recommendations for future research. Cognitive behavioral therapy is the most well-researched treatment for a wide range of concerns and is understood to be particularly well suited to digital implementations, given its structured, skill-based approach.

METHODS

We conducted systematic searches of 3 databases (PubMed, PsycINFO, and Embase). Broadly, eligible articles described a cognitive behavioral intervention delivered via smartphone app whose primary target was an emotional disorder or problem and included some level of human involvement or support (coaching). All records were reviewed by 2 authors. Information regarding the qualifications and training of coaches, stated purpose and content of the coaching, method and frequency of communication with users, and relationship between coaching and outcomes was recorded.

RESULTS

Of the 2940 titles returned by the searches, 64 (2.18%) were eligible for inclusion. This review found significant heterogeneity across all of the dimensions of coaching considered as well as considerable missing information in the published articles. Moreover, few studies had qualitatively or quantitatively evaluated how the level of coaching impacts treatment engagement or outcomes. Although users tend to self-report that coaching improves their engagement and outcomes, there is limited and mixed supporting quantitative evidence at present.

CONCLUSIONS

Digital mental health is a young but rapidly expanding field with great potential to improve the reach of evidence-based care. Researchers across the reviewed articles offered numerous approaches to encouraging and guiding users. However, with the relative infancy of these treatment approaches, this review found that the field has yet to develop standards or consensus for implementing coaching protocols, let alone those for measuring and reporting on the impact. We conclude that coaching remains a significant hole in the growing digital mental health literature and lay out recommendations for future data collection, reporting, experimentation, and analysis.

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Background

Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use.

Objective

The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both.

Methods

A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information.

Results

The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially.

Conclusions

A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.

Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach

Objective

Predicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-based predictions within digital mental health interventions. Therefore, this study evaluates the performance of machine learning in predicting treatment response in a digital mental health intervention designed for treating depression and anxiety.

Methods

Several algorithms were trained based on the data of 970 participants to predict a significant reduction in depression and anxiety symptoms using clinical and sociodemographic variables. As a random forest classifier performed best over cross-validation, it was used to predict the outcomes of 279 new participants.

Results

The random forest achieved an accuracy of 0.71 for the test set (base rate: 0.67, area under curve (AUC): 0.60, p = 0.001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their Patient Health Questionnaire-9 (PHQ-9) (-2.7, p = 0.004) and General Anxiety Disorder Screener-7 values (-3.7, p < 0.001) compared to responders. Besides pre-treatment Patient Health Questionnaire-9 and General Anxiety Disorder Screener-7 values, the self-reported motivation, type of referral into the programme (self vs. healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire items contributed most to the predictions.

Conclusions

This study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.

Engagement with mobile health interventions for depression: A systematic review

BACKGROUND

Depressive disorders are a major public health problem, and many people face barriers to accessing evidence-based mental health treatment. Mobile health (mHealth) interventions may circumvent logistical barriers to in-person care (e.g., cost, transportation), however the symptoms of depression (low motivation, concentration difficulties) may make it difficult for people with the disorder to engage with mHealth.

OBJECTIVE

The aim of this systematic review is to examine assessment and reporting of engagement in clinical trials of mHealth interventions for depression, including objective engagement (e.g., number of times program is used), subjective engagement (e.g., qualitative data on users' experiences), and associations between engagement and other clinically important variables (e.g., symptom improvement, participant characteristics).

METHODS

Three electronic databases (PsycINFO, Web of Science, PubMed) were searched in February 2020 using search terms for mHealth and depression. Studies were included in the review if they tested a mHealth intervention designed for people with depressive disorders or elevated depression symptoms.

RESULTS

Thirty studies met inclusion criteria and were reviewed. Most studies reported objective engagement (N = 23, 76.7%), approximately half reported subjective engagement (N = 16, 53.3%), and relatively few examined associations between engagement and clinical improvement, participant characteristics, or other clinically relevant variables (N = 13, 43.3%).

CONCLUSIONS

Although most studies in this small but rapidly growing literature report at least one measure of engagement, there is substantial heterogeneity. Intentional, theory-driven, and consistent measurement of engagement with mHealth interventions for depression may advance the field's understanding of effective engagement to facilitate clinical improvement, identify dose-response relationships, and maximize generalizability for underserved populations.

Significant reduction in depressive symptoms among patients with moderately-severe to severe depressive symptoms after participation in a therapist-supported, evidence-based mobile health program delivered via a smartphone app

Depression is a debilitating disorder associated with poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms of depression. As such, in an intent-to-treat study we examined whether 218 patients with at least moderately severe depressive symptoms (PHQ-9 ≥ 15) experienced significant reductions in depressive symptoms after participation in a therapist-supported, evidence-based mobile health (mHealth) program, Meru Health Program (MHP). Patients with moderately severe and severe depressive symptoms at pre-program assessment experienced significant decreases in depressive symptoms at end-of treatment (mean [standard deviation] PHQ-9 reduction = 8.30 [5.03], Hedges' g = 1.64, 95% CI [1.44, 1.85]). Also, 34% of patients with at least moderately severe depressive symptoms at baseline and 29.9% of patients with severe depressive symptoms (PHQ-9 ≥ 20) at baseline responded to the intervention at end-of-treatment, defined as experiencing ≥50% reduction in PHQ-9 score and a post-program PHQ-9 score lower than 10. Limitations include use lack of a control group and no clinical diagnostic information. Future randomized trials are warranted to test the MHP as a scalable solution for patients with more severe depressive symptoms.

Effects of Mobile App-Based Intervention for Depression in Middle-Aged and Older Adults: Mixed Methods Feasibility Study

BACKGROUND

Digital mental health interventions may help middle-aged and older adults with depression overcome barriers to accessing traditional care, but few studies have investigated their use in this population.

OBJECTIVE

This pilot study examines the feasibility, acceptability, and potential efficacy of the Meru Health Program, an 8-week mobile app-delivered intervention.

METHODS

A total of 20 community-dwelling middle-aged and older adults (age: mean 61.7 years, SD 11.3) with elevated depressive symptoms participated in a single-arm pilot study investigating the Meru Health Program, an app-delivered intervention supported by remote therapists. The program primarily uses mindfulness and cognitive behavioral skills to target depressive symptoms. A semistructured interview was completed at the baseline to establish current psychiatric diagnoses. Depressive symptoms were measured using the Patient Health Questionnaire and Patient-Reported Outcomes Measurement Information System (PROMIS) depression measures. Anxiety symptoms were measured using the Generalized Anxiety Disorder Scale and the PROMIS Anxiety measure. User experience and acceptability were examined through surveys and qualitative interviews.

RESULTS

In total, 90% (18/20) of the participants completed the program, with 75% (15/20) completing at least 7 of the 8 introductory weekly lessons. On average, participants completed 60 minutes of practice and exchanged 5 messages with their therapists every week. The app was rated as helpful by 89% (17/19) participants. Significant decreases in depressive (P=.03) and anxiety symptom measures (P=.01) were found; 45% (9/20) of participants showed clinically significant improvement in either depressive symptoms or anxiety symptoms.

CONCLUSIONS

The findings suggest that the commercially available Meru Health Program may be feasible, acceptable, and potentially beneficial to middle-aged and older adults. Although larger controlled trials are needed to demonstrate efficacy, these findings suggest that digital health interventions may benefit adults of all ages.

Feasibility and Efficacy of the Addition of Heart Rate Variability Biofeedback to a Remote Digital Health Intervention for Depression

A rise in the prevalence of depression underscores the need for accessible and effective interventions. The objectives of this study were to determine if the addition of a treatment component showing promise in treating depression, heart rate variability-biofeedback (HRV-B), to our original smartphone-based, 8-week digital intervention was feasible and whether patients in the HRV-B (“enhanced”) intervention were more likely to experience clinically significant improvements in depressive symptoms than patients in our original (“standard”) intervention. We used a quasi-experimental, non-equivalent (matched) groups design to compare changes in symptoms of depression in the enhanced group (n = 48) to historical outcome data from the standard group (n = 48). Patients in the enhanced group completed a total average of 3.86 h of HRV-B practice across 25.8 sessions, and were more likely to report a clinically significant improvement in depressive symptom score post-intervention than participants in the standard group, even after adjusting for differences in demographics and engagement between groups (adjusted OR 3.44, 95% CI [1.28–9.26], P = .015). Our findings suggest that adding HRV-B to an app-based, smartphone-delivered, remote intervention for depression is feasible and may enhance treatment outcomes.

A therapist-guided smartphone app for major depression in young adults: A randomized clinical trial

BACKGROUND

Meru Health Program (MHP) is a therapist-guided, 8-week intervention for depression delivered via smartphone. The aim was to test its efficacy in patients with clinical depression in a Finnish university student health service.

METHODS

Patients (n=124, women 72.6%, mean age 25y) were stratified based on antidepressant status, and randomized into intervention group receiving MHP plus treatment as usual (TAU), and control group receiving TAU only. Depression, measured by the Patient Health Questionnaire-9 (PHQ-9) scale, was the primary outcome. After baseline (T0), follow-ups were at mid-intervention (T4), immediately post-intervention (T8); 3 months (T20), and 6 months (T32) post-intervention.

RESULTS

The intervention group and control group did not have significant differences in depression outcomes throughout end of treatment and follow-up. Among secondary outcomes, increase in resilience (d=0.32, p=0.03) and mindfulness (d=0.57, p=0.002), and reduction in perceived stress (d=-0.52, p=0.008) were greater in MHP+TAU versus TAU at T32; no differences were found in anxiety, sleep disturbances, and quality of life between groups. Post-hoc comparisons of patients on antidepressants showed significantly greater reduction in depression at T32 for MHP+TAU versus TAU (d=-0.73, p=0.01); patients not on antidepressants showed no between-group differences.

LIMITATIONS

Limitations include unknown characteristics of TAU, potential bias from patients and providers not being blinded to treatment group, and failure to specify examination of differences by antidepressant status in the protocol.

CONCLUSIONS

Most outcomes, including depression, did not significantly differ between MHP+TAU and TAU. Exploratory analysis revealed intervention effect at the end of the 6-month follow-up among patients on antidepressant medication.

mHealth Mindfulness Intervention for Women with Moderate-to-Moderately-Severe Antenatal Depressive Symptoms: a Pilot Study Within an Integrated Health Care System

Objectives

Traditional mindfulness-based interventions have been shown to reduce depression symptoms in pregnant women, although in-person classes may pose significant accessibility barriers, particularly during the COVID-19 pandemic. Mobile technology offers greater convenience, but little is known regarding the efficacy of self-paced, mobile-delivered (mHealth) mindfulness interventions in this population. This study tested the feasibility and acceptability of offering such an intervention for pregnant women with moderate-to-moderately-severe depression symptoms.

Methods

We conducted a single-arm trial within Kaiser Permanente Northern California (KPNC). Participants were identified through KPNC’s universal perinatal depression screening program. Eligible participants included English-speaking pregnant women (<28 weeks of gestation) with moderate-to-moderately-severe depressive symptoms without a regular (<3 times/week) mindfulness/meditation practice. Participants were asked to follow a self-paced, 6-week mindfulness meditation program using a mobile app, Headspace™, 10–20 min/day. Outcome measures included feasibility, acceptability, and patient-reported outcomes (e.g., depression symptoms).

Results

Of the 27 women enrolled, 20 (74%) completed the study. Over half (55%) of participants used the app ≥50% of the days during the 6-week intervention. Responses to the semi-structured interviews indicated that women appreciated the convenience of the intervention and the ability to engage without having to attend classes or arrange childcare. We observed significant improvements in pre-postintervention scores for depression symptoms, perceived stress, sleep disturbance, and mindfulness.

Conclusions

Our study demonstrates the feasibility and acceptability of an mHealth mindfulness intervention for women with moderate-to-moderately-severe antenatal depression symptoms. The preliminary data further suggest that an efficacy trial is warranted.

Assessment of Psychological Distress in Adults With Type 2 Diabetes Mellitus Through Technologies: Literature Review

Background

The use of technological devices can support the self-management of individuals with type 2 diabetes mellitus (T2DM), particularly in addressing psychological distress. However, there is poor consistency in the literature regarding the use of psychological instruments for the web-based screening of patients’ psychological distress and subsequent monitoring of their psychological condition during digital interventions.

Objective

This study aims to review previous literature on the types of psychological instruments delivered in digital interventions for assessing depression, anxiety, and stress in patients with T2DM.

Methods

The literature review was conducted using the PsycINFO, CINAHL and PubMed databases, in which the following terms were considered: diabetes mellitus, measure, assessment, self-care, self-management, depression, anxiety, stress, technology, eHealth, mobile health, mobile phone, device, and smartphone.

Results

In most studies, psychological assessments were administered on paper. A few studies deployed self-reporting techniques employing automated telephonic assessment, a call system for screening and monitoring patients’ conditions and preferences, or through telephone interviews via interactive voice response calls, a self-management support program leveraging tailored messages and structured emails. Other studies used simple telephone interviews and included the use of apps for tablets and smartphones to assess the psychological well-being of patients. Finally, some studies deployed mood rating scales delivered through tailored text message–based support systems.

Conclusions

The deployment of appropriate psychological tools in digital interventions allows researchers and clinicians to make the screening of anxiety, stress, and depression symptoms faster and easier in patients with T2DM. Data from this literature review suggest that mobile health solutions may be preferred tools to use in such digital interventions.

Changes in Quality of Life and Loneliness Among Middle-Aged and Older Adults Participating in Therapist-Guided Digital Mental Health Intervention

Background: This study aimed to examine the effects of a 12-week multicomponent mobile app-delivered intervention, the Meru Health Program (MHP), on mental health quality of life (QoL) and loneliness among the middle-aged and older adults with depression symptoms. Methods: The eligible participants (M age = 57.06, SD = 11.26 years) were enrolled in the MHP, a therapist-supported mobile intervention. Using a non-randomized pre-post design, change in mental health QoL [WHO QoL Brief (WHOQOL-BREF) psychological health] and loneliness (UCLA Loneliness Scale) from baseline to post-treatment were examined. Time of enrollment [pre- vs. post-coronavirus disease 2019 (COVID-19)] was included as a between-subjects factor in the repeated measures analyses. Results: Forty-two participants enrolled prior to the COVID-19 pandemic; eight enrolled after the pandemic began. Among the pre-COVID-19 enrollees, increase in mental health QoL, F_{(1, 38)} = 12.61, p = 0.001, η² = 0.25 and decreases in loneliness emerged, F_{(1, 38)} = 5.42, p = 0.025, η² = 0.13. The changes in mental health QoL, but not loneliness, held for the combined sample, such as post-COVID-19 enrollees, F_{(1, 44)} = 6.02, p = 0.018, η² = 0.12. The regression analyses showed that increases in mindfulness were associated with the increased mental health QoL and decreased loneliness. Conclusion: Therapist-supported digital mental health interventions, such as the MHP, have the potential to improve mental health QoL and decrease loneliness among the middle-aged and older adults. The findings for loneliness may not hold during the periods of mandated isolation. Instead, therapists supporting digital interventions may need to tailor their approach to target loneliness.

Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study

Background

Mobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases.

Objective

The aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases.

Methods

Between December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences.

Results

Of the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information.

Conclusions

Patients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.