Exploring the features of an app-based just-in-time intervention for depression

Effects of app-based relaxation techniques on perceived momentary relaxation: Observational data analysis in people with cancer

OBJECTIVE

To examine the effects of six relaxation techniques on perceived momentary relaxation and a possible association of relaxation effects with time and practice experience in people with cancer.

METHODS

We used data from participants with cancer in a larger study practicing app-based relaxation techniques over 10 weeks, assessed momentary relaxation before and after every third relaxation practice, and analyzed momentary relaxation changes with a linear mixed-effects model.

RESULTS

The sample included 611 before-after observations from 91 participants (70 females (76.9%)) with a mean age of 55.43 years (SD 10.88). We found moderate evidence for variations in momentary relaxation changes across different techniques (P = .026), with short meditation, mindfulness meditation, guided imagery, and progressive muscle relaxation more frequently observed and leading to more relaxation than body scan and walking meditation. Furthermore, we found moderate evidence for increasing momentary relaxation changes over time (P = .046), but no evidence for an association between momentary relaxation and the number of previous observations (proxy for practice experience; P = .47).

CONCLUSION

We compared six app-based relaxation techniques in a real-life setting of people with cancer. The observed variations in perceived momentary relaxation appear to correspond with the popularity of the techniques used: The most popular relaxation techniques were the most effective and the least popular were the least effective. The effects increased over time, likely caused by dropout of individuals who gained no immediate benefit. Our findings open an interesting avenue for future research to better understand which relaxation techniques work best for whom in which situations.

TRIAL REGISTRATION

DRKS00027546; https://drks.de/search/en/trial/DRKS00027546.

Feasibility and Acceptability of a Mobile App-Based TEAM-CBT (Testing Empathy Assessment Methods-Cognitive Behavioral Therapy) Intervention (Feeling Good) for Depression: Secondary Data Analysis

BACKGROUND

The Feeling Good App is an automated stand-alone digital mobile mental health tool currently undergoing beta testing with the goal of providing evidence-informed self-help lessons and exercises to help individuals reduce depressive symptoms without guidance from a mental health provider. Users work through intensive basic training (IBT) and ongoing training models that provide education regarding cognitive behavioral therapy principles from a smartphone.

OBJECTIVE

The key objective of this study was to perform a nonsponsored third-party academic assessment of an industry-generated data set; this data set focused on the safety, feasibility, and accessibility of a commercial automated digital mobile mental health app that was developed to reduce feelings associated with depression.

METHODS

The Feeling Good App development team created a waitlist cohort crossover design and measured symptoms of depression and anxiety using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and an app-specific measure of negative feelings called the 7 Dimension Emotion Slider (7-DES). The waitlist cohort crossover design divided the participants into 2 groups, where 48.6% (141/290) of the participants were given immediate access to the apps, while 51.4% (149/290) were placed on a 2-week waitlist before being given access to the app. Data collected by the Feeling Good App development team were deidentified and provided to the authors of this paper for analysis through a nonsponsored university data use agreement. All quantitative data were analyzed using SPSS Statistics (version 28.0; IBM Corp). Descriptive statistics were calculated for demographic variables. Feasibility and acceptability were descriptively assessed. All participants included in the quantitative data were given access to the Feeling Good App; this study did not include a control group.

RESULTS

In terms of safety, there was no statistically significant change in suicidality from preintervention to postintervention time points (t288=0.0; P>.99), and there was a statistically significant decrease in hopelessness from preintervention to postintervention time points (F289=30.16; P<.01). In terms of acceptability, 72.2% (166/230) of the users who started the initial 2-day IBT went on to complete it, while 34.8% (80/230) of the users who started IBT completed the entirety of the apps' 4-week protocol (150/230, 65.22% dropout rate over 4 weeks).

CONCLUSIONS

This study is the first reported proof-of-concept evaluation of the Feeling Good App in terms of safety, feasibility, and statistical trends within the data set. It demonstrates a feasible and novel approach to industry and academic collaboration in the process of developing a digital mental health technology translated from an existing evidence-informed treatment. The results support the prototype app as safe for a select nonclinical population. The app had acceptable levels of engagement and dropouts throughout the intervention. Those who stay engaged showed reductions in symptom severity of depression warranting further investigation of the app's efficacy.

Efficacy of stand-alone digital mental health applications for anxiety and depression: A meta-analysis of randomized controlled trials

BACKGROUND

Anxiety and depressive disorders affect 20% of the population, cause functional impairment, and represent a leading cause of disability. Although evidence-based treatments exist, the shortage of trained clinicians and high demand for mental health services have resulted in limited access to evidence-based care. Digital mental health applications (DMHA) present innovative, scalable, and sustainable solutions to address disparities in mental health care.

METHODS

The present study used meta-analytic techniques to evaluate the therapeutic effect of DMHAs in randomized controlled trials (RCTs) for individuals experiencing anxiety and/or depressive symptoms. Search terms were selected based on concepts related to digital mental health applications, mental health/wellness, intervention type, trial design, and anxiety and/or depression symptoms/diagnosis outcomes to capture all potentially eligible results. Potential demographic, DMHA, and trial design characteristics were examined as moderators of therapeutic effects.

RESULTS

Random effects meta-analyses found that stand-alone DMHAs produced a modest reduction in anxiety (g = 0.31) and depressive (g = 0.35) symptom severity. Several moderators influenced the therapeutic effects of DMHAs for anxiety and/or depressive symptoms including treatment duration, participant inclusion criteria, and outcome measures.

LIMITATIONS

Minimal information was available on DMHA usability and participant engagement with DMHAs within RCTs.

CONCLUSIONS

While DMHAs have the potential to be scalable and sustainable solutions to improve access and availability of evidence-based mental healthcare, moderator analyses highlight the considerations for implementation of DMHAs in practice. Further research is needed to understand factors that influence therapeutic effects of DMHAs and investigate strategies to optimize its implementation and overcome the extant research-to-practice gap.

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.

Longitudinal Trajectories of Post-Stroke Depression Symptom Subgroups

BACKGROUND

Post-stroke depressive symptoms are prevalent and impairing, and elucidating their course and risk factors is critical for reducing their public health burden. Previous studies have examined the course of post-stroke depression, but distinct depressive symptom dimensions (eg, somatic symptoms, negative affect [eg, sadness], anhedonia [eg, loss of interest]) may vary differently over time.

OBJECTIVE

The present study examined within-person and between-person associations between depressive symptom dimensions across 3 timepoints in the year following discharge from in-patient rehabilitation hospitals, as well as the impact of multiple clinical variables (eg, aphasia).

METHODS

Stroke survivors completed the Center for Epidemiologic Depression Scale (CES-D) at discharge from post-stroke rehabilitation ("T1") and at 3-month ("T2") and 12-month ("T3") follow-ups. Scores on previously identified CES-D subscales (somatic symptoms, anhedonia, and negative affect) were calculated at each timepoint. Random intercept cross-lagged panel model analysis examined associations between symptom dimensions while disaggregating within-person and between-person effects.

RESULTS

There were reciprocal, within-person associations between somatic symptoms and anhedonia from T1 to T2 and from T2 to T3. Neither dimension was predictive of, or predicted by negative affect.

CONCLUSIONS

The reciprocal associations between somatic symptoms and anhedonia may reflect a "vicious cycle," and suggest these 2 symptom dimensions may be useful indicators of risk and/or intervention targets. Regularly assessing depression symptoms starting during inpatient rehabilitation may help identify stroke survivors at risk for depression symptoms and facilitate early intervention.

Within-person examination of the exercise intention-behavior gap among women in midlife with elevated cardiovascular disease risk

Engaging in moderate-to-vigorous intensity physical activity (MVPA) is important for protecting cardiovascular health among women in midlife (i.e., ages 40-60), particularly if they have already developed conditions that increase their risk for cardiovascular disease (e.g., hypertension). Although the gap between MVPA intentions and behavior is well documented in other populations, little is known about the intention-behavior gap in this at-risk group - particularly as it plays a role in daily life. The present study employed an ecological momentary assessment design to examine the relation between women's MVPA intentions and behavior in the subsequent 3 hours, as well as momentary moderators of this relation (i.e., affective states and body satisfaction). Surveys sent to women's smartphones 5 times per day for 10 days while they wore ActiGraph GT3X waistband accelerometers. Women achieved their exercise intentions at only 13% of occasions on which they set intentions. Although the most common intended exercise was walking, women engaged in more minutes of MVPA after setting intentions to do yoga or Pilates than any other type of exercise (sr = 0.25). Multilevel models showed a modest within-person relation between minutes of intended MVPA and observed MVPA in the next 3 hours (sr = 0.20). This relation was moderated within-person by the reported extent of positive affect (particularly contentment) and body satisfaction (srs = 0.35 and 0.28, respectively). Findings extend knowledge about the physical activity intention-behavior gap to an at-risk population of women and identify positive affect and body satisfaction as potential contextual influences for this group, which could inform improvements to existing interventions (e.g., delivering intervention content at times with lower-than-usual body satisfaction).

Evidence-Informed Approach to De-Prescribing of Atypical Antipsychotics (AAP) in the Management of Behavioral Expressions (BE) in Advanced Neurocognitive Disorders (NCD): Results of a Retrospective Study

The LuBAIR™ Paradigm is a novel approach to ascribe meaning to behavioral expressions in advanced neurocognitive disorders when the reliability of a clinical assessment is limited. The meaning ascribed to each behavioral category was used to identify those which are likely to respond to the use of atypical antipsychotics, in their management. De-prescribing was attempted on patients who qualified to enter this retrospective study. De-prescribing was defined as successful if individuals were completely withdrawn from AAP and remained off them for 60 days, without the re-emergence of behaviors. The LuBAIR™ Inventory was filled on two occasions. The data collected on the second occasion, in the successful and failed de-prescribed groups, were compared in this retrospective study. MANOVA, Chi-Square paired t-test statistical analyses were used to detect the differences in the behavioral categories between the two cohorts. Cohen d was used to measure effect size. Patients who did not have Mis-Identification and Goal-Directed Expressions were more likely to successfully de-prescribe: X2 (1, N = 40) = 29.119 p < 0.0001 and X2 (1, N = 40) = 32.374, p < 0.0001, respectively. Alternatively, the same behavioral categories were more likely to be present in patients who failed de-prescribing: MANOVA and paired t-test (p < 0.0001). Atypical antipsychotics, in their role as an antipsychotic and mood stabilizer, may be used to manage Mis-Identification and Goal-Directed Expressions, respectively.

In Search of Digital Dopamine: How Apps Can Motivate Depressed Patients, a Review and Conceptual Analysis

INTRODUCTION

Depression is highly prevalent and causes considerable suffering and disease burden despite the existence of wide-ranging treatment options. Momentary assessment is a promising tool in the management of psychiatric disorders, and particularly depression. It allows for a real-time evaluation of symptoms and an earlier detection of relapse or treatment efficacy. Treating the motivational and hedonic aspects of depression is a key target reported in the literature, but it is time-consuming in terms of human resources. Digital Applications offer a major opportunity to indirectly regulate impaired motivational circuits through dopaminergic pathways.

OBJECTIVE

The main objective of this review was twofold: (1) propose a conceptual and critical review of the literature regarding the theoretical and technical principles of digital applications focused on motivation in depression, activating dopamine, and (2) suggest recommendations on the relevance of using these tools and their potential place in the treatment of depression.

MATERIAL AND METHODS

A search for words related to "dopamine", "depression", "smartphone apps", "digital phenotype" has been conducted on PubMed.

RESULTS

Ecological momentary interventions (EMIs) differ from traditional treatments by providing relevant, useful intervention strategies in the context of people's daily lives. EMIs triggered by ecological momentary assessment (EMA) are called "Smart-EMI". Smart-EMIs can mimic the "dopamine reward system" if the intervention is tailored for motivation or hedonic enhancement, and it has been shown that a simple reward (such as a digital badge) can increase motivation.

DISCUSSION

The various studies presented support the potential interest of digital health in effectively motivating depressed patients to adopt therapeutic activation behaviors. Finding effective ways to integrate EMIs with human-provided therapeutic support may ultimately yield the most efficient and effective intervention method. This approach could be a helpful tool to increase adherence and motivation.

CONCLUSION

Smartphone apps can motivate depressed patients by enhancing dopamine, offering the opportunity to enhance motivation and behavioral changes, although longer term studies are still needed.