Isaac Moshe

The high global prevalence of depression, together with the recent acceleration of remote care owing to the COVID-19 pandemic, has prompted increased interest in the efficacy of digital interventions for the treatment of depression. We provide a summary of the latest evidence base for digital interventions in the treatment of depression based on the largest study sample to date. A systematic literature search identified 83 studies (N = 15,530) that randomly allocated participants to a digital intervention for depression versus an active or inactive control condition. Overall heterogeneity was very high (I2 = 84%). Using a random-effects multilevel metaregression model, we found a significant medium overall effect size of digital interventions compared with all control conditions (g = .52). Subgroup analyses revealed significant differences between interventions and different control conditions (WLC: g = .70; attention: g = .36; TAU: g = .31), significantly higher effect sizes in interventions that involved human therapeutic guidance (g = .63) compared with self-help interventions (g = .34), and significantly lower effect sizes for effectiveness trials (g = .30) compared with efficacy trials (g = .59). We found no significant difference in outcomes between smartphone-based apps and computer- and Internet-based interventions and no significant difference between human-guided digital interventions and face-to-face psychotherapy for depression, although the number of studies in both comparisons was low. Findings from the current meta-analysis provide evidence for the efficacy and effectiveness of digital interventions for the treatment of depression for a variety of populations. However, reported effect sizes may be exaggerated because of publication bias, and compliance with digital interventions outside of highly controlled settings remains a significant challenge. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC

Importance: While the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary. Objective: To investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness. Data Sources: A systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials. Study Selection: Titles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted. Main Outcomes and Measures: Heterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs. Results: The systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire-9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = -0.02; 95% credible interval [CrI], -0.07 to 0.03). However, HTE was higher in more severe depression levels (β̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = -0.56; 95% CrI, -0.46 to -0.66). An interaction effect between guidance and baseline severity was found (β̂ = -0.24, 95% CrI, -0.03 to -0.46). Conclusions and Relevance: In this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

BACKGROUND: Depression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression; however, high dropout rates are a major challenge, particularly in clinical settings. OBJECTIVE: This study aims to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with dropout and whether intervention usage data could help improve the identification of individuals at risk of dropout early on in treatment. METHODS: Data were collected from 2 large-scale randomized controlled trials in which 253 patients with a diagnosis of CBP and major depressive disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participants' baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed the extent to which dropout could be predicted from a combination of participants' baseline characteristics and intervention usage variables following the completion of the first module. Dropout was defined as completing <6 modules. Analyses were conducted using logistic regression. RESULTS: : OR 1.59, 95% CI 1.13-2.23), medium versus high social support (OR 3.03, 95% CI 1.25-7.33), and a higher number of days to module completion (OR 1.05, 95% CI 1.02-1.08) predicted a higher risk of dropout, whereas a self-reported negative event in the previous week was associated with a lower risk of dropout (OR 0.24, 95% CI 0.08-0.69). A model that combined baseline characteristics and intervention usage data generated the most accurate predictions (area under the receiver operating curve [AUC]=0.72) and was significantly more accurate than models based on baseline characteristics only (AUC=0.70) or intervention usage data only (AUC=0.61). We found no significant influence of pain, disability, or depression severity on dropout. CONCLUSIONS: Dropout can be predicted by participant baseline variables, and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify individuals at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.