Effectiveness and cost-effectiveness of a web-based routine assessment with integrated recommendations for action for depression and anxiety (RehaCAT+): protocol for a cluster randomised controlled trial for patients with elevated depressive symptoms in rehabilitation facilities.

INTRODUCTION: The integration of a web-based computer-adaptive patient-reported outcome test (CAT) platform with persuasive design optimised features including recommendations for action into routine healthcare could provide a promising way to translate reliable diagnostic results into action. This study aims to evaluate the effectiveness and cost-effectiveness of such a platform for depression and anxiety (RehaCAT+) compared with the standard diagnostic system (RehaCAT) in cardiological and orthopaedic health clinics in routine care. METHODS AND ANALYSIS: A two-arm, pragmatic, cluster-randomised controlled trial will be conducted. Twelve participating rehabilitation clinics in Germany will be randomly assigned to a control (RehaCAT) or experimental group (RehaCAT+) in a 1:1 design. A total sample of 1848 participants will be recruited across all clinics. The primary outcome, depression severity at 12 months follow-up (T3), will be assessed using the CAT Patient-Reported Outcome Measurement Information System Emotional Distress-Depression Item set. Secondary outcomes are depression at discharge (T1) and 6 months follow-up (T2) as well as anxiety, satisfaction with participation in social roles and activities, pain impairment, fatigue, sleep, health-related quality of life, self-efficacy, physical functioning, alcohol, personality and health economic-specific general quality of life and socioeconomic cost and benefits at T1-3. User behaviour, acceptance, facilitating and hindering factors will be assessed with semistructured qualitative interviews. Additionally, a smart sensing substudy will be conducted, with daily ecological momentary assessments and passive collection of smartphone usage variables. Data analysis will follow the intention-to-treat principle with additional per-protocol analyses. Cost-effectiveness analyses will be conducted from a societal perspective and the perspective of the statutory pension insurance. ETHICS AND DISSEMINATION: The study will be conducted according to the Declaration of Helsinki. The Ethics Committee of Ulm University, has approved the study (on 24 February 2021 ref. 509/20). Written informed consent will be obtained for all participants. Results will be published via peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00027447.

Digital interventions for the treatment of depression: A meta-analytic review.

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).

Acceptance towards digital health interventions - Model validation and further development of the Unified Theory of Acceptance and Use of Technology.

Internet- and mobile-based interventions (IMI) offer an effective way to complement health care. Acceptance of IMI, a key facilitator of their implementation in routine care, is often low. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study validates and adapts the UTAUT to digital health care. Following a systematic literature search, 10 UTAUT-grounded original studies (N = 1588) assessing patients' and health professionals' acceptance of IMI for different somatic and mental health conditions were included. All included studies assessed Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and acceptance as well as age, gender, internet experience, and internet anxiety via self-report questionnaires. For the model validation primary data was obtained and analyzed using structural equation modeling. The best fitting model (RMSEA = 0.035, SRMR = 0.029) replicated the basic structure of UTAUT's core predictors of acceptance. Performance Expectancy was the strongest predictor (γ = 0.68, p < .001). Internet anxiety was identified as an additional determinant of acceptance (γ = -0.07, p < .05) and moderated the effects of Social Influence (γ = 0.07, p < .05) and Effort Expectancy (γ = -0.05, p < .05). Age, gender and experience had no moderating effects. Acceptance is a fundamental prerequisite for harnessing the full potential of IMI. The adapted UTAUT provides a powerful model identifying important factors - primarily Performance Expectancy - to increase the acceptance across patient populations and health professionals.

Validation of the Mobile Application Rating Scale (MARS).

BACKGROUND: Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity. OBJECTIVE: This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS. METHODS: Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation. RESULTS: In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05). CONCLUSION: The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.