Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial.

Objective: Smart sensing has the potential to make psychotherapeutic treatments more effective. It involves the passive analysis and collection of data generated by digital devices. However, acceptance of smart sensing among psychotherapy patients remains unclear. Based on the unified theory of acceptance and use of technology (UTAUT), this study investigated (1) the acceptance toward smart sensing in a sample of psychotherapy patients (2) the effectiveness of an acceptance facilitating intervention (AFI) and (3) the determinants of acceptance. Methods: Patients (N = 116) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a control video. An online questionnaire was used to assess acceptance of smart sensing, performance expectancy, effort expectancy, facilitating conditions and social influence. The intervention effects of the AFI on acceptance were investigated. The determinants of acceptance were analyzed with structural equation modeling (SEM). Results: The IG showed a moderate level of acceptance (M = 3.16, SD = 0.97), while the CG showed a low level (M = 2.76, SD = 1.0). The increase in acceptance showed a moderate effect in the intervention group (p < .05, d = 0.4). For the IG, performance expectancy (M = 3.92, SD = 0.7), effort expectancy (M = 3.90, SD = 0.98) as well as facilitating conditions (M = 3.91, SD = 0.93) achieved high levels. Performance expectancy (γ = 0.63, p < .001) and effort expectancy (γ = 0.36, p < .001) were identified as the core determinants of acceptance explaining 71.1% of its variance. The fit indices supported the model's validity (CFI = .95, TLI = .93, RMSEA = .08). Discussion: The low acceptance in the CG suggests that enhancing the acceptance should be considered, potentially increasing the use and adherence to the technology. The current AFI was effective in doing so and is thus a promising approach. The IG also showed significantly higher performance expectancy and social influence and, in general, a strong expression of the UTAUT factors. The results support the applicability of the UTAUT in the context of smart sensing in a clinical sample, as the included predictors were able to explain a great amount of the variance of acceptance.

Optimizing the predictive power of depression screenings using machine learning.

Objective: Mental health self-report and clinician-rating scales with diagnoses defined by sum-score cut-offs are often used for depression screening. This study investigates whether machine learning (ML) can detect major depressive episodes (MDE) based on screening scales with higher accuracy than best-practice clinical sum-score approaches. Methods: Primary data was obtained from two RCTs on the treatment of depression. Ground truth were DSM 5 MDE diagnoses based on structured clinical interviews (SCID) and PHQ-9 self-report, clinician-rated QIDS-16, and HAM-D-17 were predictors. ML models were trained using 10-fold cross-validation. Performance was compared against best-practice sum-score cut-offs. Primary outcome was the Area Under the Curve (AUC) of the Receiver Operating Characteristic curve. DeLong's test with bootstrapping was used to test for differences in AUC. Secondary outcomes were balanced accuracy, precision, recall, F1-score, and number needed to diagnose (NND). Results: A total of k = 1030 diagnoses (no diagnosis: k = 775; MDE: k = 255) were included. ML models achieved an AUCQIDS-16 = 0.94, AUCHAM-D-17 = 0.88, and AUCPHQ-9 = 0.83 in the testing set. ML AUC was significantly higher than sum-score cut-offs for QIDS-16 and PHQ-9 (ps ≤ 0.01; HAM_D-17: p = 0.847). Applying optimal prediction thresholds, QIDS-16 classifier achieved clinically relevant improvements (Δbalanced accuracy = 8%, ΔF1-score = 14%, ΔNND = 21%). Differences for PHQ_9 and HAM-D-17 were marginal. Conclusions: ML augmented depression screenings could potentially make a major contribution to improving MDE diagnosis depending on questionnaire (e.g., QIDS-16). Confirmatory studies are needed before ML enhanced screening can be implemented into routine care practice.

Mechanisms of change in digital cognitive behavioral therapy for depression in patients with chronic back pain: A mediation analysis of a multicenter randomized clinical trial.

BACKGROUND: While there is evolving knowledge on change processes of digital cognitive behavioral therapy (CBT) in the treatment of depression, little is known about how these interventions produce therapeutic change in the comorbid constellation of chronic back pain (CBP). Here, we examined whether the effects of a digital intervention to treat depression in patients with CBP are mediated by three pain-related variables (i.e., pain self-efficacy, pain-related disability, pain intensity). METHODS: This study is a secondary analysis of a randomized clinical trial conducted in routine care at 82 orthopedic clinics across Germany. In total, 209 adults with CBP and diagnosed depression (SCID interview) were randomly assigned to the intervention (n = 104) or treatment-as-usual (n = 105). Cross-lagged mediation models were estimated to investigate longitudinal mediation effects of putative mediators with depression symptom severity (PHQ-9) as primary outcome at post-treatment. RESULTS: Longitudinal mediation effects were observed for pain self-efficacy (ß = -0.094, 95%-CI [-0.174, -0.014], p = 0.021) and pain-related disability (ß = -0.068, 95%-CI [-0.130, -0.001], p = 0.047). Furthermore, the hypothesized direction of the mediation effects was supported, reversed causation did not occur. Pain intensity did not reveal a mediation effect. CONCLUSIONS: The results suggest a relevant role of pain self-efficacy and pain-related disability as change processes in the treatment of depression for patients with CBP in routine care. However, further research is needed to disclose potential reciprocal relationships of mediators, and to extend and specify our knowledge of the mechanisms of change in digital CBT for depression.

Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials.

Background: Depression is highly prevalent among individuals with chronic back pain. Internet-based interventions can be effective in treating and preventing depression in this patient group, but it is unclear who benefits most from this intervention format. Method: In an analysis of two randomized trials (N = 504), we explored ways to predict heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain. Univariate treatment-moderator interactions were explored in a first step. Multilevel model-based recursive partitioning was then applied to develop a decision tree model predicting individualized treatment benefits. Results: The average effect on depressive symptoms was d = -0.43 (95 % CI: -0.68 to -0.17; 9 weeks; PHQ-9). Using univariate models, only back pain medication intake was detected as an effect moderator, predicting higher effects. More complex interactions were found using recursive partitioning, resulting in a final decision tree with six terminal nodes. The model explained a large amount of variation (bootstrap-bias-corrected R2 = 45 %), with predicted subgroup-conditional effects ranging from di = 0.24 to -1.31. External validation in a pilot trial among patients on sick leave (N = 76; R2 = 33 %) pointed to the transportability of the model. Conclusions: The studied intervention is effective in reducing depressive symptoms, but not among all chronic back pain patients. Predictions of the multivariate tree learning model suggest a pattern in which patients with moderate depression and relatively low pain self-efficacy benefit most, while no benefits arise when patients' self-efficacy is already high. If corroborated in further studies, the developed tree algorithm could serve as a practical decision-making tool.

Stress Management Apps: Systematic Search and Multidimensional Assessment of Quality and Characteristics.

BACKGROUND: Chronic stress poses risks for physical and mental well-being. Stress management interventions have been shown to be effective, and stress management apps (SMAs) might help to transfer strategies into everyday life. OBJECTIVE: This review aims to provide a comprehensive overview of the quality and characteristics of SMAs to give potential users or health professionals a guideline when searching for SMAs in common app stores. METHODS: SMAs were identified with a systematic search in the European Google Play Store and Apple App Store. SMAs were screened and checked according to the inclusion criteria. General characteristics and quality were assessed by 2 independent raters using the German Mobile Application Rating Scale (MARS-G). The MARS-G assesses quality (range 1 to 5) on the following four dimensions: (1) engagement, (2) functionality, (3) esthetics, and (4) information. In addition, the theory-based stress management strategies, evidence base, long-term availability, and common characteristics of the 5 top-rated SMAs were assessed and derived. RESULTS: Of 2044 identified apps, 121 SMAs were included. Frequently implemented strategies (also in the 5 top-rated SMAs) were psychoeducation, breathing, and mindfulness, as well as the use of monitoring and reminder functions. Of the 121 SMAs, 111 (91.7%) provided a privacy policy, but only 44 (36.4%) required an active confirmation of informed consent. Data sharing with third parties was disclosed in only 14.0% (17/121) of the SMAs. The average quality of the included apps was above the cutoff score of 3.5 (mean 3.59, SD 0.50). The MARS-G dimensions yielded values above this cutoff score (functionality: mean 4.14, SD 0.47; esthetics: mean 3.76, SD 0.73) and below this score (information: mean 3.42, SD 0.46; engagement: mean 3.05, SD 0.78). Most theory-based stress management strategies were regenerative stress management strategies. The evidence base for 9.1% (11/121) of the SMAs could be identified, indicating significant group differences in several variables (eg, stress or depressive symptoms) in favor of SMAs. Moreover, 38.0% (46/121) of the SMAs were no longer available after a 2-year period. CONCLUSIONS: The moderate information quality, scarce evidence base, constraints in data privacy and security features, and high volatility of SMAs pose challenges for users, health professionals, and researchers. However, owing to the scalability of SMAs and the few but promising results regarding their effectiveness, they have a high potential to reach and help a broad audience. For a holistic stress management approach, SMAs could benefit from a broader repertoire of strategies, such as more instrumental and mental stress management strategies. The common characteristics of SMAs with top-rated quality can be used as guidance for potential users and health professionals, but owing to the high volatility of SMAs, enhanced evaluation frameworks are needed.

Acceptance of smart sensing: a barrier to implementation-results from a randomized controlled trial.

Background: Accurate and timely diagnostics are essential for effective mental healthcare. Given a resource- and time-limited mental healthcare system, novel digital and scalable diagnostic approaches such as smart sensing, which utilizes digital markers collected via sensors from digital devices, are explored. While the predictive accuracy of smart sensing is promising, its acceptance remains unclear. Based on the unified theory of acceptance and use of technology, the present study investigated (1) the effectiveness of an acceptance facilitating intervention (AFI), (2) the determinants of acceptance, and (3) the acceptance of adults toward smart sensing. Methods: The participants (N = 202) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a video on mindfulness. A reliable online questionnaire was used to assess acceptance, performance expectancy, effort expectancy, facilitating conditions, social influence, and trust. The self-reported interest in using and the installation of a smart sensing app were assessed as behavioral outcomes. The intervention effects were investigated in acceptance using t-tests for observed data and latent structural equation modeling (SEM) with full information maximum likelihood to handle missing data. The behavioral outcomes were analyzed with logistic regression. The determinants of acceptance were analyzed with SEM. The root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were used to evaluate the model fit. Results: The intervention did not affect the acceptance (p = 0.357), interest (OR = 0.75, 95% CI: 0.42-1.32, p = 0.314), or installation rate (OR = 0.29, 95% CI: 0.01-2.35, p = 0.294). The performance expectancy (γ = 0.45, p < 0.001), trust (γ = 0.24, p = 0.002), and social influence (γ = 0.32, p = 0.008) were identified as the core determinants of acceptance explaining 68% of its variance. The SEM model fit was excellent (RMSEA = 0.06, SRMR = 0.05). The overall acceptance was M = 10.9 (SD = 3.73), with 35.41% of the participants showing a low, 47.92% a moderate, and 10.41% a high acceptance. Discussion: The present AFI was not effective. The low to moderate acceptance of smart sensing poses a major barrier to its implementation. The performance expectancy, social influence, and trust should be targeted as the core factors of acceptance. Further studies are needed to identify effective ways to foster the acceptance of smart sensing and to develop successful implementation strategies. Clinical Trial Registration: identifier 10.17605/OSF.IO/GJTPH.

Moderators and mediators of change of an internet-based mindfulness intervention for college students: secondary analysis from a randomized controlled trial.

Background: Existing evidence suggests internet- and mobile-based interventions (IMIs) improve depressive symptoms in college students effectively. However, there is far less knowledge about the potential mechanisms of change of mindfulness-based IMIs, which could contribute to optimizing target groups and interventions. Hence, within this secondary analysis of data from a randomized controlled trial (RCT), potential moderators and mediators of the effectiveness of the IMI StudiCare Mindfulness were investigated. Methods: Moderation and mediation analyses were based on secondary data from a RCT that examined the effectiveness of the 7-module IMI StudiCare Mindfulness in a sample of college students (intervention group: n = 217; waitlist control group: n = 127). Assessments were collected before (t0; baseline), 4 weeks after (t1; during intervention), and 8 weeks after (t2; post-intervention) randomization. Longitudinal mediation analyses using structural equation modeling were employed, with depressive symptom severity as the dependent variable. For moderation analyses, bilinear interaction models were calculated with depressive symptom severity and mindfulness at t2 as dependent variables. All data-analyses were performed on an intention-to-treat basis. Results: Mediation analyses showed a significant, full mediation of the intervention effect on depressive symptom severity through mindfulness (indirect effect, a*b = 0.153, p < 0.01). Only the number of semesters (interaction: ß = 0.24, p = 0.035) was found to moderate the intervention's effectiveness on depressive symptom severity at t2, and only baseline mindfulness (interaction: ß = -0.20, p = 0.047) and baseline self-efficacy (interaction: ß = -0.27, p = 0.012) were found to be significant moderators of the intervention effect on mindfulness at t2. Conclusion: Our results suggest a mediating role of mindfulness. Moderation analyses demonstrated that the intervention improved depressive symptom severity and mindfulness independent of most examined baseline characteristics. Future confirmatory trials will need to support these findings. Clinical Trial Registration: The trial was registered a priori at the WHO International Clinical Trials Registry Platform via the German Clinical Studies Trial Register (TRN: DRKS00014774; registration date: 18 May 2018).

Mediators of digital depression prevention in patients with chronic back pain: Findings from a multicenter randomized clinical trial.

OBJECTIVE: The mechanisms of change in digital interventions for the prevention of depression are largely unknown. Here, we explored whether five theoretically derived intervening variables (i.e., pain intensity, pain-related disability, pain self-efficacy, quality of life [QoL], and work capacity) were mediating the effectiveness of a digital intervention specifically designed to prevent depression in patients with chronic back pain (CBP). METHOD: This study is a secondary analysis of a pragmatic, observer-masked randomized clinical trial conducted at 82 orthopedic clinics in Germany. A total of 295 adults with a diagnosis of CBP and subclinical depressive symptoms were randomized to either the intervention group (n = 149) or treatment-as-usual (n = 146). Longitudinal mediation analyses were conducted with structural equation modeling and depression symptom severity as primary outcome (Patient Health Questionnaire-9 [PHQ-9]; 6 months after randomization) on an intention-to-treat basis. RESULTS: Beside the effectiveness of the digital intervention in preventing depression, we found a significant causal mediation effect for QoL as measured with the complete scale of Assessment of Quality of Life (AQoL-6D; axb: -0.234), as well as for the QoL subscales mental health (axb: -0.282) and coping (axb: -0.249). All other potential intervening variables were not significant. CONCLUSION: Our findings suggest a relevant role of QoL, including active coping, as change mechanism in the prevention of depression. Yet, more research is needed to extend and specify our knowledge on empirically supported processes in digital depression prevention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Help for insomnia from the app store? A standardized rating of mobile health applications claiming to target insomnia.

A large number of mobile health applications claiming to target insomnia are available in commercial app stores. However, limited information on the quality of these mobile health applications exists. The present study aimed to systematically search the European Google Play and Apple App Store for mobile health applications targeting insomnia, and evaluate the quality, content, evidence base and potential therapeutic benefit. Eligible mobile health applications were evaluated by two independent reviewers using the Mobile Application Rating Scale-German, which ranges from 1 - inadequate to 5 - excellent. Of 2236 identified mobile health applications, 53 were included in this study. Most mobile health applications (68%) had a moderate overall quality. Concerning the four main subscales of the Mobile Application Rating Scale-German, functionality was rated highest (M = 4.01, SD = 0.52), followed by information quality (M = 3.49, SD = 0.72), aesthetics (M = 3.31, SD = 1.04) and engagement (M = 3.02, SD = 1.03). While scientific evidence was identified for 10 mobile health applications (19%), only one study employed a randomized controlled design. Fifty mobile health applications featured sleep hygiene/psychoeducation (94%), 27 cognitive therapy (51%), 26 relaxation methods (49%), 24 stimulus control (45%), 16 sleep restriction (30%) and 24 sleep diaries (45%). Mobile health applications may have the potential to improve the care of insomnia. Yet, data on the effectiveness of mobile health applications are scarce, and this study indicates a large variance in the quality of the mobile health applications. Thus, independent information platforms are needed to provide healthcare seekers and providers with reliable information on the quality and content of mobile health applications.

Lessons Learned from an Attempted Pragmatic Randomized Controlled Trial for Improvement of Chronic Pain-Associated Disability in Green Professions: Long-Term Effectiveness of a Guided Online-Based Acceptance and Commitment Therapy (PACT-A).

Musculoskeletal symptoms are increased in farmers, whereas the prevalence of chronified pain is unknown. Online interventions based on acceptance and commitment therapy (ACT) have shown encouraging results in the general population, representing a promising approach for reducing pain interference in green professions (i.e., farmers, foresters, gardeners). We conducted a pragmatic RCT comparing a guided ACT-based online intervention to enhanced treatment-as-usual in entrepreneurs, contributing spouses, family members and pensioners in green professions with chronic pain (CPG: ≥grade II, ≥6 months). Recruitment was terminated prematurely after 2.5 years at N = 89 (of planned N = 286). Assessments were conducted at 9 weeks (T1), 6 months (T2) and 12 months (T3) post-randomization. The primary outcome was pain interference (T1). The secondary outcomes encompassed pain-, health- and intervention-related variables. No treatment effect for reduction of pain interference was found at T1 (ß = -0.16, 95%CI: -0.64-0.32, p = 0.256). Improvements in cognitive fusion, pain acceptance, anxiety, perceived stress and quality of life were found only at T3. Intervention satisfaction as well as therapeutic and technological alliances were moderate, and uptake and adherence were low. Results are restricted by low statistical power due to recruitment issues, high study attrition and low intervention adherence, standing in contrast to previous studies. Further research is warranted regarding the use of ACT-based online interventions for chronic pain in this occupational group. Trial registration: German Clinical Trial Registration: DRKS00014619. Registered: 16 April 2018.

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores.

BACKGROUND: Gastrointestinal diseases are associated with substantial cost in health care. In times of the COVID-19 pandemic and further digitalization of gastrointestinal tract health care, mobile health apps could complement routine health care. Many gastrointestinal health care apps are already available in the app stores, but the quality, data protection, and reliability often remain unclear. OBJECTIVE: This systematic review aimed to evaluate the quality characteristics as well as the privacy and security measures of mobile health apps for the management of gastrointestinal diseases. METHODS: A web crawler systematically searched for mobile health apps with a focus on gastrointestinal diseases. The identified mobile health apps were evaluated using the Mobile Application Rating Scale (MARS). Furthermore, app characteristics, data protection, and security measures were collected. Classic user star rating was correlated with overall mobile health app quality. RESULTS: The overall quality of the mobile health apps (N=109) was moderate (mean 2.90, SD 0.52; on a scale ranging from 1 to 5). The quality of the subscales ranged from low (mean 1.89, SD 0.66) to good (mean 4.08, SD 0.57). The security of data transfer was ensured only by 11 (10.1%) mobile health apps. None of the mobile health apps had an evidence base. The user star rating did not correlate with the MARS overall score or with the individual subdimensions of the MARS (all P>.05). CONCLUSIONS: Mobile health apps might have a positive impact on diagnosis, therapy, and patient guidance in gastroenterology in the future. We conclude that, to date, data security and proof of efficacy are not yet given in currently available mobile health apps.

Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials.

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: From participants' baseline characteristics, lower level of education (odds ratio [OR] 3.33, 95% CI 1.51-7.32) and both lower and higher age (a quadratic effect; age: OR 0.62, 95% CI 0.47-0.82, and age2: OR 1.55, 95% CI 1.18-2.04) were significantly associated with a higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR 0.60, 95% CI 0.42-0.85; age2: 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.

A systematic quality rating of available mobile health apps for borderline personality disorder.

BACKGROUND: Mobile health apps (MHAs) may offer a mean to overcome treatment barriers in Borderline Personality Disorder (BPD) mental health care. However, MHAs for BPD on the market lack transparency and quality assessment. METHODS: European app stores were systematically searched, and two independent trained reviewers extracted relevant MHAs. Employed methods and privacy and security details documentation of included MHAs were extracted. MHAs were then assessed and rated using the German version of the standardized Mobile Application Rating Scale (MARS-G). Mean values and standard deviations of all subscales (engagement, functionality, aesthetics, information, and therapeutic gain) and correlations with user ratings were calculated. RESULTS: Of 2977 identified MHAs, 16 were included, showing average quality across the four main subscales (M = 3.25, SD = 0.68). Shortcomings were observed with regard to engagement (M = 2.87, SD = 0.99), potential therapeutic gain (M = 2.67, SD = 0.83), existing evidence base (25.0% of included MHAs were tested empirically), and documented privacy and security details. No significant correlations were found between user ratings and the overall total score of the MARS-G or MARS-G main subscales. CONCLUSIONS: Available MHAs for BPD vary in quality and evidence on their efficacy, effectiveness, and possible adverse events is scarce. More substantial efforts to ensure the quality of MHAs available for patients and a focus on transparency, particularly regarding privacy and security documentation, are necessary.

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.

Review and Analysis of German Mobile Apps for Inflammatory Bowel Disease Management Using the Mobile Application Rating Scale: Systematic Search in App Stores and Content Analysis.

BACKGROUND: Patients suffering from inflammatory bowel disease (IBD) frequently need long-term medical treatment. Mobile apps promise to complement and improve IBD management, but so far there has been no scientific analysis of their quality. OBJECTIVE: This study evaluated the quality of German mobile apps targeting IBD patients and physicians treating IBD patients using the Mobile Application Rating Scale (MARS). METHODS: The German Apple App Store and Google Play Store were systematically searched to identify German IBD mobile apps for patient and physician use. MARS was used by 6 physicians (3 using Android smartphones and 3 using iPhones) to independently assess app quality. Apps were randomly assigned so that the 4 apps with the most downloads were rated by all raters and the remaining apps were rated by 1 Android and 1 iOS user. RESULTS: In total, we identified 1764 apps in the Apple App Store and Google Play Store. After removing apps that were not related to IBD (n=1386) or not available in German (n=317), 61 apps remained. After removing duplicates (n=3) and apps for congresses (n=7), journals (n=4), and clinical studies (n=6), as well as excluding apps that were available in only 1 of the 2 app stores (n=20) and apps that could only be used with an additional device (n=7), we included a total of 14 apps. The app "CED Dokumentation und Tipps" had the highest overall median MARS score at 4.11/5. On the whole, the median MARS scores of the 14 apps ranged between 2.38/5 and 4.11/5. As there was no significant difference between iPhone and Android raters, we used the Wilcoxon comparison test to calculate P values. CONCLUSIONS: The MARS ratings showed that the quality of German IBD apps varied. We also discovered a discrepancy between app store ratings and MARS ratings, highlighting the difficulty of assessing perceived app quality. Despite promising results from international studies, there is little evidence for the clinical benefits of German IBD apps. Clinical studies and patient inclusion in the app development process are needed to effectively implement mobile apps in routine care.

Internet- and mobile-based intervention for depression in adults with chronic back pain: A health economic evaluation.

BACKGROUND: Depression and comorbid chronic back pain (CBP) lead to high personal and economic burden. Internet- and mobile-based interventions (IMI) might be a cost-effective adjunct to established interventions. METHODS: A health economic evaluation was embedded into an observer-blinded, multicenter RCT (societal and health care perspective). We randomly assigned participants (≥18 years) with CBP and diagnosed depression from 82 orthopedic clinics across Germany to intervention (IG + treatment as usual [TAU]) or TAU control group (CG). The IG received a guided IMI. Primary outcomes were depression response and quality-adjusted life years (QALYs) at 6-months follow-up. Multiple imputation was used to address missing data. Incremental cost-effectiveness/cost-utility ratios (ICER/ICUR) and the probability of being cost-effective at different willingness-to-pay thresholds were calculated. Statistical uncertainty was estimated using bootstrapping techniques (N = 10,000). RESULTS: Between October 2015 and July 2017 210 participants were randomly assigned to IG (n = 105) and CG (n = 105). Depression response did not differ significantly between groups. QALYs were significantly higher in the IG compared to the CG. Taking the societal perspective and assuming a commonly used willingness-to-pay of €34,000/QALY, the intervention's likelihood of being cost-effective was 64%. LIMITATIONS: The main limitation is that the study was powered to detect clinical but not health economic differences between groups. CONCLUSION: The IMI is considered cost-effective (vs. CG) for individuals with depression and CBP (societal perspective). These results are promising when considering the high individual and economic burden of this patient group. Further research is needed to adequately inform political decision makers before implementation into routine care.

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).

Are guided internet-based interventions for the indicated prevention of depression in green professions effective in the long run? Longitudinal analysis of the 6- and 12-month follow-up of a pragmatic randomized controlled trial (PROD-A).

OBJECTIVE: Evidence of long-term stability for positive mental health effects of internet-based interventions (IBIs) for depression prevention is still scarce. We evaluate long-term effectiveness of a depression prevention program in green professions (i.e. agriculture, horticulture, forestry). METHODS: This pragmatic RCT (n = 360) compares a tailored IBI program to enhanced treatment as usual (TAU+) in green professions with at least subthreshold depression (PHQ ≥ 5). Intervention group (IG) received one of six IBIs shown previously to efficaciously reduce depressive symptoms. We report 6- and 12-month follow-up measures for depression, mental health and intervention-related outcomes. Intention-to-treat and per-protocol regression analyses were conducted for each measurement point and complemented by latent growth modeling. RESULTS: After 6 months, depression severity (ß = -0.30, 95%-CI: -0.52; -0.07), insomnia (ß = -0.22, 95%-CI: -0.41; -0.02), pain-associated disability (ß = -0.26, 95%-CI: -0.48; -0.04) and quality of life (ß = 0.29, 95%-CI: 0.13; 0.45) in IG were superior to TAU+. Onset of possible depression was not reduced. After 12 months, no intervention effects were found. Longitudinal modeling confirmed group effects attenuating over 12 months for most outcomes. After 12 months, 55.56% of IG had completed at least 80% of their IBI. CONCLUSIONS: Stability of intervention effects along with intervention adherence was restricted. Measures enhancing long-term effectiveness of IBIs for depression health promotion are indicated in green professions. TRIAL REGISTRATION: German Clinical Trial Registration: DRKS00014000. Registered: 09 April 2018.

"Help in a Heartbeat?": A Systematic Evaluation of Mobile Health Applications (Apps) for Coronary Heart Disease.

For patients with coronary heart disease (CHD) lifestyle changes and disease management are key aspects of treatment that could be facilitated by mobile health applications (MHA). However, the quality and functions of MHA for CHD are largely unknown, since reviews are missing. Therefore, this study assessed the general characteristics, quality, and functions of MHA for CHD. Hereby, the Google Play and Apple App stores were systematically searched using a web crawler. The general characteristics and quality of MHA were rated with the Mobile Application Rating Scale (MARS) by two independent raters. From 3078 identified MHA, 38 met the pre-defined criteria and were included in the assessment. Most MHA were affiliated with commercial companies (52.63%) and lacked an evidence-base. An overall average quality of MHA (M = 3.38, SD = 0.36) was found with deficiencies in information quality and engagement. The most common functions were provision of information and CHD risk score calculators. Further functions included reminders (e.g., for medication or exercises), feedback, and health management support. Most MHA (81.58%) had one or two functions and MHA with more features had mostly higher MARS ratings. In summary, this review demonstrated that a number of potentially helpful MHA for patients with CHD are commercially available. However, there is a lack of scientific evidence documenting their usability and clinical potential. Since it is difficult for patients and healthcare providers to find suitable and high-quality MHA, databases with professionally reviewed MHA are required.

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.