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BackgroundPhysical inactivity is a major contributor to the development and persistence of chronic diseases. Mobile health apps that foster physical activity have the potential to assist in behavior change. However, the quality of the mobile health apps available in app stores is hard to assess for making informed decisions by end users and health care providers. ObjectiveThis study aimed at systematically reviewing and analyzing the content and quality of physical activity apps available in the 2 major app stores (Google Play and App Store) by using the German version of the Mobile App Rating Scale (MARS-G). Moreover, the privacy and security measures were assessed. MethodsA web crawler was used to systematically search for apps promoting physical activity in the Google Play store and App Store. Two independent raters used the MARS-G to assess app quality. Further, app characteristics, content and functions, and privacy and security measures were assessed. The correlation between user star ratings and MARS was calculated. Exploratory regression analysis was conducted to determine relevant predictors for the overall quality of physical activity apps. ResultsOf the 2231 identified apps, 312 met the inclusion criteria. The results indicated that the overall quality was moderate (mean 3.60 [SD 0.59], range 1-4.75). The scores of the subscales, that is, information (mean 3.24 [SD 0.56], range 1.17-4.4), engagement (mean 3.19 [SD 0.82], range 1.2-5), aesthetics (mean 3.65 [SD 0.79], range 1-5), and functionality (mean 4.35 [SD 0.58], range 1.88-5) were obtained. An efficacy study could not be identified for any of the included apps. The features of data security and privacy were mainly not applied. Average user ratings showed significant small correlations with the MARS ratings (r=0.22, 95% CI 0.08-0.35; P

Summary: 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. [ABSTRACT FROM AUTHOR]

Introduction: There is neither strong evidence on effective treatments for patients with chronic back pain (CBP) and depressive disorder nor sufficiently available mental health care offers.Objective: The aim is to assess the effectiveness of internet- and mobile-based interventions (IMI) as a scalable approach for treating depression in a routine care setting.Methods: This is an observer-masked, multicenter, pragmatic randomized controlled trial with a randomization ratio of 1:1.Patients with CBP and diagnosed depressive disorder (mild to moderate severity) were recruited from 82 orthopedic rehabilitation clinics across Germany. The intervention group (IG) received a guided depression IMI tailored to CBP next to treatment-as-usual (TAU; including medication), while the control group (CG) received TAU. The primary outcome was observer-masked clinician-rated Hamilton depression severity (9-week follow-up). The secondary outcomes were: further depression outcomes, pain-related outcomes, health-related quality of life, and work capacity. Biostatistician blinded analyses using regression models were conducted by intention-to-treat and per protocol analysis.Results: Between October 2015 and July 2017, we randomly assigned 210 participants (IG, n = 105; CG, n = 105), mostly with only a mild pain intensity but substantial pain disability. No statistically significant difference in depression severity between IG and CG was observed at the 9-week follow-up (β = -0.19, 95% CI -0.43 to 0.05). Explorative secondary depression (4/9) and pain-related (4/6) outcomes were in part significant (p < 0.05). Health-related quality of life was significantly higher in the IG. No differences were found in work capacity.Conclusion: The results indicate that an IMI for patients with CBP and depression in a routine care setting has limited impact on depression. Benefits in pain and health-related outcomes suggest that an IMI might still be a useful measure to improve routine care. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Terhorst, Weilbacher, Suda, Simon, Messner, Sander and Baumeister.)