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Promoting individual resilience-that is, maintaining or regaining mental health despite stressful circumstances-is regarded as an important endeavor to prevent mental illness. However, digital resilience interventions designed to enhance mental health have yielded mixed results. Such heterogeneous effects reflect a variety of unsolved conceptual challenges in interventional resilience research. These range from grounding interventions in resilience frameworks, using theory or targeting etiologically important resilience factors as intervention content, to a lack of knowledge about the mechanisms underlying effects, and using techniques specifically developed to foster psychosocial resources. The web- and app-based resilience intervention RESIST was designed to address these challenges, mainly by using both the Positive Appraisal Style Theory of Resilience as its theoretical foundation and interventional techniques from Strengths-Based Cognitive Behavioral Therapy. This study's primary aim was to evaluate the effectiveness of RESIST on perceived stress in a general working population as a means of universal prevention, relative to a waitlist control group. A secondary study aim was to explore the resilience factors of self-efficacy, optimism, self-compassion, and perceived social support, the intervention targets as potential mediators of its effect on stress and self-perceived resilience. In total, 352 employees were randomly assigned to either a self-help version of RESIST or a waitlist control group. Data were collected via the web at baseline, postintervention, and at 3- and 6-month (intervention group [IG] only) follow-ups. The primary outcome was perceived stress, measured with the Perceived Stress Scale-10. Secondary outcomes included self-perceived resilience, the resilience factors targeted, and other mental and work-related health outcomes. The IG reported significantly less stress than controls postintervention (Δ=-3.14; d=-0.54, 95% CI -0.75 to -0.34, and P<.001) and at 3-month follow-up (Δ=-2.79; d=-0.47, 95% CI -0.71 to -0.22, and P=.002). These improvements in the IG were maintained at 6-month follow-up. Favorable between-group differences also were detected for self-perceived resilience and the resilience factors. IG participants completed on average 2.2 (SD 2.3) web-based sessions and used the app's core feature a median of 14 times (IQR 4.00-33.75, range 1-220). The positive effects of the intervention on stress and resilience were primarily mediated by changes in optimism and self-compassion. No evidence was found that self-efficacy and social support also acted as mediators. In a sample of employees experiencing heightened work-burden levels, RESIST was effective in reducing perceived stress and increasing self-perceived resilience as well as the targeted resilience factors. Mediation analyses suggested that developing a positive future outlook and a self-compassionate attitude toward oneself may be key drivers to enhance resilience. Changing the quality of social relationships and strengthening the belief in one's abilities may require more time, the involvement of others, or personal support from an eCoach to ensure sufficient learning opportunities.

Simulation study results suggest that COVID-19 contact tracing apps have the potential to achieve pandemic control. Concordantly, high app adoption rates were a stipulated prerequisite for success. Early studies on potential adoption were encouraging. Several factors predicting adoption rates were investigated, especially pertaining to user characteristics. Since then, several countries have released COVID-19 contact tracing apps. This study's primary aim is to investigate the quality characteristics of national European COVID-19 contact tracing apps, thereby shifting attention from user to app characteristics. The secondary aim is to investigate associations between app quality and adoption. Finally, app features contributing to higher app quality were identified. Eligible COVID-19 contact tracing apps were those released by national health authorities of European Union member states, former member states, and countries of the European Free Trade Association, all countries with comparable legal standards concerning personal data protection and app use voluntariness. The Mobile App Rating Scale was used to assess app quality. An interdisciplinary team, consisting of two health and two human-computer interaction scientists, independently conducted Mobile App Rating Scale ratings. To investigate associations between app quality and adoption rates and infection rates, Bayesian linear regression analyses were conducted. We discovered 21 national COVID-19 contact tracing apps, all demonstrating high quality overall and high-level functionality, aesthetics, and information quality. However, the average app adoption rate of 22.9% (SD 12.5%) was below the level recommended by simulation studies. Lower levels of engagement-oriented app design were detected, with substantial variations between apps. By regression analyses, the best-case adoption rate was calculated by assuming apps achieve the highest ratings. The mean best-case adoption rates for engagement and overall app quality were 39.5% and 43.6%, respectively. Higher adoption rates were associated with lower cumulative infection rates. Overall, we identified 5 feature categories (symptom assessment and monitoring, regularly updated information, individualization, tracing, and communication) and 14 individual features that contributed to higher app quality. These 14 features were a symptom checker, a symptom diary, statistics on COVID-19, app use, public health instructions and restrictions, information of burden on health care system, assigning personal data, regional updates, control over tracing activity, contact diary, venue check-in, chats, helplines, and app-sharing capacity. European national health authorities have generally released high quality COVID-19 contact tracing apps, with regard to functionality, aesthetics, and information quality. However, the app's engagement-oriented design generally was of lower quality, even though regression analyses results identify engagement as a promising optimization target to increase adoption rates. Associations between higher app adoption and lower infection rates are consistent with simulation study results, albeit acknowledging that app use might be part of a broader set of protective attitudes and behaviors for self and others. Various features were identified that could guide further engagement-enhancing app development.