Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

The study demands–resources framework states that study demands increase exhaustion and study resources increase engagement. Study crafting describes a student's proactive adaption to demands and resources. To date, no intervention in the higher education context has aimed to foster study crafting. Accordingly, this study developed and evaluated the STUDYCoach online intervention, which aimed to increase engagement and reduce exhaustion by promoting study crafting. The study was a randomized controlled trial with a waiting‐list control group. All participants ( N = 209) completed a questionnaire before (T1) and after (T2) the intervention and at a 20‐week follow‐up (T3). Participants in the intervention group ( n = 149) used the STUDYCoach over three consecutive weeks. Results showed that overall study crafting, decreasing hindering demands, and engagement significantly increased in the intervention group compared to the control group after the intervention. All effects remained stable at follow‐up. Notably, exhaustion decreased significantly in the intervention group from T1 to T3 and T2 to T3. Study crafting mediated the intervention's effect on engagement and exhaustion. Our study extends the study demands–resources framework and the literature on job crafting by confirming that study crafting interventions can be effective in higher education.

Small business owners often work together with their spouses in their business. They blur work-life boundaries and find it difficult to psychologically detach from work, which both jeopardize a satisfying work-life balance (WLB). This paper aims to investigate the mechanisms and outcomes of a coaching intervention for couples to foster their detachment and WLB. We study the role of coaches’ intervention fidelity and empathy. A blended coaching format was chosen, i.e., we combined face-to-face with tele-sessions and online courses, thereby using digitalization to keep the coaching flexible to the couples’ life situations and for digital support between sessions.Coaches’ behavior in regard to intervention fidelity was observed. Based on these ratings, the clients’ sample (N = 42) was partitioned into two intervention groups (“high intervention fidelity” vs. “low intervention fidelity”) using the adapted study design approach. We also observed coaches’ empathy and assessed clients’ self-reports at different times up to 4 months after the coaching intervention ended regarding the hypothesized mechanisms of change of the coaching concept and coaching outcomes. We also assessed clients’ affinity for technology (ATI) because of the blended format of the coaching.Two-factor ANOVAs with repeated measures showed large effects (d = 1) for detachment (p = .002) and middle effects (d = 0.7) for WLB (p = .042) up to 4 months after the intervention ended without any interaction effect. Only in the “high intervention fidelity” group did the mechanisms of change and ATI (p = .000 top = .036) predict general coaching outcomes 4 months after the coaching ceased. Coaches’ empathy predicted goal attainment (p = .004) in the “high intervention fidelity” group.The couple coaching was highly effective in boosting clients’ detachment and WLB independent of coaches’ intervention fidelity. The assumed change mechanisms of the coaching concept and the ATI were only effective when there was a high degree of intervention fidelity. Coaches should be aware of the conceptual foundations and the core components of their coaching approach.