How to Improve Mental Health Apps?
Jin et al. (2025) conducted a systematic analysis of 111 articles to examine user engagement challenges in mobile mental health apps. Their review integrates findings across technological, psychological, motivational, and design perspectives to explain why sustained engagement remains difficult despite the growing availability and clinical promise of these interventions.
Mental health apps use smartphone data to offer personalized support based on what users share and how they behave, in combination with individuals’ psychological traits, momentary mental states, and engagement patterns (Firth et al., 2017; Kang & Reynolds, 2024). Because these apps depend heavily on active user participation, maintaining engagement at levels necessary for clinical effectiveness has become a primary challenge (Ferguson et al., 2021).
Jin et al. (2025) describe engagement as a multidimensional and dynamic construct. It extends beyond simple usage frequency and includes behavioral engagement, such as how often and how intensively users interact with the app, as well as subjective experiences, including attention, interest, and emotional responses (Perski et al., 2017). In mental health interventions, engagement involves not only adherence but also meaningful interaction with therapeutic components and personal investment in change (Doherty et al., 2012). Engagement is therefore fluid and varies across individuals and over time.
Importantly, the review highlights that individuals experiencing mental health problems frequently exhibit motivational deficits (Treadway & Zald, 2011), impaired concentration, and difficulties establishing routines (Lyubomirsky et al., 2003; Karekla et al., 2019). These psychological characteristics are particularly relevant in the context of digital mental health support, where sustained engagement depends largely on self-initiated and consistent participation (Ferguson et al., 2021). Specifically, many mobile mental health apps require users to actively complete exercises, log moods, practice coping strategies, and engage with psychoeducational content (Firth et al., 2017; Kang & Reynolds, 2024). Such activities demand ongoing cognitive effort, planning, and self-regulation. Over time, these repeated self-management requirements can accumulate and become burdensome (Lipschitz et al., 2023).
As a result, users may experience task fatigue, defined as a gradual decline in willingness or capacity to continue performing app-based therapeutic activities. In digital mental health interventions, task fatigue emerges when required actions are perceived as effortful, repetitive, or emotionally demanding, thereby increasing the likelihood of disengagement (Jin et al., 2025).
Beyond individual-level psychological barriers such as motivational deficits and task fatigue, Jin et al. (2025) identify several structural and design-related factors that can further undermine engagement with mental health apps. Across the 111 analyzed studies, technical and usability problems were consistently associated with dropout. Issues such as crashes, errors, data loss, high memory consumption, slow loading times, and poor synchronization reduce user satisfaction and disrupt continued use (Aji et al., 2019; Balaskas et al., 2021).
In addition to technical shortcomings, content-related limitations also weaken sustained engagement. Overly general, repetitive, impractical, or non–evidence-based material, as well as limited multimedia features and missing core therapeutic functions, decrease user interest and perceived usefulness (Oyebode et al., 2020). Adolescents in particular expect interactive elements such as gamification, images, videos, and audio; when these expectations are not met, early disengagement becomes more likely (Kenny et al., 2016).
Emotional and social factors represent another important set of challenges. When apps fail to provide timely emotional support during stressful moments, users may lose trust. Limited access to professionals, peers, or community features can increase feelings of isolation and weaken sustained engagement (de Alva et al., 2015). Negative onboarding experiences and unmet expectations immediately after installation also contribute to early dropout (Oyebode et al., 2020).
In response to these challenges, many mental health apps incorporate adaptive and personalized components to enhance engagement. Tailored feedback, reminder systems, and gamification strategies are commonly used to strengthen user support. Personalized feedback delivered through progress-monitoring tools tracks indicators such as mood and stress and provides updated information over time. Gamified reward systems, including badges and achievement markers, aim to reinforce continued participation, and some apps include customizable avatars that evolve with user progress (Ferré Grau et al., 2021). Moreover, combining rule-based personalization with passive data collection can further tailor the experience and improve system effectiveness (Hornstein et al., 2003).
Drawing on Self-Determination Theory (Ryan & Deci, 2000), the authors argue that intrinsic motivation is strengthened when autonomy, competence, and relatedness are supported. While strategies such as goal setting and self-monitoring can enhance autonomy and competence, repetitive or overly demanding tasks may feel obligatory and undermine intrinsic motivation.
The review also highlights the potential of large language models in enhancing personalization. Applying Reinforcement Learning from Human Feedback with input from mental health professionals may reduce prediction errors and improve the reliability and accuracy of personalized support (Bai et al., 2022).
Overall, Jin et al. (2025) demonstrate that sustainable engagement in mobile mental health interventions depends not only on personalization and technological sophistication, but on carefully balancing structure, flexibility, and users’ psychological needs.
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