Background: Health care apps are widely used to support weight loss and lifestyle modification. Many of these apps offer tailored feedback on dietary intake and nutritional behavior. However, most lack personalized features that promote physical activity (PA), which is important for weight management, metabolic health, and chronic disease prevention. To develop future personalized PA promotion functions, it is essential to understand users' perceptions of PA.
Objective: This study aimed to explore health care app users' perception of PA, including perceived motivators and barriers.
Methods: A qualitative study was conducted using focus group interviews with health care app users. Participants were recruited regardless of age, sex, or body mass index. A thematic analysis was conducted using a combination of inductive and deductive approaches. Question 1 ("How do you perceive the importance of physical activity?") was analyzed inductively, whereas questions 2 ("What are the motivating factors for engaging in physical activity?") and 3 ("What are the barriers to engaging in physical activity?") were analyzed deductively based on the social ecological model.
Results: Eleven participants were interviewed and were unfamiliar with the term "physical activity" but recognized the importance of movement and reducing sedentary behavior. The identified motivators included improvements in mood; changes in physical appearance; support from family; alignment with personal routines and conditions (eg, goal setting, feedback, reminders, and praise); and tailoring to physical condition, daily schedules, and weather. The reported barriers included time restrictions due to work, fatigue, weather, remote work, and social pressure in workplace settings.
Conclusions: This study provides user-informed insights that can inform the design of personalized approaches better aligned with daily routines, competing demands, and situational barriers. Future work should evaluate how incorporating such user perspectives into personalized support strategies affects engagement and PA.
Background: Mobile health (mHealth) interventions can expand access to and engagement in lifesaving treatment for pregnant and postpartum people with a substance use disorder. Yet, many people with lived experience and substance use providers alike are often excluded from mHealth intervention development, limiting opportunities to provide feedback on critical design components such as usability, cultural relevance, and compatibility with real-world practice.
Objective: The study engaged pregnant and postpartum people and substance use providers in a formative evaluation to refine an mHealth intervention designed to support recovery.
Methods: Pregnant and postpartum participants (n=11) and providers working in recovery settings (n=13) across Missouri reviewed the same mHealth intervention. Participants completed a survey and semistructured qualitative questions on usability and compatibility after reviewing the same mHealth intervention. Survey responses and qualitative themes were compared across groups. Post hoc analyses examined differences between pregnant and postpartum participants who had used the app and those who had not (n=8) to identify barriers to participation.
Results: Both participant groups reported similar themes related to the usability and compatibility of the mHealth intervention, including a need for simplified navigation and greater personalization of app content. The e-coaching feature and directory of recovery-focused resources were viewed as valuable by both groups. Uniquely, pregnant and postpartum participants emphasized the need for app content addressing craving management, emotional triggers, and parenting stress. These participants also requested more frequent communication with the e-coach than providers recommended. Nonapp users differed from app users by race, education, and household characteristics, underscoring structural barriers to engagement.
Conclusions: Engaging both pregnant and postpartum people and providers in formative evaluation reveals overlapping and distinct priorities for mHealth design. Findings highlight that user-informed development is essential for improving usability, engagement, and recovery outcomes, including reaching those least likely to engage with traditional or digital treatment supports.
Background: Health information systems (HISs) are essential for strengthening health systems in underserved areas. However, many HISs in Africa are still in the early stages of implementation, and existing systems often suffer from imbalances in data availability. Their optimization is faced with various challenges, including limited resources, which restricts their scalability.
Objective: The aim of this study is to identify contextual barriers that hinder the optimization of HIS in African underserved settings. Specifically, the study adopts the lens of frugal innovation (FI) and information and communication technologies for development (ICT4D) to explore ways to enhance the quality of health care delivery for low-income populations.
Methods: A qualitative research approach involving 32 participants was used. The study was guided by the central theme: contextual barriers and challenges hindering the optimization of HISs.
Results: Four major thematic categories emerged from the data: HIS contextualization, health system factors, service provider issues, and HIS integration. The findings offer valuable insights that can contribute to transforming HISs in underserved settings and improving health care quality.
Conclusions: The findings reflect stakeholder experiences in underserved communities in Nairobi, Kenya, and may be transferable to similar settings, subject to local governance, resources, and workflows. Despite the transformative potential of HISs in low- and middle-income countries, progress remains limited due to poor digital infrastructure and contextual barriers resulting in minimal impact from capital-intensive digital health investments and persistent data challenges. Using FI and ICT4D lenses, 4 key barriers were identified: health system, HIS contextualization, HIS integration, and HIS service provider. Rethinking HIS strategies through FI and ICT4D can enable affordable and sustainable, user-centered solutions. Future research should test scalability, sustainability, and interoperability impact in diverse settings.
This formative research explored health science researchers' perspectives on the development of an artificial intelligence-based virtual study assistant and identified 8 potential features and their priorities.
Background: Case formulation (CF) is a core skill for therapists; however, creating high-quality CF requires considerable time.
Objective: This study demonstrates that providing a knowledge graph based on the meta-analytic literature can enhance CF quality.
Methods: Five groups were established, including four large language model (LLM) groups and one human expert group, each generating 25 CFs based on 25 vignettes. The Control group with Claude Sonnet 3.7 produced 25 CFs. The Personalization group served as the control group with additional personalization prompts. The Knowledge Graph group employed an LLM that generated 25 CFs, which was provided with a meta-analysis Knowledge Graph. Further incorporation of additional personalization prompts then comprised the Knowledge Graph with Personalization group. Finally, the Expert Group consisted of 25 CFs generated by a human expert. These 125 CFs in total were evaluated for general quality (i.e., correctness, completeness, feasibility, and consistency) using a 7-point scale and 18 essential elements with binary scores (0 or 1) by another human expert. The CFs were also qualitatively analyzed.
Results: The Knowledge Graph and Knowledge Graph with Personalization groups scored significantly higher than the control group in terms of correctness, completeness, and feasibility. The Expert group scored significantly higher on consistency than the machine-generated groups. Additionally, there was no significant difference in the feasibility scores between the Knowledge Graph, Knowledge Graph with Personalization, and expert groups. The qualitative evaluation suggested that human CFs narrow the text to content that is easy for the client to read, whereas machine CFs are more likely to include expressions that are unnatural to the client.
Conclusions: These results indicate that providing knowledge graphs to novice therapists increases the correctness, completeness, and feasibility of CF. Providing experienced therapists with knowledge graphs is suggested to improve the quality of their CF and mental health services.
Clinicaltrial: None.
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