Tailoring mHealth for Healthy Aging: Focus Group Study With Retirement-Age Adults
Bibliographic record
Abstract
BACKGROUND: The adoption of mobile health (mHealth) technologies among older adults remains significantly lower than in younger populations, despite their potential to promote healthier lifestyles and mitigate age-related health risks. OBJECTIVE: This study aims to explore the perspectives of retirement-age adults on mHealth interventions, identifying factors that influence their adoption, such as persuasive elements in the app design and psychological techniques. METHODS: A qualitative focus group study was conducted with 19 Spanish participants recruited from urban community settings in Madrid, Spain (mean age 61.5 years; 15/19, 79% women). Participants discussed their attitudes, barriers, and preferences for mHealth tools. Focus groups were recorded, transcribed, and coded using an iterative process to ensure rigorous data analysis. An abductive approach was followed, using the persuasive design principles framework and the behavior change techniques' taxonomy, and representing any theme outside those frameworks. RESULTS: Participants expressed generally positive attitudes toward mHealth tools, favoring intuitive, user-friendly designs that are minimally time-demanding. However, significant barriers also emerged, such as low digital literacy and concerns about technology dependence. Key design preferences (persuasive design principles) and psychological techniques (behavior change techniques) were deemed beneficial, with preferred features such as tailored and meaningful goal-setting, self-monitoring, positive feedback (eg, congratulating messages after achieving a goal; social rewards), and a moderated use of notifications and prompts. Participants also stressed the importance of age-appropriate recommendations (eg, suggested diets for their age and characteristics) and design (eg, accessible, easy-to-use interfaces and human-like communication). Additionally, some preferences appeared to be culturally grounded (eg, rejection of anglicisms and the desire for locally relevant content, such as suggested activities specific to Madrid). Social support mechanisms, such as group activities and peer interactions through mHealth, were seen as critical for fostering motivation and engagement. CONCLUSIONS: mHealth interventions for this population should offer accessible and easy-to-use interfaces along with initial tutorials, facilitating an easy onboarding to overcome low digital literacy, thereby enhancing both usability and initial adoption. Furthermore, by providing meaningful, tailored content (eg, personalized diets and goals) and social features that foster peer connection (eg, user chats or organized activities), these tools may better support sustained engagement over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".