Impact of a mobile health application on digital transformation: a randomized clinical trial on strengthening digital skills in older women (Preprint)
Bibliographic record
Abstract
Background: The rapid growth of digital technologies has transformed daily activities, health management, and social interaction. Older adults, however, continue to face challenges in adopting and using these tools due to limited previous exposure, age-related sensory or cognitive decline, and low digital confidence. In Brazil, internet access among adults aged 60 years or older has increased, yet digital exclusion persists, worsening health disparities. Mobile health (mHealth) apps offer a potential strategy to promote digital inclusion, strengthen digital competencies, and support healthy aging. Nonetheless, studies show that culturally adapted, multidisciplinary interventions for this group remain scarce and are rarely assessed through both quantitative and qualitative methods. Objective: This study aimed to evaluate the impact of a lifestyle mHealth app on improving digital skills, as well as to analyze the level of satisfaction and usability of the app. Methods: In this mixed methods study, a 14-week randomized clinical trial was conducted in Ribeirão Preto, São Paulo, Brazil. A total of 40 older adult women were randomized into an intervention group (n=21), who used the mobile app, and a control group (n=19). Digital competencies were measured before and after the intervention using a semistructured questionnaire based on the Modelo de Competências Digitais para M-learning com foco em idosos (MCDMSênior; Digital Competency Model for M-learning with a focus on older adults) framework, covering 6 domains-basic technology use, internet navigation, mobile app use, online research, digital communication, and usage of digital resources. Additionally, satisfaction with the educational content was evaluated using the suitability assessment of materials, and system usability was assessed using the System Usability Scale. Qualitative data were collected through semistructured, in-person interviews conducted immediately after the intervention with all intervention participants. Interviews explored perceptions of the app's usability, satisfaction with its content, barriers, and facilitators to engagement, and perceived changes in digital skills. All interviews were audio-recorded, transcribed, and analyzed thematically by 2 independent researchers using an inductive coding approach. Results: Postintervention analyses revealed significant differences in specific digital competencies. The intervention group demonstrated a moderate improvement in internet navigation skills, while gains in basic technology use and digital communication were minimal. Conversely, the control group exhibited moderate improvement in basic technology skills and lower effects in online research and digital communication. Overall, satisfaction with the educational content was low, and usability was rated as average. Qualitative findings indicated that, although participants valued the clarity of navigation and cultural relevance, persistent age-related fears and insecurities in using digital technologies were reported. Participants highlighted the need for more personalized guidance, ongoing motivational support, and technical adjustments to improve usability and engagement. Conclusions: mHealth apps can effectively enhance certain digital competencies in older women, particularly internet navigation, but improvements in content suitability and usability are needed. Refinements in design and tailored support are essential to overcome age-related barriers and foster digital inclusion.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".