A Video-Based Mobile App as a Health Literacy Tool for Older Adults Living at Home: Protocol for a Utility Study
Bibliographic record
Abstract
BACKGROUND: People aged ≥65 years are more likely to have health problems related to aging, polypharmacy, and low treatment adherence. Moreover, health literacy levels decrease with increasing age. OBJECTIVE: The aim of this study is to assess an app's utility in promoting health-related knowledge in people aged ≥65 years. METHODS: We developed a simple, intuitive, and video-based app (DigiAdherence) that presents a recipe, nutritional counseling, and content on physical activity, cognitive exercise, motivation to adhere to treatment, fall prevention, and health literacy. A convenience sample of 25 older adults attending the Personalized Health Care Unit of Portimão or the Family Health Unit of Portas do Arade (ACeS Algarve II - Barlavento, ARS Algarve, Portugal) will be recruited. Subjects must be aged ≥65 years, own a smartphone or tablet, be willing to participate, and consent to participate. Those who do not know how to use or do not have a smartphone/tablet will be excluded. Likewise, people with major cognitive or physical impairment as well as those living in a long-term care center will not be included in this study. Participants will have access to the app for 4 weeks and will be evaluated at 3 different timepoints (V0, before they start using the app; V1, after using it for 30 days; and V2, 60 days after stopping using it). After using the app for 30 days, using a 7-point Likert scale, participants will be asked to score the mobile tool's utility in encouraging them to take their medications correctly, improving quality of life, increasing their health-related knowledge, and preventing falls. They will also be asked to assess the app's ease of use and visual esthetics, their motivation to use the app, and their satisfaction with the app. Subjects will be assessed in a clinical interview with a semistructured questionnaire, including questions regarding user experience, satisfaction, the utility of the app, quality of life (EQ-5D-3L instrument), and treatment adherence (Morisky scale). The proportion of participants who considered the app useful for their health at V1 and V2 will be analyzed. Regarding quality of life and treatment adherence perceptions, comparisons will be made between V0 and V1, using the t test for dependent samples. The same comparisons will be made between V0 and V2. RESULTS: This study was funded in December 2019 and authorized by the Executive Board of ACeS Algarve II - Barlavento and by the Ethics Committee of NOVA Medical School (99/2019/CEFCM, June 2020). This protocol was also approved by the Ethics Committee for Health (16/2020, September 2020) and the Executive Board (December 2020) of the Regional Health Administration of the Algarve, IP (Instituto Público). Recruitment was completed in June 2021. CONCLUSIONS: Since the next generation of older adults may have higher digital literacy, information and communication technologies could potentially be used to deliver health-related content to improve lifestyles among older adults. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/29675.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".