Mindful Age and Technology: a Qualitative Analysis of a Tablet/Smartphone App Intervention Designed for Older Adults
Bibliographic record
Abstract
The global population is aging while modern healthcare systems are responding with limited success to the growing care demands of the senior population. Capitalizing on recent technological advancements, new ways to improve older adults' quality of life have recently been implemented. The current study investigated, from a qualitative point of view, the utility of a mindfulness-based smartphone application for older adults. A description of the older adults' experience with the smartphone application designed to enhance well-being and mindfulness will be presented. Participants'general beliefs about the benefits of technology for personal well-being will also be discussed. 68 older adults were recruited from different education centers for seniors. Participants were randomly assigned to two groups: a) a treatment group, which received the smartphone application intervention (n = 34), or b) a waitlist control group (n = 34). The experimental intervention included the utilization of a smartphone app designed specifically for improving older adult well-being and mindfulness levels. Participants completed semi-structured interviews evaluating participants' treatment experience and technology-acceptance at recruitment (T0, baseline) and post-intervention (T1, post-intervention). Through thematic analysis, four themes were identified from verbatim responses of both interviews: Utility of technology for health, Impressions of technology, Mindful-benefits of smartphone application usage, and Smartphone application usage as a means to improve interpersonal relationships. Participants showed a positive experience of the app intervention. Qualitative analysis underlined the main Mindfulness-benefits reported by participants and the potentially crucial role of "Langerian" mindfulness in the relationship between older adults and health technology.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".