Design Preferences, Routines, and Well-Being of Older Adults Using Voice-Guided Digital Mindfulness: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Mindfulness-based interventions have been demonstrated to be effective in improving bodily and emotional well-being. However, only a few studies have explored individual differences in the application and use of digital mindfulness among adults aged ≥65 years. Voice-guided mindfulness technologies can increase the accessibility of mindfulness training, but the expected benefits may not be similar in all user groups. OBJECTIVE: This study aims to understand how older adults incorporate mindfulness into their habits and routines, explore the digital mindfulness design preferences of older adults, and understand if and how digital mindfulness can facilitate self-perceived well-being among older adults. METHODS: A qualitative interview study built on an interpretive-constructivist paradigm was conducted among older adults in Sweden who used a voice-guided mindfulness app for a 3-week period in their homes (N=15). Semistructured interviews were conducted one-on-one with participants after using the app. Qualitative thematic analysis was used to explore the lived experiences of digital mindfulness, as articulated by participants, which allowed an open exploration of the subjective experiences of digital mindfulness and their possible effects and outcomes. RESULTS: From the coding stage, 23 codes describing the digital mindfulness experience were identified from the data. These codes were thematized to group the codes together, resulting in 7 subthemes. From these 7 subthemes, three main themes were formed to answer the research objectives: (1) the embeddedness of digital mindfulness in older adults' daily routines and habits, (2) heterogeneity in older adults' design preferences for digital mindfulness, and (3) the consequences of digital mindfulness on the self-perceived well-being of older adults. CONCLUSIONS: This study concludes that digital mindfulness offers a possibility to enhance the self-perceived well-being of older adults by fostering resilience and self-care. However, adverse effects of mindfulness, such as frustration and discomfort, can also be experienced by older adults. The digital mindfulness experiences and preferences of older adults are highly individual, diverse, and manifold, which indicates that personalized approaches are essential for effective engagement. By acknowledging and addressing the heterogeneous design preferences within this demographic, developers can create more personalized and adaptive voice-guided mindfulness apps.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".