Usability and Adoption of Smartwatches by Older Adults in Bangladesh: User Study (Preprint)
Bibliographic record
Abstract
Background: Wearables such as smartwatches can support point-of-care health management for older adults while reducing pressure on health care systems as aging populations grow. Although many studies emphasize technical accuracy, user-centered research on smartwatch adoption among older adults remains limited, particularly in low- and middle-income countries, such as Bangladesh. Objective: This study evaluated real-world usability, acceptance, and adoption of smartwatches among socioeconomically diverse older adults (aged ≥60 y) in a low- and middle-income country context. It examined how age, education, socioeconomic background, and cultural perceptions influence user experience, and identified barriers and facilitators related to comfort, usability, and long-term engagement. It also explored health-related features that older adults perceived as missing. Methods: Participants were recruited from older adult communities with diverse socioeconomic and educational backgrounds, and received a commercially available smartwatch. A mixed methods, 3-phase longitudinal design was conducted, including a short-term survey (n=27), 3 long-term survey data points (n=19, 18, and 18), and 2 focus group interviews (n=13 and 12). Quantitative analyses included paired t tests across longitudinal data points and chi-square tests for distributional associations, complemented by thematic qualitative analysis. Results: Comfort improved substantially over time, with participants rating the smartwatch as very comfortable, increasing from 26% (5/19) at baseline to 80% (16/20) at final follow-up. Heart rate monitoring remained the most frequently used feature, rising from 68.42% (13/19) to 75% (15/20). Paired t tests showed no significant longitudinal changes in daily usage (t5=0.00 to 0.22, P=.83 to ≥.99), comfort (t4=-0.07 to 0.30, P=.78 to .95), most liked features (t8=-0.63 to 0.51, P=.30 to .62), or clarity of instructions (t2=0.00 to 0.19, P=.87 to ≥.99). Significant increases were observed in feature usage (data point [DP] 2 vs DP3: t8=-4.16, P<.001; DP1 vs DP3: t8=-2.53, P=.03) and perceived difficulty of use (DP2 vs DP3: t8=-4.29, P<.001; DP1 vs DP3: t8=-2.31, P=.05). Chi-square analyses indicated a significant association between study phase and clarity of instructions (χ24=10.3, P=.03), with a trend for comfort (χ²8=13.8, P=.08); other usability dimensions were non-significant (χ²16=7.1 to 17.5, P=.35 to .97). Socioeconomic differences emerged in data-sharing preferences, with higher socioeconomic participants favoring continuous sharing and lower socioeconomic participants preferring emergency-only sharing. Conclusions: Older adults can adapt to wearable health technologies when given adequate time and support, regardless of education level. Preferences for a limited set of essential health features and faster adoption among participants with lower educational backgrounds highlight the need for simpler smartwatch designs. These findings can guide age-appropriate wearable technologies that prioritize usability, comfort, and familiarity to improve health care access in resource-limited settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".