Usability and Adoption of Smartwatches by Older Adults in Bangladesh: User Study (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».