Selection, Optimization, and Compensation Strategies Used by Older Adults to Live Well With Technology: Qualitative Study
Notice bibliographique
Résumé
Background: With rapid digitalization, technologies are increasingly integrated as part of our everyday lives and are becoming increasingly essential for individuals to participate in society. Technology presents opportunities to support healthy aging. Examples include digital health monitoring and opportunities to maintain social connectedness through online platforms. However, the processes in which older adults select and integrate technologies into their daily lives have not been well examined. objectives: This study uses the Selection, Optimization, and Compensation (SOC) model to understand how older adults integrate technology into their everyday lives to live well. The two key research questions are as follows: (1) How do older adults describe their technology use and their choices, analyzed with respect to SOC processes? (2) How do older adults perceive that technology is a part of living well? Methods: A descriptive qualitative study was conducted. Purposive sampling was used to recruit older adults who were aged 55 years and older, were living in the community, spoke and understood English, and resided in Australia. Five in-person focus groups comprising 20 older adults were conducted. Data were analyzed using hybrid inductive and deductive reflexive thematic analyses, based on the SOC model. Results: All participants resided in Brisbane, Queensland. Older adults selected technology intentionally to enhance different aspects of their daily lives. Perceived "involuntary" selection of technology could lead to feelings of resentment or frustration. Optimization strategies included self-monitoring, integrating technology into daily routines, digital literacy and proficiency, and problem-solving skills. Compensatory strategies included choosing alternative technology that suited participants' abilities or skills and seeking support through informal or formal avenues. Conclusions: These findings provide important considerations for technology developers to design technology in collaboration with older adults to ensure that they align with needs and preferences. Digital literacy is not sufficient to empower older adults to use technology; when empowering older adults to use technology, service providers should also consider facilitating other intrinsic and extrinsic resources and skills.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».