Factors Influencing Older Adults’ Perception of the Age-Friendliness of Their Environment and the Impact of Loneliness, Technology Use, and Mobility: Quantitative Analysis
Notice bibliographique
Résumé
BACKGROUND: The World Health Organization's (WHO) publication on age-friendly environments (AFEs) imagines future cities to become more age-friendly to harness the latent potential of older adults, especially those who have restricted mobility. AFE has important implications for older adults in maintaining social connections, independence, and successful aging-in-place. However, technology is notably absent in the 8 intersecting domains of AFEs that the WHO imagines improve older adult well-being, and we investigated whether technology should form a ninth domain. While mobility was severely restricted, the COVID-19 pandemic provided an opportunity to test how older adults' perceptions of their AFE changed and what role technology was playing. OBJECTIVE: This study examined how life-space mobility (LSM), a concept for assessing patterns of functional mobility over time, and loneliness impacted perceived AFEs and the moderating effect of technology. It also explores whether technology should play a greater role as the ninth domain of the WHO's imagination of the AFE of the future. METHODS: In this cross-sectional quantitative observation study, data from 92 older adults aged 65-89 years were collected in England from March 2020 to June 2021 during the COVID-19 pandemic. The Life-space Questionnaire, Technology Experience Questionnaire, UCLA (University of California, Los Angeles) Loneliness Scale, and age-friendly environment assessment tool were used. Correlation and moderation analyses were used to investigate relationships between variables. RESULTS: Most participants (86/92, 93%) had not left their immediate town in the previous 4 weeks before the interview. Restricted LSM was positively correlated to the age-friendly environment assessment tool, that is, rising physical isolation was linked to a better perception of AFEs; however, we discovered this result was due to the moderating impact of increased use of technology, and that restricted LSM actually had a negative effect on AFEs. Loneliness was correlated negatively with the perception of AFEs, but technology use was found to moderate the impact of loneliness. CONCLUSIONS: Pandemic-related LSM restrictions impacted perceived AFEs and loneliness negatively, but technology played a moderating role. The findings demonstrate that technology could be considered as a ninth domain in the WHO's assessment of AFEs for older adults and that there is a need for its explicit acknowledgment.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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 ».