Factors Influencing Older Adults’ Perception of the Age-Friendliness of Their Environment and the Impact of Loneliness, Technology Use, and Mobility: Quantitative Analysis
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".