Lessons Learned From the Integration of Ambient Assisted Living Technologies in Older Adults’ Care: Longitudinal Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 has given impetus to an already growing trend around the use of ambient assisted living (AAL) technologies to support frail older adults who live alone. However, the challenge is that systematic research on the long-term use of AAL technologies remains in its nascent stages, leaving gaps in the understanding of the predictors that contribute to the routine embedding of AAL technologies in older adults' care. OBJECTIVE: This paper aims to share key lessons from a longitudinal study on the routine embedding of AAL technologies in older adults' care within a hitherto under-studied Southeast Asian context. Our objective was to design and deploy an AAL system termed Ubiquitous Service Management and Reasoning Architecture (Ubismart), evaluate its impact on older adults' quality of life (QOL), and distill lessons to inform the sustainable and culturally sensitive adoption of AAL technologies in similar settings. METHODS: We conducted an in-depth case study using a mixed methods design. First, we designed and deployed Ubismart to unobtrusively monitor and visualize older adults' activities of daily living. To assess changes in QOL, we administered a simplified, gamified version of the validated Older People's Quality of Life Questionnaire. Finally, we conducted semistructured interviews with older adults and their caregivers to triangulate the quantitative findings and explore evolving perceptions of the technology and its integration into daily routines. RESULTS: Quantitative analysis revealed significant improvements in (1) psychological and emotional well-being (P=.01) and (2) leisure and social activities (P=.03) following the AAL intervention. Other QOL dimensions showed no statistically significant change. Qualitative findings reinforced the improvement in psychological and emotional well-being, with many participants describing a heightened sense of safety and peace of mind, often likening the technology to "insurance" or a silent companion. However, the impact on social relationships was paradoxical; some older adults felt more cared for, while others perceived a decline in in-person visits. This paradox highlighted the complexities of technology's role in caregiving, as it might simultaneously enhance feelings of safety while unintentionally diminishing social connection for some older adults. CONCLUSIONS: AAL technologies such as Ubismart enhance older adults' psychological and emotional well-being and sense of safety but may inadvertently reduce social interaction. Sustainable integration requires balancing these benefits with efforts to maintain meaningful caregiver connections, supporting both safety and social engagement for older adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT06486935; https://clinicaltrials.gov/study/NCT06486935.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".