Exploring Older Adults’ Needs for a Healthy Life and eHealth: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Aging brings physical and life changes that could benefit from eHealth services. eHealth holistically combines technology, tasks, individuals, and contexts, and all these intertwined elements should be considered in eHealth development. As users' needs change with life situations, including aging and retirement, it is important to identify these needs at different life stages to develop eHealth services for well-being and active, healthy lives. OBJECTIVE: This study aimed to (1) understand older adults' everyday lives in terms of well-being and health, (2) investigate older adults' needs for eHealth services, and (3) create design recommendations based on the findings. METHODS: A total of 20 older adults from 2 age groups (55 to 74 years: n=12, 60%; >75 years: n=8, 40%) participated in this qualitative interview study. The data were collected remotely using a cultural probes package that included diary-based tasks, sentence completion tasks, and 4 background questionnaires; we also performed remote, semistructured interviews. The data were gathered between the fall of 2020 and the spring of 2021 in Finland as a part of the Toward a Socially Inclusive Digital Society: Transforming Service Culture (DigiIN) project (2019 to 2025). RESULTS: In the daily lives of older adults, home-based activities, such as exercising (72/622, 11.6% of mentions), sleeping (51/622, 8.2% of mentions), and dining and cooking (96/622, 15.4% of mentions), promoted well-being and health. When discussing their needs for eHealth services, participants highlighted a preference for a chat function. However, they frequently mentioned barriers and concerns such as the lack of human contact, inefficiency, and difficulties using eHealth systems. Older adults value flexibility; testing possibilities (eg, trial versions); support for digital services; and relevant, empathetically offered content with eHealth services on short-term and long-term bases in their changing life situations. CONCLUSIONS: Many older adults value healthy routines and time spent at home. The diversity of older adults' needs should be considered by making it possible for them to manage their health safely and flexibly on different devices and channels. eHealth services should adapt to older adults' life changes through motivation, personalized content, and appropriate functions. Importantly, older adults should still have the option to not use eHealth services.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".