Refined Analysis of Older eHealth Users From an Agency Perspective: Quantitative Telephone Interview Study
Bibliographic record
Abstract
BACKGROUND: Most studies on the eHealth divide among older people have compared users to nonusers and found that age, gender, and education were associated with eHealth misuse. They assumed that these characteristics were structural barriers to eHealth adoption. Furthermore, eHealth practices have been examined in a narrow and incomplete way, and the studies disagree about the association between health conditions and eHealth use. Using a more dynamic theoretical lens, we investigated the potential motivations driving older adults' agential adoption of eHealth practices despite their advanced age. OBJECTIVE: This study aimed to obtain a complete and detailed description of eHealth uses among older adults; examine whether demographic characteristics such as age, gender, and education (previously related to eHealth misuse) are still associated with the various eHealth clusters; and determine whether contextual factors such as changes in the health condition of older eHealth users or their loved ones are associated with older adult eHealth use. METHODS: We conducted a 30-minute telephone interview with a representative sample of 442 Israeli adults (aged ≥50 years) with a sampling error of 2.04%. The interviews were conducted in Hebrew, Arabic, and Russian. Using factor analysis with 21 eHealth use questions, we identified 4 eHealth clusters: instrumental and administrative information seeking, information sharing, seeking information from peers, and web-based self-tracking. In addition to age, gender, education, internet experience, frequency of internet use, perceived eHealth literacy, and self-rated health, we asked respondents to indicate how much they had used offline health services because of a health crisis in the past year. RESULTS: We found differences in the number of older eHealth users in the various clusters. They used instrumental and administrative information (420/442, 95%) and obtained information from peers (348/442, 78.7%) the most; followed by web-based self-tracking related to health issues (305/442, 69%), and only a few (52/442, 11.3%) uploaded and shared health information on the web. When controlling for personal attributes, age, gender, and education were no longer predictors of eHealth use, nor was a chronic ailment. Instead, internet experience, frequency of internet use, and perceived eHealth literacy were associated with 3 eHealth clusters. Looking for health information for family and friends predicted all 4 eHealth clusters. CONCLUSIONS: Many older adults can overcome structural barriers such as age, gender, and education. The change in their or their loved ones' circumstances encouraged them to make deliberate efforts to embrace the new practices expected from today's patients. Seeking health information for family and friends and dealing with unexpected health crises motivates them to use eHealth. We suggest that health professionals ignore their tendency to label older people as nonusers and encourage them to benefit from using eHealth and overcome stereotypical ways of perceiving these patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".