Double-Bind of Recruitment of Older Adults Into Studies of Successful Aging via Assistive Information and Communication Technologies: Mapping Review
Bibliographic record
Abstract
BACKGROUND: Two fields of research and development targeting the needs of the aging population of the world are flourishing, successful aging and assistive information and communication technologies (A-ICTs). The risks of ageist stereotypes emerging from how we communicate in both discourses are long known. This raises questions about whether using specific age criteria in the context of "aging deficits" can bias participation in, or compliance with, the research process by older adults who try to avoid age-related stigma. OBJECTIVE: This study aimed to examine subject recruitment, study designs (based on age >65 years criteria), as well as discourses in research objectives and conclusions in health research on affordances of A-ICTs for older adults. METHODS: A systematic mapping approach was used to characterize rationales, methods, stated objectives, and expected outcomes of studies indexed in PubMed and retrieved through the search logic (["Older Adults" OR Seniors OR Elderly] AND [ICT OR gerontechnology OR "Assistive Technology")] AND ("Healthy Aging" OR "Successful Aging" OR "healthy ageing" OR "successful ageing"). Inclusion criteria were as follows: the study should have recruited older participants (aged >65 years), been qualitative or quantitative research, and involved the introduction of at least one A-ICT for health-related improvements. Exclusion criteria were as follows: reviews, viewpoints, surveys, or studies that used information and communication technology for data collection instead of lifestyle interventions. Content, thematic, and discourse analyses were used to map the study characteristics and synthesize results with respect to the research question. RESULTS: Of 180 studies that passed the search logic, 31 (17.2%) satisfied the inclusion criteria (6 randomized controlled trials, 4 purely quantitative studies, 9 focus groups, 2 observational studies, and 10 mixed methods studies). In all but one case, recruitment was pragmatic and nonrandom. Thematic analysis of rationales revealed a high likelihood of emphasis on the burdens of aging, such as rising costs of care (12/31, 39%) and age-related deficits (14/31, 45%). The objectives of the research fell under 4 categories: promotion of physical activity, acceptance and feasibility of robots and remote health monitoring systems, risk detection, and the future of A-ICTs in health care for older adults. Qualitative studies were more attentive to the nonageist research guidelines. Heterogeneity in the study results (both qualitative and quantitative) was not related to age but to individual agency, acceptance, and adherence. A combination of research strategies (participatory, longitudinal, playful, flexible, and need-based designs) proved successful in characterizing variations in study outcomes. Studies that documented recruitment dynamics revealed that fear of stigma was a factor that biased participants' engagement. CONCLUSIONS: This review indicates that age is not an informative criterion for recruitment and retention of participants. Charting the dynamics of adoption of, and interaction with, A-ICTs is critical for advancing research and technology development.
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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 | 0.092 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".