Reducing Sedentary Time among Older Adults in Assisted Living: Perceptions, Barriers, and Motivators
Bibliographic record
Abstract
Older adults accumulate more sedentary time (ST) than any other age group, especially those in assisted living residences (ALRs). Reducing prolonged ST could help maintain function among older adults. However, to develop effective intervention strategies, it is important to understand the factors that influence sedentary behavior. The purpose of this study was to explore perceptions of ST as well as barriers and motivators to reducing ST among older adults in assisted living, in the context of the Social Ecological Model (SEM). Using a qualitative description approach, we sought to learn about participants' perceptions of sedentary time in their daily lives. Semi-structured focus groups were held at six ALRs with 31 participants (84% women, 83.5 ± 6.5 years). Data were transcribed and coded using an inductive thematic approach. Themes were categorized based on four levels of the SEM: individual, social, physical environment, and organization. Many reported barriers were at the individual level (e.g., lack of motivation, pain, fatigue) while others were associated with the organization or social environment (e.g., safety concerns, lack of activities outside of business hours, and social norms). These findings suggest that there are unique challenges and opportunities to consider when designing ST interventions for assisted living.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".