Exploring the perspectives of older adults who are pre-frail and frail to identify interventions to reduce sedentary behaviour and improve mobility: A thematic content analysis
Bibliographic record
Abstract
Abstract Older adults who are frail are one of the most sedentary and the least physically active age group. Prolonged sedentary time is associated with increased risk of negative health outcomes. To help design effective and sustainable content and optimize the uptake of sedentary behaviour interventions, an in-depth understanding of older adults’ perceptions of sedentary behaviour is needed; however, most qualitative studies have been conducted in healthy older adults. The aim of this study was to explore perspectives of sedentary behaviour within the context of older adults who are pre-frail and frail after the winter and spring. We included participants if they: 1) spoke English or attended with a translator or caregiver, 2) were ≥ 60 years, and 3) were frail on the Morley Frail Scale. We utilized a qualitative description methodology including a semi-structured in-depth interview and thematic content analysis. Concepts from the COM-B (Capability Opportunity Motivation–Behaviour) model were used to guide the semi-structured interviews and analysis. To ensure credibility of the data, we used an audit trail and analyst triangulation. We recruited 21 older adults (72 ± 7.3 years, 13 females, 13 frail) from Southwestern Ontario, Canada. Two individuals were lost to follow-up due to medical mistrust and worse health. We transcribed 39 audio recordings. We identified three salient themes: 1) older adults rationalize their sedentary behaviours through cognitive dissonance, 2) urban cities in Southwestern Ontario may not be “age-friendly”, and 3) exercise is something people “have to do”, but hobbies are for enjoyment despite medical conditions. Perspectives of sedentary behaviour were different in the winter versus spring, with participants perceiving themselves to be less active in winter. Incorporating dissonance-based interventions may be an important component to disseminate educational material on the benefits and harms of certain types of sedentary behaviour. Future research should consider interdisciplinary collaborations with environmental gerontology to develop age-friendly communities that promote meaningful mobility to help older adults successfully age.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".