“My Wife and I Invariably Go Together”: A Mixed Methods Analysis of Physical Activity and its Influences on Older Adult Dyads
Bibliographic record
Abstract
Abstract Background Physical activity (PA) is an imperative factor to healthy cognitive and functional ageing and may act as a protective factor against cognitive decline. Evidence suggests that as we age, PA declines, leaving a large proportion of older adults (OAs) ‘underactive’ and ‘unprotected’. Socialisation/social support is considered a beneficial influence on PA in OAs. It is unclear whether dyadic (e.g. spousal) influences impact PA in healthy ageing, and how this might impact digital activity monitoring or intervention development to support prevention of cognitive decline. This research aims to quantify volume and types of PA and explore influential factors for PA participation in OAs. Method Participants were recruited via the ActivDyad study, where they wore a lumbar‐based‐accelerometer (AX6, Axivity) for 7 days, and completed a 7‐day activity diary. PA (i.e. daily steps) was derived from accelerometers using validated algorithms. Qualitative content analysis of the diaries was adopted for contextual understanding of the types and influences of PA. The Theoretical Domains Framework was utilised to identify common facilitators/barriers of PA in OAs. Result 21 dyads (42 OAs) participated in the study; 10 dyads were included in preliminary analysis (mean age (mean±SD): 68.6±4.8; 100% heterosexual & married; mean relationship time: 45±10 years; 80% retired). Participants carried out an average of 15,680 daily steps (range: 7,243‐22,668), and indicated their most frequent PA as: shopping, gardening, housekeeping, and recreational walks (Table 1). Participants identified potential facilitators (Table 2) and barriers (Table 3) for partaking in PA. Social influence (e.g. partner) was identified as most influential; 75% of participants mentioned this as a facilitator for PA. Environmental context, particularly weather conditions, were frequently discussed as both a facilitator (50%) and barrier (45%) to PA. Conclusion Results suggest that dyadic interactions are a key facilitator of PA in OAs. Weather appears to have a bi‐conditional influence, promoting and/or discouraging PA; this may impact activity monitoring during adverse weather conditions (e.g. lower steps). Adherence may be higher in group PA interventions, and outdoor PA should be promoted in suitable weather. Analysis of the full sample will inform studies focusing activity monitoring and intervention development for dementia‐carer dyads.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".