Investigating the social and environmental contexts of habitual walking ACTIVities in older adult DYADs: A mixed methods protocol for the ActivDyad study
Bibliographic record
Abstract
Abstract Background Mobility‐related ambulatory activity (e.g., walking) is essential to healthy cognitive and functional ageing. Wearable technology (e.g., accelerometers/inertial measurement units; IMUs) allows us to objectively and continuously capture digital mobility outcomes (DMOs), e.g. volume, pattern and variability of walking activities. Continuous digital mobility assessment has been shown as feasible and acceptable to older adults with and without dementia. However, interpretation of DMOs is limited as we do not yet understand the impact of different social and environmental contexts on walking activity. For example, DMOs may be influenced by the volume of walking that one’s partner participates in, or by the walkability of their local area. Understanding the impact of social and environmental contexts on DMOs will support development of socio‐ecological strategies to support mobility in ageing. We aim to conduct a feasibility study in older adults with the following objectives: (1). Objectively assess DMOs in older adult dyads using digital mobility tools (i.e., IMUs/GPS); (2). Identify key social and environmental influences on DMOs through mixed‐methods exploration; (3). Develop a novel analytical approach combining DMOs with GPS data to assess independence/interdependence in dyads’ walking activities. Method We will recruit 20 older adult dyads in North‐East England for an observational cross‐sectional study. Walking activities will be recorded continuously for seven days using an IMU attached to participants’ lower backs. DMOs include volume (e.g., daily steps), pattern (e.g. mean bout length) and variability (of bout length) of walking activities. Simultaneously, participants will carry a GPS device (i.e., smartphone) to monitor excursions outside the home. Questionnaires will capture information on cognition, function, falls risk, exercise motivation, wellbeing, relationship mutuality, spatial navigation, and walkability of local area. Participants will complete a “mobility diary” for the assessment period (e.g., daily journeys, motivations/perceptions/familiarity of journeys). Result Preliminary results will be presented regarding associations between social/environmental contexts with DMOs via flexible Bayesian statistical models and thematic qualitative analysis. Conclusion This study will assess the feasibility of the protocol and analysis strategies, with intentions of developing further research to examine the impact of social and environmental influences on DMOs in people with dementia and their carers.
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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 | 0.040 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".