Feasibility and acceptability of a continuous remote activity monitoring protocol in older adult dyads: A mixed methods pilot study
Bibliographic record
Abstract
Abstract Background Walking is a key facilitator of healthy ageing and may reduce risk of cognitive decline in older adults. To develop suitable, accessible interventions, we must objectively consider the socio‐ecological factors which influence participation in walking activities. For example, walking may be influenced by the volume and type of activities one’s partner participates in (i.e., dyadic interactions), or the walkability of their local area. Wearable technologies can continuously and remotely capture digital walking outcomes, such as volume, pattern, variability and location of activities. This pilot study aimed to explore the feasibility and acceptability of deploying a continuous remote activity monitoring toolkit in older adult dyads (i.e., couples). Methods Participants were asked to engage with three forms of remote activity monitoring over a period of seven days: (1). Wearing an inertial measurement unit (IMU; AX6, Axivity) on their lower backs, (2). Carrying a smartphone installed with a GPS app on excursions outside the home, and (3). Completing an activity diary (e.g., daily journeys, motivations/perceptions of journeys) each night. Upon study completion, participants were asked to complete open‐ended questionnaires regarding their experiences of the protocol. Feasibility was assessed by quantitatively calculating completion of each form of activity monitoring, while qualitative content analysis of the questionnaires was employed to understand the acceptability of the protocol. Results 21 dyads (n = 42) participated in the study (Age (median (range)): 69 (61‐79)). 95% of participants wore the IMU for seven days (5% removed early for holidays). 100% completed their activity diaries. 77% (n = 226) of all data collection days (n = 294) were captured from the GPS app; reasons for data loss (68 days) include possible technical error (69.12%), not leaving the house (29.41%), or forgetting the GPS device (1.47%). Most participants found all forms of activity monitoring acceptable; common themes are reported in Tables 1‐3. Conclusion Results suggest that a protocol of continuous remote activity monitoring using digital devices and an activity diary is feasible and acceptable to older dyads. Further work will explore how data acquired can be used to identify socio‐ecological predictors of walking and examine independence/interdependence in walking between members of each dyad.
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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 | 0.083 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".