Older adults, clinicians, and researchers’ preferences for measuring adherence to resistance and balance exercises
Bibliographic record
Abstract
BACKGROUND: Resistance and balance training are important exercise interventions for older populations living with chronic diseases. Accurately measuring if an individual is adhering to exercises as prescribed is important to determine if lack of improvement in health outcomes is because of issues with adherence. Measuring adherence to resistance and balance exercises is limited by current methods that depend heavily on self-report and are often better at and tailored towards capturing aerobic training parameters (e.g., step count, minutes of moderate to vigorous physical activity). Adherence measures must meet users' needs to be useful. METHODS: Using a Dillman tailored study design, we surveyed researchers who conduct exercise trials, clinicians who prescribe exercise for older adults, and older adults to determine: (1) how they are currently measuring adherence; (2) barriers and facilitators they have experienced to measurement; and (3) the information they would like collected about adherence (e.g., repetitions, sets, intensity, duration, frequency, quality). Surveys were disseminated internationally through professional networks, professional organizations, and social media. Participants completed an online survey between August 2021 and April 2022. RESULTS: Eighty-eight older adults, 149 clinicians, and 41 researchers responded to the surveys. Most clinicians and researchers were between the ages of 30 and 39 years, and 70.0% were female. Most older adults were aged 70-79 years, and 46.6% were female. Diaries and calendars (either analog or digital) were the most common current methods of collecting adherence data. Users would like information about the intensity and quality of exercises completed that are presented in clear, easy to use formats that are meaningful for older adults where all data can be tracked in one place. Most older adults did not measure adherence because they did not want to, while clinicians most frequently reported not having measurement tools for adherence. Time, resources, motivation, and health were also identified as barriers to recording adherence. CONCLUSIONS: Our work provides information about current methods of measuring exercise adherence and suggestions to inform the design of future adherence measures. Future measures should comprehensively track adherence data in one place, including the intensity and quality of exercises.
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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 | 0.001 |
| 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) | 0.000 | 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".