Motivational interviewing for physical activity among older adults: A multiple method design
Bibliographic record
Abstract
There is substantial evidence to support the idea that physical activity leads to health benefits for older adults. These benefits include a decreased rate of falls and fall-related injuries, osteoporosis, and dementia. Despite the numerous benefits of physical activity, many older adults do not meet the recommended physical activity guidelines. Motivational interviewing (MI) is a client-centred counselling style for strengthening motivation for change. MI has shown to be promising in the general adult population for improving physical activity levels. However, evidence is lacking to support MI's effectiveness in physical activity among older adults. This thesis has four objectives: 1) to synthesize evidence on the effect of MI on physical activity among older adults 2) to determine the feasibility of using virtual MI to improve physical activity among community-dwelling older adults 3) to explore the experiences of older adults and counsellors involved in virtual MI and 4) to examine the influence of counsellors’ behaviours and skills on participants’ change and sustain talk during MI sessions. For objective 1, a systematic review and meta-analysis was conducted. Objective 2 was achieved using a feasibility study with a single-group pre- and post-design. A qualitative descriptive design guided the data collection and analysis for objective 3. Lastly, objective 4 utilized a sequential observational method to examine the transition between the counsellors’ behaviours and participants’ utterances. The meta-analysis of three trials showed that the effect of MI on physical activity among older adults was not different between treatment and control groups. The findings from the feasibility study show that virtual MI should be a feasible and acceptable approach for improving physical activity among older adults. From the experiences of older adults and counsellors using virtual MI, we identified the interconnections between technology, relationships between older adults and counsellors, and MI skills and principles. Virtual MI was described as convenient and flexible. In the sequential analysis, all counsellors’ behaviours elicited participants’ change talk. MI-consistent behaviours elicited both change and sustain talk. This thesis adds to the literature by using different approaches and methods to examine the novel use of virtual MI for physical activity among older adults.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".