Integrating Technology Into Clinical Practice for the Assessment of Balance and Mobility: Perspectives of Exercise Professionals Practicing in Retirement and Long-term Care
Bibliographic record
Abstract
OBJECTIVE: To explore exercise professionals' perspectives on technology integration for balance and mobility assessment practices in retirement and long-term care. SETTING: A private residential care organization in Ontario, Canada, with 18 sites providing accommodation and services for older adults. DESIGN: A qualitative descriptive approach was used including semistructured focus group interviews. Open-ended questions explored perceptions of technology integration along with factors influencing its adoption. Analysis involved preliminary coding based on research questions, review and discussion of emerging themes, and final, resultant coding for each category. PARTICIPANTS: Exercise professionals (kinesiologists and exercise therapists) (N=18). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Not applicable. RESULTS: All participants felt that technology could enhance their practice by supporting programming, communication, and/or information management. Potential barriers to technology integration related primarily to the need to accommodate the broad range of complex health conditions present among clients, which would impact (1) their ability to engage with the technology and (2) relevance of technology-derived outcomes. Specific concerns related to individuals with significant cognitive and/or functional impairment. Solutions to these barriers emphasized the need for flexible technology and appropriate normative data to maximize the potential for uptake. CONCLUSIONS: The participating exercise professionals working in a retirement and long-term care setting saw technology as a potentially effective addition to current clinical practice. To increase the likelihood for clinical uptake, technology must be maximize flexibility in order to accommodate a wide range of physical and cognitive abilities and meet specific needs related to setting and job responsibilities. The findings emphasize the need for continuous dialogue between technology producers and end users for successful development and implementation.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".