Exploring Health Care Professionals’ Perspectives on Using Video Feedback and Movement Analysis to Facilitate Physical Functioning for Older Adults Living at Home: Co-Design Approach
Bibliographic record
Abstract
BACKGROUND: Maintaining and motivating physical functioning among older adults has substantial health-related benefits, such as reducing the risk of falls and increasing the opportunities for independent living. Supporting preventive actions among older adults also has socioeconomic relevance. Previous studies have shown that digital tools involving video feedback can facilitate reflection and learning by encouraging active engagement. OBJECTIVE: This study aimed to explore health care professionals' experiences of using a video-based tool as part of the rehabilitation to facilitate physical functioning among older adults (aged ≥65 years) living at home. METHODS: An experience-based co-design approach was used, involving 20 health care professionals. Nine iterative workshops were conducted, followed by 9 group interviews held between 2022 and 2023. The data were analyzed using reflexive thematic analysis. RESULTS: The results from this study captured the experiences of health care professionals using a video-based tool to facilitate physical functioning in older adults living at home. The participants described focusing on supporting patient commitment, creating a shared language to enhance collaboration in the rehabilitation process, and navigating barriers to adopting the video-based tool in practice. CONCLUSIONS: From the perspective of health care professionals, video feedback has the potential to improve movement performance in daily activities and may play a crucial role in providing motivation and promoting sustainable physical functioning among older adults. Clinical recommendations include training health care professionals to introduce video feedback in a patient-centered manner and using it to foster shared communication that promotes professional development and patient engagement. Further research is needed to assess the impact of video feedback on older adults' health outcomes and to identify strategies for implementation in complex rehabilitation needs.
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".