When More Than Exercise Is Needed to Increase Chances of Aging in Place: Qualitative Analysis of a Telehealth Physical Activity Program to Improve Mobility in Low-Income Older Adults
Bibliographic record
Abstract
BACKGROUND: A telehealth-delivered physical activity program was implemented within two low-income older adult housing properties utilizing the Otago exercise program, a physical therapy program endorsed by the Centers for Disease Control and Prevention to improve balance and strengthening in community dwelling older adults and by the National Council on Aging as the highest level of evidence for fall prevention programs. Participants were also given Fitbit activity monitors to help track their activity. OBJECTIVE: The goal of this project was to increase older adults' daily physical activity in hopes of decreasing chronic disease morbidity, disability, and falls, and decrease social isolation. METHODS: The Otago exercise program was conducted via telehealth twice weekly for 12 weeks. Participants also wore Fitbit activity trackers to encourage physical activity outside of the group classes. Postintervention qualitative interviews were conducted, recorded, transcribed, and analyzed using discourse analysis. RESULTS: Twenty-one older adult participants from two low-income properties in Charleston, SC, participated in the 12-week telehealth physical therapy program. Postintervention qualitative interviews revealed that the two sites were very different in their participation in the program and their main concerns surrounding aging in place. One site had a community-oriented outlook and enjoyed participating in physical activity together; whereas, the other site had very few participants and referenced depression and social isolation as main concerns. CONCLUSIONS: A telehealth physical therapy-led intervention to increase physical activity in low-income older adults aging in place was successfully implemented and attended; however, it became clear in postintervention qualitative interviews that social isolation and depression were prevalent and mental health needs to be addressed along with physical health to encourage successful aging in place.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".