Team VA Video Connect (VVC) to optimize mobility and physical activity in post-hospital discharge older veterans: baseline assessment
Bibliographic record
Abstract
BACKGROUND: Telehealth is increasingly used for rehabilitation and exercise but few studies include older adult participants with comorbidities and impairment, particularly cognitive. Using Veterans Administration Video Connect (VVC), the aim of the present study is to present the screening, recruitment, baseline assessment, and initial telehealth utilization of post-hospital discharge Veterans in a VVC home-telehealth based program to enhance mobility and physical activity. METHODS: Older adult Veterans (n = 45, mean age 73), recently discharged from the hospital with physical therapy goals, were VVC-assessed in self-report and performance-based measures, using test adaptations as necessary, by a clinical pharmacy specialist and social worker team. RESULTS: Basic and instrumental ADL disabilities were common as were low mobility (Short Portable Performance Battery) and physical activity levels (measured by actigraphy). Half had Montreal Cognitive Assessment (MoCA) scores in the mild cognitive impairment range (< 24). Over 2/3 of the participants used VA-supplied tablets. While half of the Veterans were fully successful in VVC, 1/3 of these and an additional group with at least one failed connection requested in-person visits for assistance. One-quarter had no VVC success and sought help for tablet troubleshooting, and half of these eventually "gave up" trying to connect; difficulty with using the computer and physical impairment (particularly dexterity) were described prominently in this group. On the other hand, Veterans with at least mild cognitive impairment (based on MoCA scores) were present in all connectivity groups and most of these used caregiver support to facilitate VVC. CONCLUSIONS: Disabled older post-hospital discharged Veterans with physical therapy goals can be VVC-assessed and enrolled into a mobility/physical activity intervention. A substantial proportion required technical support, including in-person support for many. Yet, VVC seems feasible in those with mild cognitive impairment, assuming the presence of an able caregiver. Modifications of assessment tools were needed for the VVC interface, and while appearing feasible, will require further study. TRIAL REGISTRATION: ClinicalTrials.gov NCT04045054 05/08/2019.
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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".