Video Call-based Fitness Assessment shows Poor Fitness in People with Type II Diabetes: Findings from Diabefly Digital Therapeutics Program
Bibliographic record
Abstract
OBJECTIVE: Exercise and physical activity are integral aspects for the effective management of diabetes. Unsupervised home exercise although very accessible is limited by poor adherence, risk of injury, and a higher dropout rate of participants. A fitness assessment by a qualified physiotherapist can help in understanding the baseline fitness of individuals and thus generating appropriate exercise prescriptions. The current study assesses the feasibility of video call-based fitness assessment for people with diabetes. The study also assesses the effect of current physical activity status and pain on performance in physical fitness tests. METHODS: One hundred participants with type II diabetes (T2D) underwent 6-minute walk test (6MWT), 1-minute push-up test, wall sit test, 1-minute sit-up test, and V-sit and reach test for measuring different components of physical fitness such as aerobic capacity, upper body strength, lower body strength, core strength, and flexibility, respectively. The performance in physical fitness of participants was analyzed after the video consult along with pain complaints and current exercise status. RESULTS: All the participants underwent the physical fitness test safely based on video call. Out of all the participants, a good range score was achieved by 52% in 6MWT, 17% in push-up test, 1% in wall sit test, 6% in sit-up test, and 9% in V-sit and reach test. Current physical activity status (aerobic exercise for minimum 20 minutes) did not show any association with performance in fitness tests (p = 0.89 for push-up test, p = 0.50 for wall sit test, p = 0.23 for sit-up test, and p = 0.10 for V-sit and reach test). Presence of upper body and lower body pain affected the performance in push-up test and wall sit test with 71.4% and 95.6% of participants achieving scores in poor to below-average range (p-value < 0.001). CONCLUSION: The study showed the safety and feasibility of conducting video call-based assessment of physical fitness by physiotherapists. The study also highlighted the poor glycemic control, high cardiovascular risk, and poor level of physical fitness in people with diabetes in India. Insights based on physical fitness, current physical activity status, and pain can help in developing personalized exercise plans for people with diabetes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".