Aiding clinical decision-making at the individual and community level using mobile sensor data – A study protocol for an experimental design (Preprint)
Bibliographic record
Abstract
Background: The mobile sensor data can provide vital information about patients' behavior and physical activity.In particular, the mobile accelerometer sensor data, if monitored effectively, can be used further for keeping the watch on the physical activity of an individual and planning an intervention to improve it.These sensor data can also be used for improving clinical decisionmaking in sedentary lifestyle-related diseases.Objective: The objective of this study is to use the accelerometer sensor metadata of the mobile device in facilitating the delivery of interventions to enhance physical activity.Methods: A sequential explanatory mixed-method research design would be used between June 2022 to December 2022.The physical activity-related accelerometer sensor data will be collected from individuals with a sedentary lifestyle between the age of 30 to 45 visiting the clinics of selected health care providers in Chennai, Tamil Nadu.Based on the initial data recorded, an individualized activity plan will be created.Periodic monitoring of physical activity levels will be conducted over a three months period.Participants will receive the message from clinicians based on their current physical activity levels for encouraging them to adhere to the prescribed regimen suggested by clinicians. Results:The proposed research will help in exploring the role of remote monitoring of sensor data in improving adherence to physical activity plan as prescribed by their clinicians.It will also help in studying the variables that influence the monitoring of remote data collection.Conclusions: This study will help to bring improvement in the physical activity levels of patients with a sedentary lifestyle and with confirmed hypertension and type-2 diabetes through remote monitoring, ongoing messaging, and facilitation through clinical decision-making.
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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 | 0.051 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".