The Use of Social Media and mEMA Technology in Comparing Compliance Rate Among Users
Bibliographic record
Abstract
Compliance can be defined as the extent to which a person's behavior corresponds with agreed recommendations. Within the context of lifestyle intervention, this may refer to physical activity, diet modifications, or lifestyle intervention program attendance or attrition. For lifestyle intervention programs to be successful (as measured against a variety of health and lifestyle markers), it is crucial for individuals to comply as best they can to the recommendations or instructions provided by the researchers. Those who disengage prematurely are likely to have poorer treatment outcomes. Hence, a better understanding and an added component, such as engagement, is vital to the development of successful compliance rates. Technology, such as the mobile-based Ecological Momentary Assessment (mEMA), has been used by researchers to collect data on participants through their smartphones. Studies have also used social media and mEMA in the past for topics such as chronic conditions, physical activity, weight management, and dietary behaviors. This article reports the results of two approaches to dietary monitoring using social media and mEMA technology and the lessons learned from the two studies for improving participant compliance.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".