ActiviTeen: A Protocol for Deployment of a Consumer Wearable Device in an Academic Setting
Bibliographic record
Abstract
BACKGROUND: Regular physical activity (PA) can be an important indicator of health across an individual's life span. Consumer wearables, such as Fitbit or Jawbone, are becoming increasingly popular to track PA. With the increased adoption of activity trackers comes the increased generation of valuable individual-based data. Generated data has the potential to provide detailed insights into the user's behavior and lifestyle. OBJECTIVE: The primary objective of the described study is to evaluate the feasibility of individual data collection from the selected consumer wearable device (the Fitbit Zip). The rate of user attrition and barriers preventing the use of consumer wearable devices will also be evaluated as secondary objectives. METHODS: The pilot study will occur in two stages and employs a long-term review and analysis with a convenience sample of 30 students attending Research Triangle High School. For the first stage, students will initially be asked to wear the Fitbit Zip over the course of 4 weeks. During which time, their activity data and step count will be collected. Students will also be asked to complete a self-administered survey at the beginning and conclusion of the first stage. The second stage will continue to collect students' activity data and step count over an additional 3-month period. RESULTS: We are anticipating results for this study by the end of 2016. CONCLUSION: This study will provide insight into the data collection procedures surrounding consumer wearable devices and could serve as the future foundation for other studies deploying consumer wearable devices in educational settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.096 | 0.042 |
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".