Flight Results from a Novel Magnetic Actuator on the LatinSat Spacecraft
Bibliographic record
Abstract
Background Evidence of scaled-up physical activity interventions is scarce. This study evaluates the uptake, engagement, and effectiveness of one such intervention program. Methods and Results The program was open to individuals aged ≥17 years in Singapore. The main intervention components comprised device-based daily physical activity recording paired with step count goals and financial rewards. According to the different reward opportunities, we divided the evaluation period (August 2017 to June 2018) into the baseline monitoring phase, the main challenge phase, and the maintenance phase. Uptake was assessed by the number of individuals registered, and engagement by the step recording duration after registration. The effectiveness was defined as changes in mean daily step count from baseline to the main challenge phase and the maintenance phase. A total of 696 907 participants registered, including more Singapore citizens (versus noncitizens), women, and younger (aged 17-39 years) individuals. The evaluation of engagement and effectiveness included 421 388 (60.5%) participants who provided plausible characteristic information and step count data. The median duration of engagement was 74 (IQR, 14-149) days. Compared with the baseline of 7509 (SD, 3467) steps, mean daily step count increased by 1579 (95% CI, 1564-1594) steps during the main challenge phase and 934 (95% CI, 916-952) steps during the maintenance phase. Greater engagement and activity increase were found in participants who are citizens, women, aged ≥40 years, non-obese, and using separate wearables (versus smartphones). Conclusions Mobile health physical activity interventions can successfully reach a large population and be effective in increasing physical activity, despite declining program engagement over time.
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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 | 0.002 |
| 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".