The Use of Small Electronic Devices and Health: Feasibility of Interventions for a Forthcoming Crossover Design
Bibliographic record
Abstract
BACKGROUND: Modern lifestyle is heavily affected by technology such as smartphones, tablets, and other small computers; yet it remains unclear how our health and well-being are affected by the heavy use of these devices. OBJECTIVE: max/kg) and the effect of (2) restricting media use on total sleep time. METHODS: Twenty healthy participants were included and equipped with a wrist-worn device tracking physical activity and sleep. Participants were allocated to either a physical activity group, which was instructed to use the wrist-worn device to support exercise, or a sleep silent group, which was instructed to remove or switch off all electronic devices in the bedroom (except the wrist-worn tracking device). The intervention lasted approximately 4 weeks. Data collected included blood pressure, submaximal cycle ergometer test, self-reported technology use, and compliance of using the wearable tracking device. RESULTS: All participants wore the wearable tracking device 95.8% (SD 4.4%) of the time. Participants in the physical activity group increased aerobic capacity from 30.38 (SD 8.98) to 32.1 (SD 8.71) mL/kg/min (t=-2.31, P=.046) and decreased their systolic blood pressure from 126.5 (SD 15.8) mm Hg to 121.8 (SD 11.7) mm Hg (t=2.72, P=.02). The sleep silent group prolonged their time offline before bedtime from 18.1 (SD 19.4) minutes to 27.2 (SD 17.3) minutes (t=-2.94, P=.02). CONCLUSIONS: The two interventions are feasible to conduct. Participants were willing to wear the tracking device on their wrist and restrict all media use in their bedroom and thereby reduce bedtime technology use. Our results also suggest that tracking physical activity using a wearable device is accompanied by noteworthy health benefits. We outline necessary adjustments for a forthcoming large-scale study.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".