Methodology to Derive Objective Screen-State from Smartphones: A SMART Platform Study
Bibliographic record
Abstract
Time on screens (screen time) on multiple digital devices (computers, mobile phones, tablets, television screens, etc.) due to varied motivations (work, leisure, entertainment, gaming, etc.) has become an integral part of population behaviour. However, a significant evidence gap exists in screen time accumulated over ubiquitous mobile devices such as smartphones. This study aimed to develop an accurate, reliable and replicable methodology to derive objective screen time (i.e., screen-state) from all types of citizen-owned smartphones. A convenience sample of 538 adults (≥18 years) from two largest urban centres in Saskatchewan, Canada (Regina and Saskatoon) was recruited in 2017 and 2018. Participants used a custom-built smartphone application to provide objective and subjective data. A novel methodology was developed to derive objective screen-state, and these data were compared with subjective measures. The findings showed that objective screen-state from smartphones can be derived and assessed across a range of cut-points that take into consideration varied measurement errors. When objective measures were compared with subjective reporting, the results indicated that participants consistently underreported screen time. This study not only provides a methodology to derive objective screen-state from ubiquitous mobile devices such as smartphones but also emphasises the need to capture context via subjective measures.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".