Directly-measured smartphone screen time predicts well-being and feelings of social connectedness
Bibliographic record
Abstract
Previous findings on the relationship between smartphone use and well-being have been mixed. This may be partially due to a reliance on cross-sectional study designs and self-reported smartphone usage. In the current study, we collected screen time data by directly tracking participants’ ( N = 325, ages 14−80 years, 58% women) smartphone usage over a period of 6 days. We combined this tracking with ecological momentary assessment, asking participants three times per day about their psychological well-being and feelings of social connectedness. Smartphone screen time was determined for the hour directly before each assessment. Results revealed that at times when participants used their smartphone more in the hour before an assessment, they reported lower psychological well-being and lower social connectedness. A bidirectional relationship emerged between smartphone screen time and social connectedness, suggesting a potential “vicious cycle” whereby smartphone usage leads to reduced social connectedness, which promotes more smartphone usage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".