Heavy Users, Mobile Gamers, and Social Networkers: Patterns of Objective Smartphone Use in Parents of Infants and Associations With Parent Depression, Sleep, Parenting, and Problematic Phone Use
Bibliographic record
Abstract
Smartphone use during parenting is common, which may lead to distraction (also known as technoference). However, it is likely that some phone activities are less disruptive to parents and children. In this study, we explored smartphone use (via passive sensing across 8 days) within 264 parents of infants, measuring parents’ application use on their phone (e.g., messaging, social media, mobile gaming, video chat) and phone use across contexts (e.g., during feeding and at bedtime). We utilized latent profile analysis to identify profiles of users, revealing five user types: Moderate User Social Networkers (37%), followed by Moderate User Gamers (20%), Moderate User Video Chatters (17%), Low Users (15%), and Heavy Users (11%). Parents varied in their use, from Low Users, who used their phone approximately 2.4 h each day, spent only 13% of their child time on their phone, and used their phone for about 18 min at bedtime, to Heavy Users, who spent approximately 8 h a day, about 50% of their child time on their phone, and about 1 h at bedtime. Heavy Users showed higher depressive symptoms and poorer sleep (although not poorer sleep than Moderate User Gamers). Surprisingly, we found no differences between groups in perceptions of parenting stress, responsiveness to their infant, or problematic phone use and distraction. We also explored demographic differences across groups. We call for future work to examine parent phone use more comprehensively and holistically and to view specific phone use activities as simultaneously interconnected with other types of use activities.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".