Understanding Problematic Smartphone and Social Media Use Among Adults in France: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: The Evaluation of Digital Addiction (EVADD) study investigates problematic smartphone use in the digital age, as global smartphone users reached 55.88 million in France in 2023. With increased screen time from digital devices, especially smartphones, the study highlights adult use issues and associated risks. Objective: The aim of the study is to assess the prevalence of compulsive smartphone use among French adults and identify patterns of problematic behaviors, including their impact on daily activities, sleep, and safety. Methods: The EVADD study used a cross-sectional, nonprobability sample design, conducted from May 3 to June 5, 2023. Participants were recruited through the French mutual insurance company PRO-BTP. Data were collected anonymously via a digital questionnaire, including the Smartphone Compulsive Use Test, capturing information on social network engagement, device ownership, and daily screen use. Results: A total of 21,244 adults (average age 53, SD 15 years; 9844 female participants) participated. Among 21,244 participants, 8025 of 12,034 (66.7%) respondents exhibited compulsive smartphone use (P<.001). Additionally, 7,020 (36.7%) participants scored ≥8 on the Smartphone Compulsion Test, indicating addiction. Younger participants (18-39 years) were significantly more likely to show signs of addiction (2504/4394, 57%; odds ratio 2.5, 95% CI 1.9-3.2) compared to participants aged ≥60 years. Problematic behaviors included unsafe smartphone use while driving (5736/12,953, 44.3%), frequent smartphone use before bedtime (9136/21,244, 43%), and using smartphones in the bathroom (7659/21,244, 36.1%). Sleep disturbances and risky behaviors correlated strongly with higher compulsion scores (P<.01). Conclusions: The EVADD study highlights the complex relationship between adults and smartphones, revealing the prevalence of compulsive behaviors and their impact on daily life, sleep, and safety. These findings emphasize the need for public awareness campaigns, preventive strategies, and therapeutic interventions to mitigate health risks associated with excessive smartphone use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".