Longitudinal Associations Between Adolescents’ mHealth App Use, Body Dissatisfaction, and Physical Self-Worth: Random Intercept Cross-Lagged Panel Study
Bibliographic record
Abstract
BACKGROUND: Longitudinal investigation of the association between mobile health (mHealth) app use and attitudes toward one's body during adolescence is scarce. mHealth apps might shape adolescents' body image perceptions by influencing their attitudes toward their bodies. Adolescents might also use mHealth apps based on how they feel and think about their bodies. OBJECTIVE: This prospective study examined the longitudinal within-person associations between mHealth app use, body dissatisfaction, and physical self-worth during adolescence. METHODS: The data were gathered from a nationally representative sample of Czech adolescents aged between 11 and 16 years (N=2500; n=1250, 50% girls; mean age 13.43, SD 1.69 years) in 3 waves with 6-month intervals. Participants completed online questionnaires assessing their mHealth app use, physical self-worth, and body dissatisfaction at each wave. The mHealth app use was determined by the frequency of using sports, weight management, and nutritional intake apps. Physical self-worth was assessed using the physical self-worth subscale of the Physical Self Inventory-Short Form. Body dissatisfaction was measured with the items from the body dissatisfaction subscale of the Eating Disorder Inventory-3. The random intercept cross-lagged panel model examined longitudinal within-person associations between the variables. A multigroup design was used to compare genders. Due to the missing values, the final analyses used data from 2232 adolescents (n=1089, 48.8% girls; mean age 13.43, SD 1.69 years). RESULTS: The results revealed a positive within-person effect of mHealth app use on the physical self-worth of girls: increased mHealth app use predicted higher physical self-worth 6 months later (β=.199, P=.04). However, this effect was not consistent from the 6th to the 12th month: a within-person increase in using apps in the 6th month did not predict changes in girls' physical self-worth in the 12th month (β=.161, P=.07). Regardless of gender, the within-person changes in the frequency of using apps did not influence adolescents' body dissatisfaction. In addition, neither body dissatisfaction nor physical self-worth predicted app use frequency at the within-person level. CONCLUSIONS: This study highlighted that within-person changes in using mHealth apps were differentially associated with adolescents' body-related attitudes. While increased use of mHealth apps did not influence body dissatisfaction across genders, it significantly predicted higher physical self-worth in adolescent girls 6 months later. A similar association was not observed among boys after 6 months. These findings indicate that using mHealth apps is unlikely to have a detrimental impact on adolescents' body dissatisfaction and physical self-worth; instead, they may have a positive influence, particularly in boosting the physical self-worth of adolescent girls.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".