ArticleBMC psychology2026
Development and validation of a nomogram to predict mobile phone addiction risk among Chinese adolescents.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo investigate the risk factors of mobile phone addiction in adolescents, and to construct and conduct validation of the nomogram model of mobile phone addiction in adolescents.
methodsSelected secondary school students in a city in Henan Province from January to December 2023 for a convenience sampling, included 6878 cases of valid samples, distributed according to the ratio of 7:3, of which 4814 cases were in the training set and 2064 cases were in the testing set, collected general information and questionnaires of the research subjects. The questionnaires mainly included Self-esteem Scale(SES), Self-Control Scale(SCS), Simplified Social Anxiety Scale for Adolescents (SAS-A), and Mobile Phone Addiction Index(MPAI). LASSO regression was applied to screen the characteristic variables, and the regression model was constructed using multifactorial logistic regression with the corresponding nomogram plotted, and the differentiation, calibration and clinical validity of the nomogram model were evaluated through the subject work characteristics (ROC) curve, calibration curve and clinical decision curve (DCA), respectively. To further verify the model's stability and generalizability, an independent external validation dataset-comprising 200 secondary school students demographically consistent with the primary sample-was used to replicate the same evaluation metrics.
resultsSeven characteristic variables were screened by LASSO regression, namely gender, academic performance, emotional abuse, self-control, perception, somatisation and depression, and the model was constructed and confirmed by multifactorial logistic regression that all of the above seven variables were significantly associated with mobile phone addiction in adolescents(P < 0.05); the area under the ROC curve(AUC) of the training set was 0.764[95% CI(0.75, 0.777)]; the AUC of the testing set was 0.756[95% CI(0.735, 0.777)], and the models showed good predictive ability; the goodness-of-fit tests of the training set and testing set as well as the net gain values performed well in terms of school accuracy and clinical validity. Further external validation with an independent sample yielded consistent performance, with an AUC of 0.752 [95% CI (0.676, 0.829)].
conclusionThe nomogram model constructed by the seven variables of gender, academic performance, emotional abuse, self-control, perception, somatisation, and depression possesses potential utility as a preliminary screening tool to identify the risk of developing mobile phone addiction in adolescents.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.