OBJECTIVE: To investigate the association of mobile phone use with sleep disorder and unhealthy eating behavior among college students of a medical university in Guangzhou. METHODS: Mobile Phone Involvement Questionnaire (MPIQ), Pittsburgh Sleep Quality Index (PSQI) and the Three Factor Eating Questionnaire Revised 21 Item (TFEQ-R21) were used to survey 2122 undergraduates of the medical university. One-sample t test, One-way ANOVA and multiple linear regression analysis were used to analyze the data. RESULTS: Age, body mass index (BMI), phone use before sleep, phone use frequency, sleep quality (assessed by total PSQI score) and the dimension scores of TFEQ-R21 for uncontrolled eating, cognitive restraint, and emotional eating were all significantly correlated with the total score of MPIQ ( < 0.05). Phone use before sleep, high frequency of mobile phone use, poor sleep quality and emotional eating were associated with high MPIQ scores, while lower cognitive restraint and emotional eating tendency were correlated with lower scores of MPIQ. Bivariate analysis revealed that age (=0.088, < 0.001), BMI (=0.055, < 0.05), PSQI scores (=0.204, < 0.001), TFEQ-UE scores (=0.199, < 0.001), TFEQ-CR scores (=-0.076, < 0.001), TFEQ-EE scores (=0.170, < 0.001), phone use before sleep (=0.429, < 0.001), and phone use frequency (=0.316, < 0.001) were all significantly correlated with MPIQ scores; multiple linear regression analysis showed that model 4 incorporating the scores of TFEQ-UE, TFEQ-CR, and TFEQ-EE explained up to 21.8% of the main effect (adjusted R= 21.5%). CONCLUSIONS Mobile phone overuse is associated with poor sleep quality and unhealthy eating behaviors, and education and interventions for mobile phone use is essential among college students.
Cell Phone ; Feeding Behavior ; Humans ; Sleep Wake Disorders ; Students ; Universities

OBJECTIVE: To test the validity and reliability of the Chinese version of Mobile Phone Involvement Questionnaire (MPIQ) in college students. METHODS: We assessed the degree of phone dependence using the MPIQ among 2122 college students. One month later, 60 students were randomly selected for assessment with the MPIQ, and the ROC curve was generated to evaluate the true positive rate (sensitivity) and false positive rate at different cutoff values to determine the optimal cutoff score of the MPIQ. RESULTS: Among 98.9% of the participants who finished all the items, their MPIQ scores show a positive skew distribution and a one-factor structure. The load scores of the items ranged from 0.54 to 0.77. The Cronbach's α coefficient and the Spearman Brown split reliability were 0.84 and 0.83, respectively, the correlation coefficients between the items and total score ranged from 0.54 to 0.76, and the test-retest reliability was 0.48 ( < 0.001). At the optimal cut-off score of 32, the sensitivity and the specificity of the MPIQ were 0.634 and 0.652, respectively. CONCLUSIONS At the optimal cut-off score of 32, the MPIQ has good validity and reliability for assessing phone dependence among college students.
Cell Phone ; Humans ; Reproducibility of Results ; Students ; Surveys and Questionnaires