Methods: This prospective cohort study was conducted at primary-care child health clinics in Greater Kuala Lumpur, Malaysia. The sample consisted of infants who fulfilled the criteria and were born in 2019. The anthropometric data of infants were assessed at birth and at 1, 3, 6, 9, and 12 months.
Results: A total of 328 infants were analysed. In total, 27.7%(n=91) of the subjects were SGA infants, and 237 of them were not. Significant differences in the median weight-for-age and length-for-age z-scores were observed between SGA and non-SGA infants at birth, 1 month, 6 months, and 12 months. There was a significant difference between the growth patterns of SGA and non-SGA infants. Birth weight and sex significantly predicted the nutritional status(stunting and underweight) of SGA infants during their first year of life.
Conclusion: SGA infants can catch up to achieve normal growth during their first year of life. Even though the nutritional status of SGA infants trends worse than non-SGA infants, adequate infant birth weight monitoring and an emphasis on nutritional advice are crucial for maintaining well-being.
Methods: We searched major English-language databases (PsycINFO, PubMed, and CINAHL) for studies from January 1, 2002 to July 12, 2019. Studies were included if they provided data on hope and its relationship with quality of life among adolescents with chronic diseases.
Results: In total, five articles were selected from the 336 studies that were retrieved. All five studies reported a positive correlation between hope and quality of life, such that people with a higher level of hope had a better quality of life. Hope was found to have direct and indirect effects on quality of life in adolescents with chronic diseases.
Conclusion: Healthcare professionals should make more efforts to enhance hope in adolescents with chronic diseases in order to improve their quality of life. Future studies exploring how hope develops in adolescents with chronic diseases and the long-term impact of hope on quality of life are necessary.
METHODS: A scoping review was conducted in accordance with the JBI Manual for Evidence Synthesis. Six databases-MEDLINE, CINAHL, PsycINFO, Scopus, the Cochrane Library, and the WHO Global Index Medicus-were searched with no date limits through April 23, 2026. Two reviewers independently screened studies and extracted data. The data were analyzed descriptively and synthesized narratively, distinguishing self-monitoring from proxy monitoring.
RESULTS: Thirty-seven studies met the inclusion criteria. Most used randomized controlled trial designs (62.2%). Target populations included parents, preschool teachers, and childcare workers. Interventions primarily addressed health promotion (75.7%) and chronic disease management (24.3%). Supporting skill practice and reinforcement was the most common purpose of caregiver self-monitoring (29.7%), whereas monitoring symptoms and treatment adherence was the most common purpose of caregiver proxy monitoring (24.3%). Self-monitoring recording tools, such as checklists, diaries, and logbooks, were the most frequently used tools for recording self-monitoring activities. Implementation typically involved caregiver training, application of self-monitoring tools, and review with feedback. Reported challenges included reliance on self-reported data, technical difficulties, and caregiver burden.
CONCLUSION: Caregiver self-monitoring and proxy monitoring are distinct but complementary strategies in early childhood health care interventions, supporting both health promotion and chronic disease management. Future studies should integrate self-monitoring by developing user-friendly tools, reducing caregiver burden, and testing hybrid parent-teacher or childcare-worker delivery models to improve sustainability and longterm child health outcomes.
METHODS: This cross-sectional study included 120 Indonesian undergraduate nursing students aged 21-23 years. Social media addiction and self-efficacy were assessed using the Bergen Social Media Addiction Scale and the General Self-Efficacy Scale, respectively. Data were analyzed using hierarchical multiple linear regression.
RESULTS: The mean social media addiction score was 19.11 (standard deviation=2.11), and the mean self-efficacy score was 24.60 (standard deviation=4.20). Self-efficacy showed a weak but statistically significant negative correlation with social media addiction (r=-.21, p=.022). Sociodemographic predictors entered in Step 1 of the hierarchical regression analysis explained 7.3% of the variance in social media addiction (R2=.073). Sex was a significant predictor (β=-0.19, p=.038), with male students reporting lower scores than female reference group. The final model explained 10.8% of the variance in social media addiction (R2=.108; F(5, 114)=2.77, p=.021).
CONCLUSION: Higher self-efficacy was weakly associated with lower social media addiction. Given the limited variance explained and the omission of other well-established correlates of social media addiction, these findings do not support general self-efficacy as a standalone protective mechanism.
METHODS: We analyzed cross-sectional surveillance data from 394 children in Pesisir Selatan District, West Sumatra, Indonesia, and classified nutritional status according to the 2006 World Health Organization Child Growth Standards. For each outcome, we fitted a parsimonious logistic model (LBW, age, maternal education, and sex) and an expanded model incorporating 8 additional variables (9 regression coefficients). We assessed discrimination using the area under the receiver operating characteristic curve (AUROC) and calibration using the Hosmer-Lemeshow test and calibration slope.
RESULTS: The prevalence of stunting, wasting, and underweight was 14.5%, 10.7%, and 17.3%, respectively. LBW was the strongest predictor in the parsimonious model for stunting (adjusted odds ratio [AOR], 4.85; 95% confidence interval [CI], 2.53-9.29), wasting (AOR, 2.88; 95% CI, 1.38-6.02), and underweight (AOR, 4.34; 95% CI, 2.30-8.19; p≤.005). A lack of formal maternal education was independently associated with stunting and underweight. The optimism-corrected AUROCs were .706, .631, and .751, with calibration slopes of .88, .75, and .90, respectively. Adding variables in the expanded model did not improve fit (likelihood-ratio p>.05) and increased optimism.
CONCLUSION: The parsimonious model showed modest discrimination for stunting and underweight. Discrimination for wasting was inadequate. A predictive contribution from the added variables could not be demonstrated with the available number of events. These preliminary findings require external validation before community-level application.