METHOD: A prospective cross-sectional study was conducted using the validated Smartphone Addiction Scale-Malay version (SAS-M) questionnaire. One-way ANOVA was used to determine the correlation between the PSU among the students categorised by their ethnicity, hand dominance and by their field of study. MLR analysis was applied to predict PSU based on socio-demographic data, usage patterns, psychological factors and fields of study.
RESULTS: A total of 1060 students completed the questionnaire. Most students had some degree of problematic usage of the smartphone. Students used smartphones predominantly to access SNAs, namely Instagram. Longer duration on the smartphone per day, younger age at first using a smartphone and underlying depression carried higher risk of developing PSU, whereas the field of study (science vs. humanities based) did not contribute to an increased risk of developing PSU.
CONCLUSION: Findings from this study can help better inform university administrators about at- risk groups of undergraduate students who may benefit from targeted intervention designed to reduce their addictive behaviour patterns.
METHODS: This prospective study was conducted among the caregivers of 443 child TB patients registered during the study. Caregivers of children were queried using a structured questionnaire consisting of sociodemographic and socio-economic factors and the role of healthcare workers during the treatment course. Risk factors for non-adherence were estimated using a logistic regression model.
RESULTS: In multivariate analysis, the independent variables that had a statistically significant positive association with non-adherence were male sex (adjusted odds ratio [AOR] 5.870 [95% confidence interval {CI} 1.99 to 17.29]), age ≥45 y (AOR 5.627 [95% CI 1.88 to 16.82]), caregivers with no formal education (AOR 3.905 [95% CI 1.29 to 11.79]), financial barriers (AOR 30.297 [95% CI 6.13 to 149.54]), insufficient counselling by healthcare workers (AOR 5.319 [95% CI 1.62 to 17.42]), insufficient counselling by health professionals (AOR 4.117 [95% CI 1.05 to 16.05]) and unfriendly attitude and poor support from healthcare professionals (AOR 11.150 [95% CI 1.91 to 65.10]).
CONCLUSIONS: Treatment adherence in the present study was 86% using the Morisky Green Levine Medication Adherence Scale and 90.7% using the visual analogue scale tool. Predictors of non-adherence need to be a focus and caregivers should be given complete knowledge about the importance of adherence to TB treatment.