METHODS: Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO+ and iCOTM Smokerlyzers®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.
RESULTS: Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO+ Smokerlyzer®, p<0.001; r=0.91 for the iCOTM Smokerlyzer®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCOTM Smokerlyzer® reading of 0.82 (95% CI: 0.69-0.94, p<0.001) compared to that of the piCO+ Smokerlyzer®, and a significant intercept of -0.34 (95% CI: -0.61 - -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCOTM Smokerlyzer® devices.
CONCLUSIONS: The iCOTM Smokerlyzer® readings are highly reproducible compared to those of the piCO+ Smokerlyzer®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.
SUBJECTS AND METHODS: This cross-sectional study was conducted among all (n = 361) consented dental undergraduate students of our dental school. A twenty-item Lay's Procrastination Scale for student population and a ten-item General Self-Efficacy Scale were used for the study after getting institutional ethical approval. The quantitative data were explained using descriptive statistics. Independent sample t-test and ANOVA were used to determine the association between self-efficacy, academic procrastination, and genders and academic years. Pearson correlation coefficient was used to determine the association between self-efficacy and procrastination. Multiple linear regression analysis was performed to determine the related factors to academic procrastination.
RESULTS: High procrastination (score ≥62) was seen among 28.5% of students. The mean self-efficacy score was 29.5. There was no significant difference between genders for procrastination scores (P = 0.835) and between academic years (P = 0.226). Males showed significantly more self-efficacy (P < 0.001), and self-efficacy did not show any significant difference (P = 0.204) between academic years though a tendency for year 5 students to have lower self-efficacy scores was observed. Academic procrastination was negatively correlated with self-efficacy (r = -0.238 and P < 0.001).
CONCLUSIONS: For dental undergraduates who have cognitive load as well as work associated with patients, procrastination and self-efficacy are negatively correlated.
SUBJECTS/METHODS: Urine color was used to measure hydration status, while fluid intake was assessed using the 15-item beverage intake questionnaire. Cognitive function was assessed using the Wechsler Intelligence Scale for Children, Fourth Edition.
RESULTS: More than half of the adolescents were mildly or moderately dehydrated (59.6%) and only one-third (33.0%) were well hydrated. Among the daily fluid types, intakes of soft drinks (r = -0.180; P = 0.006), sweetened tea (r = -0.184; P = 0.005) and total sugar-sweetened beverages (SSBs) (r = -0.199; P = 0.002) were negatively correlated with cognitive function. In terms of hydration status, cognitive function score was significantly higher (F-ratio = 4.102; P = 0.018) among hydrated adolescents (100.38 ± 12.01) than in dehydrated (92.00 ± 13.63) counterparts. Hierarchical multiple linear regression analysis, after adjusting for socio-demographic factors, showed that soft drinks (β = -0.009; P < 0.05) and sweetened tea (β = -0.019; P < 0.05) negatively predicted cognitive function (ΔR2 = 0.044). When further control for sources of fluid, hydration status (β = -2.839; P < 0.05) was shown to negatively predict cognitive function (ΔR2 = 0.021). The above variables contributed 20.1% of the variance in cognitive function.
CONCLUSIONS: The results highlight the links between fluid intake (soft drinks, sweetened tea, total SSBs) and hydration status with cognitive function in adolescents. Interventions aimed at decreasing the consumption of SSBs and increasing hydration status through healthy fluid choices, such as water, could improve cognitive performance in adolescents.