METHODS: This is a follow-up study among 84 obese housewives without co-morbidities aged 18 to 59 years old who previously participated as a control group (delayed intervention, G1) in the My Body is Fit and Fabulous at Home (MyBFF@home) Phase II. Baseline data were obtained from 12 month data collection for this group. A new group of 42 obese housewives with co-morbidities (G2) were also recruited. Both groups received a 6 month intervention (July-December 2015) consisting of dietary counselling, physical activity (PA) and self-monitoring tools (PA diary, food diary and pedometer). Study parameters included weight, height, waist circumference, blood pressure and body compositions. Body compositions were measured using a bioelectrical impedance analysis device, Inbody 720. Descriptive and repeated measures ANOVA analyses were performed using SPSS 21.
RESULTS: There were reductions in mean body fat, fat mass and visceral fat area, particularly among obese women without co-morbidities. There were also decreases fat and skeletal muscle from baseline to month six with mean difference - 0.12 (95% CI: -0.38, 0.14) and visceral fat area from month three to month six with mean difference - 9.22 (- 17.87, - 0.56) for G1. G2 showed a decreasing pattern of skeletal muscle from baseline to month six with mean difference - 0.01(95% CI: -0.38, 0.37). There was a significant difference for group effect of visceral fat area (p
METHODS: Data of 328 eligible housewives who participated in the MyBFF@Home study was used. Intervention group of 169 subjects were provided with an intervention package which includes physical activity (brisk walking, dumbbell exercise, physical activity diary, group exercise) and 159 subjects in control group received various health seminars. Physical activity level was assessed using short-International Physical Activity Questionnaire. The physical activity level was then re-categorized into 4 categories (active intervention, inactive intervention, active control and inactive control). Physical activity, blood glucose and lipid profile were measured at baseline, 3rd month and 6th month of the study. General Linear Model was used to determine the effect of physical activity on glucose and lipid profile.
RESULTS: At the 6th month, there were 99 subjects in the intervention and 79 control group who had complete data for physical activity. There was no difference on the effect of physical activity on the glucose level and lipid profile except for the Triglycerides level. Both intervention and control groups showed reduction of physical activity level over time.
CONCLUSION: The effect of physical activity on blood glucose and lipid profile could not be demonstrated possibly due to physical activity in both intervention and control groups showed decreasing trend over time.
METHODS: Baseline and sixth month data from the MyBFF@home study were used for this purpose. A total of 169 of overweight and obese respondents answered the IPAQ-SF and were asked to use a pedometer for 7 days. Data from IPAQ-SF were categorised as inactive and active while data from pedometer were categorised as insufficiently active and sufficiently active by standard classification. Data on sociodemographic and anthropometry were also obtained. Cohen's kappa was applied to measure the agreement of IPAQ-SF and pedometer in determining the physical activity level. Pre-post cross tabulation table was created to evaluate the changes in physical activity over 6 months.
RESULTS: From 169 available respondents, 167 (98.8%) completed the IPAQ-SF and 107 (63.3%) utilised the pedometer. A total of 102 (61.1%) respondents were categorised as active from the IPAQ-SF. Meanwhile, only 9 (8.4%) respondents were categorised as sufficiently active via pedometer. Cohen's κ found there was a poor agreement between the two methods, κ = 0.055, p > 0.05. After sixth months, there was + 9.4% increment in respondents who were active when assessed by IPAQ-SF but - 1.3% reductions for respondents being sufficiently active when assessed by pedometer. McNemar's test determined that there was no significant difference in the proportion of inactive and active respondents by IPAQ-SF or sufficiently active and insufficiently active by pedometer from the baseline and sixth month of intervention.
CONCLUSION: The IPAQ-SF and pedometer were both able to measure physical activity. However, poor agreement between these two methods were observed among overweight and obese women.
METHODS: A cross-sectional study was conducted in a locality within Selangor, sampling a total of 1449 young adults. The Cyberbullying and Online Aggression Survey was used to measure cyberbullying victimisation. The Family APGAR scale, General Health Questionnaire, Pittsburgh Sleep Quality Index and single-item measures were used to assess family dysfunction, psychological distress and health behaviour, respectively.
RESULTS: The 1-month prevalence of cyberbullying victimisation among young adults was 2.4%. The most common cyberbullying act experienced was mean or hurtful comments about participants online (51.7%), whereas the most common online environment for cyberbullying to occur was social media (45.8%). Male participants (adjusted OR (AOR)=3.60, 95% CI=1.58 to 8.23) had at least three times the odds of being cyberbullied compared with female participants. Meanwhile, participants with higher levels of psychological distress had increased probability of being cyberbullied compared with their peers (AOR=1.13, 95% CI=1.05 to 1.21).
CONCLUSIONS: As evident from this study, cyberbullying victimisation prevails among young adults and is significantly related to gender and psychological distress. Given its devastating effects on targeted victims, a multipronged and collaborative approach is warranted to reduce incidences of cyberbullying and safeguard the health and well-being of young adults.