Validation of Obesity Status Based on Self-Reported Data among Filipina and Indonesian Female Migrant Domestic Workers in Macao (SAR), China

Investigating the Physical and Mental Health Nexus: a Network Analysis of Depression, Cardiometabolic Health, Bone Mass, and Perceived Health Status Among Filipino Domestic Workers

BACKGROUND

Migrant domestic workers are vulnerable to physical and mental health problems given the many challenges they experience while working abroad. Using network analysis, this study examined the structure of depression, cardiometabolic health indicators (BMI, waist-hip ratio (WHR), blood pressure, and heart rate), bone mass, and perceived health status in this population. The network model allowed for an examination of central symptoms or symptoms with the most direct connections with other symptoms; bridge symptoms, or symptoms that link two or more communities; and edges, or relationships among symptoms.

METHOD

Cross-sectional data were gathered from 1375 Filipino domestic workers in Macao (SAR), China. Data from a subsample of 510 participants who met a cutoff indicating depression were analyzed. Anthropometric measurements and surveys were used to collect data, which was analyzed using R statistical software.

RESULTS

Results showed four community clusters: three communities consisted of at least two depression symptoms each and the fourth community included physical health indicators. Strong edges were formed between BMI-bone mass, psychomotor-concentration, BMI-WHR, and sad mood-anhedonia. The node with the highest expected influence was BMI. There were three bridges: worthlessness, psychomotor difficulties, and concentration difficulties.

CONCLUSION

The link between depression, cardiometabolic indicators, bone mass, and poor perceived health reinforces the need to address multimorbidity within migrant populations. Health promotion interventions that address mental and physical health may improve the health of this population.

The Impact of Parental Migration on Multidimensional Health of Children in Rural China: The Moderating Effect of Mobile Phone Addiction

Improving physical, mental and cognitive health is a strategic choice to help developing countries cross the middle-income trap. This paper used data from the 2019 China Rural Children Health and Nutrition Survey (n = 826), and used the Ordered Probit (Oprobit), Logit and ordinary least squares (OLS) analytical methods to systematically analyze the implications of parental migration on multidimensional health. The results indicate that parental migration significantly harms the physical and mental health of rural children, and that mobile phone addiction has a significant moderating effect. Moreover, parental migration has a greater impact on the physical health, mental health and cognitive ability of boys and rural children with low family income, while parents with higher nutrition knowledge and education can effectively improve the physical health and cognitive ability of their children. In conclusion, in order to improve the multidimensional health of rural children, the government should strengthen the policy of care and support for children whose parents migrate. Schools and families should pay attention to the supervision of rural children's mobile phone addiction.

Does Self-Perception Equal the Truth When Judging Own Body Weight and Height?

Background: Data from the research project “Fitness clubs—a venue for public health?” provided an opportunity to evaluate the accuracy of self-reported body weight and height, and subsequent Body Mass Index (BMI), as well as the “trueness” of novice exercisers perception of weight status category, which has not been examined in this population. The aims were to examine self-reported body weight, height, and calculated BMI data from an online survey compared with measured data at fitness club start-up, investigate how accurately novice exercisers place themselves within self-classified weight group (underweight, normal weight, overweight, and obese), and compare this with fitness club attendance at three months follow-up. Methods: Prior to anthropometric measurements, 62 men and 63 women responded to an online questionnaire, including body weight (kilogram, kg) and height (centimeters, cm), and self-classified weight group (“I think I am … underweight, normal weight, overweight, obese”). We used the following statistical analysis: Paired sample t-tests, a Bland–Altman plot kappa statistics, chi-squared tests, and a logistic regression. Results: Mean difference of BMI calculated from self-reported and measured data was 0.06 (95% CI −0.29 to 0.17, p = 0.593) in men, and 0.16 (95% CI −0.40 to 0.09, p = 0.224) in women, with four participants being outliers of the 95% limits of agreement (Bland-Altman plot). Allowing a difference of 0.5 kg between self-reported and measured weight, we found that 16% reported their weight correctly, 31.2% underreported (−1.89 ± 1.59 kg), and 52.8% overreported (1.85 ± 1.23 kg), with no sex differences (p = 0.870). Further, our results suggest that both sexes may have difficulty recognizing overweight/obesity in themselves, and particularly men are likely to underreport their perceived weight group compared with women. More than half (53.3%) of the overweight men perceived themselves to be normal weight (women: 14%), and only 33.3% of obese men and women correctly classified themselves as being obese. We did not find any difference between participants correctly or incorrectly classifying weight group and fitness club attendance (≥2 times a week) at three months follow-up. Conclusion: Both sexes reported body weight and height reasonably accurately, and BMI based on self-report appears to be valid measure. Still, a large proportion of novice exercisers do not recognise their own overweight or obesity status, which may in part explain why public health campaigns do not reach risk populations.