Psycho-social factors related to obesity and their associations with socioeconomic characteristics: the RECORD study

Determination of Quality of Life and Related Factors in Overweight Women Living in Rural Areas

Background

Obesity is more common among women living in rural areas of Türkiye and being overweight affects the quality of life of women.

Aim

Through this study, we aimed to determine the quality of life and related factors of overweight women living in rural areas.

Materials and Methods

This is a descriptive correlational study. The data were collected using the Personal Information Form and the Scale of Quality-of-Life Overweight Women (SMQLOW) between April and June 2020. The study sample comprised women with a body mass index (BMI) of 25 and above.

Results

The study included 201 women, 41.8% of the participants were in the 36-51 age group and 50.2% had a BMI between 30 and 49.2 (obese). It was determined that the quality of life of women was at a moderate level. The difference between the total SMQLOW score with respect to age, educational status, economic status, occupation of the spouse, general health status, and BMI were found to be statistically significant (P < 0.05). We examined the correlation of some variables with the total scale score of overweight women. A significant positive and weak correlation was found between weight and the SMQLOW total score (r = 0.390, P = 0.001). There was a positively weak and highly significant, correlation between waist circumference and SMQLOW total score (r = 0.277, P < 0.001).

Conclusion

Preventing obesity may improve the quality of life among rural women in the study Population.

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning

BACKGROUND

The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors.

PURPOSE

To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics.

METHODS

In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2).

RESULTS

We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design.

CONCLUSIONS

By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

Anthropometric Development in Children: Possible Changes in Body Mass, Basal Metabolic Rate and Inflammatory Status

(1) Background: Worldwide, public health policies focus on studying dietary patterns and the related anthropometric changes in children. Their aim is to improve the measures meant to reduce global malnutrition rates. Our goal was to study the main changes in the inflammatory status related to anthropometric changes and total daily energy intake. (2) Methods: We tested the study hypothesis by analyzing serum IL-6 and IL-8 levels, cholesterol and triglycerides values, as well as total proteins and creatinine levels, RMR, and food journals in a sample of 160 healthy subjects aged between 6 and 12 years old. (3) Results: IL-6 was correlated with the skinfold values. Changes in the skinfolds were significantly correlated with total proteins and triglycerides. Both weight for age and height for age were related to the skinfold values. Through the BMR measurements, peak carbohydrate metabolism changed significantly based upon IL-6 values, which were significantly correlated with the respiratory coefficient values. Based on the basal metabolic rate, an increased IL-8 ratio was related to the RQ value. (4) Conclusions: Skinfolds have been significantly correlated with IL-6 and IL-8 levels. With changes in body weight, we encountered differences in both serum cholesterol and serum triglycerides values, unlike total proteins and creatinine, which failed to change.