Identifying Patterns of Lifestyle Behaviors among People with Type 2 Diabetes in Tianjin, China: A Latent Class Analysis

Identifying Clusters of Health Behaviors in a Japanese Working Population at Risk for Non-Communicable Diseases: A Latent Class Analysis of 12,168 Individuals

Introduction

Noncommunicable diseases (NCDs) have become a significant global problem. Health behaviors are associated with NCDs, and characterizing populations using a public health approach can help provide specific interventions according to their characteristics. This study aims to examine the formation of clusters of health behavior combinations in the Japanese working population at risk of NCDs, taking into account the influences of age and gender, using latent class analysis.

Methods

Participants were individuals at risk for NCDs but had not previously been diagnosed with any. Latent class analysis (LCA) was used to study clustering based on basic characteristics and health behaviors. All statistical analyses were conducted using R (Version 4.0.4) and the "poLCA" package (Version 1.6.0).

Results

This study included 12,168 participants. LCA compared models with one to six latent classes. The five-class model was determined to be the most appropriate based on Bayesian Information Criterion, Akaike Information Criterion, and G^2 values, as well as distinguishable cluster characteristics. Cluster 1: "having healthy lifestyles but disliking hospitals"; Cluster 2: "women with healthy lifestyle behaviors"; Cluster 3: "general population"; Cluster 4: "middle-aged group in need of lifestyle improvement"; Cluster 5: "a group receiving treatment for lifestyle-related diseases."

Conclusions

This study reveals discernible health behavior patterns in a sample of the Japanese population using large real-world data, suggesting the effectiveness of distinct approaches when considering a population approach to public health.

Lifestyle behaviors and associated factors among individuals with diabetes in Brazil: a latent class analysis approach

The purpose of the cross-sectional study was to identify patterns of modifiable lifestyle behaviors and examine the relationship between sociodemographic characteristics and distinct lifestyle behaviors. The data were gathered from the National Health Survey 2019, a study that included adults with diabetes. Four domains of lifestyle behaviors were used to define these behaviors: smoking, alcohol consumption, physical activity, and diet. The association between patterns of lifestyle behaviors and variables of interest was assessed using multinomial regression analysis. The three lifestyle patterns identified were: Class 1, referred to as "unhealthy diet," comprised 17.0% of the sample and was characterized by unhealthy eating habits; Class 2 (less active and insufficient fruit and vegetable intake) represented 71.2% of the sample; Class 3 referred to as "low risk" (11.8%) is characterized by a lower probability of engaging in most risky behaviors. A person over 45 years of age with little or no education and no health care coverage was less likely to be a member of Class 1. Male individuals who do not attend a doctor regularly exhibited more chances of belonging to Class 2. Mixed-race individuals aged 45 years or more with a low level of education have a lower chance of belonging to this class.

An update on the economic burden of type 2 diabetes mellitus in China

OBJECTIVES

This study aims to update the statistics on the economic burden of T2DM and to identify the factors affecting the economic costs of T2DM in China.

METHODS

This study conducts a systematic review of the existing literature that has reported on the direct economic costs (mainly the direct medical resource consumption) and indirect economic costs (mainly non-medical costs and intangible costs) of T2DM as of 31 May 2019.

RESULTS

The total expenditure on diabetes in China's western region is still relatively low. Additionally, the mean direct costs of T2DM are high in China's northern urban areas. However, compared to urban areas, in rural areas, the largest proportion of the total economic costs of T2DM is the mean indirect costs. Furthermore, age, sex, type and number of complications, type of medical insurance, diabetes duration, level of education, and income are the primary factors that influence the economic burden of T2DM.

CONCLUSION

There is a considerable economic burden associated with T2DM in China. Therefore, to address the economic burden of T2DM, it is vital to take measures to reduce the prevalence rate of diabetes.

Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review

Background

Population segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients.

Methods

The literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed.

Results

Of 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients’ race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies.

Conclusions

Population segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01209-w.

Prevalence of comorbidities and their associated factors in patients with type 2 diabetes at a tertiary care department in Ningbo, China: a cross-sectional study

OBJECTIVES

To determine the prevalence of comorbidities in patients with type 2 diabetes mellitus (T2DM) and identify the factors independently associated with comorbidities in a tertiary care department in Ningbo, China.

DESIGN

A computerised medical records database was used to conduct a cross-sectional study.

SETTING

The study was conducted in a tertiary care department in Ningbo, China.

PARTICIPANTS

The study was conducted on adult patients with T2DM, and it included 8 years of data, from 1 January 2012 to 31 December 2019.

THE PRIMARY OUTCOME MEASURE

Comorbidity was defined as the coexistence of at least one other chronic condition, that is, either a physical non-communicable disease (duration ≥3 months), a mental health condition (duration ≥3 months) or an infectious disease (duration ≥3 months).

RESULTS

In total, 4777 patients with T2DM satisfied the eligibility criteria. Over 8 years, the prevalence of comorbidities was 93.7%. The odds of comorbidities increased with the age of patients (18 to 39 years: 1; 40 to 59 years: OR 2.80, 95% CI 1.98 to 3.96; 60 to 69 years: OR 4.43, 95% CI 3.04 to 6.44; and ≥70 years: OR 10.97, 95% CI 7.17 to 16.77). The odds were lower in female patients (OR 0.66, 95% CI 0.51 to 0.84), patients residing in rural areas (OR 0.75, 95% CI 0.59 to 0.95) and patients without health insurance (OR 0.62, 95% CI 0.46 to 0.83). The odds were higher in single/divorced/widowed patients compared with those in married patients (OR 1.95, 95% CI 1.21 to 3.12).

CONCLUSIONS

A large percentage of patients with T2DM in the tertiary care department in Ningbo, China, had comorbidities, and the factors associated with comorbidities were identified. The findings could be used in developing, evaluating and implementing interventions aimed at improving outcomes in patients with T2DM with comorbidities.

Clustering of multiple health risk behaviors and its association with diabetes in a Southern Chinese adult population: a cross-sectional study

Background

Identifying the clustering patterns of health risk behaviors (HRBs) within individuals and their health impacts are essential to develop lifestyle promotion strategies. This study aimed to explore the clustering of a range of HRBs and the associations between such identified clusters and diabetes in Southern Chinese adults.

Methods

Data from 5,734 adults aged 35-75 years and underwent health examinations from November 2012 to December 2013 at a tertiary hospital in Guangzhou were analyzed. Behavioral characteristics, including smoking, alcohol use, physical activity, and sleep duration and quality, were measured by questionnaires. Latent class analysis was conducted by gender to identify HRBs clustering patterns, and logistic regression models were used to estimate the associations between behavioral patterns and diabetes.

Results

Three distinct behavioral clusters emerged in both genders. Male classes were defined as: (1) healthy lifestyle (Class 1, 62.9%); (2) cumulate harmful habits (Class 2, 27.1%); (3) poor sleep and risky habits (Class 3, 10.0%). Female classes were: (1) healthy lifestyle (Class 1, 83.0%); (2) inactive, daytime dysfunction (Class 2, 5.7%); (3) poor sleep habits (Class 3, 11.3%). Individuals of Class 2 and Class 3 showed a higher likelihood of diabetes across genders (multivariable-adjusted ORs [95% CIs], 2.03 [1.49-2.76] and 2.61 [1.78-3.81] among males, 2.64 [1.16-5.98] and 1.81 [1.07-3.06] among females) when compared with those of Class 1.

Conclusions

Our data provided additional evidence of HRBs clustering among adults, and such clustering was associated with an increased risk of diabetes. These findings have implications for identifying vulnerable subgroups and developing diabetes prevention programs.

Efficacy of Pilates Based Mat Exercise on Quality of Life, Quality of Sleep and Satisfaction with Life in Type 2 Diabetes Mellitus

Background and Aims: Diabetes Mellitus may affect the patient’s quality of life and sleep that lead to reduced satisfaction of life. Aim of study was to improve quality of life and sleep along with satisfaction of life by giving physical therapy (pilates based on mat exercise) intervention. Material and Methods: Study design: experimental study, same subject design (pre-post). Sample size: 30 individuals (13 males,17 females) with mean age 46.05±9.01, mean weight 70.48±12.11 and mean duration of diabetes mellitus 7.88±4.49. Intervention: Pilates based mat exercises were given in experimental group. Duration of treatment:30-40minutes.Number of session:5 sessions/week. Total duration: 4 weeks. Outcomes measures: Final Qolid Questionnaire, Pittsburgh Sleep Quality Index and Satisfaction with Life Scale. Statistics: descriptive statistics used to measure mean± standard deviation and inferential statistics related t-test used to compare pre and post reading. Results: The results showed highly significant effect of exercise on quality of life and quality of sleep and significant result was found on satisfaction with life. Conclusion: Pilates based mat exercises shows positive effect on all parameters (quality of life, quality of sleep and satisfaction with life) of patients having type 2 diabetes mellitus.