The importance of daily movement in Type 2 Diabetes Mellitus was studied by examining 24-hour movement behaviors
The participants with type 2 diabetes were categorized into 3 groups, based on their BMI to analyze potential differences across weight status groups
This data could aid healthcare practitioners in developing individualized strategies to treat type 2 diabetes and improve the overall health of those living with the disease.
Type 2 diabetes mellitus is a chronic disorder characterized by excessive blood sugar levels. It is the most prevalent kind of diabetes and usually appears in adulthood. It causes the body to become resistant to the insulin it produces. Insulin is a hormone that allows cells to take in glucose from the bloodstream for energy, thereby helping to regulate blood sugar levels. Insulin resistance occurs when the body’s cells do not respond adequately to insulin, resulting in high blood sugar levels. Type 2 diabetes mellitus is caused by a combination of variables, including genetic susceptibility, lifestyle decisions, and obesity. Being overweight or obese raises the chances of getting type 2 diabetes mellitus because excess body fat interferes with insulin’s capacity to regulate blood sugar levels. If left untreated, it can lead to a variety of consequences, including heart disease, kidney damage, nerve damage, and problems with vision. Individuals with Type 2 diabetes mellitus may live healthy and fulfilling lives with adequate care, which includes lifestyle changes, medication, and regular monitoring. Of the lifestyle changes, movement in type 2 diabetes mellitus is important since it helps manage the illness and overall health outcomes.
Therefore, the purpose of this study was to look at the 24-hour movement behaviors paradigm in people with type 2 diabetes mellitus of various weight status groups. It was the first study to examine differences among people of different weight categories.
This cross-sectional study used data from a large cohort study that monitors people with type 2 diabetes. It is a dynamic cohort that has been prospectively followed since 1996. The people in this cohort have an annual visit with a general practitioner to follow up on their type 2 diabetes.
Only participants without other pathologies than type 2 diabetes were included. Their movement behaviors were registered using a hip-worn accelerometer during the waking hours for one week. At night, the accelerometer was not worn. A diary registering sleep was used to track sleep over the study period. Weight, height, and waist circumference were measured and BMI was calculated. Cardiometabolic parameters were measured in a fasting blood sample. The following parameters were analyzed:
The amount of sleep duration, light physical activity (LPA), moderate to vigorous physical activity (MVPA), and sedentary time (ST) were evaluated in this study. These were measured using accelerometry and sleep diaries to obtain data on these habits. As such, every behavior could be compared relative to the others. They sought to discover if there were any changes in movement behaviors among people with type 2 diabetes of various weights. If any significant differences were found, they were found using analysis of variance (ANOVA).
In addition, they compared particular pairs of weight groups using post-hoc analyses to determine whether there were any noteworthy changes between them. This assisted them in determining which weight groups had different movement patterns.
Other characteristics that could influence the outcomes, such as age, gender, and duration of diabetes, were also considered. They employed statistical models to test if the differences in movement behaviors were still significant after controlling for these characteristics.
The researchers assessed if there were any relevant changes in movement habits between persons with type 2 diabetes of varying BMIs. This data can assist healthcare practitioners in better understanding how movement behaviors relate to diabetes treatment and develop targeted interventions for different weight groups.
A total of 1549 adults with type 2 diabetes participated in this study. On average, they were 68.5 years old and they had a BMI of 29.5 kg/m2. More than 80% of them took glucose-lowering medication and more than 75% took lipid and blood pressure-lowering medication. Nearly 30% of the sample took insulin.
The participants with type 2 diabetes were categorized into 3 groups, based on their BMI:
It was found that the groups had different movement behaviors per 24 hours. In people with type 2 diabetes and obesity, the 24-hour movement behaviors revealed that in one day, they slept on average 19 minutes less and participated in 31 minutes less of light physical activity than people with type 2 diabetes and a normal BMI. Moreover, they had 51 minutes more sedentary time per 24 hours.
Compared to the group of people with type 2 diabetes being overweight, the obese group slept 8 minutes less, had 36 minutes more sedentary time, 26 minutes less light physical activity, and 2 minutes less moderate to vigorous activity.
The group with type 2 diabetes and overweight only differed from those with normal weight in sleep: they slept on average 10 minutes less.
BMI, waist circumference, HDL-cholesterol, and triglycerides were all associated with the 24-hour movement behaviors.
What happens with the BMI when sedentary activity is replaced?
To give meaning to these results, the authors tried to find out what happened when time durations of up to 20 minutes were reallocated into another movement behavior. Here the authors found:
What happens with waist circumference when 20 minutes of sedentary activity or sleep is replaced?
Are there differences between short- and long-sleepers?
Time reallocation was used to better understand the findings. However, these reallocations are only theoretical because they were derived from a particular analysis. This study was not a pre-post study in which, for example, waist circumference was measured before and after 20 minutes of sleep and was reassigned to an an active behavior every 24 hours. Because weight and body composition do not change rapidly, this is a clear method of giving meaning to the results. But to be sure of these findings, a pre-post design of several weeks would be needed. However, not every day is the same and I think this would be very difficult to study in a pre-post design, and then using this option of theoretical redistribution of time seems a convincing method.
The median of the participant’s sleep period was taken to divide the group into short- and long-sleepers. However, the median was not displayed. The group was divided into long sleepers when they recorded an average of 9.3 hours of sleep each night and short-to-average sleepers when they recorded 7.7 hours per night. So the median had to lie somewhere in between, but it was not sure at which point. It was clear that 94% of the participants had a sleep duration longer than 7 hours. So sleep data was likely skewed. The median is frequently the favored measure of central tendency for skewed distributions or outliers because it is more resilient to outliers than the mean.
The participants took medications to control their cardiometabolic profiles and these profiles were well-controlled. The authors point out that this may have resulted in the lack of associations in many of the cardiometabolic outcomes. But even though participants had these well-controlled cardiometabolic profiles, this study still found associations between changes in exercise behavior and BMI, waist circumference, HDL cholesterol, and triglycerides, indicating the importance of even small changes in movement in a single day.
Because the data is cross-sectional, causality cannot be assumed.
A limitation of this study was that almost one-third of the participants had non-valid hip accelerometer data. To tackle this problem, participants without valid accelerometer data for at least 5 days were excluded from the analysis. This ensured that the obtained data was reliable. Imagine if someone forgot to wear the accelerometer for several active hours per day. This would make the researcher think they spent more time sitting down.
Sleep was measured using a sleep diary. The purpose was to fill it out every day, which is a good option as it decreases the problem of recall bias. However, nothing was mentioned if this was controlled for. Maybe some participants filled the diary accurately, while some did not. However, nothing is mentioned about when the sleep data was transferred to the researchers. A daily system log would have been more reliable than a 7-week diary for example. A limitation of a sleep diary is that people fill in when they go to bed, but they may still lie awake for several hours, which is then counted as “sleep time”. An accelerometer worn at night had been a better option.
This study did not register participants’ diets throughout this one-week study period. As such, diet was not included in the analysis as a confounding variable.
The inclusion of a large sample from a large cohort is a strength of this study because it increases the generalizability of the results. However, the origin of the cohort should be considered when interpreting the findings. For example, it may affect participation in physical activity. We can think about the amount of physical activity a person has in a warm versus a cold country. The same goes for the season in which participants were followed. Some people are more likely to exercise in better weather and this may affect the results.
The observed effect sizes were mostly small, but they are in line with other research examining body composition in people with type 2 diabetes mellitus.
The researchers wanted to know how different forms of movement behaviors throughout the day affect persons with type 2 diabetes of various weights in this study. They gathered data from people with type 2 diabetes and measured their sleep length, how often they walked lightly how much moderate to strenuous physical activity they did, and how much time they spent sitting.
I. Willems, V. Verbestel, D. Dumuid, et al., Cross-sectional associations between 24-h movement behaviors and cardiometabolic health among adults with type 2 diabetes mellitus: a comparison according to weight status, Journal of Science and Medicine in Sport (2023).
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