Comparison of sleep and health behaviors among diabetic patients and non-diabetics in Phitsanulok , Thailand : a cross-sectional study

Type 2 diabetes mellitus (T2DM) is a global public health Background: problem. To avoid disease complications, diabetic patients have to control their blood glucose and maintain a healthy lifestyle including a healthy diet, weight control, moderate exercise and smoking cessation. This study aimed to survey sleep, eating and exercise behaviors Methods: of diabetic patients in Bang Rakam district, a rural community in Phitsanulok province, Thailand. The data on sleep and other health behaviors were taken from 1,385 T2DM patients and 1,394 non-T2DM controls, who were aged 30 85 years and were free from other chronic diseases. The data were collected using a structured questionnaire. Compared to the control group, the diabetic group had a Results: significantly higher body mass index (BMI). However, fewer of them were found to smoke cigarettes and drink alcohol. Most of the participants were ‘morning people’ who slept 7-9 hours per day. It was found that sleep ≥8 hours increased the risk of diabetes among women (OR = 1.27, 95% CI 1.03 1.56). The diabetic group reported eating chicken and vegetables more than the control group. They also avoided eating beef and eating more than a cup of rice per meal. However, the T2DM group did fewer physical activities, such as walking, biking or playing sports, during their leisure time. Compared to the control group, diabetic patients in a rural Conclusions: community of Thailand had healthier sleep, lifestyle and eating behaviors but not healthier exercise behaviors, especially among obese women. Diabetic prevention programs should emphasize and promote weight control and increasing levels of exercise.


Introduction
Type 2 diabetes mellitus (T2DM) is a global public health problem. It has been estimated that by the year 2030, there will be 439 million people with T2DM (Olokoba et al., 2012). The well-established risk factors for this disease are genetic factors, eating behaviors and exercise (Zheng et al., 2018). Recent studies also related T2DM to sleep and lifestyle. In a large population study in Korea, it was found that 'evening people' (those who go to bed late, being alert and prefer to work at night) had an increased diabetic risk (odds ratio, OR = 1.73, 95% CI 1.01-2.95) compared to 'morning people' (those who usually go to bed early and like to work or being active during the day) (Yu et al., 2015).
In meta-analysis studies, there a strong U-shaped dose-response association between T2DM and sleep quality and quantity has been observed (Cappuccio et al., 2010;Lee et al., 2017). Compared to men with seven hours of sleep, the risk of T2DM was about twice among for a short sleeper (under or equal to five or six hours of sleep per night) and three times among for a long sleeper (over eight hours) (Heianza et al. (2014); Yaggi et al., 2006). reported a similar result among ≤45 year-olds but not for those ≥60 years of age. In experimental studies, sleep deprivation increased insulin resistance, hunger hormone levels, appetite and food intake but reduced glucose metabolism, leading to obesity, a common predictive factor for diabetes (Beccuti & Pannain, 2011;Reutrakul & Van Cauter, 2018) .
On the other hand, T2DM itself can interfere with sleep and cause sleep apnea among diabetic patients (Barone & Menna-Barreto, 2011;Resnick et al., 2003). Poor sleep is often found among T2DM patients as compared to healthy control groups (Trento et al., 2008). A study among elderly Iranian women with T2DM found that being a poor sleeper is associated with: being middle-aged (OR = 2.03, 95% CI 1.01-4.08); having a longer duration of diabetes (OR = 1.77, 95% CI 0.98-3.13); and having high cholesterol levels ≥240 mg/dL (OR = 1.99, 95% CI 1.01-3.94) (Shamshirgaran et al., 2017). This was consistent with a previous study, which also reported a higher prevalence of sleep disorders (33.7%) among T2DM patients than in a non-diabetic control group (8.2%) (Sridhar & Madhu, 1994). A study in the United States reported that 55% of T2DM patients have poor sleep (Luyster & Dunbar-Jacob, 2011). Sleep problems among diabetic people might be caused by the disease itself, which affects neurobehavioral and endocrine functions, or due to complications of the disease, such as peripheral neuropathy, restless legs syndrome, polyuria and associated depression (Khandelwal et al., 2017). In an experimental study, sleep restriction (five hours per night) for a week can reduce insulin sensitivity and increase blood glucose; these changes affected kidney function and increased urination, which interfered with sleep (Buxton et al., 2010;Reutrakul & Van Cauter, 2018).
To avoid disease complications, diabetic patients have to control their blood glucose and maintain a healthy lifestyle through, for example, a healthy diet, weight control, moderate exercise and smoking cessation (Stolar, 2010;Tang et al., 2008). Optimal control of sleep duration and quality was also proposed as an intervention to improve blood glucose levels in patients with T2DM (Trento et al., 2008). However, studies about the health behaviors of diabetic patients is surprisingly rare. A population based survey in Australia reported that there was a minimal change in lifestyle among people after being diagnosed with T2DM. Compared to the healthy control group, the recently diagnosed T2DM group had a minimal weight loss of 1.38 kg (95% CI -1.85 to -0.89), and were more likely to stop smoking (OR of quitting = 2.71, 95% CI 1.59-4.63). However, there was no positive improvement in other lifestyle behaviors such as sitting, walking, moderate to vigorous physical activity (MVPA) and vegetable and fruit consumption (Chong et al., 2017).
This study aimed to survey the sleep, eating and exercise behaviors of diabetic patients in a rural community in Phitsanulok province, Thailand. The predictive factors of sleep and other health behaviors were also investigated. The results will be useful for local diabetic care programs and comparative studies worldwide.

Objectives
1. To explore sleep, eating and exercise behaviors among T2DM and non-T2DM groups.
2. To identify factors that affect sleep and exercise among diabetic patients.
3. To determine the association between diabetes and sleep duration.

Methods
This study is an analytical cross-sectional design with a comparison group.

Study site
This study utilized data from a previous case-control study on diabetes and pesticide exposure (Juntarawijit & Juntarawijit, 2018). The data on health behaviors were collected from February to May 2016 from diabetic patients (T2DM) and a non-T2DM control group living in the rural community of Bang Rakam, a district with 95,098 people (in the year 2018) in Phitsanulok province, Thailand. The district is located in the lower northern part of Thailand, about 400 km from Bangkok.

Study participants
The diabetic patients were those who had come to receive follow-up services at seven health promoting hospitals, which were randomly selected, using random number tables, from all 21 local hospitals in the target area. All diabetic patients who met the inclusion criteria were approached at their home by village health volunteers to take part in the study. In this study, the T2DM group was limited to those aged 30-85 years and free from other chronic diseases, such as heart disease, allergies, chronic pulmonary disease, and cancer. For each diabetic case, one healthy control (non-T2DM) who was free from diabetic disease and met the same inclusion criteria as the case was also approached by the same health volunteer based on the convenience sampling method. The control group were neighbors of the diabetic patients matched for gender and age (± five years.).

Study questionnaire
In addition to demographic information, data on sleep duration and other health behaviors were collected using an intervieweradministered questionnaire during a face-to-face interview, which was written in the Thai language (Juntarawijit, 2019b). Before use, the questionnaire was tested for question sequencing and understanding. An interview took place at home of each participant. The participants' self-reported sleep duration was collected using the question "How many hours do you usually sleep per day?". Participants were classified as 'current smoker' if they had smoked 100 cigarettes or more in their lifetime and they currently smoke cigarettes. Those who drank alcohol 2-4 times a week were classified as 'alcohol use'. Data on food consumption, including consumption of meat, sausage, vegetable, fruit, sweets, rice and sweet soft drinks, were also collected using 'yes or no' questions. Information on personal lifestyle (whether they are a morning person or an evening person) was collected using the question "What is the lifestyle that best describes you, morning people or evening people: "morning people" refer to those who usually go to bed early and like to work or being active during the day; "evening people" are those who go to bed late, being alert and prefer to work at night?". Participants were also asked to report how frequently they did certain physical activities (walking, biking, playing sports or sweating excessively from exercise or physical activity but not from hot climate or health problems) and watched television during their leisure time using two categories: absent (never, rarely) and present (sometimes, often, almost always). Body mass index (BMI) was calculated by dividing body weight (in kg) by height (in meters squared). The high BMI group was those with BMI ≥25.00. For waist to hip ratio (WHR), a high WHR referred to men with WHR ≥0.90 or women with WHR ≥85. For waist circumstance (WC), a high WC referred to men with WC ≥90.0 or women with WC ≥80. All of these measurements were assessed by the health volunteers. Data were collected by 50 village health volunteers who were trained on how to use questionnaires and how to interview study participants.

Statistical analysis
Demographic and health behaviors were analyzed using descriptive statistics and Chi-square test for comparison of categorical data. To identify predictive factors of sleep duration, logistic regression was performed, adjusted for gender, age (continuous), waist to hip ratio (WHR) and lifestyle (evening person vs morning person). The predictive factors of physical activity were also analyzed using ordinal regression, with physical activity categorized as never, rarely, sometimes, often and almost always. All analyses were performed using IBM SPSS statistics (version 19). Confidence intervals of 95% were used to determine significant statistics and all p-values are two two-sided. In this study, listwise or case deletion was used to handling of missing data.

Ethical statement
This study was approved by the Ethics Board of Naresuan University (project number 402/59). Written informed consent for an interview and participation in the study was obtained from each of the subjects before the interview process.

Results
From a dataset of 2,936, 157 (3.4%) were discarded as they were missing important information, such as age (17 cases) and sleep data (140 cases). In total, data from 2,779 participants (1,385 cases and 1,394 controls), with a 92.6% response rate, were included in data analysis.
Most of the participants (81.9% of T2DM and 82.3% of non-T2DM) slept 7-9 hours per day (Table 2). However, in comparison to control group, there was a significantly higher proportion of diabetics whose sleep hours were ≤5 h, and ≥8 h (Table 2). Nearly all of the participants (93.1% for T2DM and 93.8% for non-T2DM) classified themselves to be morning people.
Stratified analysis found that a sleeping time of ≥8 hours was associated with women (OR = 1.27, 95% CI 1.03-1.56) and a high WHR (OR = 1.28, 95% CI 1.04-1.59) ( The behavior of the T2DM that was healthier compared to the control group was in watching television. There was a slightly    lower percentage of the T2DM group who reported watching television during their leisure time compared to the control group (76.1% vs. 78.7%). Further analysis using ordinal regression found BMI to be associated with walking, riding a bicycle and exercise (Table 5).

Discussion
Health behaviors among diabetic and non-diabetic groups Most of the participants in this study had a healthy sleep pattern and lifestyle. Over 80% of the participants usually sleep 7-9 hours per day, which is considered to be a healthy amount of sleep (Ip & Mokhlesi, 2007). However, a closer look revealed that there was a higher percentage of the T2DM group compared to the control group who sleep less than six hours per day (3.2% vs. 2.4%) or more than nine hours per day (9.3%% vs. 7.5%) ( Compared to the control group, the T2DM group were more overweight and had a higher BMI, (24.9 ± 4.7 vs. 23.8 ± 4.3), higher WHR (0.91 ± 0.14 vs. 0.90 ± 0.11) and higher WC (36.8 ± 11.8 vs. 35.6 ± 11.9) ( Table 1). This was not surprising since obesity is a well-established risk factor of diabetes. In an epidemiological study, a short sleep was associated with BMI and weight gain (Leproult & Van Cauter, 2010). In laboratory studies, sleep deprivation affected sympathovagal balance, evening concentrations of cortisol and ghrelin hormones or hunger hormones, but decreased glucose tolerance, insulin sensitivity and leptin, a hormone controlling body weight (Van Cauter & Knutson, 2008). These changes increase blood glucose (Nedeltcheva & Scheer, 2014) and appetite for carbohydrate-rich food (Ip & Mokhlesi, 2007).
Compared with other studies, the number of cigarette smokers (10.8% of T2DM and 14.3% of non-T2DM) and alcohol users (6.6% of T2DM and 9.7% of non-T2DM) in this study were relatively small. In the United States, the prevalence of cigarette smoking among adults with diabetes was about 23.6% (Ford et al., 2004). A study in California, United States, reported that 50% of participants in the diabetic group consumed alcohol (Ahmed et al., 2006).
Compared to the control group, the prevalence of smoking and alcohol consumption among the T2DM group was significantly lower (p<0.01). A recent study in Australia also reported a higher rate of smoking cessation (OR for quitting smoking = 2.71, 95% CI 1.59-4.63) among recently diagnosed T2DM as compared to a healthy control group (Chong et al., 2017). This behavior change was often claimed to be a result of diabetic care programs and global trends of cigarette and alcohol consumption (Shi et al., 2013).
Concerning eating behaviors and choice of food, the diabetic group trended to be healthier than the control group and were more likely to eat foods that are believed to be good for health. Compared to non-T2DM, there was a significantly higher percentage of the T2DM group who reported eating vegetables (91.5% vs. 89.0%, p=0.03) and chicken (16.8% vs. 13.5%; p=0.03) and the opposite was true for beef (34.7% vs. 40.6%, p<0.01) and eating more than a cup of rice per meal (37.4% vs. 42.6%, p<0.01). A similar study in Australia also found a lifestyle change among newly diagnosed diabetic patients (Chong et al., 2017).
However, it must be noted that there were a large portion of participants in both T2DM and non-T2DM groups who reported eating fruit (63.6% vs. 61.7%), sweets (40.6% vs. 38.5%), and drinking sweet soft drinks (46.4% vs. 50.7%). Eating these foods might affect blood sugar and sleep pattern.
Comparing the frequency of several physical activities performed during their leisure time, participants in the T2DM group were less active than the control group. A significantly higher number of T2DM participants admitted to being less active compared with people of the same age and not doing exercise or playing sports as often as the control group (Table 1). There were also fewer T2DM participants who reported doing walking (41.5% vs. 51.1%) and riding a bicycle (17.4% vs. 26.7%) during their leisure time. These results are supported by other studies. In a study in rural communities of Missouri, Tennessee, and Arkansas, it was reported that 37% of T2DM patients had no leisure-time physical activity (Deshpande et al., 2005). Hays and Clark (1999) also found that over half of T2DM (54.6%) patients, mostly elderly females, had no weekly physical activity. However, a study in Nepal reported that 52% of diabetic patients were moderately active and 28% were highly active (Kadariya & Aro, 2018). This discrepancy in physical activity might be related to the culture and lifestyle of the patients. It was expected that study participants in this study would be more active because they are rural people who mainly work in agriculture.
Further ordinal regression analysis revealed that physical activities were associated with obesity (BMI). Those with a lower BMI trended to have a higher rate of walking, biking and exercising (Table 5). This result is well consistent with literature.
A study on outpatients with T2DM and their matched controls found that the total energy expenditure (<300 kcal/day), number of steps (<1500 /day), physical activity duration (<130 min/day) and active energy expenditure/day (<300 kcal) were all lower in the diabetic group (p<0.05) (Fagour et al., 2013;Hamasaki, 2016). This lower physical activity might be partially due to a fear of joint or leg pain (Dutton et al., 2005) or hypoglycemia (Brazeau et al., 2008).

Association between diabetes and sleep
After applying several grouping methods, the association was found to be significant only when sleep duration was grouped to ≤5 hours, 6-7 hours and ≥8 hours. In comparison to the 6-7 hours group, further analysis using logistic regression found that a significant association between diabetes and sleep was only among women with ≥8 hours of sleep (OR = 1.27, 95% CI 1.03-1.56) after being adjusted for age, gender, WHR and lifestyle (Table 3). A similar result was also reported in a study in Finland, which found that sleep of ≥8 hours increased the risk of diabetes among middle aged women (Tuomilehto et al., 2008). The effect of oversleeping on the risk of developing diabetes is well established, but most of the previous studies use 7-8 hours of sleep as a reference and defined oversleep to be 10-12 hours per day (Chattu et al., 2019). It was also noticed that the relative risk of diabetes in this study (OR = 1.27) was rather low compared with those reported in previous studies, which mostly reported sleep to increase diabetic risk by 2-3 times (Heianza et al., 2014;Yaggi et al., 2006). These results might be explained by the fact that sufficient sleep depends on both quantity and quality of sleep (Chattu et al., 2019). Since most participants in this study are rural villagers with a healthier lifestyle, they were more likely to have a good sleep and thus, require a shorter sleep duration. This was supported by the fact that only approximately 7% of the participants (T2DM and non-T2DM) who classified themselves to be evening people that like to stay up late at night (Table 1).
It was found that less than 7% of the study participants are evening people and the association between diabetes and lifestyle was not statistically significant. These results were in contrast to previous studies. In a large study in Korea, there was a large proportion of people who were evening people, and this group was at risk of diabetes (OR = 1.73, 95% CI 1.01-2.95). (Yu et al., 2015). The differences in workload, lifestyle, social activities and technology might affect sleep patterns of the two groups. Most of the participants in this study were rural villagers, while those in the Korean study were urban people with a modern lifestyle. In Thailand, most villagers usually go to bed early after being exhausted from hard, physical work on the farm during the day and they usually wake up early in the morning to have enough time to prepare food for their family members and the Buddhist monks.

Study limitations
One of the main limitations of this study was that it used a cross-sectional design and there was a lack of data on diabetes onset. Since the relationship between diabetes and sleep is double-sided, it therefore cannot be determined whether sleep causes diabetes or the disease interferes with the sleep pattern of the patient (Chattu et al., 2019). This bias will cause a positive effect and overestimate the association of diabetes and sleep. Without data on diabetic onset, behavior change and disease duration cannot be analyzed. This is also true of the effect of sleep duration on glucose control. These two issues are often reported in literature (Chong et al., 2017).

Conclusion
In conclusion, this study revealed that diabetic patients in a rural community in Thailand had healthy behaviors regarding sleep, lifestyle, eating, cigarette smoking and alcohol consumption. However, they tended to play sports, walk or ride a bicycle during their leisure time less often than the control group with a similar gender and age. This study also found that sleep of ≥8 hours increases the risk of diabetes as compared to sleep of 6-7 hours. Sleep was significantly related to gender, lifestyle and obesity. Those with high BMIs tended to have low levels of physical activity during their leisure time. In addition to weight control, diabetic prevention programs should emphasize and promote healthy sleep patterns and exercise, especially among women. More research on different societies and lifestyles are required before the effect of oversleeping on diabetes risk can be clearly understood.

Grant information
This study was supported by Naresuan University [R2560C031].
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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