Comparison of sleep and health behaviours among people with diabetes and a nondiabetic group in Phitsanulok, Thailand: a cross-sectional study [version 3; peer review: 1 approved, 1 approved with reservations]

Background: Type 2 diabetes mellitus (T2DM) is a global public health problem. To avoid disease complications, people with diabetes have to control their blood glucose and maintain a healthy lifestyle including a healthy diet, weight control, moderate exercise, and smoking cessation. Methods: This study aimed to survey sleep, eating, and exercise behaviours of people with diabetes in the Bang Rakam district, a rural community in Phitsanulok province, Thailand. The data on sleep and other health behaviours 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. Results: Compared to the control group, the people with diabetes had a significantly higher body mass index (BMI). However, only a few of them 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 people with diabetes also reported eating


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 behaviours and exercise (Zheng et al., 2018). Eating behaviours and food choices can directly affect the glucose levels in the blood. People who eat a diet high in fat, calories, and cholesterol are at risk of obesity and diabetes (Nieto-Martínez et al., 2017). However, food choices depend not only on behaviour but also on socio-economic status. Research found that low-income people often experience the problem of food insecurity, and are unable to access sufficient, safe, and nutritious food (Gucciardi et al., 2014). Exercise plays an important role for helping to control diabetes, by not only improving body fitness, it also improves blood glucose levels and increases insulin sensitivity (Colberg et al., 2016). 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 diabetes 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).
Sleep, eating, and exercise among T2DM were interrelated. Inadequate dietary habits and physical inactivity increase risk of obese, a well-established risk factor for diabetes. Recent research also found poor sleep to increase obesity by affecting hunger hormones, glucose tolerance, insulin sensitivity, and a hormone controlling body weight (Van Cauter & Knutson, 2008) (Chattu et al., 2019(Ip & Mokhlesi, 2007. People with sleep deprivation, therefore, eat more carbohydrate-rich food and easily gain weight (Nedeltcheva & Scheer, 2014) (Ip & Mokhlesi, 2007). Studies also found that people with diabetes especially those with high BMI often lack physical activity (Fagour et al., 2013;Hamasaki, 2016) because they worry about joint and leg pain or hypoglycaemia (Dutton et al., 2005) (Brazeau et al., 2008. 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 yearolds 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, the disease itself can interfere with sleep and cause sleep apnoea among people with diabetes (Barone & Menna-Barreto, 2011;Resnick et al., 2003). Poor sleep is often found among people with diabetes 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-T2DM 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 people with diabetes 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, people with diabetes 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 behaviours of people with diabetes are 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 behaviours such as sitting, walking, moderate to vigorous physical activity (MVPA) and vegetable and fruit consumption (Chong et al., 2017).
Thailand was ranked 7th among Western Pacific countries with the highest number of T2DM with approximately 10% of the population having the disease (Tunsuchart et al., 2020).
A nationwide survey revealed that about 64% of the T2DM had poor glycaemic control (HbA1c ≥ 7%). Those factors affected were gender, age, access to health services, type of health insurance, duration of diabetes diagnosis, and other comorbidity diseases, e.g., obesity, and hypertension (Sakboonyarat et al., 2021). 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. In 2017, The district of BangRakam, Phitsanulok province has about 4,942 cases (8.9%) of T2DM (Polpuak & Chutipanyapron, 2018).
Currently, there is no information on health behaviours, sleep duration, and lifestyle of people with diabetes (T2DM) in Thailand. This study aimed to survey the sleep, eating and exercise behaviours of people with diabetes in a rural community in Phitsanulok province, Thailand. The community was selected based on the number of people with diabetes. The predictive factors of sleep and other health behaviours were also investigated. The results will be useful for local diabetes care programs and comparative studies worldwide.

Objectives
1. To explore sleep, eating and exercise behaviours among people with diabetes (T2DM) and people without diabetes (non-T2DM).
2. To identify factors that affect sleep and exercise among T2DM group.
3. To determine the association between diabetes and sleep duration.

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

Study data
This study utilized data from a previous case-control study on diabetes and pesticide exposure (Juntarawijit & Juntarawijit, 2018). The data on health behaviours were collected from February to May 2016 from people with diabetes (T2DM) and a non-T2DM control group living in Bang Rakam, Phitsanulok.

Study participants
The people with diabetes (T2DM) 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 people with diabetes 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 T2DM case, one healthy control (non-T2DM) who was free from diabetes 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 neighbours of the people with diabetes matched for gender and age (± five years.).

Study questionnaire
In addition to demographic information, data on sleep duration and other health behaviours were collected using an interviewer-administered 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 the home of each participant. The participants' self-reported sleep duration was collected using the question "How many hours do you usually sleep per night?". 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. In this study, a modified Food Frequency Questionnaire (FFQ) was used (Barrat et al., 2012). The questionnaire was tested against a 7-day diet record and found to have high repeatability and good validity score (repeatability for intake was 0.62-0.90 (median 0.81), relative validity was 0.36-0.80 (0.64) (Barrat et al., 2012). Only types of foods found to be related to diabetes and those often found in Thailand were included in the survey. 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). A modified Baecke Habitual Physical Activity Questionnaire (BHPAQ) was used. The instrument had been adapted, and validated for use as a self-administered and self-evaluating instrument for measuring the physical activity of the past 12 months for many countries in various age groups (Florindo & Latorre, 2003). Body mass index (BMI) was calculated by dividing body weight (in kg) by height (in meters squared). The height 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 behaviours were analysed 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 analysed 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.
** Sweating refers to an excessive sweat from exercise or physical activity but not from hot climate or health problems.
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 behaviour of the people with diabetes 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).  for T2DM and 93.8% for non-T2DM) classified themselves to be morning people.
Logistic regression analysis found a significant association between diabetes and a sleeping time of ≥8 hours (OR = 1.21,

Discussion
The main objective of this study was to survey the sleep, eating and exercise behaviours of people with diabetes (T2DM) in a rural community in Phitsanulok province, Thailand. It was found that study participants had healthy behaviours regarding eating, smoking, alcohol consumption, sleep pattern and duration, but not for physical activities and exercise. These results contradicted the common perception that people with diabetes all have unhealthy lifestyles. A large study in Australia reported no positive improvements among the recently diagnosed T2DM in their lifestyle behaviours, such as physical activities and fruit and vegetable consumption (Chong et al., 2017). These findings implied that lifestyle prevention of diabetes (especially type 2 diabetes) included healthy lifestyle based on dietary patterns alone, may not be sufficient and should always include physical activities as an integral part.
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  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 people with diabetes 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 behaviour change was often claimed to be a result of diabetes care programs and global trends of cigarette and alcohol consumption (Shi et al., 2013).
Concerning eating behaviours and choice of food, people with diabetes trended to be healthier than the control group and were more likely to eat foods that are believed to be good for health. The group ate more vegetables and chicken but less beef and rice than the control group. A similar study in Australia also found a lifestyle change among newly diagnosed T2DM group (Chong et al., 2017). These results may be useful for the diabetic prevention program, by advising their patients to eat good quality and more healthy foods. However, it must be noted that there were a large portion of participants in both T2DM and non-T2DM groups who reported eating or drinking fruit, sweets, and soft drinks, a behaviour that might affect their blood sugar.
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 (  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 T2DM 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 indicated that physical activity were associated with obesity (BMI). Those with a lower BMI tended to have a higher rate of walking, biking and exercising (Table 5). This result is consistent with the 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 diabetes 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 hypoglycaemia (Brazeau et al., 2008). Therefore, this problem requires greater attention, and exercise plans specifically designed for those with diabetes need to be developed. A low impact activity, e.g. walking, swimming, biking or doing yoga are recommended.
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 diabetes 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). Therefore, the results supported the previous finding on the association between diabetes and sleep.
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 as they are 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. Further study should be conducted to verify this issue.
One of the main limitations of this study was that it used a cross-sectional design and there was a lack of data on the onset of diabetes. 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 the onset of diabetes, behavioural change and disease duration cannot be analysed. This is also true of the effect of sleep duration on glucose control. These two issues are often reported in the literature (Chong et al., 2017). This study only collected data regarding sleep quantity, while previous studies have suggested that both the duration and quality of sleep could have an effect on diabetes (Lee et al., 2017). Lastly, the information regarding sleep duration and other potential risk factors were self-reported and therefore, recall bias was likely to occur. However, if the bias does occur, it could equally affect both the study and the comparison group.

Conclusion
This study found the people with diabetes (T2DM) in the rural community of Thailand had healthy behaviours regarding sleep duration, sleep pattern, lifestyle, eating, smoking and alcohol consumption, except exercise and physical activity.
The findings here contrast with the common perception that people with diabetes have bad lifestyle patterns, instead it shows that a healthy lifestyle pattern based on dietary patterns alone may not be sufficient and that lifestyle prevention of diabetes should always include physical activity as an integral part. The study also found an association between diabetes and oversleeping as previously reported in the literature. Further studies on the health behaviour of people with diabetes with different backgrounds and lifestyles are still needed.

Ahmad Alkhatib
Teesside University, Middlesbrough, UK This a very good article, which is needed to demonstrate the exercise and nutritional prevention of diabetes by understanding those behaviours in a group of individuals with or without diabetes in Thailand.
The article is well written in many parts. However, its significance is not clear in the way it has been written, so I suggest a major revision to incorporate some amendments in order to be accepted for indexing. Once done, I am happy to look over it again.

Generic:
Please check the English and grammar throughout. ○ Specific: Abstract: "Diabetic prevention programmes", should be "diabetes prevention programs". ○ Terminology use "people with diabetes" rather than "diabetic".

Introduction:
Expand a little on eating behaviour and diabetes and try make a link on why/why not such factors may be important for communities with a similar lifestyle to those tested here.

Conclusion:
Avoid repetitions "in conclusion" or "this study reveals". It needs to go straight to the point and concise. ○ Again: the conclusion seems to repeat the findings. Please state the significance of your findings rather than repeating them.

○
The findings here contrast with the common perception that people with diabetes have bad lifestyle patterns, instead it showed that a healthy lifestyle pattern based on dietary patterns alone may not be sufficient and that lifestyle prevention of diabetes (esp. T2D) should always include physical activity as an integral part.

○
The authors could also make specific suggestions for how to implement physical activity in the rural community they tested based on the finding that some have better physical activity than others.
○ A conclusion about the sleep patterns finding is missing and should be added.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?
Terminology use "people with diabetes" rather than "diabetic".

Response:
The errors about the terminology use have been corrected.

Introduction: Comment:
Expand a little on eating behaviour and diabetes and try make a link on why/why not such factors may be important for communities with a similar lifestyle to those tested here.

Response:
More information on eating behaviors and diabetes have been added as suggested.

Comment:
Expand a little with statements showing some mechanisms linking the sleep/eating/exercise habits with the physiology of diabetes and its development.

Response:
A statement on the mechanism linking eating, exercise and diabetes has been added. For sleep and diabetes, it was reported that sleep deprivation increases insulin resistance and indirectly affect diabetes by increasing appetite and body weight. This information has already been presented in the second paragraph.

Comment:
Add a paragraph to justify why and what is special about why the rural communities in Thailand have been tested here. The reader needs to understand the link between these.

Response:
Actually, there were no special characteristics, the community was selected because it has a high number of people with diabetes, and it was in a convenient location.

Comment:
The last paragraph needs to have a statement about what is not known and how this study is adding to such knowledge.

Response:
Introduction has been revised as suggested (see the manuscript). In the last paragraph, a statement about what is not known has been added.

Methods: Comment:
Questionnaire: Specify what type of interview in the 1st paragraph.

Response:
Information on the types of interviews are specified in the first paragraph.

Comment:
Sleep questions, is there a valid sleep questionnaire used here? Why only one question? How do you differentiate between deep sleep/etc.? Did the interview cover more than the one question asked? Details are needed.

Response:
Yes, we only asked the participants how long they usually slept each day. For information regarding quality of sleep, we believed that it was rather subjective and hard to justify, therefore, it was not included in the survey. The questionnaire method has been widely used to collect information on sleep duration (Yaggi, Araujo, andMcKinlay, 2006) Chong et al., 2017) (Yu et al., 2015). However, we accept that this will be another important limitation of the study and the issue has been further discussed in study limitations.

Comment:
Data on food consumption requires justification, what type of questionnaire has been used or developed for this purpose? Why was there a focus on selected foods?
The questionnaire was used in previous studies Response: In this study, a modified Food Frequency Questionnaire (FFQ) was used. The questionnaire was validated and used in previous studies, e.g. Barrat et al. (2012). Due to different cultures and eating habits, only foods found to relate to diabetes and those often found in Thailand were selected. This information has been added to the method section.

Comment:
Frequency of certain physical activities. What physical activity questionnaire was used? Was it IPAQ recall? Any validated questionnaire or justification of the questions selected?

Response:
In this study, a modified Baecke Habitual Physical Activity Questionnaire (BHPAQ) was used and the questionnaire has been validated in previous studies (Florindo and Latorre, 2003). Yes, the questionnaire method might cause recall bias. However, if the problem occurs, it should equally affect both the study and comparison group and thus, not seriously affect the results. The issue has been added in study limitations.

Results: Comment:
It is not clear why Table 3 has many gaps. Is it to do with insufficient number?

Response:
In the first row of the table, the association (ORs) between sleep hours and diabetes in different models were presented. The association was then further analyzed using only data from model 3 (adjusted for all potential confounding factors) in regard to gender, by carrying out a comparison between males and females.

Discussion: Comment:
Please add a paragraph to start with, which should clearly state the most important findings and discuss what is novel.

Response:
A new paragraph which states the most important findings, has been added to the discussion section as suggested.

Comment:
Many paragraphs seem to repeat what is in the results. All paragraphs should have concluding statements stating what is new and how it is relevant for the field.