Factors Affecting HbA1c According to Sleep Duration in Adults with Diabetes
1
Department of Nursing, Daejeon University, Daejeon 34520, Republic of Korea
2
Department of Nursing, Korea National University of Transportation, Chungbuk, Jeungpyeong-gun 27909, Republic of Korea
3
Department of Nursing, Woosuk University, Jeonbuk, Wanju-gun 55338, Republic of Korea
*
Authors to whom correspondence should be addressed.
Diabetology 2025, 6(6), 46; https://doi.org/10.3390/diabetology6060046
Submission received: 31 March 2025
/
Revised: 8 May 2025
/
Accepted: 26 May 2025
/
Published: 1 June 2025
Abstract
:Background/Objectives: This study investigated factors affecting glycated hemoglobin (HbA1c) levels according to sleep duration in adults with diabetes. HbA1c is an important indicator for the diagnosis and management of diabetes, and lowering this value is important for reducing the risk of complications. Recent studies have shown that sleep duration and quality play important roles in controlling blood sugar levels in patients with diabetes. Therefore, we aimed to analyze the factors affecting HbA1c levels according to sleep duration in adult patients with diabetes and propose a personalized diabetes management strategy. Methods: This was a secondary analysis of data from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted between 2022 and 2023. The study included 1363 adults aged ≥30 years who were diagnosed with diabetes by a doctor. The participants were categorized into three groups based on their sleep duration: <7 h, 7–9 h, and ≥9 h. Results: The significant factors affecting HbA1c levels varied according to sleep duration. Age, drinking, and stress were significant for those who slept for <7 h. For those sleeping 7–9 h, energy intake, protein intake, fat intake, and education level were significant. Health checkups and drinking were significant for those who slept for >9 h. Conclusions: This study suggests that sleep duration is an important variable in diabetes management and should be considered in personalized diabetes management strategies. Future studies should explore various factors related to sleep patterns in greater depth.
1. Introduction
Diabetes mellitus (DM) is a disorder characterized by the impaired metabolism of carbohydrates, fats, and proteins, owing to defects in insulin secretion. Reduced production and secretion of insulin lead to elevated blood glucose levels, resulting in chronic hyperglycemia in affected individuals [1]. This persistently high blood sugar level can lead to a wide range of long-term complications [2].
Common complications include cardiovascular diseases, nephropathy, retinopathy, and neuropathy. These complications significantly affect the quality of life of individuals with diabetes and pose substantial challenges to healthcare systems worldwide. Moreover, diabetes is an important risk factor for heart failure, showing a more than two-fold higher incidence rate than in those without diabetes and becoming a major cause of death [3].
The global prevalence of diabetes among adults has shown a significant increase, doubling from 7% in 1990 to 14% in 2022. This alarming trend is further exacerbated by the fact that 59% of adults with diabetes aged 30 and above are not receiving treatment [4]. These findings indicate a potentially severe public health crisis.
HbA1c is essential for diagnosing and screening diabetes and serves as a critical indicator for diabetes management [5]. Maintaining HbA1c levels below 7% has been reported to reduce the risk of complications such as retinopathy, nephropathy, neuropathy, and cardiovascular disease [6]. Therefore, managing HbA1c remains the ultimate goal of diabetes management.
Furthermore, although the importance of HbA1c management is well established, it is crucial to explore the factors influencing this key biomarker. Recent research has highlighted sleep as a significant contributor to glycemic control. Studies have shown that sleep duration and quality are strongly associated with glycated hemoglobin (HbA1c) levels in patients with type 2 diabetes [7].
Sleep duration and quality play crucial roles in glucose metabolism. Excessive and insufficient sleep, as well as various sleep disorders, have been associated with disturbances in glucose homeostasis [8]. Research has consistently demonstrated a U-shaped relationship between sleep duration and glycemic control, with both short and prolonged sleep durations linked to elevated levels of HbA1c [9]. These findings underscore the critical role of sleep in diabetes management and suggest that addressing sleep insufficiency is an integral part of comprehensive diabetes care. Further investigation of sleep patterns and their impact on glycemic control could potentially unveil new strategies for improving HbA1c levels and overall diabetes outcomes.
Previous studies on glycemic control in diabetes management have predominantly focused on isolated dietary variables without considering the mediating role of sleep patterns. A previous study [10] demonstrated that a low-carbohydrate diet reduced HbA1c by 1.2%; however, their analysis neglected to consider how sleep duration might modify this relationship. Current evidence suggests that both short and long sleep durations are independently associated with poor glycemic control. However, a critical gap remains in understanding how sleep duration interacts with other determinants of HbA1c. These include meal timing variability, nutrient distribution patterns, and health-related behaviors such as weight fluctuation, physical activity, and alcohol consumption. Furthermore, few studies have examined how the influence of psychosocial factors, including subjective health perception, stress, and anxiety, on HbA1c levels varies across different sleep duration categories.
This study aimed to address these limitations by analyzing how the factors affecting HbA1c levels differ according to sleep duration among adults with diabetes. Using regression analysis, we sought to identify whether dietary behaviors, physical activities, and psychosocial variables exhibited differential associations with glycemic control across the short, normal, and long sleep duration groups. These findings may reveal sleep-specific pathways of glycemic regulation, potentially enabling more targeted interventions that consider sleep duration a critical modifier in personalized diabetes management strategies.
2. Materials and Methods
2.1. Study Design
This study was a secondary analysis of data from the Korea National Health and Nutrition Examination Survey (KNHANES), 2023, of the Korea Disease Control and Prevention Agency. This was a descriptive research study that identified the factors affecting HbA1c according to sleep duration.
2.2. Data Source and Participants
The KNHANES is a statutory survey on the health-related behavior and prevalence of chronic diseases in the nation and is conducted annually by the Korea Disease Control and Prevention Agency after receiving approval from the Research Ethics Review Committee. In compliance with the Personal Information Protection Act and the Statistics Act, the Korea Disease Control and Prevention Agency provides only de-identified data that cannot be used to infer individuals from survey data. The data for this study were downloaded from the Korea National Health and Nutrition Examination Survey website for research purposes with permission from the Korea Disease Control and Prevention Agency.
The total number of participants in the KNHANES in 2022 and 2023 was 13,194. Among them, 1363 adults aged 30 years were diagnosed with diabetes by a doctor. Sleep duration was <7 h in 556 participants, 7–9 h in 616, and ≥9 h in 191.
2.3. Study Variables
Sleep duration was categorized as <7 h (short), 7–9 h (appropriate), and ≥9 h (long) [11].
Diabetes- and diet-related factors included diabetes treatment (yes or no); HbA1c (%); meal frequency over the past year (0, 1–2, or ≥3 times per week for breakfast, lunch, and dinner); use of nutrition labels (yes or no); daily energy intake (kcal); daily protein intake (g); daily fat intake (g); daily carbohydrate intake (g); and daily sugar intake (g).
Sociodemographic factors included sex (men, women), age (30–49, 50–69, 70 years or older), household income (upper, middle, lower), education level (high school graduate or less, college graduate or higher), marital status (living with spouse, others), economic activity (yes or no), and health checkups (yes or no).
Physical and psychological factors included weight change over the past year (weight loss, weight gain, or no change), drinking (yes or no), current smoking (yes or no), sitting time per day (h/d), aerobic physical activity (yes or no), subjective health (good, average, or bad), anxiety, body mass index (BMI), and stress (feeling little or a lot). For drinking, lifelong non-drinking or drinking less than one glass per month in the past year was classified as no, and drinking more than one glass per month in the past year was classified as yes. Aerobic physical activity was classified according to whether individuals engaged in at least 2 h 30 min of moderate-intensity physical activity per week, 1 h and 15 min of vigorous-intensity activity per week, or a combination of both (1 min of high intensity equals 2 min of moderate intensity) for an equivalent amount of time per week. Anxiety was assessed using the Generalized Anxiety Disorder-7 (GAD-7) scale developed by Spitzer et al. [12]. The GAD-7 is a 4-point Likert scale with seven items that measures the frequency of symptoms experienced over the past two weeks. Scores range from 0 to 21, with higher scores indicating more severe anxiety. BMI was classified as <18.5 kg/m2 (underweight), 18.5–22.9 kg/m2 (normal weight), 23–24.9 kg/m2 (overweight), and 25 kg/m2 (obesity) based on the criteria for the Asia–Pacific region [13].
2.4. Ethical Consideration
The 2022 and 2023 KNHANES were conducted with the approval of the Research Ethics Review Board (2018-01-03-4C-A, 2022-11-16-R-A). The raw data of the KNHANES are anonymized to protect personal information, and to use the raw data of the KNHANES, a security pledge must be written and permission from the Korea Disease Control and Prevention Agency is required. This study downloaded and analyzed the data with permission from the Korea Disease Control and Prevention Agency.
2.5. Statistical Analysis
Analyses were performed after generating a complex sample plan file by assigning weights using IBM SPSS 27.0 (IBM Corp., Armonk, NY, USA). Complex sample analysis can reduce hidden sampling biases or errors because complex sample surveys identify and collect data from population units using multiple selection steps. The Korea Disease Control and Prevention Agency recommends that analyses be conducted by reflecting the elements of a complex sample design. This study analyzed the data using the chi-square and regression analyses of a complex sample, as recommended by the Korea Disease Control and Prevention Agency. The significance level was set at 0.05.
3. Results
3.1. Differences in Diabetes- and Diet-Related Factors According to Sleep Duration
Analysis of differences in diabetes- and diet-related factors according to sleep duration revealed differences in dinner frequency, diet, daily energy intake, protein intake, and fat intake between the groups (Table 1).
The group with 7–9 h of sleep ate the most dinner and had the highest energy, protein, and fat intake. The group with ≥9 h of sleep used the most dietary therapy.
3.2. Differences in Sociodemographic Factors According to Sleep Duration
Differences between groups were observed in all sociodemographic factors (Table 2).
The group that slept for <7 h included more women, more individuals aged 50–64 years, and higher household income. The group that slept for >7 h and <9 h had the highest level of education, lived with a spouse, engaged in economic activities, and received the most health checkups.
3.3. Differences in Physical and Psychological Factors According to Sleep Duration
Regarding the physical and psychological factors, significant differences were observed between the groups in terms of weight change, drinking, sitting time, and stress (Table 3).
The group that slept for <7 h had more weight gain, longer sitting time, and had higher stress. The group that slept for >7 h and <9 h drank the most alcohol.
3.4. Affecting Factors of HbA1c According to Sleep Duration
In the group that slept for <7 h, age, drinking, and stress were significant factors affecting HbA1c (Table 4). Compared with the group over 70 years of age, the 30–49 and 50–69 age groups showed an increase in HbA1c as age increased, and the HbA1c of those who drank alcohol increased more than those who did not drink alcohol. In the case of stress, the HbA1c level of those who drank less decreased more than that of those who drank a lot.
In the group that slept for 7–9 h, energy intake, protein intake, fat intake, and education level were significant factors affecting HbA1c. The higher the energy intake, the higher the HbA1c, and the higher the protein and fat intake, the lower the HbA1c level. In terms of education, HbA1c levels decreased more in participants with a high school diploma or lower than in those with a college degree or higher.
In the ≥9 h sleep group, health checkups and drinking significantly affected HbA1c. HbA1c decreased more in those who underwent health checkups than in those who did not and decreased more in participants who drank than in those who did not.
4. Discussion
This study analyzed the factors affecting HbA1c levels according to sleep duration in adults with diabetes. The results of this study indicated that HbA1c levels increased with increasing age, drinking, and stress in the group with a sleep duration of <7 h. In the group with a sleep duration of 7–9 h, lower energy intake, higher protein intake, and higher fat intake were associated with decreased HbA1c. Additionally, HbA1c levels decreased in this group when the level of education was lower than high school. In the group with a sleep duration of >9 h, undergoing a health examination and alcohol consumption were linked to a further decrease in HbA1c.
In a previous study, a U-shaped relationship was reported, and sleep durations of <7 h or >8 h increased HbA1c levels [14]. Similarly, in this study, the group with a sleep duration of 7–9 h had the highest dinner frequency, protein intake, and fat intake. This suggests that appropriate sleep duration can affect eating habits and nutrient intake, resulting in positive changes in HbA1c levels. However, in this study, the group with ≥9 h of sleep had the highest dietary therapy use. Although studies have reported that insufficient sleep is related to unhealthy eating habits and interferes with regular eating patterns [15], evidence on whether long sleep supports healthy eating habits is limited. However, as sleep and diet are significantly related [15,16,17], additional studies are needed to determine how factors such as sleep quality, circadian rhythm, and dietary compliance affect their relationship with HbA1c.
In this study, HbA1c was higher in the <7 h sleep group, but in the ≥9 h sleep group, HbA1c was lower with alcohol consumption. Insufficient sleep increases cortisol, a stress hormone that plays a role in raising blood sugar and, over time, HbA1c levels [18,19,20]. Insufficient sleep causes greater stress, and as sleep time increases, stress awareness decreases [21,22]. Studies have reported that low stress helps improve HbA1c levels because it is easier to control blood sugar levels [23,24]. Additionally, moderate drinking lowers HbA1c levels, which contributes to improved insulin sensitivity [25,26]. In the ≥9 h sleep group, the HbA1c level was lower in those who underwent health checkups than in those who did not. Excessive sleep time increases the risk of obesity, hypertension, diabetes, and cardiovascular disease [27]; however, HbA1c levels can be improved by performing health checkups more frequently or by combining sleep with a healthy diet and exercise. Therefore, even if you sleep for a long time and maintain a healthy lifestyle, moderate drinking may help manage your blood sugar level. To confirm clear evidence for this, further research on the relationship between sleep patterns, alcohol consumption, and health-related behaviors is necessary. Drinking also affects dietary control [28,29], and additional research is needed because insulin sensitivity after drinking can be increased, or HbA1c can be lowered in combination with other dietary factors.
This study analyzed KNHANES data, and since it was conducted as a cross-sectional study, it has the limitation that it is difficult to clearly identify the causal relationship between variables. However, the analysis in this study confirmed that sleep duration has a significant effect on diabetes management. In addition, sleep duration was closely related to various factors and overall health-related behaviors. Therefore, sleep duration should be considered an important variable in diabetes management, and a customized approach that considers the lifestyle patterns and sleep habits of patients with diabetes is necessary. In future studies, it will be necessary to analyze the interaction of sleep patterns, including sleep time, HbA1c levels, and health-related behaviors, and to clarify the causal relationship through a longitudinal study.
5. Conclusions
This study analyzed factors affecting HbA1c levels according to sleep duration in adults with diabetes. These findings reveal that sleep duration plays a significant role in glycemic control, with different influencing factors identified in each sleep duration group. For individuals with <7 h of sleep, age, drinking, and stress significantly determined HbA1c levels. In contrast, those with 7–9 h of sleep showed associations between HbA1c and energy, protein, fat, and education levels. Finally, for individuals with ≥9 h of sleep, health checkups and drinking were significant factors.
This study emphasizes that sleep duration is an important variable in diabetes management and suggests that sleep duration should be considered in personalized diabetes management strategies. These findings may contribute to improving the HbA1c levels in patients with diabetes and preventing long-term complications.
Author Contributions
Conceptualization, M.K., S.A.K. and J.K.; methodology, M.K. and S.A.K.; formal analysis, M.K.; writing—original draft preparation, S.A.K., M.K. and J.K.; writing—review and editing, M.K., S.A.K. and J.K.; and visualization, J.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The 2022 and 2023 KNHANES were conducted with the approval of the Research Ethics Review Board (2018-01-03-4C-A, 2022-11-16-R-A).
Informed Consent Statement
Written informed consent was secured from all KNHANES participants before their involvement in the survey. The KNHANES is implemented by the Korea Disease Control and Prevention Agency under the legal framework established by the National Health Promotion Act of Korea.
Data Availability Statement
KNHANES data are publicly accessible. The data can be accessed and downloaded from the KNHANES homepage (URL: https://knhanes.kdca.go.kr/knhanes/rawDataDwnld/rawDataDwnld.do#/, accessed on 10 January 2025).
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| KNHANES | The Korea National Health and Nutrition Examination Survey |
| HbA1c | Glycosylated Hemoglobin A1c |
| GAD-7 | Generalized Anxiety Disorder-7 |
| BMI | Body Mass Index |
References
- Reed, J.; Bain, S.; Kanamarlapudi, V. A review of current trends with type 2 diabetes epidemiology, aetiology, pathogenesis, treatments and future perspectives. Diabetes Metab. Syndr. Obes. 2021, 14, 3567–3602. [Google Scholar] [CrossRef] [PubMed]
- Roden, M.; Shulman, G.I. The integrative biology of type 2 diabetes. Nature 2019, 576, 51–60. [Google Scholar] [CrossRef] [PubMed]
- Kodama, S.; Fujihara, K.; Horikawa, C.; Sato, T.; Iwanaga, M.; Yamada, T.; Kato, K.; Watanabe, K.; Shimano, H.; Izumi, T.; et al. Diabetes mellitus and risk of new-onset and recurrent heart failure: A systematic review and meta-analysis. ESC Heart Fail. 2020, 7, 2146–2174. [Google Scholar] [CrossRef]
- World Health Organization. Urgent Action Needed as Global Diabetes Cases Increase Four-Fold over Past Decades. Available online: https://www.who.int/news/item/13-11-2024-urgent-action-needed-as-global-diabetes-cases-increase-four-fold-over-past-decades (accessed on 3 March 2025).
- Tiwari, D.; Aw, T.C. The 2024 american diabetes association guidelines on standards of medical care in diabetes: Key takeaways for laboratory. Explor. Endocr. Metab. Dis. 2024, 1, 158–166. [Google Scholar] [CrossRef]
- Nathan, D.M. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: Overview. Diabetes Care 2014, 37, 9–16. [Google Scholar] [CrossRef]
- Brouwer, A.; Van Raalte, D.H.; Rutters, F.; Elders, P.J.; Snoek, F.J.; Beekman, A.T. Sleep and HbA1c in patients with type 2 diabetes: Which sleep characteristics matter most? Diabetes Care 2020, 43, 235–243. [Google Scholar] [CrossRef] [PubMed]
- Buxton, O.M.; Marcelli, E. Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovas-cular disease among adults in the United States. Soc. Sci. Med. 2010, 71, 1027–1036. [Google Scholar] [CrossRef]
- Lee, S.W.H.; Ng, K.Y.; Chin, W.K. The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: A systematic review and meta-analysis. Sleep Med. Rev. 2017, 31, 91–101. [Google Scholar] [CrossRef]
- Dorans, K.S.; Bazzano, L.A.; Qi, L.; He, H.; Chen, J.; Appel, L.J.; Chen, C.S.; Hsieh, M.H.; Hu, F.B.; Mills, K.T.; et al. Effects of a low-carbohydrate dietary intervention on hemoglobin A1c: A randomized clinical trial. JAMA Netw. Open 2022, 5, e2238645. [Google Scholar] [CrossRef]
- Kim, S.Y.; Kim, H.J. Obesity risk was associated with alcohol intake and sleep duration among korean men: The 2016–2020 Korea National Health and Nutrition Examination Survey. Nutrients 2024, 16, 3950. [Google Scholar] [CrossRef]
- Spitzer, R.L.; Kroenke, K.; Williams, J.B.; Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 2006, 166, 1092–1097. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. The Asia-Pacific Perspective: Redefining Obesity and Its Treatment; World Health Organization: Geneva, Switzerland, 2000. [Google Scholar]
- Liu, H.; Zhu, H.; Lu, Q.; Ye, W.; Huang, T.; Li, Y.; Li, B.; Wu, Y.; Wang, P.; Chen, T.; et al. Sleep features and the risk of type 2 diabetes mellitus: A systematic review and meta-analysis. Ann. Med. 2025, 57, 2447422. [Google Scholar] [CrossRef] [PubMed]
- Theorell-Haglöw, J.; Lemming, E.W.; Michaëlsson, K.; Elmståhl, S.; Lind, L.; Lindberg, E. Sleep duration is associated with healthy diet scores and meal patterns: Results from the population-based EpiHealth study. J. Clin. Sleep. Med. 2020, 16, 9–18. [Google Scholar] [CrossRef]
- Barragán, R.; Zuraikat, F.M.; Tam, V.; RoyChoudhury, A.; St-Onge, M.P. Changes in eating patterns in response to chronic insufficient sleep and their associations with diet quality: A randomized trial. J. Clin. Sleep. Med. 2023, 19, 1867–1875. [Google Scholar] [CrossRef] [PubMed]
- Zuraikat, F.M.; Wood, R.A.; Barragán, R.; St-Onge, M.P. Sleep and Diet: Mounting Evidence of a Cyclical Relationship. Annu. Rev. Nutr. 2021, 41, 309–332. [Google Scholar] [CrossRef]
- Diabetes UK. Complications of Diabetes. Available online: https://www.diabetes.org.uk/guide-to-diabetes/complications (accessed on 15 March 2025).
- Zamani-Alavijeh, F.; Araban, M.; Koohestani, H.R.; Karimy, M. The Effectiveness of Stress Management Training on Blood Glucose Control in patients with Type 2 Diabetes. Diabetol. Metab. Syndr. 2018, 10, 39. [Google Scholar] [CrossRef]
- Shahid, S.; Maqsood, G.; Majeed, A.; Qasim, M.; Arshad, M.; Yasin, T.; Afzal, A.; Saadat, S.; Ali, R.M.S.; Shahzad, S.; et al. The effect of perceived stress on glycated hemoglobin value (HbA1c) on Diabetes type 2 patients-An observational study from Lahore, Pakistan. Med. Sci. 2025, 29, e21ms3512. [Google Scholar] [CrossRef]
- Kim, H.J.; Oh, S.Y.; Joo, J.H.; Choi, D.W.; Park, E.C. The relationship between sleep duration and perceived stress: Findings from the 2017 Community Health Survey in Korea. Int. J. Environ. Res. Public Health 2019, 16, 3208. [Google Scholar] [CrossRef]
- Choi, D.W.; Chun, S.Y.; Lee, S.A.; Han, K.T.; Park, E.C. Association between sleep duration and perceived stress: Salaried worker in circumstances of high workload. Int. J. Environ. Res. Public Health 2018, 15, 796. [Google Scholar] [CrossRef]
- Walker, R.J.; Garacci, E.; Campbell, J.A.; Egede, L.E. The influence of daily stress on glycemic control and mortality in adults with diabetes. J. Behav. Med. 2020, 43, 723–731. [Google Scholar] [CrossRef]
- Vasanth, R.; Ganesh, A.; Shanker, R. Impact of stress on type 2 diabetes mellitus management. Psychiatr. Danub. 2017, 29 (Suppl. S3), 416–421. [Google Scholar] [PubMed]
- Schrieks, I.C.; Heil, A.L.; Hendriks, H.F.; Mukamal, K.J.; Beulens, J.W. The Effect of Alcohol Consumption on Insulin Sensitivity and Glycemic Status: A Systematic Review and Meta-analysis of Intervention Studies. Diabetes Care 2015, 38, 723–732. [Google Scholar] [CrossRef] [PubMed]
- Ahmed, A.T.; Karter, A.J.; Warton, E.M.; Doan, J.U.; Weisner, C.M. The relationship between alcohol consumption and gly-cemic control among patients with diabetes: The Kaiser Permanente Northern California Diabetes Registry. J. Gen. Intern. Med. 2008, 23, 275–282. [Google Scholar] [CrossRef] [PubMed]
- Sharifnezhad, A.; Garmabi, M.; Naderi, F.; Darrudi, F.; Andishmand, Z.; Gholami, A. Association of sleep duration and quality with health-related quality of life in fresher university students. Sleep. Med. Res. 2023, 14, 50–57. [Google Scholar] [CrossRef]
- Joseph, P.V.; Zhou, Y.; Brooks, B.; McDuffie, C.; Agarwal, K.; Chao, A.M. Relationships among alcohol drinking patterns, macronutrient composition, and caloric intake: National Health and Nutrition Examination Survey 2017–2018. Alcohol Alcohol. 2022, 57, 559–565. [Google Scholar] [CrossRef]
- Ronksley, P.E.; Brien, S.E.; Turner, B.J.; Mukamal, K.J.; Ghali, W.A. Association of alcohol consumption with selected cardio-vascular disease outcomes: A systematic review and meta-analysis. BMJ 2011, 342, d671. [Google Scholar] [CrossRef]
Table 1.
Differences in Diabetes- and Diet-related Factors According to Sleep Duration (n = 1363).
| Characteristics | <7 h | ≥7 to <9 h | ≥9 h | χ2 (p) | |
|---|---|---|---|---|---|
| n (Weight %)/Mean (SE) | |||||
| Diabetes treatment | Yes | 530 (95.5) | 585 (94.4) | 185 (96.8) | 2.06 (0.414) |
| No | 26 (4.5) | 31 (5.6) | 6 (3.2) | ||
| Breakfast frequency (days/week) | 0 | 52 (11.3) | 47 (10.2) | 10 (6.6) | 5.66 (0.334) |
| 1–2 | 26 (6.1) | 20 (4.3) | 8 (7.1) | ||
| ≥3 | 460 (82.6) | 528 (85.5) | 155 (86.3) | ||
| Lunch frequency (days/week) | 0 | 15 (2.5) | 17 (3.4) | 8 (4.3) | 1.62 (0.820) |
| 1–2 | 11 (2.7) | 13 (2.6) | 4 (2.8) | ||
| ≥3 | 512 (94.8) | 565 (93.9) | 161 (92.9) | ||
| Dinner frequency (days/week) | 0 | 7 (1.3) | 2 (0.2) | 2 (1.3) | 12.02 (0.047) |
| 1–2 | 11 (2.3) | 9 (1.5) | - | ||
| ≥3 | 520 (96.4) | 584 (98.3) | 171 (98.7) | ||
| Use of nutrition labels | Yes | 126 (36.8) | 118 (32.5) | 15 (20.5) | 6.52 (0.054) |
| No | 215 (63.2) | 250 (67.5) | 55 (79.5) | ||
| Use of diet | Yes | 257 (47.0) | 264 (45.6) | 60 (34.6) | 7.29 (0.046) |
| No | 281 (53.0) | 331 (54.4) | 111 (65.4) | ||
| Energy intake (kcal/day) | 1747.0 (43.0) | 1761.4 (25.9) | 1490.0 (65.1) | 8.49 (<0.001) | |
| Protein intake (g/day) | 63.5 (1.6) | 68.4 (1.3) | 49.4 (2.3) | 33.59 (<0.001) | |
| Fat intake (days/week) | 40.8 (1.5) | 43.7 (1.3) | 32.0 (3.6) | 5.27 (0.006) | |
| Carbohydrate intake (days/week) | 263.2 (5.4) | 258.9 (4.0) | 243.5 (9.05) | 1.77 (0.172) | |
| Sugar intake (days/week) | 53.2 (2.2) | 49.3 (1.3) | 48.2 (3.7) | 1.33 (0.267) | |
SE: standard error
Table 2.
Differences in Sociodemographic Factors According to Sleep Duration (n = 1363).
| Characteristics | <7 h | ≥7 to <9 h | ≥9 h | χ2 (p) | |
|---|---|---|---|---|---|
| n (Weight %) | |||||
| Gender | Men | 256 (51.3) | 350 (60.9) | 96 (52.2) | 12.27 (0.006) |
| Women | 300 (48.7) | 266 (39.1) | 95 (47.8) | ||
| Age (years) | 30–49 | 39 (10.2) | 51 (13.5) | 8 (6.9) | 35.97 (<0.001) |
| 50–69 | 312 (58.3) | 330 (56.4) | 69 (39.1) | ||
| ≥70 | 205 (31.5) | 235 (30.1) | 114 (54.0) | ||
| Family income | Upper | 118 (25.2) | 122 (24.2) | 13 (9.5) | 49.03 (<0.001) |
| Middle | 249 (47.2) | 318 (52.1) | 74 (40.3) | ||
| Lower | 188 (27.6) | 174 (23.8) | 103 (50.2) | ||
| Education level | ≤High school | 285 (44.6) | 279 (35.8) | 70 (60.2) | 23.22 (<0.001) |
| ≥College | 271 (55.4) | 335 (64.2) | 37 (39.8) | ||
| Marital status | Living with spouse | 363 (69.8) | 477 (81.6) | 118 (61.3) | 36.04 (<0.001) |
| Other | 171 (30.2) | 115 (18.4) | 64 (38.7) | ||
| Economic activity | Yes | 269 (53.9) | 306 (57.1) | 33 (30.0) | 22.15 (<0.001) |
| No | 254 (46.1) | 270 (42.9) | 66 (70.0) | ||
| Health checkup | Yes | 394 (75.4) | 445 (76.9) | 65 (62.6) | 7.62 (0.042) |
| No | 129 (24.6) | 134 (23.1) | 38 (37.4) | ||
Table 3.
Differences in Physical and Psychological Factors According to Sleep Duration (n = 1363).
| Characteristics | <7 h | ≥7 to <9 h | ≥9 h | χ2 (p) | |
|---|---|---|---|---|---|
| n (Weight %)/Mean (SE) | |||||
| Weight change | Weight loss | 123 (21.3) | 149 (24.5) | 41 (28.8) | 12.71 (0.043) |
| Weight gain | 80 (14.9) | 65 (9.9) | 11 (6.6) | ||
| No change | 351 (63.8) | 400 (65.6) | 105 (64.6) | ||
| Drinking | Yes | 203 (41.2) | 274 (46.6) | 42 (28.7) | 14.79 (0.001) |
| No | 351 (58.8) | 341 (53.4) | 112 (71.3) | ||
| Smoking | Yes | 89 (20.0) | 109 (21.2) | 30 (24.0) | 0.99 (0.660) |
| No | 465 (80.0) | 506 (78.8) | 120 (76.0) | ||
| Sitting time (hours/day) | 9.92 (0.20) | 8.60 (0.23) | 8.32 (0.40) | 16.56 (<0.001) | |
| Aerobic physical activity | Yes | 180 (33.6) | 203 (38.1) | 22 (24.0) | 7.45 (0.072) |
| No | 342 (66.4) | 372 (61.9) | 75 (76.0) | ||
| Subjective health | Good | 90 (15.0) | 116 (19.2) | 15 (14.9) | 8.67 (0.181) |
| Normal | 224 (43.6) | 274 (46.8) | 43 (41.7) | ||
| Bad | 210 (41.4) | 192 (34.1) | 47 (43.4) | ||
| Anxiety | 2.01 (0.19) | 1.62 (0.13) | 2.25 (0.46) | 1.61 (0.202) | |
| Body Mass Index (kg/m2) | <18.5 | 11 (1.7) | 14 (2.8) | 5 (2.5) | 7.41 (0.386) |
| 18.5–<23 | 147 (26.1) | 161 (25.0) | 59 (34.1) | ||
| 23–<25 | 120 (22.6) | 153 (24.8) | 42 (21.4) | ||
| ≥25 | 260 (49.5) | 271 (47.4) | 66 (42.0) | ||
| Stress | Low | 401 (70.2) | 502 (79.6) | 113 (73.2) | 14.32 (0.001) |
| High | 153 (29.8) | 112 (20.4) | 39 (26.8) | ||
SE: standard error
Table 4.
Affecting Factors of HbA1c according to Sleep Duration (n = 1363).
| Characteristics | <7 h | ≥7 to <9 h | ≥9 h | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| β | t | p | β | t | p | β | t | p | ||
| Dinner frequency (ref: ≥3) | 0 | −0.193 | −1.01 | 0.317 | 0.302 | 1.38 | 0.167 | 0.863 | 1.12 | 0.262 |
| 1–2 | −0.255 | −0.73 | 0.463 | −0.401 | −1.06 | 0.287 | - | - | - | |
| Use of diet (ref: no) | Yes | −0.093 | −0.57 | 0.568 | 0.109 | 0.89 | 0.371 | 0.142 | 0.30 | 0.765 |
| Energy intake | 0.827 | 0.45 | 0.647 | 0.001 | 3.39 | 0.001 | 0.001 | 1.33 | 0.184 | |
| Protein intake | −0.002 | −0.67 | 0.499 | −0.009 | −2.76 | 0.006 | −0.021 | −1.57 | 0.117 | |
| Fat intake | −0.002 | −0.58 | 0.557 | −0.009 | −2.58 | 0.010 | −0.019 | −1.41 | 0.159 | |
| Gender (ref: women) | Men | 0.034 | 0.27 | 0.784 | −0.273 | −1.89 | 0.060 | −0.044 | −0.10 | 0.916 |
| Age (ref: ≥70) | 30–49 | 0.674 | 2.34 | 0.020 | −0.002 | −0.01 | 0.995 | 0.848 | 1.47 | 0.142 |
| 50–69 | 0.358 | 3.07 | 0.002 | 0.111 | 0.88 | 0.380 | 0.585 | 1.32 | 0.188 | |
| Family income (ref: lower) | Upper | −0.214 | −1.30 | 0.193 | 0.045 | 0.18 | 0.855 | −0.055 | −0.07 | 0.937 |
| Middle | −0.220 | −1.78 | 0.076 | −0.080 | −0.42 | 0.672 | 0.487 | 1.36 | 0.174 | |
| Education level (ref: ≥College) | ≤High school | −0.099 | −1.01 | 0.317 | −0.344 | −2.31 | 0.022 | −0.627 | −1.63 | 0.104 |
| Marital status (ref: others) | Living with spouse | −0.143 | −1.24 | 0.215 | −0.383 | −1.64 | 0.101 | −0.707 | −1.26 | 0.206 |
| Economic activity (ref: no) | Yes | −0.058 | −0.40 | 0.689 | 0.270 | 1.78 | 0.076 | −0.107 | −0.31 | 0.750 |
| Health checkup (ref: no) | Yes | −0.170 | −0.94 | 0.344 | −0.115 | −0.71 | 0.475 | −0.807 | −2.13 | 0.034 |
| Weight change (ref: no change) | Weight loss | 0.165 | 1.01 | 0.315 | −0.210 | −1.19 | 0.235 | 0.731 | 1.45 | 0.146 |
| Weight gain | −0.031 | −0.21 | 0.829 | −0.064 | −0.40 | 0.687 | 0.088 | 0.21 | 0.834 | |
| Drinking (ref: no) | Yes | 0.301 | 2.57 | 0.011 | −0.086 | −0.71 | 0.474 | −0.800 | −2.09 | 0.037 |
| Sitting time | 0.001 | 0.04 | 0.967 | 0.040 | 1.86 | 0.065 | 0.002 | 0.02 | 0.981 | |
| Stress (ref: high) | Low | −0.383 | −2.48 | 0.014 | 0.167 | 0.83 | 0.407 | −0.313 | −0.55 | 0.582 |
| R2/F/p | R2 = 0.083, F = 2.35, p = 0.002 | R2 = 0.103, F = 3.33, p < 0.001 | R2 = 0.401, F = 34.86, p < 0.001 | |||||||
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Kwon, M.; Kim, S.A.; Kim, J. Factors Affecting HbA1c According to Sleep Duration in Adults with Diabetes. Diabetology 2025, 6, 46. https://doi.org/10.3390/diabetology6060046
AMA Style
Kwon M, Kim SA, Kim J. Factors Affecting HbA1c According to Sleep Duration in Adults with Diabetes. Diabetology. 2025; 6(6):46. https://doi.org/10.3390/diabetology6060046
Chicago/Turabian StyleKwon, Myoungjin, Sun Ae Kim, and Jiyoung Kim. 2025. "Factors Affecting HbA1c According to Sleep Duration in Adults with Diabetes" Diabetology 6, no. 6: 46. https://doi.org/10.3390/diabetology6060046
APA StyleKwon, M., Kim, S. A., & Kim, J. (2025). Factors Affecting HbA1c According to Sleep Duration in Adults with Diabetes. Diabetology, 6(6), 46. https://doi.org/10.3390/diabetology6060046
