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Sleep Quality and Quality of Life in Adults with Type 2 Diabetes in Bangladesh

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08 December 2024

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09 December 2024

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Abstract

We conducted a cross sectional study among 180 adult type-2 diabetic patients in a primary diabetic health care center in Bangladesh to assess their sleep quality and quality of life. We used the Pittsburgh Sleep Quality Index (PSQI) to assess the sleep quality and SF- 36 v2TM for the quality of life. The mean (SD) PSQI global score was 7.28 (3.06). Score of overall quality of life ranged from 914-2910 with mean (SD) score 2044.80 (512.75). 117 participants (65%) were poor sleepers (global PSQI >5) who had significantly poor functioning on all domains of the SF-36 (all p<0.001). Overall sleep quality and quality of life had significant relationship with age (p<0.001), occupation (p<0.05), duration of diabetes (p<0.001), BMI (p<0.05), diabetes complications (p<0.001), hypertension (p<0.05), coronary heart disease (p<0.001) and insulin use (p<0.05). Our study, first ever among diabetic adults in Bangladesh, suggests that Diabetes has a significant impact on an individual’s quality of life (QoL) and sleep quality. Therefore, a comprehensive nationwide study and screening of new patients for sleep problems need to be evaluated.

Keywords: 
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1. Introduction

Diabetes mellitus is one of the most prevalent non-communicable diseases (NCDs) globally, with type 2 diabetes (T2D) accounting for 85-95% of all diabetes cases. In developed countries, T2D represents over 90% of diabetes cases [1]. The global prevalence of diabetes is estimated to be over 537 million adults aged 20-79 years, with projections to exceed 700 million by 2045 [2]. T2D is associated with numerous complications, including cardiovascular disease, kidney disease, neuropathy, vision loss, and lower extremity amputation, all of which significantly contribute to morbidity and mortality worldwide [3]. These complications often lead to an increased burden on healthcare systems and the economy, with substantial indirect costs due to premature mortality and lost productivity [4].
In Bangladesh, the prevalence of NCDs, including T2D, has sharply increased in recent years, making it a critical public health issue. While the estimated prevalence of diabetes among adults was 9.7% in 2011, it is estimated to be 13.7 million by 2045 [5]. It is estimated that 12 million people over the age of 30 are at risk of developing T2D [6]. Despite advancements in diagnostic and treatment strategies, the burden of the disease continues to have significant consequences for patients, including impaired well-being and quality of life [7].

Health-Related Quality of Life (HRQoL) in T2D

T2D has a profound impact on health-related quality of life (HRQoL), with individuals often reporting lower HRQoL compared to the general population [8]. HRQoL is particularly important for people with T2D, as it can significantly affect their ability to manage daily activities and cope with the disease. Studies have shown that HRQoL is negatively associated with various factors, including older age, female sex, depressive symptoms, diabetes-related complications, and insulin use [9,10]. T2D can impair multiple dimensions of HRQoL, including physical functioning, role functioning, social functioning, emotional well-being, and pain management [10]. A significant predictor of mortality in older patients with T2D is self-rated health, which has been found to be a reliable indicator of both physical and mental health status in this population [11].

Sleep Quality and Its Impact on HRQoL

Sleep quality is strongly linked to overall quality of life, and poor sleep can have a profound effect on both physical and mental health. Sleep quality includes both quantitative (e.g., sleep duration, latency) and subjective factors (e.g., restfulness or depth of sleep). In recent years, research has shown that poor sleep quality is common in adults, with up to 30% of the adult population reporting difficulties with sleep [12]. This trend is also prevalent among those with T2D, who experience higher rates of insomnia, daytime sleepiness, and poor sleep quality compared to age- and sex-matched controls [13]. Sleep disturbances are known to exacerbate metabolic dysfunctions and can influence endocrine and immune pathways, further compounding the challenges of managing diabetes [14].
In individuals with T2D, poor sleep quality has been shown to negatively affect HRQoL [15]. The interplay between sleep disturbances and HRQoL in T2D patients has been well-documented in Western populations, but limited studies have explored this relationship in the context of Bangladesh or South Asia.

Gaps in Research on T2D, Sleep, and HRQoL in Bangladesh

While the relationship between sleep quality and HRQoL has been studied extensively in patients with chronic illnesses globally, few studies have specifically addressed this issue in Bangladesh. Given the rising burden of T2D in the country, understanding how sleep disturbances affect HRQoL and disease-related quality of life is crucial for improving patient outcomes. Our study aims to fill this gap by assessing the impact of sleep quality on HRQoL and disease-related quality of life in Bangladeshi adults with T2D.This research will provide valuable insights that can inform public health strategies to address the dual burden of T2D and its complications in Bangladesh, ultimately improving patient care and quality of life.

2. Materials and Methods

2.1. Study Design and Settings

A cross sectional survey was undertaken to assess sleep quality and quality of life in 180 adults with type-2 diabetes mellitus. The patients were recruited from Mirpur National Health Care network, a primary health care centre for diabetic patients in Dhaka, Bangladesh which is affiliated with the Bangladesh Institute of Research and Rehabilitation for Diabetes, Endocrine and Metabolic Disorder (BIRDEM).

2.2. Study Sampling and Data Collection

A semi-structured questionnaire was used for data collection. A total of 180 diabetic patients who were attending the outpatient department of National Health Care Network were randomly selected and interviewed. All of them were willing to participate in this study. The two inclusion criteria of study participants were to be aged at least 25 and above and duration of diabetes (>1 year). We excluded the diabetes cases with pregnancy, inability to communicate and who were unwilling to participate.

2.3. Evaluation of Quality of Life

The tool used for evaluation of quality of life was the questionnaire, the Short Form 36 Version 2 (SF-36 V2TM version 2) [16]. The SF-36 contains 36 items evaluating 8 dimensions of health: physical functioning, role limitations due to physical problems, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. Each dimension is scored on a 0 to 100 scale, with higher scores indicating better functioning. In addition, the SF-36 includes 2 composite summary scores: the physical component score (PCS) and the mental component score (MCS). The PCS and MCS are standardized to a mean (SD) of 50, with lower scores indicating more significant dysfunction [17].

2.4. Measurement of Sleep Quality

Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) [18]. The PSQI assesses quality of sleep during the past month and contains 7 component scales: sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component is scored from 0 to 3, yielding a global PSQI score ranging from 0 to 21, with higher scores indicating worse sleep quality. The PSQI was utilized for several goals: (1) to provide a reliable, valid, and standardized measure of sleep quality; (2) to discriminate between “good” and “poor” sleepers; (3) to provide an index that is easy for subjects to use and for clinicians and researchers to interpret; and (4) to provide a brief, clinically useful assessment of a variety of sleep disturbances that might affect sleep quality [19].

2.5. Data Analysis

After completion of data collection, data were stored and quality control check was performed. Descriptive statistics including means, medians, standard deviations, ranges for continuous data and frequencies and proportion for categorical data was calculated. For inferential statistics, mainly one way ANOVA, t test, chi-square test and Pearson’s correlation were used. In all the tests, p˂0.05 was considered to be statistically significant.

3. Results

3.1. Socio-Demographic Characteristics of Patients

Highest proportion of the respondent (48.9%) was in ≥ 50 years age group followed by 40 – 49 years age group (30%). Mean (SD) age of the respondents were 48.31 (9.02) years. Majority proportion of the respondents were females (65%) and the rest were males. Most of the respondents (79.4%) were married. Rest 20.6% were categorized as others which included unmarried, divorced, widow/ widower and separated. Major proportion of respondents (43.3%) were in the educational level of S.S.C and H.S.C, up to primary level there were 25% patients, the rest were graduates and above. Among 180 respondents 48.3% were housewives, service holder 24.5%. and lowest group were day laborer (3.3%). The distribution of number of family members of respondents showed majority (67.2%) had <5 members in their family and 7.8% had more than 8 members in their family. Average family size was 5.1. Among the total respondents 83.3% were from urban areas and 15% from rural areas.
Table 1. Socio-demographic characteristics of the type 2 diabetic patients.
Table 1. Socio-demographic characteristics of the type 2 diabetic patients.
Characteristics Frequency Percent
Religion
Islam
Hindu
158
22
87.8
12.2
Marital status
Married
Others
143
37
79.4
20.6
Education
Up to Primary
S.S.C and H.S.C
Graduates and above
45
78
57
25.0
43.3
31.7
Occupation
Service
Business
Housewife
Retired
Day laborer
44
23
87
20
6
24.5
12.8
48.3
11.1
3.3
Monthly family income
<20000
20000 to 29000
30000 to 39000
40000 to 49000
50000 to 59000
≥60000
62
13
33
14
22
36
34.4
7.2
18.3
7.8
12.3
20.0
Family size
<5
5 – 6
7 – 8
>8
121
21
24
14
67.2
11.7
13.3
7.8
Ownership of house
No
Yes
96
84
53.3
46.7
Type of house
Pacca building
Semi- pacca building
155
25
86.1
13.9
Area of residence
Urban
Rural
153
27
85.0
15.0

3.2. Complications of Diabetes Among Participants

Among 180 respondents 111(61.7%) were suffering from different types of diabetic complications. Hypertension was more common 94(52.2%). Distribution of complications is shown in Table 2.
Table 2. Complications pattern of patients.
Table 2. Complications pattern of patients.
Characteristics Frequency Percent
Presence of complications
Yes
No
111
69
61.7
38.3
Types of complication
Hypertension 94 52.2
Coronary heart disease 27 15
Urinary infection 5 2.8
Nephropathy 10 5.6
Retinopathy 1 0.6
Cataract 1 0.6
Peripheral neuropathy 5 2.8

3.3. Relationship Among Diabetes Duration, Status, Complications and Sleep Quality

3.3.1. Duration of Diabetes and Sleep Quality Score

Our study participants’ mean (SD) duration of diabetes was 8.48(5.15) years, ranging from . 01-28 years. Mean(SD) of duration of diabetes was 8.48(5.15) years. Day time dysfunction score (r=0.323, p<0.001) had moderate positive correlation with duration of diabetes. Overall sleep quality (r=0.294, p<0.001), subjective sleep quality (r=0.289, p<0.001), sleep latency (r=0.223, p<0.05) and sleep duration (r=0.214, p<0.05) were also correlated with duration of diabetes. With the increase in duration of diabetes sleep quality score increased. We further categorized the duration of diabetes into <5, 5 to 9, 10 to 14 and ≥15 years. Duration of diabetes had significant association with overall sleep quality (p=0.001), subjective sleep quality (p=0.001), sleep latency (p=0.019), sleep disturbance (p=0.039) and day time dysfunction ( p<0.001). Post hoc test revealed that sleep quality score was significantly different in all domains and over all sleep quality in >15 years duration of diabetes respondents.
Table 3. Duration of diabetes and sleep quality score.
Table 3. Duration of diabetes and sleep quality score.
Characteristics Diabetes duration ( years) N Mean score SD F P-value P-value
Overall sleep quality <5
5 – 9
10 – 14
≥15
63
59
33
25
6.20
7.32
8.30
8.60
2.75
3.10
2.73
3.11

5.774

0.001
0.294 <0.001
Subjective sleep quality <5
5 – 9
10 – 14
≥15
63
59
33
25
1.40
1.57
1.78
1.96
0.58
0.68
0.69
0.78

5.432

0.001
0.289 <0.001
Sleep latency <5
5 – 9
10 – 14
≥15
63
59
33
25
1.39
1.72
1.84
1.92
0.81
0.80
0.93
0.95

3.418

0.019
0.223 0.003
Sleep duration <5
5 – 9
10 – 14
≥15
63
59
33
25
1.29
1.42
1.63
1.75
0.84
0.72
0.96
1.10

2.844

0.039
0.214 0.004
Habitual sleep efficiencies <5
5 – 9
10 – 14
≥15
63
59
33
25
0.79
0.96
1.27
1.24
0.96
1.11
0.98
1.23

1.955

0.122
0.184 0.013
Sleep disturbance <5
5 – 9
10 – 14
≥15
63
59
33
25
1.00
1.00
1.06
1.08
0.00
0.00
0.24
0.28

3.034

0.031
0.199 0.008
Use of sleep medication <5
5 – 9
10 – 14
≥15
63
59
33
25
0.73
0.98
1.12
1.12
0.95
1.09
0.89
0.97

1.615

0.188
0.134 0.073
Day time dysfunction <5
5 – 9
10 – 14
≥15
63
59
33
25
0.88
1.06
1.22
1.28
0.47
0.36
0.41
0.54

6.497

<0.001
0.323 <0.001

3.3.2. Glycemic Control and Sleep Quality Score

To understand the glycemic xcontrol, we collected data on HbA1c status of the participants. Correlation co-efficient between sleep quality score and HbA1c were estimated. Overall sleep quality (r=0.190, p>0.05) and sleep disturbance (r=0.183, p<0.05) had weaker positive correlation with the glycemic control. Subjective sleep quality (r=0.759, P<0.001), sleep latency (0.832, P<0.001), sleep duration (r=0.680, P<0.001), habitual sleep efficiency (r=0.688, p<0.001), use of sleep medication (r=0.796, P<0.001), and time dysfunction (r=5.70, P<0.001) had strong significant correlation with glycemic control. We also categorized HbA1c data into controlled and uncontrolled group. t test revealed significant relationship of sleep quality score in no domains with glycemic control status.
Table 4. Correlation between glycemic control and sleep quality score.
Table 4. Correlation between glycemic control and sleep quality score.
Characteristics Glycemic level N Mean score SD t P-value P-value
Overall sleep quality Controlled
Uncontrolled
25
38
7.48
8.68
2.85
3.23
-1.511 0.136 0.190 0.136
Subjective sleep quality Controlled
Uncontrolled
25
38
1.56
1.84
0.71
0.82
-1.403 0.166 0.759 <0.001
Sleep latency Controlled
Uncontrolled
25
38
1.68
2.02
0.90
0.92
-1.479 0.144 0.832 <0.001
Sleep duration Controlled
Uncontrolled
25
38
1.44
1.65
1.04
0.96
-0.848 0.400 0.680 <0.001
Habitual sleep efficiencies Controlled
Uncontrolled
25
38
1.08
1.34
1.12
1.24
-0.855 0.396 0.688 <0.001
Sleep disturbance Controlled
Uncontrolled
25
38
1.04
1.02
0.20
0.16
0.298 0.766 0.183 0.014
Use of sleep medication Controlled
Uncontrolled
25
38
1.00
1.18
1.00
0.95
-0.735 0.465 0.796 <0.001
Day time dysfunction Controlled
Uncontrolled
25
38
1.12
1.26
0.44
0.45
-1.253 0.215 0.570 <0.001
© Correlation co-efficient.

3.3.3. Complication of Diabetes and Sleep Quality

On an average, sleep quality of the respondents with diabetes complications were higher in overall sleep quality and all domains and the findings were significant in overall sleep quality and all domains (p<0.05) except sleep duration (p=0.072).
Table 5. Complication of diabetes and sleep quality score.
Table 5. Complication of diabetes and sleep quality score.
Characteristics Diabetes complication N Mean score SD t P-value
Overall sleep quality Yes
No
111
69
8.09
6.00
3.26
2.19
5.135 <0.001
Subjective sleep quality Yes
No
111
69
1.78
1.31
0.70
0.52
5.033 <0.001
Sleep latency Yes
No
111
69
1.84
1.36
0.90
0.72
3.752 <0.001
Sleep duration Yes
No
111
69
1.52
1.37
0.92
0.83
1.809 0.072
Habitual sleep efficiency Yes
No
111
69
1.15
0.75
1.12
0.92
2.577 0.011
Sleep disturbance Yes
No
111
69
1.03
1.00
0.18
0.00
2.028 0.045
Use of sleep medication Yes
No
111
69
1.09
0.68
1.06
0.81
2.963 0.003
Day time dysfunction Yes
No
111
69
1.17
0.88
0.44
0.43
4.234 <0.001

3.4. Relationship Among Diabetes Duration, Status, Complications and Quality of Life

3.4.1. Duration of Diabetes and Quality of Life

One way ANOVA test showed that there were significant association between duration of diabetes with overall quality of life and two domains (p<0.001). On an average, the respondents who were suffering from diabetes ≥15 years had lower score in overall quality of life, physical and mental health. Post Hoc test revealed that there were significant differences between the respondents who suffering from diabetes ≥15 years than other three different duration of diabetic groups
Table 6. Duration of diabetes and quality of life.
Table 6. Duration of diabetes and quality of life.
Characteristics Duration of diabetes in years N Mean score SD F P-value
Overall quality of life <5
5 – 9
10 – 14
≥15
63
59
33
25
2252.30
2177.92
1801.73
1528.60
383.54
401.67
528.46
534.40
20.917 <0.001
Physical health <5
5 – 9
10 – 14
≥15
63
59
33
25
1415.24
1329.66
1097.42
927.60
267.43
258.21
347.24
315.40
21.934 <0.001
Mental health <5
5 – 9
10 – 14
≥15
63
59
33
25
837.06
848.25
707.30
601.00
145.55
182.38
215.03
232.35
14.308 <0.001

3.4.2. Glycemic Control and Quality of Life

Mean score of quality of life in relation to glycemic control level showed higher in controlled group than the uncontrolled group. There were no significant association between the quality of life and diabetes control level (p>0.05). Correlation co-efficient between glycemic control level and quality of life showed that there were moderate negative significant correlation in overall quality of life (r= -0.509, p<0.001), physical health (r= -0.520, p <0.001) and mental health (r=-0.427, p<0.001).
Table 7. Glycemic control and quality of life.
Table 7. Glycemic control and quality of life.
Characteristics Glycemic level N Mean score SD P-value t P-value
Overall quality of life Controlled

Un-controlled
25

38
1979.32

1775.18
565.90

568.03

-0.509

<0.001

1.398

0.167
Physical health Controlled
Un-controlled
25
38
1218.00
1088.02
364.94
381.40
-0.520 <0.001 1.346 0.183
Mental health Controlled
Un-controlled
25
38
761.32
687.15
227.09
210.97
-0.427 <0.001 1.324 0.190
© Correlation coefficient.

3.4.3. Diabetic Complications and Quality of Life

Mean scores of quality of life in relation to presence of diabetic complications showed that overall quality of life, physical health, mental health were lower in the respondents suffering from complications than in who were not suffering from complications. Diabetic complications of the patients were significantly related with overall quality of life, physical health and mental health (P<0.01).
Table 8. Presence of diabetic complications and quality of life.
Table 8. Presence of diabetic complications and quality of life.
Characteristics Presence of complications N Mean score SD t P-value
Overall quality of life Yes
No
111
69
1860.39
2341.44
506.34
364.07
-7.396 <0.001
Physical health Yes
No
111
69
1138.33
1458.84
325.01
242.38
-7.548 <0.001
Mental health Yes
No
111
69
722.06
882.60
209.47
151.59
-5.949 <0.001

3.5. Relationship between Sleep Quality Score and Quality Of Life

Sleep quality and quality of life of the respondents were assessed by the Pittsburgh Sleep Quality Index and SF-36v2TM respectively.
Sleep quality score in overall sleep quality and all domains were inversely correlated with overall quality of life, physical and mental health score. There were strong significant correlation of overall sleep quality (r= -0.530, p<0.001), subjective sleep quality(r= -0.539, p< 0.001) and day time dysfunction (r= -0.529, p<0.001) with overall quality of life. Sleep latency (r= -0.462, p<0.001) and sleep duration (r= -0.322, p<0.001) moderately correlated with overall quality of life score. Overall sleep quality (r= -0.540, <0.001) and subjective sleep quality (r= -0.535, p<0.001) had strong correlation with physical health. Others domain of sleep quality had significant weaker correlation with the physical health. Only day time dysfunction (r= -0.511, p<0.001) had strong correlation with mental health. Sleep quality score in sleep disturbance had poor correlation with overall quality of life (r= -0.180, p=0.015), physical health (r= -0.186, p= 0.012) and mental health (r= -0.148, p=0.048).
Table 9. Correlation between sleep quality score and quality of life.
Table 9. Correlation between sleep quality score and quality of life.
Sleep quality Quality of life
Overall Physical health Mental health
P-value P-value P-value
Overall quality of sleep -0.530 <0.001 -0.540 <0.001 -0.447 <0.001
Subjective sleep quality -0.539 <0.001 -0.535 <0.001 -0.474 <0.001
Sleep latency -0.462 <0.001 -0.472 <0.001 -0.387 <0.001
Sleep duration -0.322 <0.001 -0.329 <0.001 -0.270 <0.001
Habitual sleep efficiency -0.281 <0.001 -0.288 <0.001 -0.234 0.002
Sleep disturbance -0.180 0.015 -0.186 0.012 -0.148 0.048
Use of sleep medication -0.284 <0.001 -0.312 <0.001 -0.201 0.007
Day time dysfunction -0.529 <0.001 -0.499 <0.001 -0.511 <0.001
© Correlation co-efficient.
Overall sleep quality was categorized into two groups good sleepers (score ≤5) and poor sleepers (score >5). Relationship between the sleep quality with the overall quality of life, physical health and mental health was significant (p>0.001). Average score in overall quality of life, physical and mental health domains were lower in poor sleeper than good sleeper.
Table 10. Overall sleep quality and quality of life.
Table 10. Overall sleep quality and quality of life.
Characteristics Sleep quality N Mean score SD t P-value
Overall quality of life Poor sleeper
Good sleeper
117
63
1905.48
2303.52
541.17
326.29
6.147 <0.001
Physical health Poor sleeper
Good sleeper
117
63
1169.31
1431.82
351.58
213.46
6.222 <0.001
Mental health Poor sleeper
Good sleeper
117
63
136.17
871.69
213.33
153.15
4.912 <0.001
According to above analysis it was found that more than half of the participants (65%) were “poor sleepers” according to the Pittsburgh Sleep Quality Index. Poor sleep quality was found to be a significant predictor of lower health-related quality of life as indicated by lower scores on both the physical and mental component scores of the Medical Outcomes Study 36-item Short Form health survey (SF-36). Poor sleep quality was also associated with diabetic profile. These results suggest that poor sleep is common in type 2 diabetes and may adversely affect quality of life

4. Discussion

This cross-sectional study aimed to explore the relationship between sleep quality and health-related quality of life (HRQoL) in individuals with type 2 diabetes (T2D). The results revealed that a significant proportion of the participants (65%) were classified as “poor sleepers” according to the Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was a significant predictor of lower HRQoL, as reflected by lower scores on both the physical (PCS) and mental (MCS) component scores of the Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) [20]. This finding is consistent with previous research that demonstrates a strong association between poor sleep quality and compromised quality of life in individuals with chronic conditions, particularly T2D [21,22].
In this study, poor sleepers were more likely to be older, female, and have multiple comorbidities, aligning with prior findings that older age and the presence of comorbidities are associated with worse sleep quality in T2D patients [15]. Additionally, poor sleep quality in this cohort was linked with diabetes-related factors, such as the duration of diabetes, number of diabetic complications, and insulin use. These results suggest that individuals with longer diabetes durations and greater disease-related complications are at higher risk of experiencing sleep disturbances, which in turn affect their overall HRQoL. A study by Meier and Sze (2022) also highlighted that prolonged diabetes duration correlates with worsened sleep quality, further substantiating our findings [23].
Our results underscore the importance of recognizing sleep disturbances as a critical factor influencing HRQoL and disease-related quality of life in T2D patients. As indicated by the negative associations between sleep quality and the SF-36 scores, poor sleep appears to impair multiple dimensions of quality of life, including physical health, mental health, and emotional well-being. The interplay between sleep and HRQoL is complex, as poor sleep contributes to both physical and mental health decline, which can exacerbate the challenges faced by individuals with T2D [24].
The study also demonstrated a significant correlation between the duration of diabetes and sleep quality. As the duration of diabetes increased, sleep quality deteriorated. This aligns with findings in other studies that suggest long-term diabetes is associated with disrupted sleep [25]. Additionally, the presence of diabetic complications was significantly related to lower HRQoL scores, particularly in the domains of physical and mental health. These results are consistent with research indicating that diabetic complications, such as neuropathy, cardiovascular disease, and nephropathy, substantially impact the HRQoL of patients [26].
The results further revealed that BMI and glycemic control levels were significant predictors of HRQoL, with higher BMI and poor glycemic control being associated with lower HRQoL scores. This finding is consistent with other studies, which suggest that individuals with uncontrolled diabetes and higher BMI are at an increased risk of experiencing poor quality of life [27,28] Additionally, these factors contribute to the overall burden of diabetes, reinforcing the need for comprehensive management strategies that address both metabolic control and lifestyle factors, including sleep quality [13,29].
This study highlights the need for a holistic approach to managing T2D, where sleep quality is considered a key component of diabetes care. Adequate sleep, along with proper weight management, physical activity, and glycemic control, should be prioritized as part of a healthy lifestyle for T2D patients [30]. Interventions aimed at improving sleep hygiene could significantly enhance the quality of life for individuals with T2D, particularly in settings where sleep disorders are prevalent [31].
Despite the valuable insights provided, this study has some limitations. The cross-sectional design limits the ability to draw causal conclusions, and the sample was drawn from a single hospital, which may not be representative of the broader population. Future studies could benefit from longitudinal designs to further explore the causal relationships between sleep quality, HRQoL, and diabetes-related factors in larger, more diverse populations [32].
In summary, the study reinforces the importance of sleep quality as a critical determinant of HRQoL in individuals with type 2 diabetes [33]. Poor sleep quality is not only a common issue among T2D patients but also a significant factor influencing both physical and mental health outcomes. Addressing sleep disturbances in this population could lead to improved overall health and a better quality of life.

5. Conclusions

The study findings emphasize the importance of screening new patients for sleep problems, making a referral to a sleep medicine specialist if appropriate, and suggesting sleep hygiene strategies as part of diabetes management. The diabetes educator can play a key role in assessing sleep and providing easy to implement interventions to improve sleep hygiene. The study results indicate that having diabetes mellitus is associated with lower health-related quality of life scores. Duration of diabetes, insulin use, and diabetes-related complications are all factors associated with health-related quality of life scores. Strategies should designed to diagnose diabetes early and aggressively manage blood pressure, hyperlipidemia, and albuminuria may not only prevent diabetes-related complications, but may also prevent irreversible deterioration of health-related quality of life in diabetic patients. A comprehensive nationwide study about sleep quality and quality of life of type 2 diabetes patients should be conducted with a view to get national level information which will help to draw an actual picture of these patients and will help on planning for appropriate interventions.

Author Contributions

“Conceptualization, F.Z.; methodology, F.Z; software, X.X.; validation, M.A., M.M.H, M.K.R, and M.M.M.J.; formal analysis, F.Z. and M.A.; investigation, F.Z.; resources, M.A.; data curation, F.Z.; writing—original draft preparation, F.Z. and M.A.; writing—review and editing, M.M.H, M.K.R, and M.M.M.J.; project administration, F.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the corresponding authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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