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Table of Contents
ARTICLE
Year : 2017  |  Volume : 18  |  Issue : 2  |  Page : 39-45

Relationship between sleep quality and glycaemic control among subjects with type 2 diabetes mellitus


1 Junior Lecturer, College of Nursing, CMC, Vellore, India
2 Retd.. Professor, College of Nursing, CMC, Vellore, India
3 Professor, College of Nursing, CMC, Vellore, India
4 Associate Professor, Dept of Endocrinology, CMC, Vellore, India
5 Professor, Biostatistics, CMC, Vellore, India

Date of Web Publication9-Jun-2020

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Source of Support: None, Conflict of Interest: None


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  Abstract 


Sleep disturbance has evolved as an unrecognised health issue among the Diabetic patients. Information on sleep quality in Type 2 Diabetes in relation to the glycaemic control is limited and is rarely explored. This study was designed to assess the relationship between sleep quality and glycaemic control among subjects with Type 2 Diabetes Mellitus attending the Endocrinology Outpatient Department of a tertiary care centre in South India. A non-experimental descriptive design was undertaken. A total of 500 Type 2 Diabetic subjects (male- 242, female-258) aged between 20-70 years were selected based on total enumeration sampling technique. Sleep quality was assessed using Pittsburgh Sleep Quality Index (PSQI) and glycaemic control was estimated using HbA1c levels obtained within the past six months. Descriptive and inferential non parametric statistics such as frequency distributions, median, range, Spearman rank’s correlation, Mann Whitney U test, Kruskal Wallis test, and Chi square tests were used to analyse the data. Among the subjects 63.6% had poor sleep quality (PSQI >5) and 74.6% of them had poor glycaemic control (HbA1c >7%). The median HbA1c of poor quality sleepers was 8.25% in comparison with good quality sleepers (Median HbA1c-7.80 %). Low positive correlation existed between sleep quality and glycemic control (rs= .09, p=.036). Majority of Type 2 Diabetics have poor sleep quality and poor glycaemic control. Progressive increase in HbA1c existed as the sleep quality worsened. This warrants further exploration into the details of the relationship between sleep quality and glycaemic control.

Keywords: sleep quality, glycaemic control, type 2 diabetes mellitus


How to cite this article:
Princy A, Babu V, Durai S, Asha HS, Belavendra A. Relationship between sleep quality and glycaemic control among subjects with type 2 diabetes mellitus. Indian J Cont Nsg Edn 2017;18:39-45

How to cite this URL:
Princy A, Babu V, Durai S, Asha HS, Belavendra A. Relationship between sleep quality and glycaemic control among subjects with type 2 diabetes mellitus. Indian J Cont Nsg Edn [serial online] 2017 [cited 2021 May 8];18:39-45. Available from: https://www.ijcne.org/text.asp?2017/18/2/39/286268






  Introduction Top


“Sleep”, one of the most beautiful biological omponents of human activity is a benevolent boon from God. This state of unconsciousness rejuvenates the body and nourishes the soul. It enhances health and healing in all the domains concerned with man.

On an average an adult has to sleep for 8 hours in a day. At a global level, 40 million in the U.S. have a chronic sleep disorder (National Sleep Foundation, 2012). Among the South Indian population, the average duration of actual sleep is 7 hours and females (10.3%) experience higher insomnia rates compared to males (8.3%) (Panda et al., 2012). Insufficient sleep can wreak havoc on mental and physical health. Sleep deprivation is rampant in the present day scenario either due to lack of time owing to the busy life schedules or due to sleep disorders. In this context of a sleep deprived world, Type 2 Diabetes Mellitus has emerged as one of the conditions which contribute to the causes of sleep deprivation and is a relatively new concept that has evolved.

Sleep curtailment in Type 2 Diabetes Mellitus provokes insulin resistance leading to a cascade of metabolic changes such as increase of sympathetic neuronal activity, decrease in cerebral utilisation of glucose, increase of evening cortisol and growth hormone levels, glucose intolerance, insulin resistance (increased HbA1c), and reduced acute insulin response to glucose (AlDabal, & BaHammam, 2011). The study reported that the sleep duration was 6.0 ± 1.6 hours in Diabetic patients, and 71% were classified as having poor quality sleep (Knutson, Ryden, Mander, & Van Cauter, 2006). Similar findings were reported in Hyderabad, where the mean amount of self-reported sleep was 6.10 ± 1.66 hours. The mean Pittsburgh Sleep Quality Index score was 8.3 and 71% of patients had a score of > 5 which is clinically diagnostic for poor sleep (Vigg, Vigg, & Vigg, 2003).

With this background, this study was conducted with the following objectives

  • To assess the sleep quality and glycemic control among subjects with Type 2 Diabetes Mellitus
  • To determine the relationship between sleep quality and glycaemic control among subjects with Type 2 Diabetes Mellitus
  • To determine the association of sleep quality and glycaemic control with selected demographic and clinical variables


Hypotheses

H1: There is a significant relationship between sleep quality and glycaemic control among subjects with Type 2 Diabetes Mellitus

H2: There is a significant association between sleep quality, glycaemic control and selected demographic and clinical variables

Conceptual Framework

The conceptual framework for this study is adopted from the two process model of sleep regulation developed by a Swiss pharmacologist and sleep researcher Boberly in 1982.

The balance of the sleep/wake regulation is equivalent to a double pendulum mechanism. The destabilisation causes a chaotic movement. The breaking of the balance can express itself in two modes, either combined effect or separately.

  • Quantitative lack of sleep causes sleepiness
  • Qualitative lack of sleep causes tiredness


The circadian pendulum can be seen like a true internal clock which synchronizes the periods of alertness and sleepiness on the day and night alternation. It appears like one of the main determinants of the gates of sleep. External time givers (light, heat, food, and social contacts) intervene to synchronise sleep on the day/night alternation. It is thus possible to reduce sleep duration but that doesn’t mean that one can reduce his/her need for sleep. Above all, it depends not on the living conditions, but very outstanding individual factors explain the great variabilities that can be observed from one sleeper to the other.

The homeostatic pendulum can be compared with a simple hourglass that is turned upside twice in 24 hours. In the zone of adaptive equilibrium, the duration of each period of wakefulness or sleep is proportional to the duration of the preceding. The return to balance expresses itself through an increase of sleepiness, proportional to the duration of the wake on one hand and a rebound of efficiency of the compensatory sleep on the other hand. That compensatory rebound expresses itself through a quantitative as well as a qualitative increase.

In keeping with the two process approach model of sleep regulation, the circadian pendulum is correlated with the demographic variables such as age, sex, religion, education, occupation, size and type of family, and area of living of the subjects as these outstanding individual variations can disturb the internal clock. The homeostatic pendulum is correlated with the clinical variables such as age of onset of illness, duration of illness, mode of treatment, other illnesses, history of intake of medications influencing sleep quality, history of day time sleepiness, height, weight, BMI, neck circumference, waist circumference, physical activity, alcohol intake, smoking, and tea/coffee intake as they may lead to disequilibrium in the body (see [Figure 1]).
Figure 1: Conceptual framework based on Two Process Model of Sleep Regulation (Boberly, 1982)

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Glycemic control, which is assessed by HbA1c can influence both the pendulums affecting the sleep quality leading to increased wakefulness. Appropriate nursing interventions to maintain the sleep wake cycle will keep the pendulums at a state of homeostasis. These nursing interventions for sleep were not assessed in the study.


  Methods Top


Quantitative non experimental approach and descriptive design was used. A total of 500 subjects with Type 2 Diabetes Mellitus who fulfilled the study criteria were selected using total enumeration sampling technique. The data collection instrument was a structured questionnaire with three parts. Part I consisted of individual’s personal data such as age, sex, religion, education, occupation, size and type of family, and locality of residence. Part II was the clinical proforma of the patient which included clinical diagnosis, age of onset of illness, duration of illness, mode of treatment, other illnesses, sleep affecting medication, history of day time sleepiness, tea/coffee intake, smoking, alcohol intake, height, weight, BMI, neck circumference, waist circumference, and HbA1c. In this study HbA1c value > 7% was considered as poor glycemic control. Part III was the Pittsburgh Sleep Quality Index (PSQI) scale (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). It is a self-rating questionnaire for measuring patient’s sleep quality and specifically assesses the sleep quality in the preceding one-month period. The PSQI yields a global score that is the sum of seven domain’s scores (0 - no difficulty to 3 - severe difficulty), each of which addresses a specific aspect of subjective sleep quality differentiating “poor” from “good” sleep. The component scores were summed to produce a global score (range 0 to 21). Higher scores indicated worse sleep quality. A PSQI of >5 was considered poor quality of sleep. The PSQI has internal consistency and a reliability coefficient (Cronbach’s alpha) of 0.83 for its seven components. Numerous studies using PSQI internationally supported high validity and reliability (Manzar et al., 2015).

Data were collected by the investigator from 500 subjects with type 2 Diabetes Mellitus at the outpatient Department over a period of six weeks. All subjects who met the inclusion criteria were invited to participate in the study. Written informed consent was obtained from those who agreed to participate. The PSQI scale was administered and demographic and clinical variables were noted. Recent HbA1c data was obtained from patient records. The tool was translated into Hindi, Tamil, and Bengali languages. Back translation was done by experts. Approval from the research committee and written informed consent was obtained from the subjects before data collection. Privacy of the subjects and confidentiality of the information was maintained throughout the study.


  Results and Discussion Top


Among the subjects 39.6% belonged to the age group between 50-59 years, females were 51.6% and 80.4% were from Hindu religion. In a similar study showed similar demographics with the average age as 47.4 years and 56% were females (Zizi et al., 2012). Many ofthem had secondary education (41.0%). More than half of the subjects were unemployed (55.2%) and most of the subjects (54.2%) had four to six family members residing along with them. More than half of them belonged to nuclear family (59.4%) and were from urban areas (59%). The age of onset of Diabetes Mellitus was between 40-49 years in 33.4% of the subjects.

In the present study 35.4% suffered from the illness for 6 to 10 years. Oral hypo glycaemic agents were the main mode of treatment for majority of the subjects (66.6%). More than three fourths (85.8%) of the subjects did not consume any medications affecting the sleep quality. Tea consumption was common among the subjects (46%). Most of the subjects did not consume alcohol (94.8%) or smoke (94.6%). Physical activities were not performed by 92.0% of the subjects and 4.4% of the subjects belonged to overweight category. Neck circumference was within the normal range (<35cm) in 36.8% of the subjects while waist circumference was above the normal range in 56.4% of the subjects.

[Figure 2] shows that 63.6% of them had poor sleep quality (PSQI score > 5). The study finding is congruent with the study by Rajendran, Parthsarathy, Tamilselvan, Seshadri, and Shuaib (2012) in which 69% of subjects with type 2 Diabetes had a PSQI score of > 5.
Figure 2: Distribution of subjects with Type 2 Diabetes Mellitus based on overall Sleep Quality

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[Figure 3] shows that majority (74.6%) had poor glycaemic control (HbA1c) and 25.4% of them had good glycemic control. Female subjects had poor sleep (67.1%). Many of the subjects with primary education (70.8%), semiskilled workers (70.9%), and rural residents (72.2%) had poor sleep quality. Poor sleep quality was observed in many of the subjects having an onset of Diabetes Mellitus type 2 between 40-49 years (68.3%), with 21-25 years of Diabetes (80%), with Dyslipidaemia (70%), on sedatives (81.7%), having 2-3 hours of day time sleep (81.3%), smokers (70.4%), with a BMI of < 18.5 (85.7%), with a waist circumference of > 120 cm (80%). In a similar study conducted in Japan on diabetic patients it was noted that higher body mass index, presence of smoking habit, and living alone were significantly associated with sleep disturbance/insomnia symptoms (Narisawa et al., 2017).
Figure 3: Distribution of subjects with Type 2 Diabetes Mellitus based on overall Glycaemic Control

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Subjects between the age group of 50-59 years (72.3%), males (78.5%), Muslims (82.8%) and semi-skilled workers (86.1%) had poor glycaemic control. Poor glycaemic control was noticed in many of the subjects with 21-25 years of Diabetes Mellitus (86.7%), on oral hypoglycaemic agents and insulin therapy (93.1 %), who were hypertensive (79.4%), having day time sleep of 2 to 3 hours (81.2%), and 100% of subjects with a neck circumference of >45cm. Similar studies have reported that longer duration of diabetes, treatment with oral hypoglycaemic agents, treatment with insulin, and lower educational level were associated with poor glycaemic control (Goudswaard, Stolk, Zuithoff, & Rutten, 2004; Jin, Chen, Yu, & Li, 2012).

[Table 1] reveals that statistically significant relationship existed between subjective sleep quality, sleep duration, day time dysfunction, and glycaemic levels (p< .05). Similar studies have reported that poor sleep quality and less-efficient sleep significantly correlated with worse glycaemic control in patients with type 2 Diabetes Mellitus (Knutson et al., 2006; Tsai et al., 2011). In a study conducted by Gozashti, Eslami, Radfar, and Pakmanesh, (2016) subjects who napped (66%) had a lower HbA1c (7.6 ± 1) compared to others (8.1 ± 1.3) (p=0.04) and it was concluded that napping and segmented sleep are associated with a better glycemic control in type 2 Diabetes Mellitus and there is a linear correlation between sleep duration and better glycemic control. Contrary to these study findings in a Jamaican study there was no significant association between sleep and glucose control (Cumberbatch et al., 2011).
Table 1: Correlation of various components of Sleep Quality with Glycaemic Control (HbA1c) among subjects with Type 2 Diabetes Mellitus

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[Table 2] reveals that subjects with poor sleep quality have poor glycaemic control (Median HbA1c-8.25%) compared to subjects with good sleep quality (Median HbA1c-7.80%) and the difference was statistically significant. This finding is congruent with the studies which showed that sleep quality was a significant predictor of HbA1c (Knutson et al., 2006; Tsai et al., 2012). In this study a low positive correlation of 0.09 (p=0.036) existed between sleep quality and glycaemic control. Statistically significant relationship existed between glycaemic control and sleep disturbance (p=0.01). Statistically significant association existed between sleep quality and the location of the house with a likelihood ratio of 11.26 and p value of 0.001. This finding was congruent with a study which found that rural life may have bearing on short sleep duration (Kohatsu et al., 2006). Significant association also existed between glycaemic control with gender and occupation with a likelihood ratio of 3.81 and p value of 0.051. There was significant association between sleep quality with medication intake (likelihood ratio-14.98, p-0.005) and day time sleep (likelihood ratio-15.66, p-0.02). There was a significant association between glycaemic control with duration of illness (likelihood ratio-17.82, p-0.003), mode of treatment (likelihood ratio-39.66, p - 0.000) and tea/coffee intake (likelihood ratio - 8.33, p - 0.040). This is in line with the study conducted among South East Asian Diabetic subjects where PSQI score positively correlated with the duration of Diabetes Mellitus (Rajendran et al., 2012). In yet another study results showed that shorter sleep duration (<6 hrs of sleep duration per night) was associated with a risk of Impaired Glucose Tolerance/Diabetes Mellitus independent of other lifestyle habits and metabolic risk factors (Katano et al., 2011)
Table 2: Median Glycaemic Index according to the Level of Sleep Quality among Subjects with Type 2 Diabetes Mellitus

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Nursing Implications of the Study

Sleep hygiene should be included as one of the aspects in the management of Type 2 Diabetes Mellitus. The focus at this level is to develop knowledge and skills to intervene effectively among those with poor sleep quality.

This emphasizes the need for

  • regular assessment of sleep quality in individuals with Diabetes Mellitus
  • health educating about the need for adequate sleep and sleep hygiene
  • supportive counseling to all patients having poor sleep quality especially those with Diabetes Mellitus



  Conclusion Top


India continues to be the hub of Type 2 Diabetes Mellitusadding to the global burden of the disease. This study has shown a relationship between sleep and glycemic control therefore it demands the attention of health care providers in meeting the global concerns of poor sleep among these patients. Besides medications and other life style modifications, sleep hygiene also plays a role in glycaemic control. Hence, regular sleep quality assessment and health education on sleep hygiene will enhance sleep quality and promote optimal glycaemic control in subjects with Type 2 Diabetes Mellitus.

Conflicts of Interest: The authors have declared no conflicts of interest.



 
  References Top

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