The Science of Diabetes Self-Management and Care2023, Vol. 49(4) 303 –313© The Author(s) 2023Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/26350106231173675journals.sagepub.com/home/tde
AbstractPurpose: The purpose of the study was to explore the effect of synergistic interaction and the independence of physical activity and inflammatory markers, including platelet–lymphocyte ratio (PLR) and neutrophil-tolymphocyte ratio (NLR), on sleep quality in persons with type 2 diabetes mellitus (T2DM).
Methods: This descriptive cross-sectional study included 294 persons with T2DM in East Java, Indonesia. Fasting blood was analyzed for inflammation markers, including NLR and PLR. Physical activity and sleep quality were assessed using Metabolic Equivalent of Task and Pittsburgh Sleep Quality Index, respectively. Multivariate logistic regression, chi-square test, and independent t test were performed.
Results: There was an independently significant relationship between physical activity, NLR, and PLR with sleep quality. Persons with both active physical activity and low PLRs were 12.52 times more likely to have high sleep quality scores than those with low physical activity and high PLRs. A positive additive interaction of active physical activity and low PLRs for high sleep quality scores was identified.
Conclusion: Physical activity, NLR, and PLR revealed an independently significant relationship with sleep quality. Persons with both active physical activity levels and low PLR were the most substantial synergistic effect of high sleep quality. Further studies are necessary to examine the problems and solutions for sleep quality in this population.
Type 2 diabetes mellitus (T2DM) is the leading cause of health burden and disability in most countries world-wide.1,2 The global mortality rate from T2DM is projected to increase fastest, to approximately 700 million by 2045.3,4 An epidemiological investigation projected a growing incidence of T2DM in Indonesia from 19.5 million in 2021 to 28.6 million in 2045.4 The American Diabetes Association recommends assessing sleep duration, patterns, and disorders in individuals with T2DM.5 Interestingly, sleep problems are a predominant factor but a less known burden and risk factor for developing T2DM complications.6
Sleep problems were significantly related to impairments glucose metabolism regulation,7 increased inflammation,8 and immunity system.9 Thus, impaired sleep contributes to the rising prevalence of T2DM.9 Previous studies showed that 42% to 76.8% of individuals with T2DM have low sleep quality.10-12 Low sleep quality is an independent factor for T2DM-risk complications13 and can reduce quality of life.14,15 A meta-analysis reported that numerous studies on sleep quality among individuals with T2DM had been explored in Asian countries, such as China, India, Taiwan, South Korea, and Japan, to recognize the predictor of sleep quality.16 However, this information is rarely explored in Indonesia. Indonesia is a country with a majority Muslim population. A previous study highlighted that Muslims have certain sleeping habits that adhere to specific sleep quality regulations.17 Moreover, a study in United Arab Emirates suggested that the nursing profession is encouraged to determine how to evaluate and integrate culturally relevant sleep treatment programs for Muslims that could help persons with T2DM achieve sleep quality.15
A systematic review analysis suggested that moderate to vigorous physical activity enhanced sleep quality levels.18 Furthermore, physical activity is considered an efficient and straightforward nonpharmaceutical method that will improve sleep quality among people with T2DM.5 Higher significant daily physical activity was strongly correlated with high sleep quality compared with individuals who had physical inactivity.19 A study in Taiwan revealed that 10 to 25 minutes of moderate to vigorous physical activity per day was correlated with a 2.8%-fold increase in sleep quality.8 Hence, a lack of investigation has been published on the connection between low sleep quality and physical inactivity in individuals with T2DM.8,19 Therefore, estimating their association is essential for improving sleep quality.
Various standard blood test markers of inflammation have received considerable interest due to their low-cost test.20 Platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) measurements are reproducible and easy to undertake as inflammation markers.21-23 Additionally, persons with schizophrenia revealed a negative association between high PLR and NLR scores and low sleep quality.24 High NLRs were significantly related to the severity of obstructive sleep apnea syndrome.25 Moreover, Koseoglu et al26 revealed that individuals with high PLR scores had a 1.03-fold risk of developing obstructive sleep apnea syndrome, which indicated low level of sleep quality in persons with cardiovascular disease. However, the role of PLR and NLR scores on sleep quality in persons with T2DM remains undetermined.
Remarkably, physical inactivity was positively related to high NLR counts.27,28 Low midarm muscle circumference levels could decrease activity performance, indicating those who had low physical activity or inactivity. Conversely, physical activity might help reduce inflammation and mortality risk among those with a high PLR count.29 Consequently, physical activity may decrease PLR, and NLR counts underline the anti-inflammatory process and can greatly improve sleep quality. The synergistic effect of physical activity with inflammation markers on sleep quality among individuals with T2DM remains controversial, especially in Indonesia, with a majority Muslim population. Thus, this study’s purpose was to explore the independent relationship between physical activity and low PLR and NLR levels and their synergistic interaction on sleep quality levels in persons with T2DM.
A descriptive cross-sectional design was applied for this study. This is because this study aimed to describe the effect of synergistic interaction and the independence of physical activity and inflammatory markers, including PLR and NLR, on sleep quality in persons with T2DM in Indonesia at one point of time. Participants were recruited from East Java Province, Indonesia; the study was conducted from July to November 2018 and used stratified multistage cluster sampling from 5 community clinics in 3 urban and 2 rural areas. Initially, this study separated the province of East Java into 38 distincts. Then, 2 rural and 2 urban areas were chosen from the 38 regions. Thus, 8 community clinics from each area were randomly selected for data collection. However, 3 of the selected community clinics declined the request to conduct research. Thus, 5 community clinics, including 2 community clinics in Surabaya, 1 community clinic in Kediri, and 1 community clinic in Lamongan and Sidoarjo, agreed to participate in the study.
The eligible criteria for the participants include the following: (1) Indonesian nationals between 17 and 79 years old (2) with a T2DM diagnosis with fasting plasma glucose of >126 mg/dL (as verified by medical practitioners)30 and (3) without dementia diagnosis. The following participants with T2DM were excluded: (1) pregnant, (2) used antidepressants, (3) had auditory and vision deficiencies, and (4) unable to communicate and walk independently. This study was conducted following the Declaration of Helsinki and was approved by the Ethics Review Board of the Siti Khodijah Muhammadiyah Sepanjang Hospital Ethical Review Board (reference: 009/KET-TEP/X-2018). Informed consent was granted to each respondent online, who was assured of anonymity and confidentiality; they were free to withdraw from the survey at any time.
This study utilized G*Power 3.1 to calculate the total sample size with an a priori F-test power analysis with logistic regression. Importantly, the standard power level was 0.80, and the alpha was 0.05 with the Odds ratio of 1.5.12 The power analysis, with a total of 14 predictors, revealed that this study needed at least 224 participants. The sample size of eligible persons with T2DM was increased from 288 to 300 with an estimated 30% to 35% attrition rate. Finally, total sample size was 294 respondents after collecting data and indicating adequate power.
All the respondents were evaluated by trained nursing professionals using a questionnaire that consists of questions related to participant demographics, including age, marital status, smoking status, education, gender, diabetes duration, body mass index (BMI), and income.31
The key biological measures included BMI, PLR, and NLR. Research assistants were responsible for taking the height and weight measurements, which were then doublechecked with the medical records. BMI was determined by dividing body weight in kilograms (kg) by the square of their height in meters (m2). The BMI was categorized into 2 groups: recommended nonobese (BMI of <25 kg/m2) and obese (BMI of ≥25 kg/m2).32 Platelet counts, neutrophils, and lymphocytes were detected using a fasting blood sample in a Sysmex hematology cell counter XP-100 automated analyzer in a private laboratory (International Organization for Standardization 9001:2015 and 14001:2015 certified). PLR was determined as the ratio of platelets to lymphocytes, whereas NLR was determined as the ratio of neutrophils to lymphocytes. PLR values utilized the area under the curve (AUC) of 0.914 with 2 categories, such as <121.79 and ≥121.79 of 0.914 scores, and had 80% sensitivity and 92% specificity. Moreover, NLR values based on an AUC of 0.83 with 2 groups were identified as high (NLR of ≥1.94) and low (NLR of <1.94) scores with 80% sensitivity and 80% specificity.
Sleep quality was investigated with the Pittsburgh Sleep Quality Index (PSQI). Buysse et al33 reported that the PSQI is a validated self-report that analyzes sleep quality during the previous month. PSQI consists of 19 items and 7 subscales, including sleep efficiency, quality, latency, use of sleep medication, duration, daytime dysfunction, and disturbances. Each score subscale varied between 0 (better) and 3 (worse) to obtain a sum score. Consequently, a total PSQI score from 0 to 21 was based on the total values of the 7 subscales.33 The PSQI score was interpreted as a categorical scale for sleep quality (low sleep quality with a score of ≥5 and high sleep quality with a score of <5). The total PSQI Bahasa Indonesia Version score with a cutoff value of 5 was presented with a sensitivity of 1.0 and a specificity of .81.34 Moreover, the validity of the Indonesian version PSQI was high (r = 0.89), and the internal consistency was adequate with Cronbach’s alpha of 0.80.34 In this study, Cronbach’s alpha coefficient was 0.92.
The physical activity level was determined using the modified physical activity guidelines from Advisory Committee for Americans and Godin Leisure-Time Exercise by calculating the metabolic equivalent of task (MET)-hour/week.35,36 The MET are categorized in terms of physical activity intensity, type, and duration in minutes as performed weekly, with activity types categorized as follows: mild (eg, fishing from a river bank, yoga, walking), moderate (eg, tennis, bicycling, folk dancing), and vigorous (eg, running, squash, football). Furthermore, the number of time spent and frequency of moderate, vigorous, and mild are multiplied by 5, 9, and 3, respectively. Thus, each participant’s total physical activity score is low (< 7.49 MET-h/wk) and high (≥ 7.5 MET-h/wk). Consequently, this study calculated the total amount of physical activity in arbitrary units by adding the frequency of using each element for exercise. For illustration, the total MET-hour/week score would be calculated as follows: (3 [mild] × 1 h/time × 3 times/wk) + (5 [moderate] × 1 h/time × 2 times/wk) + (9 [vigorous] × 1 h/time × 1 time/wk) = 9 + 5 + 9 = 23 MET-hour/week when a participant performed 1 hour of walking 3 times per week, 1 hour of gym exercise 2 times per week, and 1 hour of soccer once a week, which indicates a high physical activity.
The psychological problem level was measured using the Depression, Anxiety, and Stress Scale 21 (DASS-21) items. DASS-21 has a 4-point severity scale, where persons with T2DM were asked to rate the frequency with which they experienced depression, anxiety, and stress symptoms. Each domain consists of 7 items with scores ranging from 0 to 21.37 The present study categorized (1) depression data with a “yes†for scores of ≥10 and a “no†for scores of <10, (2) anxiety data with a “yes†for scores of ≥8 and a “no†in scores for <8, and (3) stress data with a “yes†for scores of ≥15 and a “no†for scores of <15. Cronbach’s alpha values for the Bahasa Indonesia translation of stress, anxiety, and depression are 0.72, 0.85, and 0.87, respectively.37 In this study, Cronbach’s alpha values was 0.90, 0.85, and 0.67 for anxiety, depression, and stress, respectively.
All the statistical calculations were conducted using Statistical Package for Social Science Version 25.0 (Chicago, IL, USA), with a P value of <.05 indicating significant differences. Distributions of sociodemographic and determining factors between groups are reported as a percentage (%) and frequency (No.). The mean with standard deviations (SDs) was analyzed using an independent t test and chi-square. The normality of data distribution indicated the Z scores for kurtosis and skewness of <3.29.28,38 This result on the skewness and the kurtosis of the Z score was 0.83 and 1.20, respectively. The maximum variance inflation factor (VIF) in this study was 2.83. A VIF score of <10 indicated low multicollinearity.39 The adjusted odds ratios (AORs) for sleep quality via multivariate logistic regression were calculated by adjusting for potential confounding factors, including age, gender, marital status, income, education, duration of diabetes, body mass index, smoking status, and stress, anxiety, and depression levels.
Synergistic interaction between PLR and physical activity on sleep quality was evaluated after creating 4 dummy variables for the following 4 (2 × 2) conditions: (a) low physical activity and high PLRs (the reference condition or ßa), (b) low physical activity and low PLRs (ßb), (c) high physical activity and high PLRs (ßc), and (d) high physical activity and low PLRs (ßd). The additive interaction using the following categories: (a) ßd = ßc × ßb indicated no interaction, (b) ßd > ßc × ßb indicated a positive interaction, and (c) ßd < ßc × ßb indicated a negative interaction.40,41 Additionally, the synergistic effect computed from the logistic regression with the 95% confidence intervals (CIs).
Table 1 lists the remaining demographic characteristics of the participants. The present investigations included 294 participants. High sleep quality was observed in 24.83% of the sample (n = 73), which had a mean age of 55.49 ± 6.81 years and 55.27 ± 7.27 years for individuals with low and high sleep qualities, respectively. Moreover, no significant differences were found regarding their age, marital status, gender, and education between individuals with low and high level of sleep quality scores.
Table 2 shows the substantial differences in PLR, NLR, physical activity, anxiety, and smoking status distribution. Interestingly, there were no significant differences in disease duration, stress, depression, and BMI. The proportion of those who had PLR of <121.79 was higher in the high sleep quality (65.8%) than in the low sleep quality group (28.1%).
Table 3 shows the multivariate logistic regression of physical activity, NLR, and PLR levels for sleep quality in participants with T2DM. Additionally, those with high physical activity were 2.91 times more likely to present high sleep quality than those with low physical activity (AOR = 2.91, 95% CI, 1.0, 8.52). Notably, elevated high sleep quality by 3.47 was identified in subjects with a low PLR (<121.79) compared to those with a PLR of ≥121.79.
Table 4 demonstrates a significantly escalated 12.52 times high sleep quality in a combination of high physical activity and low PLR compared to those with a combination of low physical activity and high PLR. Furthermore, high physical activity and low PLR for high sleep quality had a positive additive interaction (formula of synergistic additive interaction; AOR value of both high physical activity and low PLR > AOR value of high physical activity and high PLR × both low physical activity and low PLR; 12.52 > 3.68 × 3.37; 12.52 > 12.40) after controlling for confounding variables for age, gender, marital status, smoking status, income, education, stress, anxiety, depression, duration of diabetes, BMI, and NLR levels.
This study revealed the empirical evidence of the synergistic interaction between physical activity and PLR on sleep quality in participants with T2DM. This result suggested that physical activity, NLR, and PLR were related to sleep quality. Additionally, results revealed that high physical activity and low PLR were synergistically associated with improved high sleep quality score.
Current findings suggested that most of the participants (75.17%) had low sleep quality. The proportion of individuals with T2DM who had low sleep quality was 81% in Jordan,13 80% in the United States,42 77.4% in Turkey,43 and 72% in Saudi Arabia.44 The proportions in this study are higher than those of studies conducted in Kenya with 53.4%,45 48.4% in Myanmar,46 63.9% in Trinidad and Tobago,47 and 63.3% in Thailand.48 Moreover, a significant proportion of 87.5% of individuals with T2DM reported that they typically take >15 minutes to fall asleep each night.49 These contradictions can be attributed to variances in lifestyle, socioeconomic status, treatment regimen, and cultural differences.13,15,50 Mandatory Muslim practices in Indonesia include early awakening every morning for the first prayer at dawn or approximately 1 hour before sunrise and avoiding sleep in the evening approximately 1.5 to 2 hour before sunset. Additionally, behaviors, such as the brief midday nap (known as Qailulah in Islamic culture) and sleeping on the right side while avoiding sleeping in the prone position, are strongly reflected in Muslim culture.51
Moreover, this study revealed that physical activity was related to high sleep quality. Previous studies in this field revealed that persons who had moderate to vigorous physical activity had high sleep quality.8,52 Previous research supports the current finding to investigate the common factors affecting sleep quality among Arabians with T2DM, in which authors revealed that individuals with physical inactivity had a 1.56-fold risk of having improved low sleep quality compared with individuals who had active physical activity.19 A randomized control trial study in Sweden revealed that Nordic walking for 5 hours per week over 4 months significantly improved sleep quality among individuals with T2DM.53 Several mechanisms potentially provide the pathways that affect both physical activities and sleep quality among individuals with T2DM. First, a review study demonstrating that physical activity increases slow-wave sleep, which is believed to be the most restorative sleep component, provides evidence for restoring sleep mechanisms.54 Second, the restoration mechanism can enhance energy expenditure and stimulate prolonged sleep to restore physical health.54,55 Third, the thermoregulation mechanism explains the present findings; specifically, increased physical activity-induced temperature stimulates subsequent temperature downregulation, resulting in deeper sleep.54,56
Notably, previous studies revealed that sleep quality was strongly significant concerning inflammatory markers.21 Importantly, PLR and NLR are novelty systemic inflammatory markers in persons with T2DM.21,22 A meta-analysis established that NLR was connected with complete upper airway obstruction during sleep, thereby predicting the disease severity in persons with obstructive sleep apnea.57 Another meta-analysis revealed that high NLR and PLR counts were associated with declined total sleep time. These findings suggest that enhancing sleep quality may assist in decreasing inflammatory markers such as NLR and PLR.24 Low PLR levels were strongly correlated with declined nocturnal catecholamine levels,58 and higher NLRs were strongly associated with insomnia.59 Sleep disturbances accompanied by reduced slow-wave sleep contribute to higher nocturnal catecholamine levels.60 Thus, low sleep quality with restricted sleep indicates increased nocturnal catecholamine levels. The previous findings of another study confirm that endocrine alterations and pro-inflammatory cytokine responses may be involved in leukocyte production in reactions to sleep disturbance. Moreover, low sleep quality will provoke worse psychopathology and increase systemic inflammation.61 Those mechanisms may contribute to a better understanding of insight pathways that PLR and NLR, as systemic inflammation markers, may regulate sleep quality in persons with T2DM.
Previous study results revealed that the combination of high physical activity and low PLR levels synergistically improved high sleep quality. The possible mechanism for this synergistic effect on sleep quality may be clarified by the interaction between PLR and physical activity. PLR may be represented as an alternative to NLR by replacing platelets for neutrophils in calculating cellular immune inflammatory biomarkers.22,62 Concerning physical activity-induced platelets, PLR appears similarly useful for depicting inflammation in response to moderate to vigorous physical activity.22 These studies are consistent with current finding that respondents with high PLR counts and physical activity of ≥7.5 MET-h/wk had high sleep quality than participants with only 1 of these characteristics. Moreover, increasing physical activity is an important treatment to avoid high PLR count strategies against low sleep quality among T2DM.
Notably, the present study has several limitations. First, the quality of sleep in this study was measured using a self-reported PSQI questionnaire, which has potential for appropriate self-report biases. Thus, objective measures such as research-grade actigraphy, consumer-grade actigraphy, and polysomnography should be investigated in the future to validate and minimize self-report biases. However, the PSQI has been recognized as an international standard measurement, utilizing reliable and validated questions to overcome this limitation. Second, this study was cross-sectional, and therefore, causal relationships observed cannot be drawn. Third, researchers did not rule out the possibility that PLR and NLR are influenced by T2DM-associated causes; however, this investigation has already controlled for possible confounding variables. In addition, future investigations should investigate other novel systemic inflammation markers and possible factors that can affect NLR and PLR scores. Furthermore, it may have missed other confounding factors, such as work time, fatigue, and neuropathic pain, which should be investigated in future research, although this study has already adjusted several confounding factors.
Low level of sleep quality is a common public health issue in persons with T2DM. Intriguingly, the physical activity and low PLRs work synergistically to improve sleep quality among persons with T2DM. This finding could help identify and promote physical activity and low NLR strategies to control and optimize sleep quality in T2DM. Additionally, diabetes care and education specialists might educate individuals with T2DM on restricting the variables that contribute to low sleep quality by promoting physical activity and monitoring PLR count.
This results have several implications for research and clinical practice. When evaluating the level of physical activity and inflammation markers, such as PLR and NLR among persons with T2DM who also have sleep quality problems, medical staff should consider sleep problems into consideration. To develop a suitable intervention approach to improve sleep quality, diabetes care and education specialists need information regarding physical activity and inflammatory markers. Diabetes care and education specialists must comprehend their perspectives and sleeping habits to provide culturally acceptable care for persons with T2DM. Consequently, diabetes care and education specialists should develop intervention measures, such as increased physical activity and monitoring inflammation markers, to improve sleep quality.
Y. A. Rias contributed to conception, designed, acquisition, analysis, interpretation, drafted manuscript, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. R. Thato contributed to acquisition, analysis, interpretation, drafted manuscript, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. H. T. Tsai contributed to acquisition, analysis, interpretation, drafted manuscript, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. All authors read and approved the final manuscript.
The authors thank all patients who participated in the study. The authors would like to thank Postdoctoral Fellowship, Ratchadapisek Somphot Fund, Chulalongkorn University, Faculty of Nursing, Chulalongkorn University, and School of Nursing, College of Nursing, Taipei Medical University, and Institut Ilmu Kesehatan Bhakti Wiyata Kediri for the academic support.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, funding, and/or publication of this article.
This study was supported by the Postdoctoral Fellowship, Ratchadapisek Somphot Fund, Chulalongkorn University.
Yohanes Andy Rias https://orcid.org/0000-0001-5403-2161
From Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand (Dr Rias, Prof Thato); Faculty of Health and Medicine, College of Nursing, Institut Ilmu Kesehatan Bhakti Wiyata, Kediri, Indonesia (Dr Rias); Post-Baccalaureate Program in Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan (Prof Tsai); and School of Nursing, College of Nursing, Taipei Medical University, Taipei Taiwan (Prof Tsai).
Corresponding Author:Ratsiri Thato, Faculty of Nursing, Chulalongkorn University, Borommaratchachonnanisisattaphat, 11, Rama I Rd, Wang Mai, Pathum Wan, Bangkok, 10030, Thailand.Emails: ratsiri.T@chula.ac.th; ratsiri99@gmail.com
