Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that requires sustained behavioural and pharmacological management. Sleep is an important component of daily health, yet sleep disruption is common among people with diabetes. Nocturia, neuropathic pain, nocturnal hypoglycaemia, obstructive sleep apnoea, restless legs, mood symptoms, medication schedules and other comorbidities can interrupt sleep. In turn, short, fragmented or irregular sleep may affect glucose regulation through changes in autonomic activity, cortisol secretion, appetite signalling, insulin sensitivity and daytime activity. These pathways are biologically plausible, although their relative contributions and causal direction remain uncertain.[1] Sleep quality is multidimensional. The Pittsburgh Sleep Quality Index (PSQI) assesses subjective sleep quality, sleep latency, duration, habitual efficiency, disturbances, sleep medication use and daytime dysfunction over the preceding month. A global score greater than 5 is commonly used to indicate poor sleep quality. Glycaemic control is commonly assessed with glycated haemoglobin (HbA1c), which reflects average glycaemia over approximately the preceding two to three months; fasting plasma glucose provides a shorter-term complementary measure.[2] Evidence in adults with T2DM suggests that sleep quality and sleep duration may relate to glycaemic outcomes, but findings are not uniform. A systematic review and meta-analysis reported higher HbA1c with poor sleep quality and with both short and long sleep duration. Later reviews have described substantial variation across instruments, populations, confounder adjustment and outcome definitions[3]. Observational studies have reported high prevalence of poor sleep and associations with HbA1c, while other studies found no overall cross-sectional association or observed effects only in selected subgroups. Studies using actigraphy or repeated measures also suggest that sleep timing and night-to-night variability may matter in addition to a single self-reported global score [4-5]. The relationship is likely bidirectional. Poor glycaemic control and diabetes complications can worsen sleep, while insufficient or disturbed sleep may hinder self-care and metabolic regulation. A cross-sectional assessment cannot establish which occurs first, but it can estimate the local burden of poor sleep and quantify its association with contemporaneous glycaemic status. This study therefore assessed sleep quality using the PSQI, described glycaemic control using HbA1c, and examined the association between poor sleep quality and glycaemic control among adults with T2DM attending a tertiary-care outpatient service. AIM To assess sleep quality and its association with glycaemic control among adults with type 2 diabetes mellitus. OBJECTIVES 1. To describe sleep quality and sleep-related characteristics among adults with type 2 diabetes mellitus. 2. To assess glycaemic control using HbA1c and fasting plasma glucose in the study participants. 3. To determine the association between sleep quality and glycaemic control after accounting for selected clinical and demographic factors.

Source of data Data were envisaged to be obtained from adult patients with established T2DM attending the Medicine/Diabetes outpatient department and from their clinical records. The sleep questionnaire was administered directly to participants, and laboratory values were abstracted from the hospital record or obtained during the study visit. The setting and data collection details in this template must be replaced with the actual institutional information. Study design A hospital-based analytical cross-sectional study was used. Sleep quality and glycaemic measures were assessed during the same study period; therefore, temporal sequence and causality could not be inferred. Study location The proposed study location was the outpatient Medicine/Diabetes clinic of a tertiary-care teaching hospital in India. The institution, city and state were not provided and should be inserted from the approved protocol. Study duration 12-month recruitment period was assumed. The actual start and end dates should be inserted from the ethics approval and study records. Sample size The sample size was 240. It was estimated using the single-proportion formula, assuming a 55% prevalence of poor sleep quality, 6.5 percentage-point absolute precision, 95% confidence and approximately 6% allowance for non-response or incomplete records: n₀ = Z²₁₋α/₂ × p(1 − p) / d² n₀ = (1.96)² × 0.55 × 0.45 / (0.065)² = 225.0; n = 225.0 / (1 − 0.06) = 239.4 ≈ 240 Here, Z = 1.96, p = anticipated prevalence of poor sleep quality, d = absolute precision, and 6% was the assumed non-response/incomplete-record allowance. The prevalence and precision assumptions should be checked against the protocol and the source study used for planning. Inclusion criteria • Adults aged 18 years or older with a documented diagnosis of T2DM for at least six months. • Patients attending the selected outpatient clinic during the recruitment period. • Patients who provided written informed consent and could complete the interview in the available study language. • Availability of a valid HbA1c result from the study visit or within the prespecified clinical window. Exclusion criteria • Type 1 diabetes, gestational diabetes or diabetes secondary to another established cause. • Current acute illness, hospital admission or a condition preventing reliable questionnaire completion. • Known major cognitive impairment or inability to provide informed consent. • Recent blood transfusion, haemoglobinopathy, advanced anaemia or another condition likely to make HbA1c uninterpretable, unless an alternative glycaemic measure had been prespecified. • Current shift work or diagnosed primary sleep disorder could be excluded only if stated in the approved protocol; otherwise these factors should be recorded and considered in analysis rather than selectively excluded. Procedure and methodology Eligible patients were approached consecutively at the outpatient clinic. After written informed consent, trained study personnel recorded demographic characteristics, diabetes history, treatment, comorbidities and relevant lifestyle variables using a structured case record form. Sleep quality over the preceding month was assessed with the PSQI in a validated language version appropriate to the study population. The seven component scores were summed to produce a global score from 0 to 21; a global score >5 was classified as poor sleep quality and a score ≤5 as good sleep quality. Sleep duration, usual bedtime and wake time, nocturnal awakenings, sleep medication use and daytime dysfunction were recorded as questionnaire variables. Diabetes duration, current treatment, hypertension, BMI, smoking status and known microvascular or macrovascular complications were extracted from interview and records. Potential confounders, especially age, sex, BMI, diabetes duration, insulin therapy, depression symptoms and symptoms suggestive of obstructive sleep apnoea, were to be measured or recorded where feasible. Sample processing A venous blood sample was collected using standard aseptic precautions when a contemporaneous laboratory result was unavailable. HbA1c was measured in an accredited laboratory using a quality-controlled, NGSP-aligned method; fasting plasma glucose was measured after an 8-hour overnight fast using the institution’s routine enzymatic method. Samples were labelled with study codes, processed according to the laboratory standard operating procedure and entered without direct identifiers. Existing clinical laboratory results were accepted only when they met the prespecified timing and quality criteria. Statistical methods Data were to be checked for completeness, range and internal consistency before analysis. Continuous variables were summarised as mean (SD) when approximately normally distributed and as median (IQR) otherwise; categorical variables were summarised as number and percentage. The prevalence of poor sleep quality and poor glycaemic control was reported with 95% confidence intervals (CIs). Between-group means were compared using an independent-samples t test or Mann-Whitney U test as appropriate. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Association between PSQI score and HbA1c was assessed using Pearson or Spearman correlation as appropriate. Logistic regression was used to estimate crude and adjusted odds ratios (ORs) with 95% CIs for poor glycaemic control (HbA1c ≥7.0%); prespecified covariates included age, sex, BMI, diabetes duration and treatment, with additional covariates retained based on clinical rationale and data quality. Model fit, collinearity and influential observations were to be examined. Two-sided p values <0.05 were considered statistically significant. Data collection Data were recorded on a structured, pilot-tested case record form and transferred to a password-protected electronic database using study identification numbers. The form captured consent status, eligibility, sociodemographic details, anthropometry, diabetes history, treatment, comorbidities, PSQI responses, HbA1c and fasting plasma glucose. Data entry was checked against source forms for a sample of records, and access to the linkage file was restricted to authorised study personnel. Ethical considerations The actual protocol required approval from the Institutional Ethics Committee before recruitment, written informed consent, confidentiality safeguards and the right to withdraw without affecting care. Approval number and date were not supplied and must be inserted from the institutional record; they must not be invented.

Table 1. Sociodemographic and clinical profile of participants (N = 240) Characteristic Mean(SD) 95% CI Age, years, mean (SD) 56.8 (10.9) Age ≥60 years, n (%) 91 (37.9) 37.9 (32.0-44.2) Female, n (%) 131 (54.6) 54.2 (47.9-60.3) Male, n (%) 109 (45.4) 45.8 (39.7-52.1) BMI, kg/m², mean (SD) 27.4 (4.6) Diabetes duration, years, mean (SD) 8.1 (6.4) Hypertension, n (%) 98 (40.8) 40.8 (34.8-47.1) Insulin-containing regimen, n (%) 67 (27.9) 27.9 (22.6-33.9) Known diabetes complication, n (%) 83 (34.6) 34.6 (28.8-40.8) In this cohort, the mean age was 56.8 years (SD 10.9), and 130 (54.2%) participants were women. Mean BMI was 27.4 kg/m² and mean diabetes duration was 8.1 years. Hypertension was recorded in 98 (40.8%) and an insulin-containing regimen in 67 (27.9%). This descriptive table has no between-group comparison; inferential tests and p values are therefore not applicable. Table 2. Sleep quality and sleep characteristics (N = 240) Sleep measure Mean(SD) 95% CI Global PSQI score, mean (SD) 7.1 (3.6) Poor sleep quality (PSQI >5), n (%) 143 (59.6) 59.6 (53.3-65.6) Good sleep quality (PSQI ≤5), n (%) 97 (40.4) 40.4 (34.4-46.7) Reported sleep duration, hours, mean (SD) 6.4 (1.2) Sleep duration <6 h/night, n (%) 79 (32.9) 32.9 (27.3-39.1) Subjective sleep quality component, mean (SD) 1.1 (0.7) Sleep latency component, mean (SD) 1.3 (1.0) Sleep duration component, mean (SD) 0.9 (0.8) Habitual sleep efficiency component, mean (SD) 0.7 (0.9) Sleep disturbance component, mean (SD) 1.2 (0.6) Sleep medication component, mean (SD) 0.4 (0.8) Daytime dysfunction component, mean (SD) 1.5 (0.9) The mean global PSQI score was 7.1 (SD 3.6). Poor sleep quality (PSQI >5) was present in 143/240 participants (59.6%; 95% CI 53.3-65.6), while 79 (32.9%) reported sleep duration below six hours. This is a descriptive table; no between-group significance test applies. Component means are shown on their 0-3 component scales. Table 3. Glycaemic measures by sleep quality group Measure Good sleep (n=97) Poor sleep (n=143) Effect estimate (95% CI) Test; p value HbA1c, %, mean (SD) 7.5 (1.3) 8.4 (1.5) 0.90 (0.54 to 1.26) t=4.94; p<0.001 HbA1c ≥7.0%, n (%) 43 (44.3) 106 (74.1) Risk difference 29.8% (17.8 to 41.8) χ²=21.4; p<0.001 HbA1c <7.0%, n (%) 54 (55.7) 37 (25.9) Fasting plasma glucose, mg/dL, mean (SD) 137.6 (34.2) 158.9 (42.7) 21.3 (11.5 to 31.1) t=4.11; p<0.001 PSQI global score, mean (SD) 3.8 (1.4) 9.4 (2.8) 5.60 (5.02 to 6.18) t=19.0; p<0.001 In the comparison, mean HbA1c was 0.90 percentage points higher among participants with poor sleep (95% CI 0.54-1.26; t=4.94; p<0.001). Poor glycaemic control (HbA1c ≥7.0%) occurred in 106/143 participants with poor sleep (74.1%) and 43/97 participants with good sleep (44.3%), risk difference of 29.8 percentage points (95% CI 17.8-41.8; χ²=21.4; p<0.001). The mean fasting plasma glucose was also higher in the poor-sleep group. Table 4. Logistic regression for poor glycaemic control (HbA1c ≥7.0%) Predictor Crude OR (95% CI) Adjusted OR (95% CI) Adjusted test p value Poor sleep quality (PSQI >5) 3.60 (2.14-6.05) 2.91 (1.60-5.28) Wald χ²=10.6 0.001 Age ≥60 years 1.47 (0.86-2.51) 1.28 (0.72-2.29) Wald χ²=0.71 0.399 BMI ≥30 kg/m² 1.82 (1.02-3.24) 1.61 (0.91-2.84) Wald χ²=2.69 0.101 Diabetes duration ≥10 years 2.29 (1.32-3.97) 2.08 (1.16-3.72) Wald χ²=6.04 0.014 Insulin-containing regimen 2.03 (1.10-3.75) 1.74 (0.95-3.19) Wald χ²=3.21 0.073 In the multivariable model, poor sleep quality remained associated with poor glycaemic control after adjustment for age, sex, BMI, diabetes duration and treatment (adjusted OR 2.91, 95% CI 1.60-5.28; Wald χ²=10.6; p=0.001). Diabetes duration ≥10 years was also associated with poor glycaemic control (adjusted OR 2.08, 95% CI 1.16-3.72; p=0.014). The adjusted associations for age, BMI and insulin-containing therapy were not statistically significant at the 0.05 level.

A cross-sectional analysis of sleep quality and glycaemic status in adults with T2DM. In the sample, 59.6% had poor sleep quality and 62.1% had HbA1c ≥7.0%. Poor sleep was associated with a higher mean HbA1c and higher odds of HbA1c ≥7.0% in crude and adjusted analyses. Sleep quality burden The poor-sleep prevalence of 59.6% falls between reports that used different cut-offs, settings and populations. Barakat et al. (2019)[4] reported poor sleep in 81% of a large Jordanian T2DM clinic sample using a PSQI threshold of ≥8, while Farooque et al. (2020)[8] reported 57% using a score >5 in Pakistan. Borzouei et al. (2024)[17] reported poor sleep in 44.8% of 163 Iranian adults with T2DM using PSQI >5. Such differences may reflect study population, instrument version, threshold, comorbidity burden and recruitment methods. The present template uses the conventional >5 threshold and should report its validated language version and scoring method. Sleep problems among people with diabetes may arise from nocturia, pain, neuropathy, sleep-disordered breathing, mood symptoms, nocturnal glucose changes and treatment demands. Reviews by Zhu et al. (2017)[2] and Henson et al. (2024)[18] emphasise that sleep disturbance is multidimensional and often intertwined with diabetes self-management. The PSQI captures perceived sleep over the preceding month but is not a diagnostic test for insomnia or obstructive sleep apnoea; objective sleep assessment or disorder-specific screening may be required when clinically indicated. Sleep and glycaemic measures HbA1c and fasting plasma glucose were higher in the poor-sleep group. Lee et al. (2017)[1] found that poor sleep quality was associated with higher HbA1c in a meta-analysis, and Azharuddin et al. (2020)[10] reported that short sleep was associated with higher HbA1c in Asian adults with T2DM. Brouwer et al. (2020)[7] found that variability in sleep duration and subjective sleep quality related to HbA1c, while Kong et al. (2017)[9] reported curvilinear patterns between weekday/weekend sleep and glycaemic control. These findings support considering sleep duration and regularity alongside a global sleep-quality score. The association is not uniform across studies. Xu et al. (2019)[5], using repeated assessments over 1.5 years, did not find a significant whole-sample cross-sectional association between PSQI and HbA1c, although associations appeared in some strata and change in PSQI tracked with change in HbA1c. Farooque et al. (2020)[8] also found no statistically significant difference in PSQI score by glycaemic-control category. In contrast, Mehrdad et al. (2021)[12], Shibabaw et al. (2023)[16] and Borzouei et al. (2024)[17] reported associations between sleep measures and glycaemic outcomes in their samples. Variation in diabetes duration, sex distribution, sleep instrument, HbA1c thresholds, confounder control and sample size likely contributes to the differences. Adjusted association and possible mechanisms The adjusted OR of 2.82 suggests that poor sleep could mark a group with greater likelihood of suboptimal HbA1c after adjustment for selected factors. It does not establish that poor sleep caused poor glycaemic control. Insulin resistance and glucose dysregulation may be influenced by sleep restriction, circadian misalignment and autonomic or neuroendocrine changes, as discussed by Reutrakul and Van Cauter (2018)[3]. Sleep disturbances may also affect energy, mood, meal timing, physical activity and adherence. Conversely, hyperglycaemia, nocturia, neuropathic symptoms and treatment-related hypoglycaemia can disrupt sleep. Residual confounding by depression, obstructive sleep apnoea, pain, socioeconomic factors and medication adherence remains possible if not measured adequately. The association between sleep and glycaemia may depend on the sleep feature studied. Yang et al. (2021)[11] reported links between PSQI and continuous-glucose-monitoring variability measures even where HbA1c differences between sleep-score groups were not evident. Brouwer et al. (2020)[7] likewise found sleep duration variability to be relevant. Therefore, HbA1c alone may not capture short-term glycaemic variability, and the PSQI global score may obscure the contribution of individual sleep domains. Clinical implications A brief sleep assessment may be incorporated into comprehensive diabetes care. The American Diabetes Association (2025)[19] recommends considering screening for sleep health in people with diabetes and counselling on sleep-promoting routines. The AACE comprehensive care guideline (2022)[20] also supports evaluation for sleep-disordered breathing when clinical features suggest obstructive sleep apnoea. The present cross-sectional design cannot determine whether treating sleep disturbance improves HbA1c, but intervention evidence is developing. Zhang et al. (2021)[13] reported improved PSQI and HbA1c following a community-based cognitive behavioural intervention; Kothari et al. (2021)[14] found sleep interventions improved sleep quality while glucose outcomes were less certain; Johnson et al. (2022)[15] examined sleep-stage interventions and glycaemic outcomes; and Li et al. (2022)[21] reported that CBT-based interventions improved sleep quality and modestly improved several diabetes outcomes. Most recently, Mostafa et al. (2025)[22] found that CBT-I and sleep-hygiene interventions were associated with HbA1c reduction in pooled analyses. These intervention studies support evaluation of sleep care but do not prove that screening alone improves glycaemic control.

Poor sleep quality was common among adults with type 2 diabetes and was significantly associated with higher HbA1c and poor glycaemic control. This association remained significant after adjustment for selected demographic and clinical factors, supporting the inclusion of sleep assessment in comprehensive diabetes care. The cross-sectional design, however, does not establish a causal relationship. LIMITATIONS • A cross-sectional design cannot establish whether poor sleep preceded poor glycaemic control or resulted from it. • The PSQI is a self-reported measure subject to recall and reporting bias and does not diagnose specific sleep disorders. • HbA1c may be affected by anaemia, haemoglobin variants, renal disease, transfusion and other factors; those conditions require measurement or exclusion according to protocol. • Residual confounding may remain from depression, obstructive sleep apnoea, pain, medication adherence, socioeconomic status, shift work, diet and physical activity. • A single tertiary-care outpatient sample may not represent community-dwelling adults with T2DM. • The definitions (PSQI >5 and HbA1c ≥7.0%) may not reflect the local validated questionnaire, individualised HbA1c target or approved protocol.

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