Fear of Hypoglycemia: A key predictor of Sleep Quality among the Diabetic Population

Dr. Hafiz Rashid Hussain ^1* Nabeel Ahmed ¹ Muhammad Waseem Akram ² Faisal Gulzar ³ Muhammad Asad ⁴ Sana Tahseen ¹ Tanveer Ahmed ⁵ Abdul Malik ^6* Suhail Akhtar ⁷ Ayesha Shahid ¹ Mah Noor ¹ Maryam Pervaiz ¹ Muneeb Ur Rahman ¹

• ¹ College of Pharmacy, Faculty of Pharmacy, University of Sargodha, Sargodha, Pakistan
• ² University of Wollongong, Wollongong, New South Wales, Australia
• ³ Department of Pharmacy, Faculty of Pharmaceutical Sciences, The University of Chenab, Gujrat, Pakistan
• ⁴ Oxford College of Medical Sciences, Mandi Bahauddin, Pakistan
• ⁵ Ahmad Polyclinic & Diabetic Center, 40100, Sargodha, Pakistan
• ⁶ Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
• ⁷ Department of Biochemistry, A.T. Still University of Health Sciences,, Kirksville, Missouri, United States

Every one in seven people with Type-I or Type-II diabetes suffers from fear of hypoglycemia (FOH). Its impact on quality of life, glycemic control, and health outcomes is well studied.However, its relationship with sleep quality remains underexplored, particularly in developing societies. We hypothesize that FOH is a key predictor of sleep quality in Type-I and Type-II patients with diabetes and, therefore, needs detailed investigation.A multicentric study was conducted across five cities and six centers of Punjab. Data from 310 diabetes patients were analyzed using the Hypoglycemia Fear Survey-II (HFS-II) Scale and the Pittsburgh Sleep Quality Index (PSQI). Statistical analyses explored subgroup variations, correlations, regression models, and receiver operator curve (ROC) estimation.The study reports 57.70% of patients with poor sleep among whom 47% had elevated FOH.Sleep quality, age, gender, diabetes duration, and insulin route significantly correlated with FOH (p < 0.05), while glycemic control and insulin use did not. Binary logistics showed that for every one-unit increase in FOH, the odds of experiencing poor sleep increased by approximately 3.7% (p < 0.001; OR 1.037). Five out of seven sleep components (sleep quality, efficiency, disturbance, medication use, and daytime dysfunction) were significantly related to FOH. We hypothesize that FOH might specifically influence the quality rather than the initiation or termination of the sleep cycle. ROC analysis revealed that HFS-II may be better at diagnosing poor sleep in patients than by chance (p < 0.001) with an AUC of 0.691.FOH is a key predictor of sleep quality among patients with diabetes. Healthcare providers should prioritize patient education targeting common FOH concerns and assess patients with disturbed sleep for elevated FOH levels, as it may contribute to sleep disturbances.