Poor sleep quality among hospital-based healthcare workers: Quality of life and pre-sleep digital device use in a single-centre cross-sectional study

Purpose: Healthcare professionals work in demanding environments that may adversely affect sleep and well-being. This study examined associations between sleep quality, quality of life, pre-sleep smartphone use, and cognitive function among healthcare professionals. Methods: This study was originally registered as a longitudinal investigation of sleep pattern and cognitive change among hospital-based healthcare professionals; attrition arising from participant shift and duty reassignment prevented completion of the longitudinal follow-up, and the analysis reported here is therefore a cross-sectional analysis of baseline data. Ninety-eight participants (58 nurses, 31 clinical research coordinators, 8 postgraduate medical trainees, 1 other hospital-based technician) from a tertiary-care teaching hospital in Karnataka, India, completed the Pittsburgh Sleep Quality Index (PSQI), Montreal Cognitive Assessment (MoCA), WHO Quality of Life-BREF (WHOQOL-BREF), and a questionnaire on pre-sleep smartphone use. Two additional enrolled participants were excluded after audit revealed PSQI totals outside the instrument's valid range with no recoverable source data. Spearman correlations (designated primary given non-normal distributions, with Pearson reported as a sensitivity analysis), independent-samples comparisons, and multiple linear regression (adjusting for age, sex, phone use, occupation, and lifestyle covariates) were performed. Results: Poor sleep quality (PSQI >5) was observed in 45.9% of participants (45/98; 95% CI 36.4–55.8%; mean PSQI 5.33 ± 2.75). PSQI scores were significantly negatively correlated with WHOQOL-BREF scores (Spearman ρ = −0.41, Pearson r = −0.47; both p < 0.001). Participants with poor sleep quality reported lower quality of life than good sleepers (mean difference = 10.21 points; Cohen’s d = 0.76). Pre-sleep smartphone use was positively correlated with poorer sleep quality (Spearman ρ = 0.30, Pearson r = 0.28; both p < 0.01). In a multivariable model adjusting for age, sex, phone use, occupation, and lifestyle covariates, PSQI was independently associated with quality of life within the fitted model (standardised β = −0.42, p < 0.001). No significant association was found between sleep quality and MoCA scores (Spearman ρ = 0.02, p = 0.88); an unanticipated exploratory association between pre-sleep phone use and MoCA scores (Spearman ρ = 0.31, p = 0.002, BH-adjusted p = 0.005) is reported and interpreted with caution. Conclusion: Poor sleep quality is common in this single-centre convenience sample of nurses, clinical research coordinators, postgraduate medical trainees, and other hospital-based technical staff and is associated with reduced quality of life within the fitted model. Pre-sleep smartphone use is a cross-sectional behavioural correlate of poorer sleep and a candidate target for future intervention research. This study was originally registered as a longitudinal investigation of sleep and cognitive change; attrition prevented completion of the longitudinal follow-up, and findings reported here are cross-sectional, baseline associations only.