Abstract Poor sleep quality is a critical health burden among older adults, potentially exacerbated by sensory deficits like hearing loss. This study investigates the impact of hearing loss on sleep quality in community-dwelling older adults in Vietnam. A cross-sectional study evaluated 779 adults aged ≥60 (mean age 71.1; 61.9% female) in Hung Yen Province. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI; poor sleep >5) and hearing loss via pure tone audiometry (PTA ≥20 dB). Poor sleep quality was alarmingly prevalent (67.9%; mean global PSQI = 8.0 ± 3.9). Participants with hearing loss exhibited significantly higher PSQI scores than normal-hearing peers (median 8.0 vs. 7.0, P = 0.003), with subjective sleep quality, sleep latency, and sleep disturbances most severely affected. Multivariable logistic regression demonstrated that hearing loss independently increased the odds of poor sleep by 62% (aOR=1.62, 95% CI: 1.15–2.29, P = 0.006). A significant dose-response trend emerged: poor sleep prevalence rose from 58.4% (normal hearing) to 71.1% (mild hearing loss) and 72.4% (moderate+ hearing loss) (P = 0.002). Beyond hearing status, advanced age and hypertension further impaired sleep, whereas higher education and moderate-to-high physical activity were strongly protective (aOR=0.55–0.58). Poor sleep is highly prevalent among older Vietnamese adults and is significantly compounded by hearing loss in a dose-dependent manner. Integrating comprehensive sleep assessments into routine geriatric and audiological care is strongly warranted to improve overall well-being.

Keywords: Hearing loss, Sleep quality, Pittsburgh sleep quality index, Older adults, Vietnam

Funding: This study was funded by the Hung Yen Department of Science and Technology. The funder had no role in study design, data collection, analysis, interpretation, or manuscript preparation.

Citation:  Nguyen, X.T., Hoa, P.T., Hung, N.Q., Ha, N.N., Dao, D.T., and Tran, V.D. 2027. The impact of hearing loss on sleep quality in older adults: Evidence from Vietnam. Natural and Life Sciences Communications. 26(1): e2027011.

Graphical Abstract:

INTRODUCTION

Sleep quality is a fundamental determinant of health and well-being in older adults. A recent meta-analysis estimated that approximately 40% of adults aged 60 years and older worldwide experience poor sleep quality, as measured by the Pittsburgh Sleep Quality Index (PSQI) (Canever et al., 2024). Poor sleep in older adults is associated with increased risks of cognitive decline, depression, frailty, cardiovascular disease, and mortality (Livingston et al., 2020; Chen et al., 2023; Ganidagli et al., 2023; Banerjee and Satpathy, 2025; Yu et al., 2025). Several factors contribute to age-related changes in sleep architecture, including chronic medical conditions, medication use, psychosocial stressors, and environmental factors (Ganidagli et al., 2023). In China, studies of community-dwelling older adults have reported poor sleep prevalence ranging from 21% to 50% depending on the PSQI threshold applied (Wang et al., 2020; Y.-S. Zhang et al., 2020; Su et al., 2025). In Thailand, the prevalence was 44% among community-dwelling adults aged 60 years and older (Nanthakwang et al., 2020). In Vietnam, where the proportion of the population aged 60 and older is projected to reach 25% by 2049, community-level data on sleep quality among older adults remain scarce (General Statistics Office, 2021).

Hearing loss is highly prevalent among older adults; globally, over 65% of individuals aged 60 years and older experience some degree of hearing loss (World Health Organization, 2021). Several biological and psychosocial pathways may link hearing loss to poor sleep quality. Hearing loss may impair sleep through increased communication stress and social isolation, which promote anxiety and depressive symptoms (Shukla et al., 2020; Gallagher et al., 2024). Social isolation, in turn, disrupts circadian entrainment cues and reduces daytime physical activity (Schrempft et al., 2019; Fernandes et al., 2021). A Chinese longitudinal study demonstrated that depression mediated 70.8% of the association between sleep duration and hearing loss (Wang et al., 2025). Despite these emerging findings, community-based studies using standardized sleep instruments alongside pure tone audiometry remain limited, and none have been conducted in Southeast Asian settings.

This study aimed to investigate the association between hearing loss and sleep quality among community-dwelling older adults aged 60 years and older in Hung Yen Province, Vietnam, using pure tone audiometry and the Pittsburgh Sleep Quality Index.

MATERIALS AND METHODS

Study design and setting

This community-based cross-sectional study was conducted in Hung Yen Province, northern Vietnam, between January and March 2025.

Study population and sampling

The study population comprised community-dwelling adults aged 60 years and older residing in Hung Yen Province. Participants were recruited through a multi-stage cluster sampling method. Communes were randomly selected from both urban and rural districts; within each commune, older adults were identified through the commune health station registry and invited to participate.

The minimum sample size was calculated using the Cochran formula for prevalence estimation:

where Z = 1.96 (95% confidence level), p = 0.50, and d = 0.05, yielding n_₀ = 385. After adjusting for design effect (DEFF = 1.5) and a 10% non-response rate, the minimum required sample size was 641. A total of 800 participants were initially screened for the study. After excluding 13 individuals due to data quality issues and 8 due to incomplete PSQI responses, a final analytic sample of 779 participants was included in the analysis.

Inclusion criteria were: (1) age ≥60 years, (2) permanent residence in Hung Yen Province, and (3) ability to cooperate with audiological testing and questionnaire administration. Exclusion criteria were: (1) acute ear infection at the time of examination, (2) severe cognitive impairment precluding reliable audiometric or questionnaire responses, and (3) refusal to provide informed consent.

Data collection

Data were collected by trained research teams from the Vietnam Audiology Society and Hung Yen Provincial Health Department. Each participant underwent a structured interview, clinical examination, and audiological assessment in a single session.

Sociodemographic and clinical data

A standardized questionnaire collected information on sociodemographic characteristics (age, sex, urban/rural residence, education level), lifestyle factors (smoking status, physical activity level assessed by the International Physical Activity Questionnaire [IPAQ] short form, alcohol consumption), medical history (hypertension, diabetes mellitus), and occupational noise exposure history.

Body mass index (BMI) was calculated and categorized per the WPRO-WHO classification for Asian populations: underweight (<18.5), normal (18.5–22.9), overweight (23.0–24.9), and obese (≥25.0 kg/m²).

Audiological assessment

All participants underwent pure tone audiometry using the Amplivox Model 270 diagnostic audiometer (Amplivox Ltd., Oxfordshire, UK) in a portable sound-attenuating booth. Air conduction thresholds were measured at 500, 1,000, 2,000, and 4,000 Hz. The pure tone average (PTA) was calculated as the mean of thresholds at these four frequencies in the better ear.

Sleep quality assessment

Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), a validated 19-item self-report questionnaire that evaluates sleep quality over the preceding month (Buysse et al., 1989; C. Zhang et al., 2020). The PSQI yields seven component scores (C1–C7, each scored 0–3) and a global score (range 0–21): subjective sleep quality (C1), sleep latency (C2), sleep duration (C3), habitual sleep efficiency (C4), sleep disturbances (C5), use of sleep medication (C6), and daytime dysfunction (C7). Higher scores indicate poorer sleep quality. PSQI scoring followed the original algorithm (Buysse et al., 1989).

Outcome definitions

Primary outcomes

Two primary outcomes were analyzed:

1. PSQI global score (continuous, 0–21): the sum of seven component scores
2. Poor sleep quality (binary): PSQI Global Score >5 (Buysse et al., 1989).

Hearing loss definitions

Hearing loss was defined using the PTA in the better ear according to the WHO 2021 classification:

• Any hearing loss: PTA ≥20 dB HL
• Severity grading: normal (<20 dB), mild (20–34 dB), moderate (35–49 dB), severe (50–79 dB), and profound (≥80 dB)

Statistical analysis

Continuous variables were assessed for normality using the Shapiro-Wilk test. Non-normally distributed variables were presented as median (interquartile range [IQR]) and compared using the Mann-Whitney U test. Normally distributed variables were presented as mean ± standard deviation (SD). Categorical variables were presented as frequencies and percentages and compared using the chi-square test.

Bivariate comparisons of PSQI component scores between hearing groups were performed using the Mann-Whitney U test with Bonferroni correction for multiple comparisons. Dose-response relationships between hearing loss severity and PSQI scores were assessed using the Kruskal-Wallis test, with post-hoc pairwise Mann-Whitney U tests for between-group comparisons. Chi-square tests were used to compare poor sleep prevalence across severity groups.

Multivariable logistic regression was used to model poor sleep quality (PSQI >5) as the binary outcome, with hearing loss (≥20 dB) as the primary exposure. Covariates were selected a priori based on clinical relevance and prior literature: age (5-year groups), sex, residence (urban/rural), education level, BMI category, hypertension, diabetes mellitus, smoking status, alcohol use, physical activity level (IPAQ categories), and occupational noise exposure. Multicollinearity was evaluated using the variance inflation factor (VIF), with VIF >5 indicating problematic collinearity. Variables with P > 0.25 in bivariate analysis (BMI, diabetes, alcohol) were excluded from the final multivariable model to improve model parsimony and the events-per-variable (EPV) ratio. The EPV rule of ≥10 was verified.

All tests were two-sided, and P <0.05 was considered statistically significant. Statistical analyses were performed using Python 3.12 with scipy (version 1.14), statsmodels (version 0.14), and matplotlib (version 3.10).

RESULTS

Participant characteristics

Of 800 community-dwelling older adults initially screened, 13 were excluded for data quality issues and 8 for incomplete PSQI responses, yielding a final analytic sample of 779 participants. The mean age was 71.1 ± 6.7 years, and 482 (61.9%) were female. The majority resided in rural areas (75.1%) and had secondary-level education (73.7%). The prevalence of hearing loss (PTA ≥20 dB) was 71.9% (n = 560). Self-reported hypertension was present in 79.1%, diabetes mellitus in 18.1%. Baseline characteristics stratified by sleep quality status are presented in Table 1.

Participants with poor sleep quality (PSQI > 5) were older (71.6 ± 6.7 vs. 69.9 ± 6.4 years; P < 0.001) and had a lower proportion of college/university education (3.6% vs. 9.2%; P = 0.005) compared with good sleepers. They also reported lower levels of physical activity (P = 0.004) and had a higher prevalence of hypertension (81.7% vs. 73.6%; P = 0.010). Notably, the prevalence of hearing loss (75.8% vs. 63.6%; P < 0.001) was significantly higher among those with poor sleep quality.

Table 1. Baseline characteristics of study participants by sleep quality status (N = 779).

Note:  Values are presented as mean ± SD for continuous variables and n (%) for categorical variables. Continuous variables compared   using Mann-Whitney U test; categorical variables using chi-square test. PSQI = Pittsburgh Sleep Quality Index; HL = hearing loss; BMI = body mass index (Asian classification).

Sleep quality profile

Among the seven PSQI components, subjective sleep quality (C1; P = 0.007), sleep latency (C2; P = 0.003), and sleep disturbances (C5; P = 0.001) showed statistically significant differences between hearing groups after Bonferroni correction (α = 0.0071). Sleep efficiency (C4; P = 0.017) was significant at the nominal level but did not survive Bonferroni correction. Sleep medication use (C6; P = 0.820) and daytime dysfunction (C7; P = 0.909) did not differ between groups. Results are presented in Table 2.

Table 2. PSQI component scores by hearing loss status.

Note:  Values are presented as median [interquartile range]. Comparisons performed using the Mann-Whitney U test. **P< 0.01 (significant after Bonferroni correction, α = 0.05/7 = 0.0071); *P < 0.05 (nominal significance only).

Dose-response relationship

To examine the dose-response relationship between hearing loss severity and sleep quality, participants were classified into three groups: normal hearing (n = 219), mild hearing loss (n = 346), and moderate-or-greater hearing loss (n = 214; moderate, severe, and profound categories were combined due to small cell sizes in the severe [n = 61] and profound [n = 2] groups). A significant dose-response relationship was observed across the three severity groups (Kruskal-Wallis H = 9.3, P = 0.010). Mean PSQI scores increased progressively from normal hearing (7.4 ± 3.8) through mild (8.2 ± 3.8) to moderate-or-greater hearing loss (8.4 ± 4.0). Post-hoc pairwise comparisons revealed that both mild (P = 0.009) and moderate-or-greater (P = 0.006) hearing loss groups had significantly higher PSQI scores than the normal hearing group, whereas no significant difference was observed between the mild and moderate-or-greater groups (P = 0.625). The prevalence of poor sleep quality (PSQI >5) also increased across severity groups: 58.4% (normal), 71.1% (mild), and 72.4% (moderate+; overall χ² = 12.6, P = 0.002), as shown in Table 3.

Table 3. Dose-response relationship between hearing loss severity and sleep quality (N = 779).

Note:   HL = hearing loss; PSQI = Pittsburgh Sleep Quality Index; poor sleep quality defined as PSQI > 5; M = mean; Mdn = median; IQR = interquartile range. Overall comparison by Kruskal–Wallis test: H = 9.3, P = 0.010. Post-hoc pairwise Mann–Whitney U tests with Bonferroni correction (α = 0.017): Normal vs. Mild, P = 0.009; Normal vs. Moderate+, P = 0.006; Mild vs. Moderate+, P = 0.625. Poor sleep prevalence across groups: χ²(2) = 12.6, P = 0.002.

Multivariable logistic regression

Hearing loss was independently associated with poor sleep quality (aOR = 1.62, 95% CI: 1.15–2.29, P = 0.006; Figure 1). Other significant predictors included age ≥75 years (aOR = 1.76 for 75–79 years; aOR = 2.15 for ≥80 years), female sex (aOR = 1.60, P = 0.010), higher education as a protective factor (college/university: aOR = 0.40, P = 0.017), and hypertension (aOR = 1.50, P = 0.038). Moderate and high physical activity remained protective (aOR = 0.55, P = 0.008; aOR = 0.58, P = 0.035).

Figure 1. Forest plot of adjusted odds ratios from multivariable logistic regression for poor sleep quality (PSQI >5) using the reduced model.

DISCUSSION

In this community-based cross-sectional study of 779 older adults in Hung Yen Province, Vietnam, hearing loss was independently associated with poorer sleep quality as measured by the binary classification of poor sleep (PSQI >5). In unadjusted analyses, participants with hearing loss had a higher median global PSQI score than those with normal hearing (8.0 vs. 7.0; Mann-Whitney U P = 0.003). After multivariable adjustment for sociodemographic, lifestyle, and medical factors, hearing loss was associated with 62% greater odds of poor sleep quality (aOR = 1.62, 95% CI: 1.15–2.29, P = 0.006). A dose-response trend was observed, with PSQI scores increasing progressively across hearing loss severity groups (Kruskal-Wallis P = 0.010).

The overall prevalence of poor sleep quality (PSQI >5) was 67.9%, which exceeds the pooled global estimate of 40% reported by Canever et al. (2024) in a meta-analysis of 252 studies across 36 countries and the 45% (95% CI: 40–50%) reported by Fu et al. (2025) in a more focused meta-analysis of 41 studies among community-dwelling older adults. The figure is also higher than the 50% reported by Kavousi et al. (2025) in another systematic review using the same PSQI >5 cutoff. In the Asia-Pacific region, Wang et al. (2020) reported a 33.8% prevalence using the stricter PSQI >7 threshold among community-dwelling adults aged 65 years and older in rural Shandong, China. Zhang et al. (2020) found 21.0% in rural Hebei province, also using PSQI >7. In Thailand, Nanthakwang et al. (2020) reported 44.15% among adults aged 60 and older using PSQI >5, and Thichumpa et al. (2018) reported 44.0% in rural Chiang Rai. Direct comparison is complicated by differences in PSQI cutoffs.

Within Vietnam, community-based studies using the PSQI remain limited, though clinical samples consistently show high rates of sleep impairment among older adults. Dao-Tran and Seib (2018) reported that 38.9% of women aged 60 and older across 16 rural and urban sites experienced sleep disturbance. Recent hospital- and clinic-based studies report much higher prevalence rates: 87.3% among elderly outpatients at a polyclinic in Ho Chi Minh City [25], 75.4% among older adults with type 2 diabetes at Thu Duc City Hospital, and 60.7% among older patients at Hue University Hospital (Nguyen et al., 2018; Hai et al., 2024; Duong Thi Ngoc et al., 2025). The high prevalence in our community-based cohort (67.9%) bridges the gap between earlier population estimates and these high-burden clinical samples, likely reflecting the advanced mean age (71.1 years), high burden of chronic comorbidities (79.1% hypertension, 18.1% diabetes), and the predominantly rural agricultural setting. In line with this, the prevalence of poor sleep quality increased with the number of chronic conditions, consistent with findings from Su et al. (2025) in Hunan Province, China.

Our baseline analysis further highlighted that poor sleep quality was significantly associated with older age, lower educational attainment, physical inactivity, and hypertension. These findings align with recent Vietnamese literature demonstrating that poor sleep in older adults is exacerbated by chronic conditions and lifestyle factors. For instance, studies by Nguyen et al. (2018) and Duong Thi Ngoc et al. (2025) highlighted that advanced age (≥80 years), rural living, lack of physical exercise, and smoking are independent predictors of sleep impairment among older Vietnamese adults. Similarly, Hai et al. (2024) emphasized that longer duration of chronic diseases, obesity, and polypharmacy significantly increase the risk of sleep disorders. Additionally, regional studies on geriatric care highlight that sleep disturbances, particularly insomnia, represent a major clinical concern in older adults with chronic comorbidities (Pungchompoo and Udomkhwamsuk, 2021). Beyond the Vietnamese context, global evidence confirms that lower physical activity and lower education independently predict poor sleep quality, which bidirectionally exacerbates conditions like hypertension (Brewster et al., 2021; Santa Helena et al., 2024). This multidimensional relationship highlights the complex etiology of sleep disturbances in our aging rural population, making isolated interventions less effective.

Hearing loss and sleep quality

The finding that hearing loss is independently associated with poor sleep quality aligns with the limited but growing evidence base. Jiang et al. (2022) found that longer sleep duration was marginally associated with poorer high-frequency hearing among US adults aged 70 years and older in the NHANES 2005–2006 cycle, although the direction of association differs from our study, which focused on sleep quality rather than duration.

The effect size observed in our study (aOR = 1.62) is modest but clinically relevant. A one-point increase in the PSQI score is a meaningful shift in sleep quality, particularly given that the threshold for poor sleep is only 5 points. The 62% increase in odds of poor sleep among those with hearing loss is comparable to the magnitude of the association between hypertension and poor sleep observed in other Asian populations (Wang et al., 2020). The high co-occurrence of hearing loss and chronic comorbidities in Vietnamese older adults may amplify the sleep-disrupting effects of sensory impairment.

PSQI components affected by hearing loss

The component-level analysis provides insight into the specific domains of sleep quality affected by hearing loss. Subjective sleep quality (C1), sleep latency (C2), and sleep disturbances (C5) showed the strongest associations, each surviving Bonferroni correction. The lack of association with sleep medication use (C6) and daytime dysfunction (C7) suggests that the sleep impairment related to hearing loss is primarily nocturnal in nature, involving sleep initiation and maintenance rather than compensatory daytime behaviors. Brewster et al. (2021) proposed a model in which hearing loss leads to deafferentation-induced atrophy of frontotemporal brain regions, increased listening effort with dysregulation of cognitive control networks, and reduced social engagement — each of which can independently promote depressive symptoms and circadian disruption. In Chinese older adults, Cao et al. (2023) demonstrated that depressive symptoms partially mediated the association between hearing loss and cognitive impairment, with social relationships moderating both the direct and indirect effects. Huang et al. (2022) similarly found that social isolation mediated approximately 9% of the hearing loss–depression association in Guangzhou. These cascading effects represent a plausible pathway that our cross-sectional design cannot disentangle but that merits investigation in longitudinal studies.

In a Vietnamese context, Han et al. (2021) found that the association between hearing loss and cognitive impairment among 725 older adults in Thua Thien Hue province was attenuated among those who maintained frequent communication using devices. This finding suggests that communication-enhancing interventions may buffer the downstream effects of hearing loss, potentially including sleep disturbances. The fact that our study did not assess depression, anxiety, or social engagement is a limitation in fully characterizing the mechanism.

Sex and age effects

Female sex was independently associated with greater odds of poor sleep quality in the multivariable model (aOR = 1.60, 95% CI: 1.12–2.30, P = 0.010), consistent with the well-documented female predominance in sleep complaints across populations (Y.-S. Zhang et al., 2020; Su et al., 2025). This finding is strongly supported by recent data from older adults with type 2 diabetes in Vietnam, where female sex was a significant predictor of sleep disorders (OR = 2.48) (Hai et al., 2024). Dao‐Tran and Seib (2018) reported that among Vietnamese women aged 60 and older, body mass index and physical health were the primary correlates of sleep disturbance. Furthermore, Shukla et al. (2020) noted that among women, hearing loss was more strongly associated with loneliness and social isolation than among men, which may exacerbate sleep disruptions. Advanced age was also a strong independent predictor of poor sleep (aOR = 1.76 for 75–79 years; aOR = 2.15 for ≥80 years), reflecting the cumulative burden of age-related physiological changes and multiple comorbidities that dominate sleep quality in the oldest age groups.

Other predictors of sleep quality

Hypertension was independently associated with greater odds of poor sleep quality in the multivariable model (aOR = 1.50, P = 0.038), consistent with the known cardio-metabolic contributors to sleep disturbance in older adults (Y.-S. Zhang et al., 2020; Chen et al., 2023). This aligns with recent Vietnamese data from Hai et al. (2024) which emphasized that chronic diseases exacerbate sleep disorders. Although occupational noise exposure showed a trend toward poorer sleep in bivariate analysis (P = 0.133) and was included in the multivariable model, it did not emerge as an independent predictor, suggesting that its effect may be mediated by or confounded with hearing loss itself. Higher physical activity (moderate and high levels) was independently protective against poor sleep (moderate: aOR = 0.55, P = 0.008; high: aOR = 0.58, P = 0.035), confirming the established relationship between physical activity and improved sleep quality (Banerjee and Satpathy, 2025). These findings support the implementation of targeted lifestyle interventions. Specifically, structured physical activity, such as the E75 exercise program evaluated in Thai older adults, improves physical fitness, thereby helping to alleviate sleep disturbances (Paknapa et al., 2025). Higher education was also associated with better sleep quality (college/university: aOR = 0.40, P = 0.017), likely reflecting greater health literacy, better living conditions, and access to healthcare resources. These findings mirror those from other community-based Asian cohorts where socioeconomic and lifestyle factors strongly determine sleep health (Nanthakwang et al., 2020; Y.-S. Zhang et al., 2020).

CONCLUSION

In this community-based cross-sectional study of 779 older adults in Vietnam, hearing loss was independently associated with poorer sleep quality and 62% greater odds of poor sleep (PSQI >5). The association was primarily driven by impairment in subjective sleep quality, sleep latency, and sleep disturbances.

LIMITATION

Several limitations warrant consideration. First, the cross-sectional design precludes determination of temporal or causal relationships; hearing loss may cause poor sleep, or both may share common antecedents such as vascular aging. Second, PSQI relies on self-report and may be subject to recall bias; objective sleep measures such as polysomnography or actigraphy would provide complementary data. Third, the study did not assess depression or anxiety, which are important mediators in the hearing loss–sleep pathway.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the People's Committee of Hung Yen Province and the Hung Yen Department of Science and Technology for approving and funding this research. The authors also thank all study participants for their cooperation.

AUTHOR CONTRIBUTIONS

Xuong Tuyet Nguyen: Conceptualization (Lead), Methodology (Lead), Formal Analysis (Equal), Investigation (Lead), Data Curation (Equal), Writing – Original Draft (Lead), Writing – Review & Editing (Equal), Supervision (Lead), Project Administration (Lead); Phung Thi Hoa: Investigation (Equal); Data Curation (Equal); Nguyen Quang Hung: Investigation (Supporting), Data Curation (Supporting); Nguyen Ngoc Ha: Investigation (Supporting); Dinh Thi Dao: Methodology (Supporting), Writing – Review & Editing (Supporting); Van Dinh Tran: Conceptualization (Supporting), Methodology (Supporting), Formal Analysis (Equal), Writing – Review & Editing (Equal), Supervision (Supporting).

ETHICAL CONSIDERATIONS

This study was approved by the Institutional Medical-Pharmaceutical Ethics Committee (approval number: 04.25/GCN-HĐĐĐ NCYSH-IMP). Written informed consent was obtained from all participants prior to enrollment. All data were de-identified and stored securely. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies.

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