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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2025 Jun 24;80(9):gbaf115. doi: 10.1093/geronb/gbaf115

Longitudinal associations between speech-in-noise deficits and cognitive function over 8 years: the role of hearing aid use and environmental noise exposure

Semere Bekena 1,✉, Ramkrishna K Singh 2, Nikitha Damera 3, Yiqi Zhu 4, Jean-Francois Trani 5,6, Kate McClannahan 7, Mollie Webb 8, Ganesh M Babulal 9,10
Editor: Brittany S Cassidy
PMCID: PMC12416495  PMID: 40581785

Abstract

Objective

Hearing loss is a potentially modifiable risk factor for dementia, yet questions remain about its impact on cognitive trajectories. Hearing aid use has been proposed as a protective intervention, but findings are mixed. This study examined longitudinal associations between hearing loss, hearing aid use, and cognitive function, and explored the role of environmental noise exposure.

Methods

We prospectively studied 398 adults aged ≥65 years. Hearing status was assessed using the National Institute of Health Toolbox Words-in-Noise test and Hearing Handicap Inventory for the Elderly. Cognitive function was measured using a Preclinical Alzheimer’s Cognitive Composite (PACC). Associations between hearing status, hearing aid use, and cognitive outcomes were evaluated using linear regression and mixed-effects models, adjusting for demographic and environmental covariates.

Results

Participants had a mean age of 73 years, with 53.5% female and 86.9% identifying as non-Hispanic White. Hearing loss was associated with lower PACC scores cross-sectionally (β = −0.04, p < .001). Longitudinally, poorer speech-in-noise performance was associated with lower cognitive scores (β = −0.02, p = .003). Compared to those with normal hearing, participants with hearing loss who did not use hearing aids had lower PACC scores (β = −0.17, p = .039). Hearing aid users did not differ significantly from the group with normal hearing. However, hearing aid use was not a significant predictor in adjusted models. Environmental noise and neighborhood deprivation were not significant predictors.

Discussion

Hearing loss was associated with lower cognitive function, however hearing aid use did not significantly influence these trajectories. Further research is warranted to clarify its role.

Keywords: cognitive aging, speech-in-noise processing, self-perceived hearing handicap, dementia risk, environmental noise exposure

Dementia burden and future prediction in the United States

The population of older adults (age ≥65) in the United States is projected to increase from 58 million in 2022 to 88 million by 2050, raising their share of the total population from 17% to 23% (U.S. Census Bureau, 2023). Alzheimer’s disease and related dementias (ADRD) are the fifth-leading cause of death among older adults, with ADRD-related deaths rising by over 140% between 2000 and 2021, while deaths from stroke, heart disease, and HIV declined (Alzheimer’s Association, 2024). In 2024, the total cost of healthcare, long-term care, and hospice services for older adults with dementia reached $360 billion, with an additional estimated $346.6 billion in unpaid caregiving provided by family members and other informal caregivers (Alzheimer’s Association, 2024).

Hearing loss and dementia risk: epidemiology and mechanisms

Hearing loss affects approximately 68% of adults over the age of 70 and is an established risk factor for age-related cognitive decline, dementia, and disability. The 2024 Lancet Commission on Dementia Prevention, Intervention, and Care identified midlife hearing loss as a significant, potentially modifiable risk factor for dementia. According to the report, hearing loss is one of the largest potentially modifiable contributors, responsible for 7% of the population-attributable risk for dementia (Livingston et al., 2024). Given this growing recognition of hearing loss as a key modifiable risk factor, a substantial body of research has examined its relationship with cognitive aging. These studies have used both cross-sectional and longitudinal designs, with outcomes ranging from performance-based cognitive test scores to clinical diagnoses of dementia. Cross-sectional studies consistently show that individuals with hearing impairment perform more poorly on cognitive assessments (Deal et al., 2015; Lin et al., 2011), while longitudinal studies have linked hearing loss to accelerated cognitive decline and greater risk of incident dementia (Loughrey et al., 2018; Yuan et al., 2018).

Several potential mechanisms have been proposed to explain the relationship between hearing loss and an increased risk of dementia. One proposed explanation avers that hearing loss can influence cognitive test performance since most assessments rely on verbal tasks or spoken instructions (Amieva & Ouvrard, 2020). Another possible explanation is the “deprivation hypothesis,” initially proposed by Lindenberger and Baltes (Lindenberger & Baltes, 1994), which posits that sensory impairments may lead to reduced cognitive stimulation, affecting brain integrity, and accelerating cognitive decline (Baltes & Lindenberger, 1997; Phillips et al., 2022). Neuroimaging studies indicate that hearing loss is associated with reduced gray matter volume, poor white matter integrity in the temporal lobe, and accelerated atrophy in both the right temporal lobe and the whole brain over time (Croll et al., 2020; Lin et al., 2014; Wang et al., 2022; Wright et al., 2021). Psychosocial factors, such as loneliness, depression, and social isolation, may also contribute to the relationship between auditory sensory function and cognitive decline (Griffiths et al., 2020; Livingston et al., 2024). Other proposed mechanisms suggest that hearing loss may contribute to dementia through increased cognitive load and the reallocation of cortical resources toward auditory processing, which may impair other cognitive functions (Griffiths et al., 2020; Park et al., 2016). The strong association between hearing loss and dementia underscores the need for early detection and intervention for midlife hearing loss.

Chronic environmental noise exposure

Human and animal studies link chronic noise to amyloid-β accumulation and tau hyperphosphorylation in key brain regions like the hippocampus (Hahad et al., 2019; Huang et al., 2021; Tzivian et al., 2018). Animal models further indicate that chronic noise exposure may accelerate AD-like pathology, particularly in the hippocampus, while also activating hypothalamic stress pathways, which leads to noradrenaline and dopamine dysregulation and may impair prefrontal cortex function and cognitive performance (Cui et al., 2015; Jafari et al., 2020). In line with these findings, a recent study found that long-term exposure to aircraft and railway noise was associated with accelerated cognitive decline and increased risk of cognitive impairment in older adults (Wu et al., 2024). In addition, higher exposure to local traffic noise above 75 dB during the day and 65 dB at night has been associated with an increased risk of dementia or cognitive impairment in older Mexican American adults over a 10-year period (Yu et al., 2020). It is also important to note that environmental noise often cooccurs with air pollution, particularly in urban areas near high-traffic roadways and airports. Both exposures have been independently associated with neuroinflammation and cognitive decline, raising the possibility of shared or synergistic effects on brain health (­Livingston et al., 2024; Tzivian et al., 2018).

Hearing aid use as a potential intervention and its conflicting results

Given the evidence linking hearing loss and dementia, hearing aid use has emerged as a practical intervention to potentially slow cognitive decline and dementia progression. However, many older adults with hearing loss remain untreated, with fewer than 20% using hearing aids among those with clinically significant hearing loss, major barriers include limited access to healthcare, high costs, and stigma (Brewster et al., 2022; Gahlon et al., 2024; Reed et al., 2021).

Evidence suggests that hearing aid use may be associated with better cognitive outcomes and a lower risk of cognitive decline and dementia among users (Deal et al., 2015; West et al., 2022; Yeo et al., 2023). A systematic review and meta-analysis of eight cohort studies, including 126,903 participants, found that hearing aid users had a significantly reduced risk of cognitive decline and dementia, with hazard ratios of 0.81 and 0.83, respectively (Yeo et al., 2023); however, as noted by Denham et al. (2023), many individual studies included in the meta-analysis did not report significant associations, and the aggregation of cognitively normal and mildly impaired participants limits interpretation regarding baseline cognitive status. Moreover, other studies have not observed a significant cognitive benefit, particularly among individuals with a dementia diagnosis or in population-based cohorts (Atef et al., 2023; Grenier et al., 2024; Lin et al., 2013). In a large-scale study of 62,072 middle-aged adults, hearing aid use did not significantly mitigate the risk of cognitive impairment except among those with comorbid depression (Grenier et al., 2024). While hearing interventions may offer cognitive benefits, their effectiveness likely depends on factors such as adherence, timing of intervention, and underlying cognitive reserve (Lin et al., 2023; Livingston et al., 2024).

In light of the inconsistent findings on the cognitive impact of hearing aid use, this study investigated the longitudinal relationships between hearing loss, self-reported hearing aid use, and cognitive function, while also assessing the influence of chronic environmental noise exposure. We hypothesized that baseline hearing loss would be associated with lower cognitive performance. Additionally, we hypothesized that hearing impairment would be linked to persistently lower cognitive trajectories over time, while hearing aid use would mitigate this decline. Environmental noise exposure and neighborhood deprivation would negatively influence cognitive outcomes.

Methodology

This prospective, longitudinal observational study included community-dwelling older adults enrolled in the DRIVES Project (R01 AG056466, R01AG068183, and R01AG067428) at Washington University in St Louis. All participants were native English speakers. This study was approved by the Washington University Institutional Review Board and the Human Research Protection Office (IRB numbers: 202010214 and 202003209). All participants provided written informed consent before enrollment. Eligible participants aged 65 years or older were required to complete a comprehensive neuropsychiatric assessment and a hearing evaluation at baseline.

Participants underwent annual assessments of cognitive function, hearing status, and environmental exposures. Individuals with only a single baseline visit were excluded from the present analyses. Among the included participants, the mean number of cognitive assessments was 2.4 (range: 2–6 visits). Attrition occurred due to deaths, withdrawal, or loss to follow-up, which is expected in aging cohorts. Demographic data was self-­reported, and the Area Deprivation Index (ADI) was obtained from the census block group where participants’ home addresses are located. This report adheres to the STROBE guidelines for cohort studies, which are designed to strengthen the reporting of observational epidemiological research.

Cognitive assessment

A Preclinical Alzheimer’s Cognitive Composite (PACC) score was derived from four neuropsychological assessments: Animal Fluency (Goodglass & Kaplan, 1983), the Selective Reminding Test—Free Recall (Grober et al., 1988), and Trail Making Tests A and B (Armitage, 1946). For the Trail-Making Tests, the directionality was reversed so that higher values (reflecting longer completion times) corresponded to lower cognitive function. These tests were chosen to evaluate critical cognitive functions, including verbal fluency, episodic memory, processing speed, and executive function. Raw test scores were transformed into z-scores, standardized based on the mean and standard deviation of the group. The final composite score was determined by averaging these z-scores, yielding an integrated measure of overall cognitive function (Roe et al., 2018). Participants also completed the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005). Following the standardized scoring guidelines, each participant’s raw score was calculated and then adjusted for educational level by adding one point for those with 12 or fewer years of formal education.

Hearing assessment

Hearing ability was assessed using objective and self-reported measures to capture different aspects of auditory function. The National Institute of Health (NIH) Toolbox Words-in-Noise (WIN) Test assessed participants’ ability to perceive speech in noisy environments. This test is a component of the NIH Toolbox Auditory Test Battery, developed to evaluate various auditory functions across different age groups (Weintraub et al., 2013). The WIN Test measures a participant’s ability to recognize words presented against background noise, a critical ability for effective communication in real-world settings (Zecker et al., 2013). The NIH Toolbox WIN Test was administered using an automated procedure that ensured consistent test delivery and allowed for reliable assessment by nonspecialists. During the test, participants listened to single spoken words (35 in total) embedded in multi-talker background conversation, with the speech-to-noise ratio systematically varied to increase task difficulty. Responses were scored based on accuracy, and a threshold score was calculated for each participant. The scores for each ear can range from −2.0 to 26.0 decibels of signal-to-noise ratio (dB SNR) based on the formula “WIN_Score = 26—0.8*WIN_NCorrect,” with lower values reflecting better performance and higher values potentially indicating hearing challenges. The better-ear threshold was used as the primary measure of speech-in-noise perception.

The WIN test was administered in a quiet room (not sound-treated) using Sennheiser HD 201 headphones. For participants who used hearing aids, the assessment was conducted with the hearing aids removed to standardize across sessions. A DOHM sound conditioner was used to generate natural white noise to minimize ambient noise interference. A cutoff of 10 dB SNR was applied to categorize participants into normal hearing (≤10 dB SNR) and hearing loss (>10 dB SNR) groups, following established guidelines for the NIH Toolbox version of the test (Gershon et al., 2013).

Hearing aid use was assessed with a question and binary response, “Do you wear a hearing aid?”, categorized as yes or no. The Hearing Handicap Inventory for the Elderly (HHIE) was used to assess the self-perceived impact of hearing loss on daily life. This 25-item questionnaire evaluates the emotional and social consequences of hearing difficulties, with questions addressing aspects such as embarrassment, frustration, and communication challenges. The total score ranges from 0 (no handicap) to 100 (maximum handicap) (Kawata et al., 2021). Participants were instructed to respond to the HHIE based on their perceived hearing difficulties without considering the use of hearing aids (i.e., “as if unaided”).

Noise data acquisition and processing

Noise exposure data were obtained from the U.S. Bureau of Transportation’s National Transportation Noise Map, which provides modeled estimates of transportation noise from road traffic and aviation. The dataset integrated noise exposure information based on transportation models from the Federal Highway Administration (FHWA) Traffic Noise Model (TNM) and the Federal Aviation Administration (FAA) Integrated Noise Model (INM) and Aviation Environmental Design Tool (AEDT). Noise estimates for aviation and road traffic sources were derived separately. Noise levels were expressed in A-weighted decibels (dB(A)), consistent with the U.S. National Transportation Noise Map specifications. A 45 dB(A) cutoff was applied, indicating that noise levels below this threshold were not included in the dataset. Aviation noise exposure was calculated based on flight operation data, aircraft types, and airport characteristics, while road traffic noise was estimated using vehicle counts, speeds, and roadway geometry. The noise exposure data were represented as 24-hr equivalent sound levels (L_eq,24) and categorized into exposure bands (e.g., >50 dB(A), >60 dB(A), etc.).

To spatially integrate this noise exposure data with participant locations, geospatial analyses were conducted using ArcGIS Pro 3.3.1. Addresses for participants were geocoded using Esri’s Streetmap Premium USA Address Locator to generate geospatial points. Census tract and block group boundaries were obtained from the U.S. Census Bureau TIGER files, and noise exposure estimates were linked to participant locations at the census tract and block group level. Using the Zonal Statistics tool, the mean noise level for each census tract and block group was calculated, and these values were extracted for each participant location using the Extract Multi Values to Points tool. The processed noise exposure data were exported as structured datasets in CSV format, linking individual noise estimates with hearing and cognitive outcome variables for subsequent statistical analysis.

Statistical analysis

Baseline characteristics, including age, sex, education, and hearing-related measures, were summarized using means and standard deviation (SD) for continuous variables and frequencies (percentages) for categorical variables. Multiple linear regression (MLR) models examined the association between baseline PACC scores and two hearing-related measures, the HHIE and WIN threshold while, assessing the moderating effects of hearing aid use and race through interaction terms. Longitudinal associations were assessed using linear mixed-­effects (LME) models. We constructed a composite hearing loss measure by categorizing participants who (a) demonstrated hearing loss on the baseline WIN test and (b) reported hearing aid use at baseline. One model examined PACC scores over time in relation to the longitudinal WIN test, while another assessed differences in the association between PACC trajectories and the WIN test across four hearing groups, defined by baseline hearing impairment and hearing aid use. The second model included fixed effects for time, the hearing group variable, and their interaction while accounting for inter-individual variability through random effects. All analyses were conducted using R version 4.4.2.

Result

Cohort characteristics and follow-up

There were 398 participants included in the analysis, with a mean age of 73.0 (SD = 4.79) years at baseline (Table 1). Overall, 53.5% were female, and 86.9% identified as non-Hispanic White. Participants with hearing loss (n = 95, 23.8%) were, on average, older (75.5 ± 5.58 vs 72.2 ± 4.23, p < .001), more likely to be male (67.4%vs 32.6%, p < .001), and had a significantly lower baseline PACC score (−0.17 ± 0.81 vs 0.07 ± 0.62, p = .008) relative to those with normal hearing (n = 303). Self-reported hearing handicap was also higher among participants with impaired hearing (20.9 ± 17.7 vs 9.93 ± 12.5, p < .001). Hearing aid use was reported by 66 (16.6%). The ADI did not significantly differ between those with and without hearing loss (mean [SD]: 42.0[22.5] vs 47.1[24.5], p = .421). Similarly, mean environmental noise exposure at the census block group level was comparable between hearing groups (52.7 dB(A) [2.61] vs 52.2 dB(A) [2.56], p = .101).

Table 1.

Baseline characteristics stratified by hearing status using the words in noise test.

Variables Total (N = 398) Hearing loss
 p-valuea
No (n = 303) Yes (n = 95)
Age, mean (SD) 73.0 (4.79) 72.0 (4.06) 75.5 (5.58) <.001***
Sex
 Female, n (%) 213 (53.5%) 182 (60.1%) 31 (32.6%) <.001***
 Male (Ref) 183 (46.0%) 119 (39.3%) 64 (67.4%)
Race, n (%)
 Black or African American 50 (12.6%) 44 (14.5%) 6 (6.3%) .051
 Non-Hispanic White (Ref) 346 (86.9%) 257 (84.8%) 89 (93.7%)
Education (years), mean (SD) 16.4 (2.42) 16.4 (2.42) 16.3 (2.43) .749
Area deprivation index, mean (SD) 45.8 (24.1) 47.1 (24.5) 42.0 (22.5) .063
Hearing aid use, n (%)
 No 327 (82.2%) 280 (92.4%) 47 (49.5%) <.001***
 Yes (Ref) 66 (16.6%) 20 (6.6%) 46 (48.4%)
MoCA, mean (SD) 25.7 (2.88) 26.0 (2.72) 24.8 (3.16) <.001***
PACC score, mean (SD) 0.014 (0.677) 0.072 (0.622) −0.168 (0.806) .008**
Hearing handicap inventory, mean (SD) 12.6 (14.7) 9.93 (12.5) 20.9 (17.7) <.001***
Noise at census block group level (dB[A]), mean (SD) 52.3 (2.58) 52.2 (2.56) 52.7 (2.61) .101
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Note. MoCA = Montreal Cognitive Assessment; PACC = Preclinical Alzheimer’s Cognitive Composite.

a

p-Values were calculated based on t-test and chi-square results.

***

p < .001.

**

p < .01.

Participants were drawn from an existing cohort, with follow-up spanning 8 years (2016 to 2024). Participants were followed for a mean of 3.79 (SD = 1.72) years (median 3.43; range 1.00–8.84). Repeated assessments of hearing status, cognitive function, and environmental exposures (noise levels, ADI) were conducted during this period.

Cross-sectional association between PACC and hearing measures

Table 2 summarizes the MLR model results for the association of baseline PACC scores with two measures of hearing: WIN threshold and HHIE total score, adjusting for age, sex, education, race, ADI, hearing aid use, and noise exposure. A higher (worse) WIN threshold was significantly associated with lower baseline PACC scores (β = −0.04, 95% CI: −0.06, −0.02; p < .001). In contrast, the HHIE total score was not associated with PACC (β = 0.00, 95% CI: −0.01, 0.00; p = .073). Among covariates, older age was consistently linked to lower baseline PACC scores (β=−0.03 per year, 95% CI: −0.04, −0.01; p < .001), while being non-Hispanic White (vs Black) was associated with higher PACC scores (β = 0.51, 95% CI: 0.30, 0.71; p < .001). Hearing aid use was not significantly associated with PACC (WIN model: β = 0.16, 95% CI: −0.03, 0.36; p = .094; HHIE model: β = 0.11, 95% CI: −0.09, 0.31; p = .270). Noise exposure at the census block group level (mean approximately 52 dB (A)) was not significantly associated with baseline PACC (p > .05). Similarly, the ADI was not significantly associated. Comparable results were obtained when substituting MoCA scores for PACC (see Supplementary material, Supplementary Table 1).

Table 2.

Multiple linear regression models predicting Preclinical Alzheimer’s Cognitive Composite (PACC) scores at baseline.

Variables Model 1: WIN thresholda  
 Model 2: HHI total scoreb  
β 95% CI p-value β 95% CI p-value
Age −0.03 −0.04, −0.01 <.001*** −0.03 −0.05, −0.02 <.001***
Sex
 Female 0.14 0.01, 0.28 .041* 0.20 0.06, 0.33 .004**
Education (years) 0.01 −0.01, 0.04 .313 0.02 −0.01, 0.04 .246
Race
 Non-Hispanic White 0.51 0.31, 0.71 <.001*** 0.51 0.30, 0.71 <.001***
Area deprivation index 0.00 0.00, 0.00 .808 0.00 0.00, 0.00 .779
Hearing aid use
 Yes 0.16 −0.03, 0.36 .094 0.11 −0.09, 0.31 .270
Noise at census block  group level (dB[A]) 0.00 −0.02, 0.03 .751 0.00 −0.02, 0.03 .864
Words in noise −0.04 −0.06, −0.02 .001***
HHIE 0.00 −0.01, 0.00 .073
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Note. PACC = Preclinical Alzheimer’s Cognitive Composite; CI = confidence interval.

a

Model 1 (“WIN Threshold”) includes Words-in-Noise (WIN) Threshold as the primary predictor.

b

Model 2 (“HHI Total Score”) includes Hearing Handicap Inventory (HHIE) Total Score as the primary predictor.

***

p < .001.

**

p < .01.

*

p < .05.

Linear mixed-effects models

Longitudinal cognitive outcome was examined using LME models to evaluate the associations between hearing loss, hearing aid use, and cognitive function over time (Table 3). A higher (worse) WIN threshold was significantly associated with lower PACC scores over time (β = −0.02, 95% CI: −0.03, −0.01; p = .003). Similarly, higher HHIE scores, indicating greater self-perceived hearing handicap, were associated with lower PACC scores (β = 0.00, 95% CI: −0.01, 0.00; p = .012). Hearing aid use was not significantly associated in either model (WIN model: β = −0.01, 95% CI: −0.10, 0.08; p = .872; HHIE model: β = 0.00, 95% CI: −0.09, 0.09; p > .999). Among covariates, older age was consistently associated with lower PACC scores (WIN model: β=−0.03, 95% CI: −0.05, −0.02; p < .001; HHIE model: β = −0.04, 95% CI: −0.05, −0.02; p < .001), while non-Hispanic White participants had higher PACC scores compared to Black participants (β = 0.43, 95% CI: 0.24, 0.63; p < .001). Female participants had significantly higher PACC scores in both models (WIN model: β = 0.14, 95% CI: 0.01, 0.27; p = .030; HHIE model: β = 0.16, 95% CI: 0.03, 0.28; p = .014). Education was not significantly associated (p > .05) in either model. Noise exposure at the census block level and ADI were also not significant predictors of PACC score (p > .05). When substituting MoCA scores for PACC, demographic associations (age, race, sex) remained consistent; however, hearing measures (WIN, HHIE) were not significantly associated with longitudinal MoCA trajectories (see Supplementary Table 2).

Table 3.

Linear mixed effects model: longitudinal associations between Preclinical Alzheimer’s Cognitive Composite (PACC) scores and hearing measures (WIN Threshold and HHIE Total Score).

Variables Model 1: WIN thresholda  
 Model 2: HHI total scoreb  
β 95% CI p-value β 95% CI p-value
Age −0.03 −0.05, −0.02 <.001*** −0.04 −0.05, −0.02 <.001***
Race
 Non-Hispanic White 0.43 0.24, 0.63 <.001*** 0.43 0.24, 0.63 <.001***
Sex
 Female 0.14 0.01, 0.27 .030* 0.16 0.03, 0.28 .014*
Education (years) 0.01 −0.01, 0.04 .388 0.01 −0.02, 0.04 .403
Area deprivation index 0.00 −0.01, 0.00 .103 0.00 −0.01, 0.00 .083
Hearing aid use
 Yes −0.01 −0.10, 0.08 .872 0.00 −0.09, 0.09 >.999
Hearing handicap  inventory 0.00 −0.01, 0.00 .012*
Words in noise −0.02 −0.03, −0.01 .003*
Noise at census block  group level (dB[A]) 0.00 −0.02, 0.03 .812 0.00 −0.02, 0.03 .816
Open in a new tab

Note. PACC = Preclinical Alzheimer’s Cognitive Composite; CI = confidence interval.

a

Model 1 (“WIN Threshold”) includes Words-in-Noise Threshold as the primary predictor.

b

Model 2 (“HHIE Total Score”) includes Hearing Handicap Inventory Total Score as the primary predictor.

***

p < .001.

*

p < .05.

In a separate LME analysis (Table 4), participants were categorized by baseline hearing impairment and hearing aid use to evaluate group differences in cognitive trajectories (see ­Figure 1). Compared to those with normal hearing and no hearing aid use, individuals with hearing loss who did not use a hearing aid exhibited significantly lower PACC scores over time (β=−0.17, 95% CI: −0.32, −0.01; p = .039). In contrast, those with hearing loss who used hearing aids did not significantly differ from the normal-hearing/no-aid group (β = −0.06, 95% CI: −0.25, 0.14; p = .578). Among covariates, older age was significantly associated with lower PACC scores over time (β = −0.04, 95% CI: −0.05, −0.02; p < .001), and non-Hispanic White participants had significantly higher PACC scores than Black participants (β = 0.42, 95% CI: 0.22, 0.62; p < .001). Neither the ADI (β = 0.00, 95% CI: −0.01, 0.00; p = .085) nor noise exposure at the census block group level (β = 0.00, 95% CI: −0.02, 0.02; p = .989) was significantly associated with longitudinal PACC score. The interaction between time and hearing aid groups was initially included but was not statistically significant and was removed from the final model. The individual data points and predicted trajectories, illustrating differential rates of cognitive decline by hearing loss and hearing aid status, are presented in Supplementary Figure 1.

Table 4.

Linear mixed effects model: longitudinal associations between Preclinical Alzheimer’s Cognitive Composite (PACC) scores and baseline hearing aid use and WIN Group.

Variables Longitudinal PACC scores
β 95% CI p-value
Age −0.04 −0.05, −0.02 <.001***
Sex
 Female 0.12 −0.01, 0.25 .074
Race
 Non-Hispanic White 0.42 0.22, 0.62 <.001***
Education (years) 0.01 −0.02, 0.03 .666
Hearing aid and words in noise group
 Hearing loss: Yes, Hearing aid: No −0.17 −0.32, −0.01 .039*
 Hearing loss: Yes, Hearing aid: Yes −0.06 −0.25, 0.14 .578
Area deprivation index 0.00 −0.01, 0.00 .070
Noise at census block group level  (dB[A]) 0.00 −0.02, 0.02 .994
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Note. PACC = Preclinical Alzheimer’s Cognitive Composite; WIN = Words-in-Noise; CI = confidence interval.

***

p < .001.

*

p < .05.

Figure 1.

Open in a new tab

Longitudinal changes in Preclinical Alzheimer’s Cognitive Composite (PACC) scores by baseline hearing loss status and hearing aid use. Predicted trajectories show cognitive decline over time, with the steepest decline observed in individuals with hearing loss who did not use hearing aids (▲). Individuals with hearing loss who used hearing aids (■) demonstrated more gradual decline, closely resembling those with normal hearing (●).

Discussion

The present study examined the longitudinal association between hearing loss operationalized via the WIN test and cognitive function over an eight-year period in a cohort of 398 participants. We also explored whether hearing aid use might attenuate cognitive performance decline. We hypothesized that hearing loss would be associated with lower cognitive performance and faster cognitive decline and that hearing aid use would modify this relationship.

Our findings indicate that a 10 dB SNR worsening in the WIN threshold was associated with a 0.20-unit lower PACC score across all time points, indicating that individuals with poorer speech-in-noise perception exhibited consistently lower cognitive function. While this effect size is smaller at the individual level, it suggests that those with poorer speech perception of noise will have lower cognitive function. Given that hearing loss often progresses gradually, these cumulative declines could contribute to clinically meaningful cognitive changes over several years. This supports the growing body of evidence linking hearing loss to cognitive decline (Ghisletta et al., 2022; Grenier et al., 2024; Hwang et al., 2022; Liang et al., 2021; Loughrey et al., 2018; Thomson et al., 2017). Findings from the MLR models indicate that poorer speech perception in noise (higher WIN threshold) was significantly associated with a lower baseline PACC score. In contrast, self-reported hearing difficulties (HHIE total score) were not significantly associated with cognition. This suggests that objective auditory processing deficits, rather than perceived hearing loss (or lack thereof), are more closely linked to cognitive function. The lack of association with the HHIE may reflect individual differences in self-awareness of hearing loss, coping mechanisms, or variability in how hearing loss affects social interactions and daily life (Atef et al., 2023; Hallberg & Carlsson, 1991; Newman et al., 1997)

Prior research has shown that self-reported hearing loss, particularly when combined with social isolation, increases dementia risk (Shukla et al., 2020). Consistent with this, a large epidemiological study found that older adults with hearing loss were more likely to experience social isolation, limiting their conversational engagement with family, friends, and acquaintances (Tomida et al., 2024). The combination of hearing loss and reduced social interaction has been linked to an increased risk of dementia and functional disability due to its impact on daily living activities (Tomida et al., 2024). The absence of a significant association between self-reported hearing difficulties and PACC score in our study could be partly explained by the variability in social engagement among individuals with hearing loss. Some may actively compensate for their difficulties through social strategies, while others may become increasingly isolated (Cormier et al., 2024; Motala et al., 2024; Shukla et al., 2020).

Although we did not observe a significant interaction between hearing aids and time, our results indicate that individuals with hearing loss who did not use hearing aids exhibited significantly lower PACC scores over the study follow-up period. Compared to the normal-hearing/no-aid group, those with untreated hearing loss had persistently lower PACC scores across follow-ups, suggesting a sustained cognitive disadvantage. In contrast, hearing-impaired participants who used hearing aids followed cognitive trajectories similar to those with normal hearing, with no significant difference in PACC scores over time. This finding suggests that hearing aid use may be associated with preserved cognitive performance over time. While our participants were primarily cognitively intact at baseline, this pattern may reflect a protective effect of hearing rehabilitation in preventing decline rather than reversing impairment. Prior observational studies have reported similar trends, although findings vary depending on cognitive status at baseline and follow-up duration (Deal et al., 2015; Yeo et al., 2023). However, it is important to note that in most observational studies, including ours, hearing aid use is assessed via self-report without information about device appropriateness, adherence, or duration of use. These limitations highlight the value of randomized controlled trials such as the ACHIEVE study (Lin et al., 2023), which implemented a comprehensive hearing rehabilitation intervention. Importantly, the ACHIEVE intervention included both hearing aid fitting and additional rehabilitative services, highlighting that hearing care involves more than device provision alone.

While some randomized controlled trials have found no substantial benefit of hearing aid use over short follow-up periods, recent findings suggest that hearing interventions may be more effective for individuals at higher risk of cognitive decline. In a randomized controlled trial assessing 977 participants over 8 years, participants were assigned to either a hearing intervention or a health education control group (Lin et al., 2023). While no significant difference was observed in overall cognitive trajectories, a sensitivity analysis suggested cognitive benefits for participants with higher baseline risk. These findings highlight the potential value of early intervention, particularly in populations vulnerable to accelerated decline, where preserving cognitive function may be more feasible than reversing impairment.

Furthermore, the cognitive benefits of hearing aid use appear to vary by demographic factors. It is possible that differences in cognitive trajectories between hearing aid users and nonusers reflect underlying social factors that influence both hearing aid adoption and cognitive health. For instance, individuals with greater social support, less stigma, or stronger networks may be more likely to pursue hearing rehabilitation and also experience protective cognitive effects related to these same social advantages (Singh et al., 2025). One study reported that hearing aid use was linked to better cognitive function in non-Hispanic White and Hispanic older adults but not in Black older adults (West et al., 2022). The reasons for this disparity remain unclear but suggest potential differences in access to care, hearing aid utilization, or other social determinants of health. Notably, in all our models, the ADI was not significantly associated with cognitive outcomes, while race remained significant, with non-Hispanic White participants consistently exhibiting higher PACC scores than Black participants. Interestingly, when race was removed from the models, ADI emerged as a strong predictor, suggesting that the observed racial difference may, in part, reflect the impact of structural socioeconomic factors that were otherwise accounted for by race in the full models.

Female participants exhibited significantly higher PACC scores than males in both the WIN and HHIE models. This is consistent with prior research demonstrating sex-specific differences in the associations between hearing loss, cognitive function, and memory outcomes (Al-Yawer et al., 2022, 2023). Several factors may contribute to these sex differences, including greater cognitive reserve, stronger social networks, and differences in health behaviors among females, all of which have been proposed as protective factors for cognitive aging (Mielke, 2018; Stern, 2012).

Despite prior research linking chronic noise exposure to cognitive decline and dementia risk, our findings did not indicate a significant association between environmental noise levels and cognitive outcomes in our models (Hahad et al., 2019; Huang et al., 2021; Tzivian et al., 2018; Wu et al., 2024; Yu et al., 2020). The mean noise level in our dataset (∼52 dB(A)) may have been too low to induce detectable changes in cognitive trajectories, as prior studies suggest that higher thresholds (>75 dB(A) daytime, >65 dB(A) nighttime) may be required to observe measurable cognitive effects (Yu et al., 2020). Additionally, our use of modeled noise exposure at the census block group level may not fully capture individual variations in actual noise perception, particularly differences in indoor versus outdoor exposure and personal noise mitigation strategies.

This study has several limitations. First, hearing aid use was self-reported, without objective measures of adherence, duration, or real-world effectiveness, limiting our ability to assess how consistent use may have influenced cognitive outcomes. Second, while the WIN test assessed hearing ability, it was more cognitively demanding than traditional auditory assessments like pure-tone audiometry, potentially introducing a cognitive load component that may overestimate the relationship between auditory function and cognitive performance. Third, our models adjusted for socioeconomic deprivation using the ADI, but the ADI did not significantly predict cognitive outcomes when race was included. However, when race was removed, ADI emerged as a strong predictor, suggesting that our findings on racial disparities in cognition may, in part, reflect underlying structural inequalities. This underscores the need for more granular measures of socioeconomic status and healthcare access to understand these disparities better. Third, environmental noise exposure was estimated using modeled data from the U.S. Bureau of Transportation’s National Transportation Noise Map rather than direct measurements at the household level. Estimates were based on predictive models from the FHWA and FAA, incorporating factors such as traffic volume, vehicle speed, roadway characteristics, flight operations, and aircraft types. GIS-based analysis assigned census block group-level mean noise levels to participants, which may not fully capture individual noise exposure variations. While necessary for large-scale studies, this approach introduces potential spatial misclassification, as census block group averages may not capture personal noise exposure variations, indoor insulation effects, or short-term fluctuations. Additionally, the noise levels estimated near participants’ current residences may not capture cumulative lifetime noise exposures, such as occupational, military, or earlier residential exposures, which could contribute to long-term neural degeneration (Kujawa & Liberman, 2009). Finally, while this study benefited from a well-characterized longitudinal cohort, the follow-up period (mean: 3.79 years) may not have been long enough to detect more subtle cognitive changes related to hearing loss or hearing aid use. Future research should incorporate more extended follow-up periods, more diverse populations, and multimodal assessments of hearing and cognitive function to elucidate further the complex relationships between auditory health and cognitive aging.

Conclusion

Speech-in-noise deficits were significantly associated with sustained cognitive disadvantage. These findings highlight the importance of proactive hearing care; however, the extent to which hearing aid use itself contributes to maintaining cognitive function remains uncertain and may reflect underlying social or health-related factors. Future research should focus on adherence, intervention timing, and the potential role of social engagement in mitigating cognitive risk.

Supplementary Material

gbaf115_Supplementary_Data
gbaf115_supplementary_data.zip (434.5KB, zip)

Acknowledgments

The authors extend their sincere gratitude to the dedicated participants of The DRIVES Project, whose generous contribution of time and effort made this research possible. We also wish to thank the committed lab members and staff of The DRIVES Project for their invaluable support and diligence throughout the data collection and analysis process. Their unwavering collaboration has been essential to the success of this study.

Contributor Information

Semere Bekena, Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Ramkrishna K Singh, Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Nikitha Damera, Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Yiqi Zhu, Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Jean-Francois Trani, School of Public Health, Washington University in St Louis, St. Louis, MO, USA; Department of Psychology, University of Johannesburg, Johannesburg, South Africa.

Kate McClannahan, Department of Otolaryngology–Head & Neck Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Mollie Webb, Data Services, WashU Libraries, Washington University in St Louis, St. Louis, MO, USA.

Ganesh M Babulal, Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Department of Psychology, University of Johannesburg, Johannesburg, South Africa.

Supplementary material

Supplementary material is available at The Journals of Gerontology Series B: Psychological Sciences and Social Sciences online.

Funding

This work was supported by the National Institutes of Health (NIH) and the National Institute on Aging (NIA) (R01 AG068183, R01 AG067428, and R01 AG074302 to G. M. Babulal). The funding agencies had no role in the design, conduct, data collection, management, analysis, or interpretation of the study, nor in the preparation, review, or approval of the manuscript.

Conflict of interest

The authors report no competing interests.

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