Abstract
Objectives.
Numerous health disparities are documented in deaf population research, but few empirical sleep assessments exist for this under-served population, despite knowledge that sleep contributes to physical and mental health disparities. We sought to document subjective and objective sleep in deaf adults with cross-sectional and prospective measures.
Methods.
Twenty deaf participants completed validated sleep and mental health questionnaires, two-weeks of nightly sleep diaries and continuous wrist-worn actigraphy monitoring, and one-week of nightly, reduced-montage EEG recordings.
Results.
Questionnaire data suggest high prevalence of insomnia (70%), poor sleep (75%), daytime sleepiness (25%) and nightmares (20%) among participants. Strong correlations were found between depression and sleep quality, fear of sleep, and insomnia severity (p’s<0.005). Objective sleep assessments suggest elevated wake after sleep onset and low sleep efficiency and sleep duration.
Conclusions.
The prevalence of sleep disturbance recorded from self-report and objective sleep measures provides preliminary evidence of sleep health disparity among deaf adults.
Keywords: Deaf, Deafness, Deaf Wellness, Health Disparity, Insomnia, Sleep, Sleep Health
INTRODUCTION
Relative to the general population, deaf communities experience significant health and life disparities including elevated rates of both physical and mental health conditions1,2. Though sleep disturbance is a critical public health issue associated with increased risk for a variety of psychiatric and medical illness3,4, the sleep of deaf individuals is poorly characterized and limited to recent cross-sectional survey research (using single items retrospectively reporting on sleep) indicating a high prevalence of insomnia (73%, compared to 10% in the general population5) and shorter sleep duration among deaf adults6,7. Objective sleep data in this population are lacking. Given that sleep heath disparities contribute to other physical and mental health disparities, and are impacted by socioeconomic status, access to healthcare, and cultural beliefs, among others8, assessing sleep is critical to understanding and improving all aspects of heath in underserved populations. We therefore performed a prospective cohort study of 20 deaf individuals and collected both objective and subjective measures of sleep over a 2-week period along with a full battery of validated self-report measures.
PARTICIPANTS AND METHODS
Study Population
Candidates between the ages of 21–64 were considered eligible if they self-identified as a deaf individual fluent in American Sign Language (ASL), and had self-reported hearing loss greater than 70 decibels. Exclusion criteria included presence of a cochlear implant, and/or any active serious medical conditions. We enrolled 23 individuals in the study; three withdrew prior to data collection for a final sample of 20 participants (Informed consent waived by the University of Rochester Research Subjects Review Board).
Baseline Assessments
Participants provided sociodemographic and medical history information, and completed five sleep items from the Behavioral Risk Factor Surveillance Survey (BRFSS)9, the Epworth Sleepiness Scale (ESS)10, Pittsburgh Sleep Quality Index (PSQI)11, Insomnia Severity Index (ISI)12, Fear of Sleep Inventory (FOSI)13, Disturbing Dreams and Nightmare Severity Index (DDNSI)14, and a report of their most recent dream. Additionally, they completed the Perceived Stress Scale (PSS)15, Patient Health Questionnaire for Depression (PHQ-9)16, Generalized Anxiety Disorder-7 (GAD-7), PTSD Checklist (PTSD-CL)17, and the Adverse Childhood Experiences Questionnaire (ACES)18, with supplemental questions about communication access. In addition to characterizing the sample with these measures, we calculated Pearson correlations for relationships of interest (2-tailed, alpha of .005 to correct for multiple comparisons).
Longitudinal Sleep Assessments
Over a two-week period, participants completed a daily sleep and dream diary online upon awakening, including 8 items related to bed and rise times, minutes to fall asleep, number and duration of awakenings, recall of dreams, and optional dream report via text or ASL-video recording. Participants wore an actigraph (Actiwatch2; Philips Respironics, Murrysville, PA) on their non-dominant wrist continuously for two weeks. Actigraphs collected data on movement and indicated off-wrist time at the default sampling rate of 30 Hz. Home diary and actigraphy data are often collected in tandem for one to two weeks19. Finally, participants were given a reduced-montage dry-electroencephalographic (EEG) device (Dreem 1 headband; Dreem, Inc., NY) to wear for 7 nights during the study period, which is consistent with the length of at-home sleep monitoring studies20. The headband uses 4 EEG electrodes (2 frontal electrodes and 2 occipital electrodes). Data were transmitted to a secure cloud server owned by Dreem via an application through an anonymized account.
Respironics Actiware software was used to score Actigraphy data; the default Actiware protocol automatically sets the rest interval when it detects periods of low activity that are longer than 3 hours. The Actiware algorithm permits automated labeling of sleep/wake status for each 30-second epoch, and weights the activity counts in relationship to activity levels in the surrounding 2-minute periods, using a wake threshold activity count of 40. Sleep onset was defined as 10 minutes of immobile time and sleep offset as the last epoch in the rest interval.
From these data, we calculated mean values for sleep latency (SL), minutes of wake after sleep onset (WASO), total sleep time (TST), sleep efficiency (SE), number of awakenings (NOA), and number of dreams recalled. Additionally, sleep stage data from wearable EEG were automatically scored by Dreem algorithms, and from this output we calculated means for percentage time spent in REM sleep and non-REM stages (N1, N2, and N3). Statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC).
RESULTS
Participants were a mean age of 29 ± 8 years, 15/20 (75%) were white, and 13/20 (65%) were women. Nearly all (19/20; 95%) participants reported onset of hearing loss before the age of 3, and 16/20 self-reported the severity of their hearing loss to be profound (80+ dB threshold in better ear). Half of the cohort rated their sleep health as inconsistent or poor at baseline (see Table 1 for sample characteristics).
Table 1.
Characteristics of Study Sample
| Characteristic | (N = 20) |
|---|---|
| Age, y | 28.9 ± 8.3 |
| Race/Ethnicity | |
| Asian | 5 (25%) |
| Hispanic | 2 (10%) |
| White | 15 (75%) |
| Gender | |
| Male | 6 (30%) |
| Female | 13 (65%) |
| Non-Binary | 1 (5%) |
| Smoking Habits | |
| Never smoker | 12 (60%) |
| Prior smoker, but not active | 7 (35%) |
| Active smoker | 1 (5%) |
| Yearly Income, Dollars | |
| < 25,000 | 14 (70%) |
| 25,000–34,999 | 3 (15%) |
| 35,000–49,999 | 2 (10%) |
| 50,000–74,999 | 0 |
| > 75,000 | 1 (5%) |
| Onset of Hearing Loss | |
| At birth | 13 (65%) |
| Before 3 years of age | 6 (30%) |
| Unknown | 1 (5%) |
| Severity of Hearing Loss in Better Ear | |
| Severe (61–80 dB) | 3 (15%) |
| Profound (80+ dB) | 16 (65%) |
| Unknown | 1 (5%) |
| Self-Rated Sleep Health | |
| Poor | 2 (10%) |
| Inconsistently Good/Bad | 8 (40%) |
| Moderate | 5 (25%) |
| Good | 5 (25%) |
| Self-Rated Average Hours of Sleep per Night | 6.7 ± 1.1 |
Values are displayed as means ± standard deviation or as frequency (%).
Among participants, 70% reported clinically meaningful insomnia (defined as a score of ≥10 on the ISI; mean=11.75±5.26), 75% reported poor sleep (defined as a score of ≥6 on the PSQI; mean=7.4±3.68), 25% excessive daytime sleepiness (defined as a score of ≥11 on the ESS; mean=7.2±4.80), and 20% frequent/severe nightmares (defined as a score of ≥10 on the DDNSI; mean=5.4±6.80). Participants also had high scores on the FOSI compared to a non-clinical sample (mean=12.00±12.03 vs non-clinical sample mean=4.80±7.72).21 Only 3/20 participants (15%) could be considered good sleepers defined as having total scores of ISI < 10, ESS < 11 and PSQI < 6.
Pearson correlations between mental health measures of depression (PHQ; mean=7.5±5.15), anxiety (GAD; mean=5.5±5.0), perceived stress (PSS; mean=17.25±6.52), and adverse childhood experiences (ACES; mean=2.65±2.54) with main sleep measures (ISI, ESS, PSQI, FOSI, DDNSI) revealed strong relationships between depression and poor sleep quality (r=.78, p<.001), fear of sleep (r=.62, p=.003), and insomnia severity (r=.75, p<.001), and between perceived stress and fear of sleep (r=.62, p=.004). Other trends were apparent but did not survive correction for multiple comparisons (corrected p<.005, two-tailed tests), including between anxiety and insomnia severity (r=.46, p=.04), fear of sleep (r=.48, p=.03), and poor sleep quality (r=.44, p=.05); and between nightmare severity and depression (r=.42, p=.07), and adverse childhood experiences (r=.42, p=.07).
There were a total of 247 days of sleep diary (n=20 participants), 73 days of reduced-montage EEG (n=14 participants), and 184 days of actigraphy data (n=14 participants) collected over the study period. Sleep diaries showed mean values for TST (7.9±1.4 hrs), SE (95.5±3.2%), and SL (18.1±14.6 min) within normal ranges. Reduced-montage dry EEG and actigraphy also indicated normal SL (20.1±19.1 and 26.6±20.5 min, respectively, see Figure 1). Reduced-montage dry EEG suggested low TST (6.6±1.2 hrs) and normal SE (88.4%±7.2%), whereas actigraphy suggested normal TST (7.1±1.1 hrs) and low SE (82.8%±4.4%; see Figure 1). Both reduced-montage dry EEG and actigraphy suggested high WASO (26.2.±24.0 and 43.2.±13.3 min, respectively) and a high number of awakenings per night (22.2±9.6 and 37.2±11.0), respectively. Mean percentages of each sleep stage as measured by EEG were within normal ranges (N1= 5.8±1.4%; N2=43.8±5.4%; N3=27.0±6.1%; REM=23.5±4.3%).
Figure 1. Characterization of Sleep by Self Report and Objective Measures in 20 Deaf Individuals.
Abbreviations: EEG = Electroencephalography. Strip-plot of sleep duration, onset latency and efficiency, with each measurement modality. Each vertical gridline represents an individual participant, with different markers to indicate diary, actigraphy, or EEG assessment measures. Group means for each assessment method are shown with a dotted line. The green highlighted portions represent healthy ranges per respective sleep metric25,26.
Diary TST correlated with actigraphy (n=14, r=.71, p=.004) but not with EEG measured TST (n=14, r=−.22, p=.45); diary SL correlated with EEG (n=14, r=.69, p=.006) but not with actigraphy SL (n=14, r=.42, p=.14); and diary SE correlated with EEG (n=13, r=.74, p=.004) but not with actigraphy SE (n=13, r=.42, p=.15). Actigraphy and EEG were not highly correlated on any measure: TST (n=9, r=.30, p=.43), SL (n=9, r=.65, p=.06) or SE (n=9, r=.52, p=.15). Given that only a subgroup of participants had recordings for all three assessment methods (n=9), we calculated means for this subgroup including: diary measures of TST (8.1±1.2 hrs), SE (95.9±2.6%), and SL (12.6±7.5 min); EEG-measured TST (6.4±1.3 hrs), SE (88.0±5.3%), and SL (20.3±14.8 min); and actigraphy measured TST (7.2±0.7 hrs), SE (82.0±5.0%), and SL (31.8±24.2 min). We did not find any significant differences (two-tailed t-test comparisons) between the subgroup and the whole sample on any sleep measures within diary (all p>.31), EEG (all p>.77), or actigraphy assessments (all p>.62).
Finally, Pearson correlations (two-tailed) between psychological tests and subjective and objective measures of TST, SL and SE, revealed a negative correlation between depression and diary-reported TST (n=20, r=−.58, p=.007); and a trend toward a positive correlation between adverse experiences and EEG-measured TST (n=14, r=.55, p=.04). No other correlations were significant (all p>.2).
DISCUSSION
These data represent the first characterization of habitual sleep in a sample of deaf individuals, indicating a potential health disparity that has been largely unexplored. Our results focus on self-reported sleep measurements (validated questionnaires and daily dairies), with preliminary evidence for objective sleep measurements in a subsample. Self-report sleep health questionnaires revealed only 15% of the sample qualified as good sleepers, whereas the majority, 85%, reported above clinical thresholds on insomnia symptoms (ISS), daytime sleepiness (ESS), and/or poor sleep (PSQI). Prevalence rates for insomnia (70%), daytime sleepiness (25%), and frequent nightmares (20%) in our sample were up to 7-fold higher than in the general population (insomnia 10%5, daytime sleepiness 5–15%5, nightmares 5%22).
Objective data provided by EEG-headband and actigraphy suggested elevated levels of WASO and nocturnal awakenings, with SE and sleep duration below or at the lower end of normal ranges.
Given the strong links we found between depression and various aspects of sleep, we speculate that the unique psychosocial stressors and higher rates of mental health problems faced by deaf individuals may negatively impact their sleep. This is supported by findings that depression is higher in the deaf population23 and that depression is known to negatively impact sleep24. Conversely, poor sleep may also contribute to or compound such stressors. Either way, the study findings highlight potential clinical sleep need in this community.
Our study was limited by a small sample size resulting in a cohort consisting largely of young white women, restricting generalizability. We were, however, able to obtain both self-report and multiple objective assessments of sleep (and to do so prospectively), which allowed us to identify potential areas of clinical need in this cohort. Our findings of 70% prevalence of insomnia is comparable to single-item survey response data from prior research (73% insomnia6). Nevertheless, there was a discrepancy between sleep diary and objective measures of sleep continuity, which are both well-known and often more pronounced in poor sleepers19, which seems to have comprised a large percentage of our sample. It is also unknown whether use of the EEG headband interfered with typical sleep as measured by diaries or actigraphy in our study, although we attempted to reduce any such interference by limiting headband use to only 7 of 14 nights. Work with larger samples is needed to understand the prevalence and types of sleep disturbance in deaf communities and how to address them if our preliminary observation of a sleep health disparity is supported.
Funding
This work was supported by University of Rochester Public Health Sciences Pilot Grant; and funding from the National Institutes of Health [NIGMS K12 GM106997 (MC); 5T32NS007338-32 (AY)].
Footnotes
Declaration of Competing Interest
Authors MC and AY report financial support provided by the National Institutes of Health.
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