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. 2024 Dec 30;34(4):e14456. doi: 10.1111/jsr.14456

Actigraphy‐based assessment of circadian rhythmicity and sleep in patients with Usher syndrome type 2a: A case–control study

Jessie M Hendricks 1,2, Juriaan R Metz 3, H Myrthe Boss 4, Rob W J Collin 5, Erik de Vrieze 1, Erwin van Wijk 1,✉
PMCID: PMC12215243  PMID: 39740053

Summary

This study aimed to improve our understanding of sleep problems as a comorbidity of hereditary deaf–blindness due to Usher syndrome type 2a. Fifteen patients with Usher syndrome type 2a with a conclusive genetic diagnosis and 15 unaffected controls participated in comprehensive sleep and activity assessments for 2 weeks, using the MotionWatch 8 actigraph and consensus sleep diary. Various sleep parameters including sleep opportunity window, sleep latency, sleep efficiency, and self‐reported sleep quality were analysed. Non‐parametric circadian rhythm analysis was performed to evaluate circadian rhythmicity. Additionally, regression analyses were conducted to study potential correlations between sleep parameters and patients' demographics and disease progression. Patients with Usher syndrome type 2a exhibited significantly longer sleep latency and lower self‐reported sleep and rest quality compared with controls. Additionally, day‐to‐day variability of sleep efficiency and sleep latency were significantly higher in the patient population. Non‐parametric circadian rhythm analysis revealed no significant differences in circadian rhythmicity. Regression analyses indicated that having Usher syndrome type 2a was a significant predictor of poor sleep outcomes. No clear correlations were found between the level of visual impairment and sleep parameters, suggesting that the negative effects of Usher syndrome type 2a on sleep manifest independently of the progressive visual impairment. These findings suggest that, while circadian sleep–wake rhythm remain intact, patients with Usher syndrome type 2a suffer from sleep disturbances that likely arise from factors beyond their progressive blindness. With sleep problems being a major risk factor for physical and mental health problems, we advocate that sleep problems should be recognized as a hallmark symptom of Usher syndrome type 2a, warranting in‐depth research for potential targeted therapeutic interventions.

Keywords: actigraphy, circadian rhythm, sleep, USH2a, usher syndrome

Patients with Usher syndrome type 2a experience significant sleep disturbances, including increased sleep latency, reduced sleep quality and increased variability in sleep efficiency, manifesting independently of the progressive visual impairment. These findings highlight the need to recognize sleep disturbances as a comorbidity, which is a critical step toward targeted therapeutic interventions.

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1. INTRODUCTION

Usher syndrome (USH) is the most prevalent form of hereditary deaf–blindness, affecting approximately 400,000 individuals worldwide. USH is both genetically and clinically heterogeneous. To date, pathogenic variants in at least 11 different genes have been identified to result in USH (Ullah et al., 2024). USH is categorized into four distinct clinical types based on differences in the presence, severity and age of onset of the different symptoms. The degree of the (mostly congenital) hearing impairment, the manifestation of vestibular dysfunction, and age of onset of the progressive loss of vision caused by retinitis pigmentosa (RP) can vary significantly between the different clinical types. Usher syndrome type 2 (USH2) is seen most frequently, accounting for about two‐thirds of all USH cases. USH2 is characterized by moderate to severe congenital hearing impairment in the largely absence of vestibular problems, and onset of RP during the second decade of life. About 85% of all patients with USH2 are genetically classified as Usher syndrome type 2a (USH2a), caused by pathogenic variants in the USH2A gene (van Wijk et al., 2004), thus establishing it as the most common subtype (Millán et al., 2010; Yan & Liu, 2010).

In addition to congenital hearing impairment and progressive vision loss, many patients with USH2a suffer from fatigue (Ehn et al., 2016; Ehn et al., 2020; Wahlqvist et al., 2013). This complaint is poorly understood and rarely addressed. It is often attributed to the burden of dual sensory impairment or considered a consequence of mental health issues resulting from the diagnosis of this debilitating condition. Extensive dialogues with patients recently unveiled the prevalence of sleep problems alongside fatigue. A questionnaire‐based study was initiated after patient anamnesis, exploring the prevalence, severity and nature of sleep problems and fatigue among genetically confirmed USH2a patients. This study revealed that patients with USH2a experienced poorer sleep quality, a higher incidence of sleep disorders, and elevated levels of fatigue and daytime sleepiness compared with the control population (Hendricks et al., 2023).

The critical role of light as a synchronizer for the circadian clock has been widely recognized (LeGates et al., 2014) and, indeed, sleep disorders are frequently found among the severely visually impaired population. However, sleep problems are primarily observed in individuals without residual light perception (Hartley et al., 2018). In these studies, participants are often screened on the absence of pupillary reflex, absence of conscious light perception, a negative electroretinogram, or absence of the complete eye (Aubin et al., 2016; Leger et al., 2002). Although RP can eventually progress into legal blindness, patients with USH2a do still have central vision or residual light perception (Stemerdink et al., 2021). However, our questionnaire‐based sleep assessment of patients with USH2a revealed that their sleep disturbances were not correlated with the level of visual impairment (Hendricks et al., 2023). This suggests that sleep disturbances in the USH2a population may have a different underlying cause than solely a reduction in light perception. This distinction prompted us to perform sleep research in this specific population, with the following two goals: (1) expand the findings of the USH2a sleep questionnaire study with more comprehensive and objective data, to characterize circadian rhythmicity and sleep in this specific population; and (2) study potential correlations between sleep and the patients' demographics and disease phenotype to determine whether the nature of these sleep problems differs from those experienced by the general blind community.

To expand the findings of the sleep questionnaires with more comprehensive and objective data, various methods can be applied, such as sleep diaries, actigraphy or polysomnography (Ibáñez et al., 2018). While a sleep diary is the easiest and most cost‐effective option, it remains subjective and prone to missing data due to its reliance on the active participation of the study subjects. Actigraphy, using a wrist‐worn accelerometer, is a commonly used method to measure activity and rest. Sleep–wake data can be deduced from the activity measurements. This non‐invasive monitoring can be executed over prolonged periods, enabling the opportunity to also study sleep variability and the stability of the circadian sleep–wake rhythm (Smith et al., 2018). However, it lacks the capability to distinguish between the different sleep stages. To study the latter, polysomnography can be performed. While polysomnography enables the characterization of sleep in much more detail, it is also more invasive, does not provide information about circadian sleep–wake rhythms, and is often performed for only one night and in a laboratory setting rather than in the subjects' own environment (Marino et al., 2013).

To enhance our understanding of sleep problems as a comorbidity of Usher syndrome and explore potential treatment options, we conducted a study that expanded upon the findings of the USH2a sleep questionnaire. In the current study, we combined actigraphy measurements with a sleep diary to assess sleep patterns and circadian rhythmicity in 15 genetically confirmed USH2a patients and 15 control individuals. Additionally, potential correlations between these data and the patients' demographics and disease progression were studied.

2. METHODS

2.1. Ethics

This study was classified as non‐WMO (Wet Medisch‐wetenschappelijk Onderzoek [Medical Scientific Research Act]) research by the local medical ethics review committee (case number 2022‐16034). Therefore, no full review according to the Dutch Medical Research Involving Human Subjects Act was performed. Visual field (VF) and visual acuity (VA) measurements have been conducted as a part of the USH2A natural history study (ClinicalTrials.gov Identifier NCT04820244).

2.2. Study design

This observational case–control study was designed to evaluate sleep patterns and circadian sleep–wake rhythms in patients with USH2a compared with controls. All participants lived throughout the Netherlands and were studied in their natural daily environment. Recruitment began in March 2023, and all measurements were performed between April and July 2023 (spring and summer periods). The study was executed in four rounds. In every round, three or four patients with USH2a and three or four unaffected controls were studied in parallel. To ensure accessibility, participants received the instructions via (video) call and the actigraphy device via regular mail, or during an at‐home visit. During the instructions, the purpose of the study was explained, as well as instructions on the actigraphy device and the sleep diary. Most importantly, the preferred format of the sleep diary was chosen (printed on A4, large‐print, or digital with the possibility to use text‐to‐speech software) and any accessibility issues were resolved. After completion of the experiment, the supplies were returned by regular mail. The participants did not have to visit the Radboud University Medical Center to participate in this study.

2.3. Participants

Patients who participated both in the Dutch USH2A natural history study (Characterizing Rate of Progression in USHer Syndrome, ClinicalTrials.gov Identifier NCT04820244) and the USH2a sleep questionnaire (Hendricks et al., 2023) were invited to participate in this follow‐up study (n = 29 invitations). Fifteen patients with USH2a enrolled, three declined and 11 did not respond. All patients were adults with a conclusive genetic diagnosis. The level of remaining visual function was not an inclusion criterion, and no selection was performed based on the results of the sleep survey.

Control participants were recruited from the control population of the USH2a sleep questionnaire (Hendricks et al., 2023). To closely resemble the USH2a group, controls were selected based on sex and age. Only individuals with the same sex and a maximum age difference of 4 years as one of the recruited patients were invited to participate. Exclusion criteria for control participants were night shift work and any type of visual impairment uncorrectable by glasses or lenses. Fifteen individuals participated as a control. All of the enrolled participants completed the study.

2.4. Measurements and data analysis

Sleep and circadian rhythmicity were assessed with the MotionWatch 8 actigraphy device (CamNTech, Cambridge, UK), accompanied by the consensus sleep diary (CSD), for 14 days. In addition, the Hospital Anxiety and Depression Scale (HADS) was filled in once. Patient VF and VA data were requested from the natural history study.

2.4.1. Consensus sleep diary (CSD)

The expanded version of the CSD was used, which consisted of a morning and an evening survey (Carney et al., 2012). The survey and corresponding instructions were translated into Dutch, the native language of all participants. One additional question was added: “Did you have an appointment or reason in the morning for which you had to get up? For example, (volunteer) work, meeting friends/family, sports. If yes, at what time?”. From the CSD, the following parameters were calculated for every night: sleep opportunity window (SOW); total sleep time (TST); sleep efficiency (SE); sleep‐onset latency (SOL); wake after sleep onset (WASO); self‐reported sleep quality; and self‐reported rest quality. The self‐reported sleep quality was rated by answering the following question: “How would you rate the quality of your sleep?”, with scores ranging between 1 and 5, where 1 = very poor and 5 = very good. The self‐reported rest quality was rated by asking “How rested or refreshed did you feel when you woke‐up for the day?” using the same scoring system. For every participant, the mean and the standard deviation of all CSD parameters were calculated, of which the latter indicates the variability between the 14 nights. Missing entries in the sleep diary were left blank, and thus not included in the calculation of mean or standard deviation values for any parameter.

2.4.2. Actigraphy

Participants wore the MotionWatch 8 actigraphy device on their non‐dominant wrist for 24 hr per day. The watch was set up with the following settings: recording mode 1, light recording enabled, data compression disabled, epoch 30 s, marker button enabled. The event marker button was used twice a day to mark the moment the participant was trying to go to sleep (Lights out), and the moment the participant stepped out of bed (Got up). Actigraphy data were analysed with the Sleep & Circadian Rhythm analysis of the MotionWare 1.3.33 software (CamNTech, Cambridge, UK). The Auto‐Sleep Configuration was used with the standard parameters: sleep activity threshold 20 cpm, minimum sleep fraction 60%, marker button limit 3.0 hr, and Prescan sleep fraction 80%. The four sleep analysis parameters: Lights out, Fell asleep, Woke up, and Got up, were checked with the CSD and adjusted if needed. The sleep analysis threshold was set on the validated high‐sensitivity setting of 20 cpm. For all sleep parameters, the mean and the standard deviation of the 14 nights were calculated, of which the latter indicates the variability. Circadian rhythmicity was assessed with the built‐in non‐parametric circadian rhythm analysis (NPCRA) tool (Van Someren et al., 1999). Twenty‐four‐hour average plots were exported with a smoothing period of 30 min. Results tables of all participants were exported and analysed with a custom R script.

2.4.3. Hospital Anxiety and Depression Scale (HADS)

To assess mental well‐being, every participant filled in the HADS, which consists of 14 multiple‐choice questions. Scores could range from 0 to 21 for both depression and anxiety, where a score of 11 or higher indicated a suspected anxiety disorder or depression (Zigmond & Snaith, 1983).

2.4.4. VF size and VA

Thirteen patient participants provided consent to share their VF and VA data from the USH2A natural history study for use in this study. All measurements were performed between 1 and 2 years before the sleep study, and were measured by the same optometrist in the same centre. VF was expressed as the mean deviation (MD), and VA as the letter score (Hendricks et al., 2023).

2.5. Statistics

Statistical analysis was performed in R. Normality of the data was assessed with the Shapiro–Wilk test. To assess differences between the USH2a and control population, a Student's unpaired t‐test or Wilcoxon rank sum test was performed. Potential relationships between the sleep variables and the following independent variables were assessed: morning appointment frequency, HADS anxiety score, and HADS depression score. This was done by calculating the Pearson or Spearman's rank correlation coefficients (for normal and non‐normal data, respectively). Two multiple linear regression models were built that adjusted for the independent variables that affected any of the sleep parameters. The first model included group (USH2a/Control) and morning appointment frequency (%) as independent variables. The second model adjusted for the following two additional independent variables: the presence of a suspected anxiety disorder (HADS anxiety score > 10), and a suspected depression (HADS depression score > 10). Lastly, to study potential relationships between any of the sleep variables and USH2a disease progression, all sleep variables of the patient participants were combined with the following three disease progression parameters: age, VF, and VA. Pearson or Spearman's rank correlation coefficients were calculated for normal and non‐normal data, respectively.

3. RESULTS

3.1. Participant demographics

Both the control and the USH2a study population consisted of six males and nine females. The control group had an age of 44 ± 10 years (mean ± SD) with a range of 27–59 years, and the USH2a group was 43 ± 10 years with a range of 25–58 years (Student's t‐test p = 0.914; Table 1). Mental well‐being of the participants was assessed with the HADS. Both the absolute scores for anxiety, as well as the number of participants scoring above the cut‐off for a suspected anxiety disorder, were not significantly different between the two groups (Student's t‐test p = 0.285 and Fisher Exact test p = 0.206, respectively). The depression score of the USH2a participants was significantly higher than the control (Wilcoxon rank‐sum test p = 0.017). However, only one of the 15 patients with USH2a had a score that indicated a suspected depression, and this frequency was not significantly different from the control population (Fisher Exact test p = 1.000). Lastly, the frequency of morning appointments, such as (volunteer) work, meeting friends/family or sports was assessed with an additional question in the CSD. The control participants had a morning appointment frequency of 64% ± 13%, equivalent to about 4.5 days per week. The USH2a group had a morning appointment on 60% ± 20% of the days, which is an average of 4.2 days per week. No difference was found between the two groups (Wilcoxon rank‐sum test p = 0.947). Altogether, the participant demographics show a high degree of similarity between both groups.

TABLE 1.

Demographics, CSD, actigraphy sleep, and actigraphy circadian rhythm results for control participants and patients with USH2a.

Analysis Variable Mean ± SD p‐Value
Control USH2a
Demographics Male/female 6/9 6/9 1.000 a
Age 44 ± 10 43 ± 10 0.914 b
Anxiety score 3.20 ± 2.04 4.53 ± 4.24 0.285 b
Suspected anxiety disorder 0/15 2/15 0.206 a
Depression score 1.40 ± 1.72 4.53 ± 4.29 0.017 c
Suspected depression 0/15 1/15 1.000 a
Morning appointment (%) 64 ± 13 60 ± 20 0.947 c
CSD SOW (hr) 8.05 ± 0.79 8.45 ± 0.48 0.108 b
SOW (hr) variability 0.91 ± 0.36 0.97 ± 0.47 0.696 b
TST (hr) 7.35 ± 0.66 7.54 ± 0.51 0.387 b
TST (min) variability 56.73 ± 26.38 60.75 ± 24.95 0.419 c
SE (%) 91.64 ± 5.41 89.21 ± 5.76 0.252 b
SE (%) variability 8.00 ± 2.92 8.42 ± 3.87 0.748 b
SOL (min) 12.35 ± 9.22 23.87 ± 16.91 0.036 b
SOL (min) variability 10.16 ± 9.28 21.47 ± 19.08 0.093 c
WASO (min) 35.37 ± 30.09 54.85 ± 40.71 0.269 c
WASO (min) variability 32.85 ± 20.63 44.08 ± 32.95 0.381 c
Self‐reported sleep quality 3.72 ± 0.49 3.20 ± 0.55 0.012 b
Self‐reported sleep quality variability 0.63 ± 0.27 0.74 ± 0.32 0.313 b
Self‐reported rest quality 3.51 ± 0.63 2.78 ± 0.55 0.002 c
Self‐reported rest quality variability 0.64 ± 0.22 0.78 ± 0.28 0.106 c
Actigraphy sleep Time in bed (hr) 8.15 ± 0.88 8.30 ± 0.79 0.648 c
Time in bed (hr) variability 0.90 ± 0.34 1.01 ± 0.46 0.934 c
Actual sleep time (hr) 6.89 ± 0.52 6.77 ± 0.71 0.581 b
Actual sleep time (hr) variability 0.79 ± 0.33 1.03 ± 0.44 0.115 c
SE (%) 84.96 ± 4.28 81.78 ± 7.20 0.155 b
SE (%) variability 4.29 ± 1.08 6.61 ± 3.63 0.030 b
Sleep latency (min) 5.62 ± 4.63 14.78 ± 11.83 0.012 b
Sleep latency (min) variability 6.24 ± 4.12 19.30 ± 17.20 0.012 b
Rest per 24 hr (%) 39.58 ± 5.23 44.08 ± 5.24 0.026 b
Central phase measure (min) 216.07 ± 36.84 222 ± 61.99 0.753 b
Central phase measure (min) variability 42.43 ± 24.87 58.15 ± 59.59 0.507 c
Actigraphy NPCRA Relative amplitude 0.91 ± 0.04 0.91 ± 0.06 0.384 c
Inter‐daily stability 0.49 ± 0.09 0.49 ± 0.06 0.990 b
Intra‐daily variability 0.88 ± 0.24 0.90 ± 0.17 0.772 b
L5 start hour 1.07 ± 0.96 1.33 ± 1.11 0.570 c
M10 start hour 9.60 ± 1.64 9.27 ± 1.79 0.485 c
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Statistical tests were chosen based on the type of data and the normality. Significant p‐values are indicated in bold.

CSD, consensus sleep diary; L5, least five active hours; M10, most 10 active hours; NPCRA, non‐parametric circadian rhythm analysis; SE, sleep efficiency; SOL, sleep‐onset latency; SOW, sleep opportunity window; TST, total sleep time; USH2a, Usher syndrome type 2a; WASO, wake after sleep onset.

a

Fisher exact test.

b

Student's t‐test.

c

Wilcoxon rank‐sum test.

3.2. Patients with USH2a experience poorer sleep quality and SE, as well as higher day‐to‐day sleep variability

From the CSD, the following parameters were calculated for every single night: SOW; TST; SE; SOL; WASO; self‐reported sleep quality; and self‐reported rest quality. Then, the mean and SD were calculated for all participants individually. The SD is used to express the day‐to‐day variability (Rösler et al., 2023). All CSD scores for the two groups, as well as the statistical tests, are shown in the CSD section of Table S1. The SOW of the USH2a population was 8.45 ± 0.48 hr, compared with 8.05 ± 0.79 hr in the control population (p = 0.108). No difference was found between the SOW variability of the two groups (p = 0.696). With an average TST of 7.35 ± 0.66 hr and 7.54 ± 0.51 hr for control and USH2a individuals, respectively, no significant difference was found in the amount of sleep of both groups (p = 0.387). The same is observed for the TST variability (p = 0.419). Although the self‐reported SE was slightly lower in the USH2a population, and the SE variability was slightly increased in the patient population, no significant differences were found for both parameters (p = 0.252 and p = 0.748 for mean SE and SE SD, respectively). However, the SOL was nearly twice as high in the patients compared with the controls: 12.35 ± 9.22 min for controls and 23.87 ± 16.91 min for patients (p = 0.036). Four patients with USH2a had an average SOL above 30 min, compared with zero of the control participants. Although not statistically significant, the patients with USH2a seem to have a higher SOL variability as well (p = 0.093). The control participants had a WASO of 35.37 ± 30.09 min, and the patients 54.85 ± 40.71 min (p = 0.269), again with a non‐significant but slightly higher day‐to‐day variability in the patients (p = 0.381). Control participants gave their sleep quality an average of 3.72 ± 0.49 out of 5, where 1 indicates very poor sleep and 5 very good sleep. The USH2a population had a significantly lower self‐reported sleep quality of 3.20 ± 0.55 (p = 0.012). Lastly, with an average of 2.78 ± 0.55, the patients clearly showed a reduced self‐reported rest quality compared with the control population, which scored 3.51 ± 0.63 (p = 0.002). In summary, the sleep diary indicates that patients take significantly longer to fall asleep and, although not significant, tend to be more awake during the night and exhibit greater day‐to‐day variability. Lastly, they also rate their sleep and rest quality lower compared with the control population.

Besides all sleep scores from the sleep diary, the following parameters were measured with the actigraphy devices: time in bed, actual sleep time, SE, sleep latency, percentage of rest per 24 hr, and the central phase measure. The results are shown in Table 1. “Average time in bed” and “time in bed variability” measured with the actigraph were very comparable to the outcomes of the sleep diary, with no significant differences between the groups (p = 0.648 and p = 0.934, respectively). No significant differences were found between the “actual sleep time” and “actual sleep time variability” of USH2a and controls (p = 0.581 and p = 0.115). Although not significantly different, the USH2a population had a slightly lower SE (p = 0.155). However, they did present with a significantly higher SE variability (p = 0.030). Strikingly, the sleep latency of the patient population was almost three times higher than the control population: 14.78 ± 11.83 min versus 5.62 ± 4.63 min for USH2a and control population, respectively (p = 0.012). In addition, the sleep latency variability was more than three times as high in the USH2a population: 19.30 ± 17.20 min versus 6.24 ± 4.12 min for USH2a and control participants, respectively (p = 0.012). The “rest per 24‐hr” parameter showed that USH2a participants have more moments of inactivity (p = 0.026). Lastly, the midpoint between fell asleep and wake‐up time was calculated. This parameter is called the “central phase measure”, expressed as the number of minutes past midnight. The central phase measure and its variability were not significantly different between the two groups (p = 0.753 and p = 0.507, respectively; Table 1). Results from the female and male participants separately were rather comparable (Tables S1 and S2). The outcomes derived from the CSD and the actigraphy measurements showed a high correlation (Table S3). Altogether, the sleep diary and actigraphy data show that the subjects with USH2a have a significantly increased sleep latency and higher day‐to‐day variability of sleep latency and SE as compared with control individuals, which is reflected in their self‐reported sleep and rest quality.

3.3. Patients with USH2a display normal circadian sleep–wake rhythm

To study the potential aberrations in circadian rhythmicity, a NPCRA was performed on the actigraphy data. The following five parameters were calculated: relative amplitude; inter‐daily stability; intra‐daily variability; start hour of the five least active hours (L5); and start hour of the 10 most active hours (M10). The results are shown in the Actigraphy NPCRA section of Table 1. The 24‐hr average activity plots of all participants are shown in Figure S1. No differences were found in any of the five parameters when the patients with USH2a were compared with the control population, showing that the USH2a participants have a clear circadian rhythm in their sleep–wake activity.

3.4. Regression models reveal negative effects of USH2a on sleep latency, SE and sleep quality

To study whether the differences in the USH2a and control populations can be explained by having USH, or are an indirect effect of the participant's demographics, the data were analysed using two different multiple linear regression models: (1) group and morning appointment frequency as independent variables; and (2) group, morning appointment frequency, presence of a suspected anxiety disorder (HADS anxiety score > 10), and a suspected depression (HADS depression score > 10) as independent variables. These independent variables were chosen based on the results of single linear regression analyses (Table S4). Each independent variable had a significant effect on at least one of the sleep parameters. According to model 1, the SOL is negatively impacted by having USH2a (p = 0.026), while a higher morning appointment frequency significantly shortens, and thus improves, the SOL (p = 0.003). When adjusting for the presence of a potential anxiety disorder or depression in model 2, having USH2a still increases the SOL, although not significantly (p = 0.087). The presence of USH2a decreases the self‐reported sleep quality according to model 1 (p = 0.014) and model 2 (p = 0.048). Similarly, the self‐reported rest quality is also significantly decreased by the presence of USH2a in both model 1 and model 2 (p = 0.003 and p = 0.012, respectively). The effect (b 1 + 95% confidence interval [CI]) and strength (R 2) of these correlations, as well as the effect of all other independent variables, can be found in Table 2.

TABLE 2.

Multiple regression results of the following three CSD parameters.

SOL (min) SSQ SRQ
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Predictors b 1 [CI] p b 1 [CI] p b 1 [CI] p b 1 [CI] p b 1 [CI] p b 1 [CI] p
(Intercept) 40.0 [21.86, 58.14] < 0.001 28.04 [6.52, 49.55] 0.013 3.09 [2.30, 3.88] < 0.001 3.59 [2.65, 4.52] < 0.001 3.02 [2.09, 3.95] < 0.001 3.53 [2.42, 4.65] < 0.001
Group [USH2a] 10.12 [1.29, 18.95] 0.026 7.71 [−1.19, 16.61] 0.087 −0.49 [−0.88, −0.11] 0.014 −0.39 [−0.77, −0.00] 0.048 −0.71 [−1.16, −0.26] 0.003 −0.61 [−1.07, −0.15] 0.012
Morning appointment frequency −0.43 [−0.70, −0.17] 0.003 −0.25 [−0.57, 0.08] 0.131 0.01 [−0.00, 0.02] 0.094 0.00 [−0.01, 0.02] 0.762 0.01 [−0.01, 0.02] 0.255 0.00 [−0.02, 0.02] 0.974
Anxiety [Yes] 21.37 [−5.40, 48.14] 0.112 −1.11 [−2.28, 0.05] 0.059 −1.13 [−2.52, 0.26] 0.106
Depression [Yes] −0.52 [−32.93, 31.90] 0.974 0.44 [−0.97, 1.85] 0.524 0.43 [−1.25, 2.11] 0.605
R 2/R 2 adjusted 0.407/0.308 0.497/0.413 0.296/0.241 0.403/0.303 0.329/0.278 0.407/0.308
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Model 1 includes group and morning appointment frequency as independent variables. Model 2 includes two additional independent variables: presence of a suspected anxiety disorder (HADS anxiety score > 10) and a suspected depression (HADS depression score > 10). b 1 = slope, [CI] = confidence interval, R 2 = strength. Significant p‐values are indicated in bold.

CI, confidence interval; SOL, sleep‐onset latency; SRQ, self‐reported rest quality; SSQ, self‐reported sleep quality; USH2a, Usher syndrome type 2a.

The same multiple linear regression models were used to assess the results of the actigraphy data. According to both model 1 and model 2, the SE variability is significantly increased in the participants with USH2a (p = 0.041 and p = 0.038, respectively), while the SE variability is decreased in participants with a higher morning appointment frequency (p = 0.019 and p = 0.029 for model 1 and model 2, respectively). The sleep latency shows the same trend of a significant increase in USH2a participants (p = 0.006 and p = 0.011), and a significant decrease with higher morning appointment frequency (p = 0.001 and p = 0.011, respectively). The same is observed for the effect of having USH2a on increased sleep latency variability (p = 0.006 and p = 0.018). In model 1, a higher morning appointment frequency leads to a decreased sleep latency variability (p = 0.003). Lastly, the presence of bi‐allelic pathogenic variants in USH2A leads to an increase in rest per 24 hr in model 1 (p = 0.028). The effect and strength of these correlations, and the effect of all other independent variables can be found in Table 3. Overall, the regression models indicate that having USH2a negatively affects sleep latency, SE and sleep quality, while participants' demographics have minimal impact on the outcomes of the study. Among all demographic predictors, only the morning appointment frequency showed significant correlations: a higher morning appointment frequency has a positive influence on the sleep latency and SE.

TABLE 3.

Multiple regression results of the following four actigraphy sleep parameters: SE SD, sleep latency, sleep latency SD, and rest per 24 hr.

SE (%) variability Sleep latency
Model 1 Model 2 Model 1 Model 2
Predictors b 1 [CI] P b 1 [CI] p b 1 [CI] p b 1 [CI] p
(Intercept) 8.78 [4.87, 12.70] < 0.001 9.65 [4.71, 14.60] < 0.001 26.13 [14.40, 37.87] < 0.001 24.48 [9.86, 39.11] 0.002
Group [USH2a] 1.99 [0.09, 3.90] 0.041 2.18 [0.13, 4.22] 0.038 8.37 [2.66, 14.08] 0.006 8.10 [2.05, 14.16] 0.011
Morning appointment frequency −0.07 [−0.13, −0.01] 0.019 −0.08 [−0.16, −0.01] 0.029 −0.32 [−0.50, −0.15] 0.001 −0.30 [−0.52, −0.08] 0.011
Anxiety [Yes] −2.35 [−8.51, 3.80] 0.438 −3.52 [−21.72, 14.68] 0.693
Depression [Yes] 1.51 [−5.94, 8.97] 0.679 11.91 [−10.12, 33.95] 0.275
R 2/R 2 adjusted 0.317/0.265 0.335/0.224 0.501/0.463 0.528/0.449
Sleep latency variability Rest per 24 hr (%)
Model 1 Model 2 Model 1 Model 2
Predictors b 1 [CI] p b 1 [CI] p b 1 [CI] p b 1 [CI] p
(Intercept) 31.70 [14.48, 48.92] 0.001 18.30 [−0.68, 37.28] 0.058 41.98 [33.56, 50.39] < 0.001 36.89 [27.39, 46.39] < 0.001
Group [USH2a] 12.19 [3.80, 20.57] 0.006 9.62 [1.77, 17.47] 0.018 4.65 [0.55, 8.74] 0.028 3.70 [−0.23, 7.63] 0.064
Morning appointment frequency −0.40 [−0.65, −0.15] 0.003 −0.19 [−0.48, 0.10] 0.185 −0.04 [−0.16, 0.09] 0.540 0.04 [−0.10, 0.19] 0.549
Anxiety [Yes] 11.23 [−12.40, 34.85] 0.336 1.61 [−10.21, 13.43] 0.781
Depression [Yes] 22.96 [−5.64, 51.56] 0.111 13.65 [−0.66, 27.96] 0.061
R 2/R 2 adjusted 0.452/0.410 0.594/0.526 0.192/0.130 0.372/0.268
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Model 1 includes group and morning appointment frequency as independent variables. Model 2 includes two additional independent variables: Presence of a suspected anxiety disorder (HADS anxiety score > 10) and a suspected depression (HADS depression score > 10). b 1 = slope, [CI] = confidence interval, R 2 = strength. Significant p‐values are indicated in bold.

CI, confidence interval; SE sleep efficiency; USH2a, Usher syndrome type 2a.

3.5. Sleep problems are not correlated to the disease progression

To better study the potential effect of the level of vision loss on the sleep of patients with USH2a, the sleep parameters of the 15 USH2a participants were correlated to three disease progression parameters: VF; VA (available for 13 participants); and age (available for all 15 patient participants). The VF was described as the MD, where a higher MD represented a smaller VF. An MD between 0 and 2 dB represents normal vision, and our patient population ranged between 10 and 26 dB, with a mean of 21 dB. The restriction in the VF affected only one of the 30 sleep variables: a positive correlation between the MD and intra‐daily variability was found, meaning that a more restricted VF led to a significantly higher intra‐daily variability of the circadian rhythm (Pearson's correlation coefficient [r] = 0.794, p = 0.010, slope [b] = 23.128, and CI = 6.86–39.4). The second disease progression parameter was VA. VA was expressed as the letter score, where a lower letter score represents lower VA. Letter scores can range from 0 to 100, where 100 represents the sharpest vision. Average healthy participants score 84–88, and our patient cohort ranged between 59 and 89, with a mean of 76. None of the 30 sleep variables was correlated to VA. Because USH2A‐associated vision loss is progressive, the final disease progression parameter used was age. Age influenced on two of the 30 sleep variables: a higher age increased the rest per 24‐hr percentage in patients (r = 0.641, p = 0.018, b = 1.113, CI = 0.20–2.02). In addition, a higher age increased the intra‐daily variability of the circadian rhythm (r = 0.564, p = 0.045, b = 31.6, CI = 2.01–61.19). The correlation coefficients of the three disease progression parameters versus the 30 sleep variables can be found in Table S5. In total, only three out of the 90 correlations between disease progression and sleep demonstrated a statistically significant relationship. Altogether, these results suggest that the negative effects of USH2a on sleep latency, SE and sleep quality present themselves independent of their progressive visual impairment. An overview of the study's key findings is presented in Figure 1.

FIGURE 1.

FIGURE 1

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Overview of the key findings. (a) Consensus sleep diary (CSD) data indicate that patients with Usher syndrome type 2a (USH2a) experience poorer sleep quality, including a lower self‐reported rest quality. (b) Non‐parametric circadian rhythm analysis (NPCRA) suggests that the circadian rhythm of patients remained intact, for example, indicated by non‐significant differences in inter‐daily stability. (c) Actigraphy‐derived sleep parameters reveal several significant differences between USH2a and controls, including a higher day‐to‐day variability in sleep efficiency (SE), a prolonged sleep latency and higher sleep latency variability. (d) Regression analysis shows no significant correlation between sleep parameters and disease progression, here indicated by visual field (VF) mean deviation (MD). Boxes display first to third quartiles, in which the black horizontal line indicates the median. Whiskers extend to the most extreme data point in the 1.5 interquartile range. Scores were compared with a Student's t‐test or Wilcoxon rank‐sum test for normal and non‐normal data, respectively (*p < 0.05; **p < 0.01).

4. DISCUSSION

In this study, sleep and circadian rhythmicity of 15 genetically confirmed USH2a patients and 15 controls were assessed with an actigraphy device and a sleep diary. Compared with the control population, patients with USH2a reported a significantly increased SOL in the sleep diary, as well as significantly lower self‐reported sleep quality and rest quality. The actigraphy data corroborated the significantly increased sleep latency in the patient population. In addition, the day‐to‐day variability of SE and sleep latency, and amount of rest per 24 hr, were significantly higher in patients with USH2a compared with the control population.

In addition to the sleep analysis, a NPCRA was conducted (Van Someren et al., 1999). No differences were found in any of the NPCRA parameters, indicating that the USH2a population had an identical amplitude in their circadian activity as the control population, as well as a similar inter‐daily stability, intra‐daily variability, and timing of their least and most active hours. The absence of aberrations in the circadian sleep–wake rhythm aligns with the result that the SOW and central phase measure, as well as the day‐to‐day variability between these two parameters, were not significantly different between USH2a participants and healthy controls. Furthermore, the similar morning appointment frequencies between USH2a and controls indicate that the patient participants maintain a structured daily routine too, which is well known to play an essential role in establishing and sustaining the circadian rhythm (Moss et al., 2015).

Sleep is a complex process affected by many factors. These can be physical, for example activity levels and daily routine consistency (Moss et al., 2015; Wang & Boros, 2021), as well as psychological, such as stress and mental health (Fang et al., 2019; Kim & Dimsdale, 2007). In addition, other biological factors such as age and sex can influence sleep and sleep quality (Redline et al., 2004). In this study, we aimed to control for these variables by age‐ and sex‐matching the control population to the USH2a cohort. Moreover, we assessed the potential influence of the morning appointment frequency on all sleep variables. The morning appointment frequency positively impacted 7/30 sleep variables. These findings align with the consensus that regularity in daytime activities is important for sleep (Monk et al., 2003; Moss et al., 2015). To further understand the psychological factors, mental wellbeing was evaluated with the HADS. Although the USH2a population had significantly higher HADS depression scores, the mean score remained far below the cut‐off score for suspected depression. Nevertheless, because the slightly elevated HADS scores could be an indicator of poorer mental health in the USH2a population, the potential influence on the sleep parameters was evaluated. Indeed, higher anxiety and depression scores were correlated with lower self‐reported sleep and rest qualities. Additionally, higher depression scores negatively impacted the sleep latency and its variability, or vice versa. These correlations are well documented (Baglioni et al., 2011; Fang et al., 2019). The increased HADS scores of the USH2a population are in line with previously published results about the mental well‐being of patients with USH2. In the study of Wahlqvist et al. (2013), the physical and psychological health of 96 patients with USH2 was assessed. The authors reported that the physical and psychological health of the USH2 participants was significantly poorer compared with the reference group. Among other variables, the presence of anxiety and depression was significantly increased (Wahlqvist et al., 2013). Anxiety, depression and sleep problems often co‐occur (Taylor et al., 2005). Commonly, poor mental well‐being is seen as a potential cause of sleep problems. However, there is increasing evidence for the reverse; sleep problems are actually a risk factor for the development of depression and other mental health issues (Baglioni et al., 2011; Blanken et al., 2019; Fang et al., 2019; Hertenstein et al., 2019; Riemann et al., 2022). Although all correlations found in our study correspond to findings in literature, multiple linear regression models were employed to correct for these influences. When correcting for morning appointment frequency, the model showed that having USH2a significantly affected seven of the measured sleep parameters. However, when we also adjusted for the presence of a suspected anxiety or depression disorder, only five parameters remained significantly affected. When sleep problems are indeed a risk factor for anxiety and depression, this raises the question of whether it is fair to correct for anxiety and depression in the multilinear regression models. But, most importantly, it again highlights the importance of recognizing (and eventually also treating) these sleep problems in patients with USH2a. Altogether, the multiple regression models show that, from all predictors, having USH2a exerts the strongest effect on the sleep parameters.

Because direct relationships exist between light perception and circadian rhythmicity (LeGates et al., 2014), and legal blindness and sleep problems (Atan et al., 2023), one could assume that an impaired light perception also underlies the sleep problems observed in patients with USH2a. Despite the abundance of literature on the high frequency of sleep disorders in blind individuals, this concerns almost exclusively fully blind people with no residual light perception, suffering from non‐24‐hr sleep–wake disorders (Atan et al., 2023). Interestingly, this is completely different in our USH2a study cohort: participants still have residual light perception and, according to our study, their circadian sleep–wake rhythm seems to be intact. In addition, patients with USH declare that the sleep problems were already experienced long before the initial clinical symptoms of vision loss became apparent. Therefore, one of our goals was to investigate whether potential correlations exist between sleep parameters of sleep deficiency and the level of visual dysfunction, to investigate whether these sleep problems are of a different nature than the sleep problems in the general blind community. To study this, VF, VA and age were used as disease progression parameters. VF only significantly affected one of the 30 sleep parameters: a lower VF increased the intra‐daily variability of the circadian rhythm. The VA affected none of the sleep variables. Age only correlated with 2/30 sleep parameters: a higher age increased the rest percentage and increased the intra‐daily variability. It is important to note that the intra‐daily variability was not significantly different in the patient population compared with controls. Altogether, we conclude that there is no clear correlation between the disease progression and the onset or severity of the sleep problems, suggesting that the negative effects of USH2a on sleep manifest independently of the progressive visual impairment. These findings are in accordance with the results obtained from the previously executed questionnaire‐based USH2a sleep study (Hendricks et al., 2023).

To further investigate whether the origin of the sleep problems observed in patients with USH2a are of a different nature than in fully blind individuals, we compared our results with other actigraphy studies. Lazreg and colleagues studied participants who had either congenital or acquired blindness, with varying causes. The blind adults had a significantly decreased actual sleep time and SE compared with the control adults. Additionally, NPCRA revealed a decreased relative amplitude of the circadian sleep–wake rhythm in the blind adults (Lazreg et al., 2011). Aubin and colleagues studied blind individuals without residual light perception, and identified a greater variability in SE, TST and time in bed of the blind participants compared with the controls. The authors concluded this suggested an increased incidence of free‐running, non‐24‐hr circadian rhythms in blind individuals, although no NPCRA was performed (Aubin et al., 2016). And lastly, Bittner and colleagues studied sleep disturbance in patients with RP with a confirmed diagnosis. The genetic diversity of the RP diagnoses was not mentioned and no control participants were measured. However, their study population included patients with widely varying levels of VA and VF, from healthy vision to complete blindness. The study showed that VA and VF had no significant effect on the sleep quality rating and actigraphy‐based sleep parameters (fragmentation and WASO; Bittner et al., 2018). Our findings differ from the two studies in fully blind individuals (Aubin et al., 2016; Lazreg et al., 2011) as no indications of a non‐24‐hr circadian rhythm disorder in the patients with USH2a were obtained. Of course, an intrinsic shift in circadian rhythm could potentially be masked by their relatively strict schedule, as indicated by the normal morning appointment frequency and normal SOW. However, patient declarations about the early onset of sleep problems, as well as our data showing an absence of correlation with disease progression, suggest at least a partial non‐retinal or extra‐retinal origin of the sleep problems. This finding aligns with the results from Bittner and colleagues, where no direct correlation between vision and sleep was found in patients with RP (Bittner et al., 2018). Lastly, our main finding of high day‐to‐day variability aligns with actigraphy results from a sighted insomnia population (Rösler et al., 2023), indicating that the sleep problems identified in our USH2a study population mirror those experienced by individuals without visual impairment.

Taken together, the patients' declarations about the early onset of sleep problems, the absence of clear correlations with disease progression and the absence of a circadian rhythm disorder, it seems likely that the sleep problems in patients with USH2a have a different underlying origin than an impaired light perception. Currently, we can only speculate about the underlying mechanism, but it is not unlikely that the USH2A‐encoded protein usherin is involved in more processes than solely retinal photoreceptor cell maintenance and cochlear hair cell development (Liu et al., 2007). Notably, USH2A transcripts have been detected in various human tissues beyond retina and cochlea, including fetal and adult brain (van Wijk et al., 2004). The potential presence of usherin in sleep‐regulating brain regions, however, has not yet been investigated to date. And although not much is known about the potential correlation between sleep characteristics and hearing loss (Jiang et al., 2021), the effect of hearing impairment on sleep in patients with USH2a should be considered for future research. Additionally, it is known that the usherin functions in protein complexes together with the other USH2 proteins ADGRV1 and whirlin (Slijkerman et al., 2017). Further research should investigate whether USH2A‐associated sleep problems are also present in people suffering from other types of USH2 or USH2A‐associated non‐syndromic RP.

This study added an extra layer of objectively measured and in‐depth sleep and rest–activity data of patients with USH2a on top of the already published questionnaire‐based data on experienced sleep quality and fatigue. Both studies clearly show a relationship between USH2a and poor sleep, independent of the vision loss. With sleep problems being a major risk factor for physical and mental health, we advocate that poor sleep quality should be recognized as an important comorbidity of USH2a that has a significant impact on the quality of life of affected individuals. Recognition of these sleep problems as comorbidity is the first step toward improved patient care. In addition, unravelling the origin of the sleep disturbances experienced by patients with USH2a will be an important subject for future research, as understanding the underlying biological cause is crucial for the development of targeted therapeutic interventions.

AUTHOR CONTRIBUTIONS

Jessie M. Hendricks: Conceptualization; writing – original draft; writing – review and editing; methodology; visualization; formal analysis; investigation. Juriaan R. Metz: Conceptualization; writing – review and editing; funding acquisition; methodology; supervision. H. Myrthe Boss: Conceptualization; writing – review and editing; methodology; supervision. Rob W. J. Collin: Conceptualization; writing – review and editing; supervision. Erik de Vrieze: Conceptualization; funding acquisition; methodology; supervision. Erwin van Wijk: Conceptualization; writing – review and editing; funding acquisition; methodology; supervision.

FUNDING INFORMATION

This study was supported by Stichting Ushersyndroom, Dr C.J. Vaillantfonds, Landelijke Stichting voor Blinden en Slechtzienden, Steunfonds Uitzicht (Beheer't Schild), Algemene Nederlandse Vereniging ter Voorkoming van Blindheid (UZ2020‐16), de Gelderse Blindenstichting, and Radboud Institute for Biological and Environmental Sciences (Radboud University). The funders had no role in the study design, data collection, analysis, interpretation or publication of the findings.

CONFLICT OF INTEREST STATEMENT

The authors do not have any conflicts of interest to disclose.

INFORMED CONSENT

Participants provided informed consent to take part in the study. They were adequately informed about the study and given the opportunity to ask questions. Consent was given for the data collected using the actigraphy wristband and associated questionnaires to be anonymously analysed for scientific research purposes.

Supporting information

FIGURE S1. Circadian activity of all participants. Twenty‐four‐hour average plots with a smoothing period of 30 min. All graphs share a consistent y‐axis range of 0 to 350 motion watch activity counts per 30‐s epoch.

JSR-34-e14456-s006.pdf (272.9KB, pdf)

TABLE S1. Demographics, CSD, actigraphy sleep and chronotype results for female control participants and Usher syndrome type 2a (USH2a) patients.

JSR-34-e14456-s001.pdf (99.7KB, pdf)

TABLE S2. Demographics, CSD, actigraphy sleep and chronotype results for male control participants and Usher syndrome type 2a (USH2a) patients.

JSR-34-e14456-s003.pdf (99.4KB, pdf)

TABLE S3. Correlation between CSD and MotionWare sleep parameters.

JSR-34-e14456-s005.pdf (67KB, pdf)

TABLE S4. Correlation matrix of the independent variables anxiety score, depression score and morning appointment frequency versus all sleep scores.

JSR-34-e14456-s004.pdf (132.1KB, pdf)

TABLE S5. Correlation matrix of disease progression parameters age, VF and VA versus all sleep scores of the patient group.

JSR-34-e14456-s002.pdf (141.3KB, pdf)

ACKNOWLEDGEMENTS

The authors would like to sincerely thank all participants for contributing to this study, as well as Professor A. Schenck and L.V. van Renssen for providing us with the MotionWatches and their knowledge.

Hendricks, J. M. , Metz, J. R. , Boss, H. M. , Collin, R. W. J. , de Vrieze, E. , & van Wijk, E. (2025). Actigraphy‐based assessment of circadian rhythmicity and sleep in patients with Usher syndrome type 2a: A case–control study. Journal of Sleep Research, 34(4), e14456. 10.1111/jsr.14456

Erik de Vrieze and Erwin van Wijk shared last authorship.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

FIGURE S1. Circadian activity of all participants. Twenty‐four‐hour average plots with a smoothing period of 30 min. All graphs share a consistent y‐axis range of 0 to 350 motion watch activity counts per 30‐s epoch.

JSR-34-e14456-s006.pdf (272.9KB, pdf)

TABLE S1. Demographics, CSD, actigraphy sleep and chronotype results for female control participants and Usher syndrome type 2a (USH2a) patients.

JSR-34-e14456-s001.pdf (99.7KB, pdf)

TABLE S2. Demographics, CSD, actigraphy sleep and chronotype results for male control participants and Usher syndrome type 2a (USH2a) patients.

JSR-34-e14456-s003.pdf (99.4KB, pdf)

TABLE S3. Correlation between CSD and MotionWare sleep parameters.

JSR-34-e14456-s005.pdf (67KB, pdf)

TABLE S4. Correlation matrix of the independent variables anxiety score, depression score and morning appointment frequency versus all sleep scores.

JSR-34-e14456-s004.pdf (132.1KB, pdf)

TABLE S5. Correlation matrix of disease progression parameters age, VF and VA versus all sleep scores of the patient group.

JSR-34-e14456-s002.pdf (141.3KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Journal of Sleep Research are provided here courtesy of Wiley

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