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. Author manuscript; available in PMC: 2026 Sep 29.
Published before final editing as: Br J Ophthalmol. 2026 Aug 18:bjo-2026-329606. doi: 10.1136/bjo-2026-329606

Assessing Physical Activity and Sleep Patterns in Glaucoma Using Wearable Devices

Isaac Bernstein a, Sheharbano Jafry a, Kuldev Singh a, Sophia Y Wang a
PMCID: PMC13618013  NIHMSID: NIHMS2209288  PMID: 42613167

Abstract

Background/Aims

Glaucoma has been linked to reduced physical activity and disturbed sleep, but large-scale studies using objective wearable data are scarce. We compared activity and sleep metrics between adults with and without glaucoma using commercial wrist-worn devices.

Methods

We conducted a retrospective cross-sectional study within the All of Us Research Program (Registered Tier v8). Participants aged ≥40 years with Fitbit data and with or without a glaucoma diagnosis were included (n = 40,743). Daily averages of steps, activity intensity, active calories, and time in sleep stages were calculated. Multivariable linear mixed effects models were adjusted for demographics, BMI, comorbid sleep disorders and cardiovascular diseases.

Results

Of 40,743 participants with complete data, 1,245 (3.1%) had glaucoma, contributing a mean 2.5 years of wearable data. Compared with controls, glaucoma participants had lower daily active calories, fewer lightly and fairly active minutes, and fewer steps. They spent less time in bed and asleep, with relatively more light sleep but less deep sleep, REM sleep, and restless time. After adjustment, glaucoma remained associated with fewer restless minutes of sleep.

Conclusion

In this large wearable-based cohort, glaucoma was independently associated with modest alterations in sleep architecture and reduced daily active energy expenditure.

Precis:

Using multiyear Fitbit data from 40,743 All of Us participants, glaucoma was associated with modest reductions in restless minutes, indicating measurable impacts of glaucoma on sleep.

Introduction

Glaucoma is a leading cause of blindness worldwide, affecting approximately 3.5% of adults globally.1,2 Characterized by progressive optic nerve damage, glaucoma leads to visual field loss that correlates with the extent of neuronal injury. Glaucoma is also associated with disruption of both waking activities and sleep.3 While awake, patients with glaucoma face physical challenges, such as reduced mobility and an increased risk of falls. Some older adults with glaucoma may reduce or avoid physical activity altogether out of fear of injury, particularly if they have visual deficits that may heighten the risk of falls or other accidents.4 While evidence may suggest that physical activity can protect against incident glaucoma or progression, few large-scale studies have employed objective physical activity measures to evaluate glaucoma patients.5–9

The exact nature of sleep disruption amongst glaucoma patients is also unclear. Glaucoma patients report poorer sleep quality, which may also be associated with the severity of their disease.10–13 Progressive degeneration of retinal ganglion cells, including those which are intrinsically photosensitive, may impact modulation of circadian rhythms in response to light stimuli, providing a potential biological mechanism for sleep disruption in glaucoma.14–16 Glaucoma patients have also been found to demonstrate abnormal secretion of melatonin, an essential hormone for sleep-wake regulation. Sleep apnea is another potential risk factor associated with glaucomatous disease.17–19 Despite clinical evidence and biologically plausible mechanisms suggesting glaucoma-related sleep disruption, population-level data of objective sleep measures has been lacking.

Over the past decade, wearable devices that measure sleeping and waking activities have undergone substantial advancements in technological capability and accessibility, with current estimates suggesting use by one-third of U.S. adults.20 These devices have been shown to be robust in assessing measures of physical activity, including heart rate, step count, and energy expenditure.21 Using movement detection, heart rate monitoring, and machine learning algorithms, wearable devices are also capable of measuring and classifying the various stages of sleep. This technology has enabled the exploration of sleep and its association with comorbid conditions, such as cardiovascular disease, mood disorders, and cognitive dysfunction.22–25 Curated by the National Institutes of Health, the All of Us research program includes over 800,000 participants,26 and has recently incorporated data from participant-owned Fitbit devices. Despite known links between glaucoma and both sleep disruption and reduced physical activity, large-scale objective assessments of these relationships using wearable technology remain limited. The goal of this study was to investigate potential effects of glaucoma diagnosis on physical activity and sleep using biometric data. A deeper understanding of how glaucoma impacts patients’ physical activity and sleep would clarify the disease’s broader effects on daily functioning, informing physicians on how to optimally support their patients.

Materials and Methods

Data source

The All of Us Research Program maintains a longitudinal database that integrates clinical, environmental, and lifestyle data.26 Non-incarcerated adults 18 years of age and older residing in the U.S. can participate either through the program’s website or via more than 60 affiliated healthcare organizations. Comprehensive data were collected, including participant demographics and survey responses during digital enrollment, physical measurements and vital signs recorded at a participating healthcare organization, voluntarily shared electronic health record (EHR) data from partnered healthcare providers, and optionally shared Fitbit data by linking a Google Fitbit account through the All of Us participant portal (Table 1). For this study, the Registered Tier Dataset v8 was used. This study was not considered human subjects research as all data were de-identified.

Table 1:

Cohort demographics and co-morbidities

All subjects (n = 40,743) Glaucoma (n = 1,245) No glaucoma (n = 39,498) p*
Age 63.2 (12.6) 71.6 (10.0) 62.9 (12.6) <0.01
Female 26,662 (66.5) 740 (60.4) 25,922 (66.7) <0.01
White 29,754 (73.0) 882 (70.8) 28,872 (73.1) >0.90
Black 3,240 (8.0) 159 (12.8) 3,081 (7.8) <0.01
Asian 1,427 (3.5) 46 (3.7) 1,381 (3.5) >0.90
Mixed 2,445 (6.0) 57 (4.6) 2,388 (6.0) 0.60
Other 3,877 (9.5) 101 (8.1) 3,776 (9.6) >0.90
Hispanic or Latinx 3,963 (9.7) 68 (5.5) 3,895 (9.9) <0.01
BMI 30.1 (7.5) 29.6 (6.7) 30.1 (7.5) >0.90
Cerebrovascular disease 1,860 (4.6) 199 (16.0) 1,661 (4.2) <0.01
Chronic heart disease 1,817 (4.5) 180 (14.5) 1,637 (4.1) <0.01
Diabetes mellitus 4,611 (11.3) 366 (29.4) 4,245 (10.7) <0.01
Hypertensive disorder 12,048 (29.6) 817 (65.6) 11,231 (28.4) <0.01
Peripheral vascular disease 1,339 (3.3) 145 (11.6) 1,194 (3.0) <0.01
Taking topical beta blocker 539 (1.3) 306 (24.6) 233 (0.6) <0.01
Sleep disorder 10,177 (25.0) 677 (54.4) 9,500 (24.1) <0.01
Sleep apnea 6,681 (16.4) 472 (37.9) 6,209 (15.7) <0.01
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*

Categorical variables compared using a chi-squared test or Fisher’s exact test. Continuous variables compared using t-test or Wilcoxon rank-sum test. Bonferroni correction applied to adjust for multiple comparisons.

Study population/Inclusion criteria

The cohort included subjects with and without a diagnosis of glaucoma age ≥ 40 years with Fitbit data. Subjects were excluded if they were missing demographics or BMI data. Comorbid diagnosis of “sleep disorder” and “sleep apnea” were defined during cohort creation in the All of Us Research Workbench using The Observational Medical Outcomes Partnership (OMOP) common data model concept IDs 435524 and 313459, respectively. Comorbid cardiovascular variables were determined from OMOP concept IDs and their descendants: 381591 (cerebrovascular disease), 4134586 (chronic heart disease), 201820 (diabetes mellitus), 316866 (hypertensive disorder), and 321052 (peripheral vascular disease). Glaucoma diagnoses were determined using diagnosis codes from the International Classification of Diseases, Ninth (ICD-9) and Tenth (ICD-10) Revisions, as well as the Systematized Nomenclature of Medicine (SNOMED). Any glaucoma diagnosis was defined during study cohort creation in the All of Us Research Workbench using OMOP concept ID 437541. Patients with only borderline glaucoma or glaucoma suspects and without presence of a true glaucoma diagnosis were excluded from the full study cohort using diagnosis codes for “borderline glaucoma,” “glaucoma suspect,” “Open-angle glaucoma - borderline,” or “Open angle with borderline intraocular pressure” and OMOP concept IDs 35207556 and 44823052. There were no control subjects with a glaucoma diagnosis, including borderline glaucoma or glaucoma suspects. The five most common glaucoma diagnostic codes in the dataset were “glaucoma,” “primary open angle glaucoma,” “glaucomatous atrophy of optic disc,” “chronic angle-closure glaucoma,” and “low tension glaucoma.” These diagnosis codes were often variable, such that multiple codes described the same diagnosis, e.g. “open-angle glaucoma” vs. “primary open angle glaucoma.” Subjects with glaucoma but without wearable data were also compared to glaucoma participants with wearable data. Those without wearable data were more ethno-racially diverse and with higher prevalence of cardiovascular diseases (Supplemental Table 1).

Fitbit data

Fitbit is a line of wearable activity trackers, with the first model released in 2009. This cohort included Fitbit owners; those who purchased Fitbits independently, or those given Fitbits such as participants of the WEAR study, which provided free Fitbit devices to participants from underrepresented communities. The Fitbit data was collected starting when the participant created a Fitbit account, not on the date of enrollment in All of Us. Six categories of Fitbit data were included in the analysis: Activity Summary, Heart Rate Summary, Minute-Level Heart Rate, Steps Intraday, Sleep Daily Summary, and Sleep Level. Fitbit measures physical activity using metabolic equivalents (METS), which is a ratio of rate of energy expended during an activity to rate of energy expended during rest, accounting for body-mass index. with 1 MET representing energy expenditure at rest, and activities at or above 3 METs considered moderate-intensity exercise.27 Specific Fitbit activity variables included daily calories, sedentary minutes, lightly active minutes, fairly active minutes, very active minutes, and daily steps. Fitbit sleep logs are generated automatically during sleep every 30- or 60 seconds and can also be entered manually by participant subjects. For each log, Fitbit estimates sleep stages using a proprietary algorithm based on heart rate and movement, applying sleep stage estimation only to sleep periods exceeding three hours.28 Validation studies have shown that Fitbit categorizes ‘light’ sleep as N1 + N2, ‘deep’ sleep as N3, and ‘REM’ as rapid eye movement sleep.29 Specific Fitbit sleep variables included in this study were minutes in bed, minutes asleep, minutes light sleep, minutes deep sleep, minutes rapid eye movement (REM) sleep, minutes restless, and minutes awake.

Statistical analysis

All statistical analyses were performed in R (v4.4.0) using Jupyter Notebook within the All of Us Research Workbench, a secure cloud-based platform. The “naniar” package (v1.1.0) was used to evaluate for missingness. An index date was defined for each subject; only biometric data after this date were used for analysis. For individuals without glaucoma, the index date was the earliest recorded biometric measurement. For individuals with glaucoma, if the documented glaucoma onset occurred on or before the earliest biometric measurement, the index date was defined as the earliest biometric measurement. If glaucoma onset occurred after the earliest biometric measurement but within the range of available biometric data, the index date was defined as the glaucoma onset date. Individuals whose glaucoma onset occurred after the last available biometric measurement were excluded. Histograms and Q-Q plots were used to evaluate continuous variables for normality. Categorical variables were compared between participant groups using a Pearson’s chi-squared test or Fisher’s exact test, whereas continuous variables were compared using a t-test or Wilcoxon rank-sum test. To adjust for multiple comparisons, Bonferroni correction was performed using the “stats” package (v4.5.0) in R to calculate adjusted p-values. Categorical variables were presented as frequency and weighted percentage of sample cohort, and continuous variables were presented as weighted mean ± standard deviation (SD). Linear Mixed Effects Models were used via the “lme4” package (v2.0) in R to evaluate association between glaucoma and wearable measures, presented as coefficients and 95% confidence intervals (CI) with Bonferroni-adjusted p-values. Multivariable models were adjusted for age, gender, race, ethnicity, BMI, presence of any sleep disorder, whether using an ophthalmic topical beta blocker, and presence of cerebrovascular disease, chronic heart disease, diabetes mellitus, hypertensive disorder, or peripheral vascular disease. Correlation and multicollinearity among covariates were assessed using pairwise correlation coefficients and variance inflation factors (VIFs). The maximum absolute correlation observed was 0.26, and all VIF values were below 2. A sensitivity analysis also was performed using linear mixed effects models to evaluate the association between biometric variables and glaucoma while excluding subjects with sleep disorders. A p-value of < 0.05 was considered statistically significant.

Results

Of the 40,743 subjects meeting inclusion criteria, 1,245 (3.1%) had been diagnosed with glaucoma (Table 1). The mean age of the cohort was 63.2 ± 12.6, of which 66.5% were female and 73.0% self-categorized as White. Twenty-five percent of subjects had been diagnosed with any sleep disorder, and 16.4% with sleep apnea. Compared to subjects without glaucoma, those with glaucoma were older, less female, more Black, and less Hispanic or Latinx. Subjects with glaucoma also had higher prevalence of various cardiovascular comorbidities, sleep apnea, and any sleep disorder (Table 1).

Participants each had a mean 2.5 ± 2.7 years of Fitbit data that was available for analysis (Supplemental Figure 1). Table 2 shows results comparing biometric data between those with glaucoma and those without. Compared to control subjects, those with glaucoma had lower average heart rate (74.6 vs. 76.7 bpm; p < 0.01), daily active calorie expenditure (734.6 vs. 805.9; p < 0.01), lightly active minutes (166.2 vs. 175.8; p < 0.01), fairly active minutes (13.1 vs. 14.9; p < 0.01), and average daily steps taken (5,698.7 vs. 6,364.3; p < 0.01). In terms of sleep data, subjects with glaucoma spent fewer minutes in bed (363.6 vs. 377.8; p < 0.01) and fewer minutes asleep (319.4 vs. 332.3; p < 0.01). Subjects with glaucoma had greater minutes of light sleep (257.1 vs. 252.0; p < 0.01) but fewer minutes of deep sleep (50.2 vs. 55.3; p < 0.01), minutes of REM sleep (68.4 vs. 74.9; p < 0.01), and minutes of restless (13.2 vs. 17.2; p < 0.01). Subjects in the two groups did not significantly differ regarding sedentary minutes, very active minutes, or minutes awake.

Table 2:

Biometrics of subjects with and without glaucoma

All subjects (n = 40,743) Glaucoma (n = 1,245) No glaucoma (n = 39,498) P**
Average Heart rate 76.6 (9.0) 74.6 (8.7) 76.7 (9.0) <0.01
Active calories* 803.8 (455.5) 734.6 (399.7) 805.9 (456.9) <0.01
Sedentary minutes 934.2 (231.8) 931.2 (222.5) 934.3 (232.1) >0.90
Lightly active minutes 175.5 (81.7) 166.2 (75.1) 175.8 (81.9) <0.01
Fairly active minutes 14.8 (14.8) 13.1 (12.6) 14.9 (14.9) <0.01
Very active minutes 15.5 (18.8) 15.1 (18.5) 15.5 (18.8) >0.90
Average daily steps 6,344.8 (43,140.0) 5,698.7 (3,612.8) 6,364.3 (43,784.1) <0.01
Minutes in bed 377.4 (93.2) 363.6 (95.0) 377.8 (93.1) <0.01
Minutes awake 44.1 (17.2) 43.3 (16.4) 44.1 (17.2) >0.90
Minutes asleep 331.9 (82.4) 319.4 (83.6) 332.3 (82.3) <0.01
Minutes light sleep 252.2 (44.3) 257.1 (46.4) 252.0 (44.3) <0.01
Minutes deep sleep 55.2 (15.6) 50.2 (15.0) 55.3 (15.6) <0.01
Minutes REM sleep 74.7 (21.6) 68.4 (22.4) 74.9 (21.5) <0.01
Minutes restless 17.0 (22.7) 13.2 (18.2) 17.2 (22.8) <0.01
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*

Calories burned during the day for periods of time when the user was active above sedentary level. This value is calculated minute by minute for minutes that fall within this criteria. This includes activity burned calories and BMR.

**

Categorical variables compared using a chi-squared test or Fisher’s exact test. Continuous variables compared using t-test or Wilcoxon rank-sum test. Bonferroni correction applied to adjust for multiple comparisons.

Table 3 shows results of univariate and multivariate linear regressions for the association between various biometric factors and glaucoma. In the univariate regression analyses, glaucoma diagnosis was significantly associated with lower average heart rate, fewer active calories per day, fewer lightly active minutes, fewer fairly active minutes, fewer minutes in bed, fewer minutes asleep, fewer minutes of deep sleep, fewer minutes of REM sleep, and fewer minutes of restless sleep. After adjusting the model for age, gender, race, ethnicity, BMI, beta blocker use, and presence of co-morbid sleep disorder, and cardiovascular comorbidities, glaucoma diagnosis remained associated only with decreased minutes of restless sleep (β = −2.93, p < 0.01).

Table 3:

Linear Mixed Effects Models for association between biometrics and glaucoma

Univariate Multivariate**
Coef (95% CI) p Coef (95% CI) p
Average heart rate (bpm) −2.11 (−2.64, −1.57) <0.01 0.08 (−0.45, 0.60) >0.90
Active calories* −64.55 (−91.75, −37.35) <0.01 7.82 (−19.61, 35.24) >0.90
Lightly active minutes −9.14 (−13.80, −4.47) <0.01 1.31 (−3.68,6.30) >0.90
Fairly active minutes −1.77 (−2.61, −0.94) <0.01 −0.38 (−1.25, 0.49) >0.90
Minutes in bed −12.94 (−18.53, −7.35) <0.01 −5.32 (−11.27, 0.62) 0.79
Minutes asleep −11.60 (−16.51, −6.69) <0.01 −4.19 (−9.42, 1.03) >0.90
Minutes light sleep 4.94 (2.29, 7.59) <0.01 −1.59 (−4.28, 1.10) >0.90
Minutes deep sleep −5.18 (−6.12, −4.24) <0.01 −0.09 (−1.02, 0.84) >0.90
Minutes REM sleep −6.34 (−7.64, −5.05) <0.01 −0.33 (−1.64, 0.98) >0.90
Minutes restless −3.92 (−5.29, −2.54) <0.01 −2.93 (−4.43, −1.42) <0.01
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*

Calories burned during the day for periods of time when the user was active above sedentary level. This value is calculated minute by minute for minutes that fall within this criteria. This includes activity burned calories and BMR.

**

Multivariate models adjusted for age, gender, race, ethnicity, BMI, whether taking topical beta blocker, and presence of co-morbid sleep disorder, chronic heart disease, diabetes mellitus, hypertensive disorder, and peripheral vascular disease. Bonferroni correction was applied to adjust for multiple comparisons.

Additional sensitivity analyses were performed as subjects with any sleep disorder were older, less likely to be female, and more often white, with higher BMI and greater prevalence of cardiovascular comorbidities compared to those without sleep disorder (Supplemental Table 2). Linear mixed effects models evaluating biometric variables and glaucoma were repeated while excluding subjects with sleep disorders (Supplemental Table 3), with minutes restless retaining significance in multivariable modeling, similar to the primary analysis.

Discussion

In this cross-sectional study of US adults, we investigated relationships between glaucoma, sleep, and physical activity using multiyear wearable data. Subjects with glaucoma had lower heart rates, reduced activity, and altered sleep, including less REM and deep sleep. After adjustment for demographics and co-morbid sleep disorders, glaucoma remained significantly associated with fewer restless minutes, although effect sizes were modest with uncertain clinical significance. Sensitivity analyses investigating the potential confounding effect of underlying sleep disorders largely corroborated the findings of the primary analysis.

Prior work on sleep in glaucoma has yielded mixed results. Polysomnography studies found either no differences in sleep stages, or fewer minutes asleep without stage differences in small cohorts of glaucoma patients versus controls.16,30 Other analyses also reported no differences in sleep stages or minutes asleep after matching on age, sex, and apnea–hypopnea index.30 Wrist-actigraphy studies, more comparable to our approach, were typically short in duration. One week of monitoring in 102 glaucoma patients and 31 controls showed no difference in minutes asleep, whereas another 20-day study in 9 severe glaucoma patients and 9 controls reported fewer minutes asleep and more minutes awake in glaucomatous subjects.31, 32 Several studies have suggested a U-shaped relationship between glaucoma and sleep duration, primarily based on patient self-reported sleep duration. In the Korean National Health and Nutrition Examination Survey, Lee et al. found that subjects sleeping fewer than 5 hours had the highest glaucoma prevalence (5.55%), followed by those sleeping 9 or more hours (4.56%), particularly pronounced in subjects who were overweight.40 Another study of UK Biobank participants found that short (<7 hours) or long (≥ 9 hours) sleep duration was associated with higher glaucoma risk,41 corroborated by a separate UK Biobank study.42 Mechanisms may involve glaucomatous degeneration of intrinsically photosensitive retinal ganglion cells.14–19 Although our study found reduced sleep duration among glaucoma subjects, this relationship did not persist after adjusting for covariates, though our study is unique in measuring sleep duration objectively using wearable devices over multiple days and up to several years, yielding more objective sleep duration metrics than patient self-report.

We evaluated 1,245 glaucoma and 39,498 control participants, each contributing on average 2.5 years of sleep data. In multivariable models, glaucoma was associated with decreased minutes of restless sleep, though this finding warrants careful interpretation. Restlessness during sleep is typically associated with sleep-disordered breathing, periodic limb movements, and other sleep disturbances, and would generally be expected to be elevated in a population with higher comorbidity burden. However, this may reflect a limitation of consumer-grade wearable accelerometry rather than a true physiological phenomenon. Restless sleep is determined by Fitbit devices based on accelerometer-detected movement and elevated heart rate, which may not necessarily reflect better sleep quality.28,38 Glaucoma patients in this cohort were older and had higher rates of cardiovascular and metabolic comorbidities, conditions associated with reduced mobility and motor activity. Reduced limb movement and heart rate fluctuations during sleep in this population may reflect underlying comorbidity rather than more restful sleep. Furthermore, systemic factors beyond glaucoma diagnosis may have contributed to the observed findings. Adjusting for BMI and topical beta blocker use attenuated associations observed in univariate analyses, suggesting that these variables may be contributing factors influencing sleep and activity outcomes. Systemic absorption of topical beta blockers, while limited, has been documented and may exert central nervous system effects including sedation and reduced nocturnal motor activity, potentially suppressing movement-based restlessness as detected by accelerometry independent of glaucoma severity.46 Similarly, higher BMI is associated with obstructive sleep apnea, reduced sleep quality, and decreased physical activity, all of which may confound the relationship between glaucoma and wearable-derived sleep metrics.47 Although many potential confounding factors (age, cardiovascular comorbidities, BMI) were adjusted for in the multivariable models, it is possible that residual confounding persisted. To our knowledge, this is the first study to assess sleep stages in glaucoma using wrist actigraphy at this scale, and the overall literature, including our findings, highlights a complex, method- and stage-dependent relationship between glaucoma and sleep.

For physical activity, we did not observe significant adjusted differences between glaucoma and control participants. Absence of severity information may have diluted associations, as most patients likely had mild disease that did not substantially limit activity. Shorter studies have demonstrated that activity decreases with increasing glaucoma severity. Huang et al. reported lower daily step counts in subjects with advanced glaucoma despite no overall difference between glaucoma and normal participants.33 Another clinic-based study showed a proportional relationship between visual field loss and reduced moderate or vigorous activity.34 Location also appears important: accelerometer and GPS data revealed fewer steps and less moderate or vigorous activity away from home, but not at home, among glaucoma subjects.35 In contrast, a UK Biobank analysis of over 20,000 glaucoma patients found no association between diagnosis and activity.36 Collectively, these studies and ours suggest that severity, rather than diagnosis alone, may drive reductions in physical activity.33–36

Our investigation is the largest to use wearable devices to assess sleep and activity in glaucoma. Wearables allowed objective, long-term measurement and adjustment for multiple confounders, including sleep disorders. However, wrist actigraphy has limitations relative to polysomnography. A 2024 systematic review comparing modern wearables with polysomnography or electroencephalography identified discrepancies in sleep stage classification, though it concluded that wearables provide generally acceptable estimates of sleep parameters.37 Additional limitations of Fitbit-derived activity metrics are described elsewhere.38 We were unable to quantify visual function or glaucoma severity because All of Us does not include detailed ophthalmic examinations or visual field data, an important limitation given evidence that severity can act as an effect modifier.33–36 We also could not robustly compare glaucoma subtypes due to small, overlapping diagnostic subsets. Glaucoma diagnoses were based on billing codes, which may be prone to misclassification. This study was also unable to evaluate nocturnal intraocular pressure (IOP) or sleep position. Prior research has demonstrated IOP increases related to sleep position, which may be more pronounced in glaucoma patients.43,44,45 Finally, although some participants received complimentary devices through the Wearables Enhancing All of Us Research (WEAR) study, most participants purchased Fitbit devices independently and may differ from the general US population in health engagement, income, and technology access, contributing to potential selection bias.39

Overall, this large-scale cross-sectional analysis of 40,743 US adults utilizing objective wearable device data demonstrates that individuals with glaucoma may exhibit modest but statistically significant differences in restless sleep minutes, but not physical activity patterns compared to controls. While the clinical significance of these findings remains unclear, our results contribute to the growing body of evidence suggesting complex relationships between glaucoma, sleep disturbances, and physical activity levels. The utilization of multi-year objective data from commercial wearable devices represents a novel approach to understanding these associations at a population level, offering advantages over traditional short-term assessments. Future research should continue to measure sleep and activity with objective, longitudinal measures, while incorporating glaucoma severity or visual function to better characterize proportional relationships to further elucidate the relationships between glaucoma, sleep, and activity.

Supplementary Material

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Key Message.

What was known before:

Glaucoma has been linked to poorer sleep quality and reduced physical activity, but most prior studies relied on self-report or short-term monitoring rather than large-scale wearable data.

What this study adds:

Using multi-year Fitbit data from 40,743 All of Us participants, including 1,245 with glaucoma, this study finds that glaucoma is independently associated with modest reductions in restless minutes, but not physical activity nor other measures of sleep architecture.

How this study might affect research, practice or policy:

These results may support incorporating sleep and activity assessment into holistic glaucoma care and highlight commercial wearables as scalable tools for longitudinal research in glaucoma and activity measurement.

Funding Sources:

National Eye Institute K23EY03263501 (SYW); unrestricted departmental grant from Research to Prevent Blindness (all authors); American Glaucoma Society Young Clinician Scientist Award (SYW); Alcon Research Institute Young Investigator Award (SYW): departmental grant National Eye Institute P30-EY026877 (all authors).

Financial Disclosures:

Kuldev Singh is a consultant for the following companies: Alcon Laboratories, Diorsis, Elios, Novartis, Ocular Therapeutix, Oculis, Qlaris, Radiance Therapeutics, Sight Sciences, Thieme Medical Publishers.

Footnotes

Conflicts of Interest: The authors report no conflicts of interest.

Ethics Statement: This study was not considered human subjects research as all data were de-identified.

References

  • 1.Sun Y, Chen A, Zou M, et al. Time trends, associations and prevalence of blindness and vision loss due to glaucoma: an analysis of observational data from the Global Burden of Disease Study 2017. BMJ Open. 2022;12(1):e053805. doi: 10.1136/bmjopen-2021-053805 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis. Ophthalmology. 2014;121(11):2081–2090. doi: 10.1016/j.ophtha.2014.05.013 [DOI] [PubMed] [Google Scholar]
  • 3.Ramulu P. Glaucoma and Disability: Which tasks are affected, and at what stage of disease? Curr Opin Ophthalmol. 2009;20(2):92–98. doi: 10.1097/ICU.0b013e32832401a9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Delbaere K, Crombez G, Vanderstraeten G, Willems T, Cambier D. Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study. Age Ageing. 2004;33(4):368–373. doi: 10.1093/ageing/afh106 [DOI] [PubMed] [Google Scholar]
  • 5.Yuan Y, Lin TPH, Gao K, et al. Aerobic exercise reduces intraocular pressure and expands Schlemm’s canal dimensions in healthy and primary open-angle glaucoma eyes. Indian J Ophthalmol. 2021;69(5):1127–1134. doi: 10.4103/ijo.IJO_2858_20 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Chrysostomou V, Kezic JM, Trounce IA, Crowston JG. Forced exercise protects the aged optic nerve against intraocular pressure injury. Neurobiol Aging. 2014;35(7):1722–1725. doi: 10.1016/j.neurobiolaging.2014.01.019 [DOI] [PubMed] [Google Scholar]
  • 7.Chrysostomou V, Galic S, van Wijngaarden P, Trounce IA, Steinberg GR, Crowston JG. Exercise reverses age-related vulnerability of the retina to injury by preventing complement-mediated synapse elimination via a BDNF-dependent pathway. Aging Cell. 2016;15(6):1082–1091. doi: 10.1111/acel.12512 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.He YY, Wang L, Zhang T, Weng SJ, Lu J, Zhong YM. Aerobic exercise delays retinal ganglion cell death after optic nerve injury. Exp Eye Res. 2020;200:108240. doi: 10.1016/j.exer.2020.108240 [DOI] [PubMed] [Google Scholar]
  • 9.Meier NF, Lee D chul, Sui X, Blair SN. Physical Activity, Cardiorespiratory Fitness, and Incident Glaucoma. Med Sci Sports Exerc. 2018;50(11):2253–2258. doi: 10.1249/MSS.0000000000001692 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chin JY, Toh ZH, Lo YT, et al. Effects of primary glaucoma on sleep quality and daytime sleepiness of patients residing at an equatorial latitude. Int J Ophthalmol. 2020;13(9):1451–1458. doi: 10.18240/ijo.2020.09.18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Wang H, Zhang Y, Ding J, Wang N. Changes in the Circadian Rhythm in Patients with Primary Glaucoma. PLoS ONE. 2013;8(4):e62841. doi: 10.1371/journal.pone.0062841 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Agorastos A, Skevas C, Matthaei M, et al. Depression, Anxiety, and Disturbed Sleep in Glaucoma. J Neuropsychiatry Clin Neurosci. 2013;25(3):205–213. doi: 10.1176/appi.neuropsych.12020030 [DOI] [PubMed] [Google Scholar]
  • 13.Ayaki M, Shiba D, Negishi K, Tsubota K. Depressed visual field and mood are associated with sleep disorder in glaucoma patients. Sci Rep. 2016;6:25699. doi: 10.1038/srep25699 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Feng KM, Tsung TH, Chen YH, Lu DW. The Role of Retinal Ganglion Cell Structure and Function in Glaucoma. Cells. 2023;12(24):2797. doi: 10.3390/cells12242797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Gao J, Provencio I, Liu X. Intrinsically photosensitive retinal ganglion cells in glaucoma. Front Cell Neurosci. 2022;16:992747. doi: 10.3389/fncel.2022.992747 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gracitelli CPB, Duque-Chica GL, Roizenblatt M, et al. Intrinsically Photosensitive Retinal Ganglion Cell Activity Is Associated with Decreased Sleep Quality in Patients with Glaucoma. Ophthalmology. 2015;122(6):1139–1148. doi: 10.1016/j.ophtha.2015.02.030 [DOI] [PubMed] [Google Scholar]
  • 17.Ma XP, Shen MY, Shen GL, Qi QR, Sun XH. Melatonin concentrations in serum of primary glaucoma patients. Int J Ophthalmol. 2018;11(8):1337–1341. doi: 10.18240/ijo.2018.08.14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Yoshikawa T, Obayashi K, Miyata K, Saeki K, Ogata N. Decreased melatonin secretion in patients with glaucoma: Quantitative association with glaucoma severity in the LIGHT study. J Pineal Res. 2020;69(2):e12662. doi: 10.1111/jpi.12662 [DOI] [PubMed] [Google Scholar]
  • 19.Bahr K, Bopp M, Kewader W, et al. Obstructive sleep apnea as a risk factor for primary open angle glaucoma and ocular hypertension in a monocentric pilot study. Respir Res. 2020;21:258. doi: 10.1186/s12931-020-01533-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Dhingra LS, Aminorroaya A, Oikonomou EK, et al. Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020. JAMA Netw Open. 2023;6(6):e2316634. doi: 10.1001/jamanetworkopen.2023.16634 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Germini F, Noronha N, Debono VB, et al. Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature. J Med Internet Res. 2022;24(1):e30791. doi: 10.2196/30791 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Huang T, Mariani S, Redline S. Sleep Irregularity and Risk of Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis. J Am Coll Cardiol. 2020;75(9):991–999. doi: 10.1016/j.jacc.2019.12.054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Zhou M, Liang YY, Ai S, et al. Associations of accelerometer-measured sleep duration with incident cardiovascular disease and cardiovascular mortality. Sleep. 2024;47(11):zsae157. doi: 10.1093/sleep/zsae157 [DOI] [PubMed] [Google Scholar]
  • 24.Lyall LM, Wyse CA, Graham N, et al. Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank. Lancet Psychiatry. 2018;5(6):507–514. doi: 10.1016/S2215-0366(18)30139-1 [DOI] [PubMed] [Google Scholar]
  • 25.Canazei M, Papousek I, Weiss EM. Light Intervention Effects on Circadian Activity Rhythm Parameters and Nighttime Sleep in Dementia Assessed by Wrist Actigraphy: A Systematic Review and Meta-Analysis. The Gerontologist. 2022;62(10):e614–e628. doi: 10.1093/geront/gnab168 [DOI] [PubMed] [Google Scholar]
  • 26.The “All of Us” Research Program | New England Journal of Medicine. Accessed June 19, 2025. https://www.nejm.org/doi/10.1056/NEJMsr1809937?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed
  • 27.Jetté M, Sidney K, Blümchen G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin Cardiol. 1990;13(8):555–565. doi: 10.1002/clc.4960130809 [DOI] [PubMed] [Google Scholar]
  • 28.Master H, Kouame A, Marginean K, Basford M, Harris P, Holko M. How Fitbit data are being made available to registered researchers in All of Us Research Program. Pac Symp Biocomput Pac Symp Biocomput. 2023;28:19–30. [PMC free article] [PubMed] [Google Scholar]
  • 29.Beattie Z, Oyang Y, Statan A, et al. Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol Meas. 2017;38(11):1968. doi: 10.1088/1361-6579/aa9047 [DOI] [PubMed] [Google Scholar]
  • 30.Bec L, Herber R, Bailly S, et al. Sleep quality in glaucoma patients. Sci Rep. 2024;14:25593. doi: 10.1038/s41598-024-76368-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Stoner AM, Patnaik JL, Ertel MK, et al. Subjective and Objective Measurement of Sleep Quality and Activity in Glaucoma. J Glaucoma. 2023;32(4):265. doi: 10.1097/IJG.0000000000002186 [DOI] [PubMed] [Google Scholar]
  • 32.Lanzani MF, de Zavalía Nuria, Fontana Héctor, Sarmiento María Ines Keller, Golombek Diego, and Rosenstein RE. Alterations of Locomotor Activity Rhythm and Sleep Parameters in Patients With Advanced Glaucoma. Chronobiol Int. 2012;29(7):911–919. doi: 10.3109/07420528.2012.691146 [DOI] [PubMed] [Google Scholar]
  • 33.Huang W, Gao K, Liu Y, Liang M, Zhang X. The Adverse Impact of Glaucoma on Psychological Function and Daily Physical Activity. J Ophthalmol. 2020;2020:9606420. doi: 10.1155/2020/9606420 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Ramulu PY, Maul E, Hochberg C, Chan ES, Ferrucci L, Friedman DS. Real-World Assessment of Physical Activity in Glaucoma Using an Accelerometer. Ophthalmology. 2012;119(6):1159–1166. doi: 10.1016/j.ophtha.2012.01.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.E JY, Mihailovic A, Garzon C, et al. Importance and severity dependence of physical activity by GPS-tracked location in glaucoma patients: GPS-tracked physical activity in glaucoma. Am J Ophthalmol. 2021;230:276–284. doi: 10.1016/j.ajo.2021.04.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Madjedi KM, Stuart KV, Chua SYL, et al. The Association of Physical Activity with Glaucoma and Related Traits in the UK Biobank. Ophthalmology. 2023;130(10):1024–1036. doi: 10.1016/j.ophtha.2023.06.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Schyvens AM, Van Oost NC, Aerts JM, et al. Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review. JMIR MHealth UHealth. 2024;12:52192. doi: 10.2196/52192 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Considerations while using Fitbit Data in the All of Us Research Program. User Support. April 25, 2025. Accessed June 19, 2025. https://support.researchallofus.org/hc/en-us/articles/9651723386388-Considerations-while-using-Fitbit-Data-in-the-All-of-Us-Research-Program [Google Scholar]
  • 39.Wear Study. Accessed June 19, 2025. https://www.joinallofus.org/wear-study?utm_source=blavity&utm_medium=email&utm_campaign=tpc-y6&utm_content=wear_voice_heard_join_aourp&utm_source=Blavity+News&utm_campaign=2d92430b39-EMAIL_CAMPAIGN_2024_05_16_09_41&utm_medium=email&utm_term=0_−2d92430b39-%5BLIST_EMAIL_ID%5D&mc_cid=2d92430b39&mc_eid=60f6f2be7f
  • 40.Lee JA, Han K, Min JA, Choi JA; Epidemiologic Survey Committee of the Korean Ophthalmological Society. Associations of sleep duration with open-angle glaucoma in the Korea National Health and Nutrition Examination Survey. Medicine (Baltimore). 2016;95(52):e5704. doi: 10.1097/MD.0000000000005704 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Sun C, Yang H, Hu Y, et al. Association of sleep behaviour and pattern with the risk of glaucoma: a prospective cohort study in the UK Biobank. BMJ Open. 2022;12(11):e063676. doi: 10.1136/bmjopen-2022-063676 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Huang Z, Chen C, Meng J, et al. Associations among sleep duration, sleep quality, and age-related ocular diseases: insights from longitudinal and mediation analyses. Invest Ophthalmol Vis Sci. 2025;66(11):15. doi: 10.1167/iovs.66.11.15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Koppen C, Tassignon MJ, Goethals M. Posture-induced intraocular pressure changes: considerations regarding body position in glaucoma patients. Surv Ophthalmol. 2010;55(5):445–453. doi: 10.1016/j.survophthal.2009.12.002 [DOI] [PubMed] [Google Scholar]
  • 44.Lazzaro EC, Mallick A, Singh M, Reich I, Elmann S, Stefanov DG, Lazzaro DR. The effect of positional changes on intraocular pressure during sleep in patients with and without glaucoma. J Glaucoma. 2014;23(5):282–287. doi: 10.1097/01.ijg.0000435848.90957.fe [DOI] [PubMed] [Google Scholar]
  • 45.Park JH, Yoo C, Yoo E, Kim YY. Intraocular pressure elevation during lateral body posture in side-sleeping glaucoma patients. Optom Vis Sci. 2019;96(1):62–70. doi: 10.1097/OPX.0000000000001322 [DOI] [PubMed] [Google Scholar]
  • 46.Elhusseiny AM, Abdelrazek HMA, Aljohani A, et al. A review of ocular and systemic side effects in glaucoma pharmacotherapy. J Clin Transl Ophthalmol. 2025;3(1):2. doi: 10.3390/jcto3010002 [DOI] [Google Scholar]
  • 47.Sochor O, Jaresova J, Lanska V, et al. Sleep duration and excessive daytime sleepiness are associated with obesity independent of diet and physical activity. Nutrients. 2018;10(10):1311. doi: 10.3390/nu10101311 [DOI] [PMC free article] [PubMed] [Google Scholar]

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NIHMS2209288-supplement-Supp1.pdf (75.7KB, pdf)
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NIHMS2209288-supplement-Supp2.pdf (67.8KB, pdf)
Supp3
NIHMS2209288-supplement-Supp3.pdf (67.7KB, pdf)
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