Sleep and activity patterns in older patients discharged from the hospital

Abstract

Study Objectives

Although sleep disturbance is common in acutely ill patients during and after a hospitalization, how hospitalization affects sleep in general medicine patients has not been well characterized. We describe how sleep and activity patterns vary during and after hospitalization in a small population of older, predominately African American general medicine patients.

Methods

Patients wore a wrist accelerometer during hospitalization and post-discharge to provide objective measurements of sleep duration, efficiency, and physical activity. Random effects linear regression models clustered by subject were used to test associations between sleep and activity parameters across study days from hospitalization through post-discharge.

Results

We recorded 404 nights and 384 days from 54 patients. Neither nighttime sleep duration nor sleep efficiency increased from hospitalization through post-discharge (320.2 vs. 320.2 min, p = 0.99; 74.0% vs. 71.7%, p = 0.24). Daytime sleep duration also showed no significant change (26.3 vs. 25.8 min/day, p = 0.5). Daytime physical activity was significantly less in-hospital compared to post-discharge (128.6 vs. 173.2 counts/min, p < 0.01) and increased 23.3 counts/min (95% CI = 16.5 to 30.6, p < 0.01) per hospital day. A study day and post-discharge period interaction was observed demonstrating slowed recovery of activity post-discharge (β ₃ = −20.8, 95% CI = −28.8 to −12.8, p < 0.01).

Conclusions

Nighttime sleep duration and efficiency and daytime sleep duration were similar in-hospital and post-discharge. Daytime physical activity, however, was greater post-discharge and increased more rapidly during hospitalization than post-discharge. Interventions, both in hospital and at home, to restore patient sleep and sustain activity improvements may improve patient recovery from illness.

Statement of Significance.

Disrupted sleep during hospitalization can hinder recovery from illness and continued disruption after patients return home may leave them vulnerable to readmission. No study to date has documented sleep and activity patterns both during hospitalization on a general medicine floor and immediately after discharge. We show that our patients’ sleep disturbance persisted post-discharge with no significant change in sleep duration, sleep efficiency, or daytime sleep. There was, however, a trajectory of increasing physical activity across study days. It is important to investigate whether patient sleep and activity patterns are eventually restored after discharge or if hospitalization is associated with chronic sleep disorders. Future research on how sleep and activity trajectories could predict patient outcomes is warranted.

Introduction

Patients are in a particularly vulnerable state during the time just after discharge from the hospital. Not only are they recovering from their illnesses, but they also continue to be affected by stressors associated with hospitalization. Altered sleep patterns, reduced activity, poor nutrition, pain and discomfort, new medications, and mental stress can all affect recovery [1, 2]. The cumulative effect may result in posthospital syndrome, a high-risk period after discharge when patients may acquire adverse medical conditions leading to re-hospitalization [1, 2]. More than one in six Medicare patients discharged from the hospital are readmitted within 30 days and many are readmitted for a different illness than their index admission, indicating a period of increased vulnerability [1–3]. Thus it has become important to consider the factors associated with a hospital admission that may impair recovery post-discharge and predispose patients to readmission.

Both sleep and physical activity patterns are disrupted during hospitalization and may affect patient recovery [4–8]. Sleep deprivation is harmful to immune [9, 10], metabolic [11, 12], and cardiovascular health [13, 14], allostasis [2], and cognition and memory [15, 16]. These effects may be more significant in older adults who may have preexisting vulnerabilities. Disruptions to the circadian sleep–wake cycle can further exacerbate these problems by imposing a jetlag-like syndrome leading to excessive fatigue and daytime sleepiness that can limit active participation in one’s medical care and rehabilitation [17]. Furthermore, lack of physical activity can leave the patient deconditioned, limiting self-care and increasing the chance of falls. Thus acute disturbance of sleep and physical activity patterns associated with hospitalization may hinder recovery and continued disruption post-discharge may increase risk for readmission.

Previous studies have demonstrated sleep disturbance weeks to months after critical illness requiring an intensive care unit (ICU) stay and after cardiac surgery [18–22]. However, no study has used actigraphy to characterize sleep and activity patterns in patients both during their inpatient stays on a general medicine service and immediately post-discharge. Characterizing these patterns is an important step in understanding how they may influence a patient’s recovery and risk for 30-day readmission and is a critical step in targeting interventions to address posthospital syndrome. This study aims to provide insight into immediate post-discharge sleep and activity patterns in a cohort of predominately African American patients cared for on an inpatient general medicine service.

Methods

Subjects

Subjects were a subset of a larger sleep study of hospitalized patients conducted at the University of Chicago Medical Center [23]. Patients eligible for actigraphy monitoring were ambulatory adults age 50 years or older hospitalized on the general medicine or hematology/oncology services and living in the community before admission. Patients were considered ambulatory if they reported being able to walk across the room independently or with a walker. As our sample was drawn from a general medicine service, no neurology patients were included in the study (Table 1). Exclusion criteria included being transferred from the ICU or another hospital, being in droplet or airborne isolation, cognitive impairment as measured by the Mini-Mental State Examination or Short Portable Mental Status Questionnaire [24, 25], having a documented sleep disorder (including chronic insomnia, obstructive sleep apnea, sleep disordered breathing, and restless legs syndrome), being on bed rest, and having been hospitalized for greater than 72 hours prior to enrollment. Exclusion criteria were designed to ensure participants’ actigraphy results reflected their sleep and activity as any of the aforementioned conditions may affect patients’ mobility or sleep/wake patterns. Patients were eligible if they wore the actigraphy monitor for at least one night in the hospital and at least one night post-discharge. Patients received a total of $50; they received the first $25 as they were discharged from the hospital and the second $25 after completing a 2-week follow-up assessment by phone. The University of Chicago institutional review board approved this study and all patients provided written consent.

Table 1.

Sample characteristics (N = 54)

Characteristic	Value
Age (years)	61 ± 9.4
African American, n (%)	43 (79.6%)
Female, n (%)	32 (59.3%)
Major diagnostic category for primary diagnoses
Diseases and disorders of the digestive system, hepatobiliary system and pancreas	16
Other*	11
Diseases and disorders of the respiratory system	10
Diseases and disorders of the circulatory system, blood, blood forming organs, immunologic disorders	7
Endocrine, nutritional, and metabolic diseases and disorders	5
Diseases and disorders of the kidney and urinary tract	5
Mean global PSQI score (mean ± SD)†	9.5 ± 5.3
Number of patients with global PSQI >5‡	36 (75%)
Baseline self-reported sleep duration (mean ± SD)§
Weekday (minutes)	348 ± 114
Weekend (minutes)	362 ± 120
Mean ESS score (mean ± SD)	8.2 ± 5.5
Number of patients with ESS >10||	19 (35.2%)
Total number of nights recorded	404
Total number of days recorded	384
Mean length of stay in the hospital (days, mean ± SD)	3.6 ± 1.6
Mean follow-up time post-discharge (nights, mean ± SD)	6.4 ± 1.6
Number of patients readmitted within 30-days, n (%)	3 (5.6%)
Mean AHRQ Elixhauser Comorbidity Index (mean ± SD, min, max)
30-day readmission score	15.3 ± 12.1, −3, 47
In-hospital mortality score	3.7 ± 8.8, −11, 28

*Diseases and disorders of the ear, nose, mouth, and throat; diseases and disorders of the musculoskeletal system and connective tissue; diseases and disorders of the skin, subcutaneous tissue, and breast; myeloproliferative diseases and disorders, poorly differentiated neoplasms; infectious and parasitic diseases, systemic or unspecified sites; mental diseases and disorders; alcohol/drug use and alcohol-/drug-induced organic mental disorders; human immunodeficiency virus infections.

^† N = 48.

^‡PSQI > 5 represents poor sleep quality.

^§Based on self-reported sleep from month before admission on the PSQI.

^||ESS > 10 represents excessive daytime sleepiness.

Data collection

Objective measurements of sleep and activity were made using actigraphy following a protocol previously described by our group [26]. Patients wore Acitwatch Spectrum Pro (Phillips Respironics, Bend, OR) wrist actigraphy monitors to collect data on sleep duration and quality and activity. The monitor collects acceleration data with a 32 Hertz sampling frequency and returns estimates of sleep timing and quality and has been validated against polysomnography in both healthy individuals and insomniacs [27, 28]. The intensity of physical activity is expressed in activity counts using a proprietary metric developed by Phillips Respironics and derived within the Actiware software from collected accelerometry data. Actiware 5 software was used to calculate sleep duration, sleep efficiency, and activity levels [29]. To estimate nighttime sleep duration and efficiency, the assumed sleep period was determined by the Karolinska Sleep Diary while patients were in the hospital and a sleep log while they were at home [30–32]. The Actiware software was used to derive objective estimates of sleep duration and efficiency based on periods with low activity over all 15-second epochs during the assumed sleep period and time in bed, respectively, based on self reported measures from the Karolinska Sleep Diary and home sleep logs. Sleep duration is the total time spent asleep and sleep efficiency is the ratio of time asleep to time in bed. Similar to previous reports, daytime sleep duration was calculated from Actiware defined minor rest intervals that occurred during the patient reported wake period or between 7 am and 11 pm if patient reported wake time was not available [33]. A value of zero was entered into the database for days when no daytime sleep occurred. Activity was measured as average activity counts per minute over the assumed wake period. Because patients were able to remove the Actiwatch, the number of days and nights recorded per participant represents the total number of days and nights over the study period with adequate sleep and activity data rather than the number of consecutive days and nights they were in the study. To maximize our study population, we included patients who had data from at least one in-hospital and one post-discharge night allowing for intervening non-recorded nights. Our term “study day” does reflect consecutive days in the study.

Baseline sleep characteristics

Baseline sleep characteristics were assessed upon enrollment using the Epworth Sleepiness Scale (ESS) to measure excessive daytime sleepiness in common situations and the Pittsburgh Sleep Quality Index (PSQI) to measure baseline sleep quality and hygiene over the previous month [34–36].

Baseline patient characteristics

Demographic information (age, race, ethnicity, sex) and length of stay were collected from patient charts through an ongoing study of patients admitted to the University of Chicago inpatient general medicine service [23]. To account for severity of disease, we calculated Agency for Healthcare Research and Quality (AHRQ) Elixhauser Comorbidity Index scores for 30-day readmission and in-hospital mortality using 29 binary Elixhauser comorbidity variables according to the algorithm developed by the Healthcare Cost and Utilization Project [37]. Elixhauser comorbidity measures are a group of clinical conditions that can be used in administrative data sets to assign weights to preadmission diseases to derive a single comorbidity score that can be used as an adjustment factor to control for disease severity and can help indentify risk for mortality and 30-day readmission [37].

Data analysis

All data were collected and entered into REDCap [38], a secure web application used to create a secure database. Descriptive statistics were used to summarize daytime and nighttime sleep duration, sleep efficiency, and physical activity in-hospital and post-discharge. The primary outcome used for physical activity was average activity counts per minute, which is a frequently reported activity measure from actigraphy [27, 39].

To test for differences between in-hospital and post-discharge nighttime sleep duration, sleep efficiency, daytime sleep duration, and daytime physical activity, the average value for each variable was calculated per subject both in-hospital and post-discharge. When the data were normally distributed (nighttime sleep duration), a paired t-test was used to compare the means. When the data were not normally distributed (nighttime sleep efficiency, daytime sleep duration, and daytime physical activity), a Wilcoxon matched-pairs signed rank test was used to test for a difference between in-hospital and post-discharge variables. To characterize the association between sleep duration, sleep efficiency, daytime sleep duration, and daytime physical activity across each day of the study from the inpatient stay through post-discharge, random effects linear regression models clustered by subject were used to account for multiple observations per participant. We controlled for the following covariates in our final regression model: age (<65 or ≥65 years), sex, ethnicity, and 30-day readmission score for AHRQ Elixhauser Comorbidity Index. When the covariates were significant, we added an interaction term using cross product terms to specifically test the differential effect of study days in the post-discharge period compared to study days while in the hospital.

Statistical significance was set at p < 0.05. All data were analyzed in STATA 14.0 (Stata Corp., College Station, TX).

Results

From October 2012 to November 2017, 404 nights (27% in-hospital, 73% post-discharge) and 384 days (28% in-hospital, 72% post-discharge) were recorded from 54 patients (Table 1). Average number of in-hospital nights and post-discharge nights per participant were 2.1 ± 1.3 and 5.4 ± 2.1 nights, respectively. Average daytime physical activity periods per participant were 2.0 ± 1.2 and 5.1 ± 1.9 days, respectively. Daytime sleep data were available from 45 patients over 333 total days with an average of 2.1 ± 1.3 days in-hospital and 5.3 ± 1.6 days post-discharge per participant.

The study population had an average age of 61 ± 9.4 years and was predominately female (59.3%) and African American (79.6%) (Table 1). Average global PSQI at baseline was 9.5 ± 5.3 of 21 with 75% of patients having a score higher than five, representing poor baseline sleep quality. Average baseline ESS score was 8.2 ± 5.5 of 24, with 35.2% of patients recording scores greater than 10, representing excessive daytime sleepiness. Average AHRQ Elixhauser Cormorbidity Index score for 30-day readmission was 15.3 ± 12.1 and for in-hospital mortality was 3.7 ± 8.8.

Nighttime sleep duration and efficiency

Actigraphy data demonstrated no significant difference in average sleep duration in-hospital compared to post-discharge (320.2 ± 128.5 vs. 320.2 ± 103.0 min; p = 0.99) (Figure 1). Similar to sleep duration, there was no difference in sleep efficiency between settings (median in-hospital = 74.0% vs. median post-discharge = 71.7%, z = −1.2, p = 0.24) (Figure 2). In random effects linear regression models clustered by subject, neither sleep duration nor sleep efficiency changed significantly across study days from hospitalization through post-discharge (1.2 min, 95% CI = −2.5 to 5.0, p = 0.5; −.5%, 95% CI = −1.1 to .1, p = 0.1). None of the covariates were significant in either model.

Figure 1.

Nighttime sleep duration. Left: There is no significant difference between mean in-hospital sleep duration and mean post-discharge sleep duration (320.2 ± 128.53 vs. 320.2 ± 103.03 min, p = 0.99). Right: There is no significant change in sleep duration by study day, where day 0 is the day of discharge (p = 0.5). Best fit lines are derived from a linear regression model adjusted for age, sex, ethnicity, and AHRQ Elixhauser Comorbidity Index score for 30-day readmission.

Figure 2.

Nighttime sleep efficiency. Left: There is no significant difference between median in-hospital sleep efficiency and median post-discharge sleep efficiency (74.0% vs. 71.7% z = −1.2, p = 0.24). Right: There is no significant change in sleep efficiency by study day, where day 0 is the day of discharge (p = 0.1). Best fit lines are derived from a linear regression model adjusted for age, sex, ethnicity, and AHRQ Elixhauser Comorbidity Index score for 30-day readmission.

Daytime sleep duration and physical activity

Like nighttime sleep parameters, daytime sleep duration was similar across settings. Median daytime sleep duration calculated based on 45 patients over a total of 333 days, was not different between in-hospital (26.3 min/day) and post-discharge (25.8 min/day) settings (z = 0.6, p = 0.5) (Figure 3). Random effects linear regression clustered by subject demonstrated no significant change across days of the study (−1.2 min, 95% CI = −3.2 to 0.7, p = 0.2). Daytime physical activity, however, did show significant change. Calculated from all 54 patients over a total of 384 days, daytime physical activity was lower in-hospital (median = 128.6 counts/min) than post-discharge (median = 173.2 counts/min) (z = −4.2, p < 0.01) (Figure 4). Random effects linear regression clustered by subject showed an increase of 23.5 counts/min (95% CI = 16.5 to 30.6, p < 0.01) per hospital day. A significant interaction between study day and post-discharge period was observed (β ₃ = −20.8, 95% CI = −28.8 to −12.8, p < 0.01) demonstrating a slower rate of increase after discharge (Table 2). Age was the only significant covariate in the regression model.

Figure 3.

Daytime sleep duration. Left: There is no significant difference between median in-hospital daytime sleep duration and median post-discharge daytime sleep duration (26.3 vs. 25.8 min/day, z = 0.6, p = 0.5). Right: There is no significant change in daytime sleep duration by study day, where day 0 is the day of discharge (p = 0.2). Best fit lines are derived from a linear regression model adjusted for age, sex, ethnicity, and AHRQ Elixhauser Comorbidity Index score for 30-day readmission.

Figure 4.

Daytime physical activity. Left: Median post-discharge daytime physical activity is greater than median in-hospital daytime physical activity (173.2 vs. 128.6 counts/min, z = −4.2, p < 0.01). Right: Daytime physical activity increased 23.5 counts/min (95% CI = 16.5 to 30.6, p < 0.01) per hospital day. A significant interaction between study day and post-discharge period was observed (β3 = −20.8, 95% CI = −28.8 to −12.8, p < 0.01). Best fit lines are derived from a linear regression model adjusted for age, sex, ethnicity, and AHRQ Elixhauser Comorbidity Index score for 30-day readmission and an interaction term for Study day × Post-discharge.

Table 2.

Random effects linear regression models for daytime physical activity

		β Coefficient	P-value	95% Confidence interval
Model 1
	Study day	9.6	<0.01	6.9 to 12.3
	Constant	164.3	<0.01	141.9 to 186.6
Model 2
	Study day	23.5	<0.01	16.5 to 30.6
	Post-discharge period	2.8	0.77	−16.5 to 22.2
	Study day × Post-discharge period	−20.8	<0.01	−28.8 to −12.8
	Age*	−56.4	0.03	−106.3 to −6.5
	Sex	19.9	0.37	−23.3 to 63.1
	Ethnicity	−4.7	0.86	−58.0 to 48.5
	AHRQ Elixhauser Comorbidity Index: 30-day readmission score	1.2	0.17	−0.5 to 3.0
	Constant	165.5	<0.01	101.5 to 229.5

N = 54.

*Age ≥ 65 years.

Discussion

This is the first study to the authors’ knowledge to characterize sleep and activity patterns in older community-dwelling adults during an inpatient general medicine stay and immediately following discharge to home. Neither sleep duration nor sleep efficiency was significantly improved post-discharge relative to during hospitalization and neither showed significant change across days of the study. Similar to previous findings in critically ill patients, our results suggest that sleep disturbance experienced by general medicine patients while in the hospital may persist once patients are discharged. Although the average post-discharge follow-up time for our study was 6.4 ± 1.6 nights, further research will be necessary to determine how long this effect lasts.

Importantly, the nighttime sleep durations we observed both in the hospital and at home are shorter than the recommended 7–9 hours of sleep per night for adults [40]. For patients recovering from illness, this sleep deprivation may be particularly detrimental. The baseline short sleep durations reported on the PSQI by our patient population, suggest they are chronically sleep deprived, as are 65% of Americans who get less than 7 hours of sleep per weeknight [40]. Given the harms of sleep deprivation, it is concerning that we did not observe improved sleep post-discharge when patients should be continuing to recover and are no longer directly subject to the sleep disruptions of the hospital. Even when daytime sleep is factored in, total sleep time remains less than the recommended 7–9 hours per day. Furthermore, the presence of daytime sleep may be a signal that nighttime sleep is not sufficient and/or daytime sleep could delay nighttime sleep and reduce homeostatic sleep drive contributing to short nighttime sleep durations and altered circadian rhythms.

Daytime physical activity increased significantly across days of the study and may be a marker of recovery. Average activity in the hospital (132 counts/min) fell between the thresholds for sitting and watching television (67 counts/min) and sitting and eating (177 counts/min) whereas average post-discharge activity (193 counts/min) fell just above the threshold for sitting and being active with one’s hands (190 counts/min) [41]. Age was a significant covariate of daytime physical activity, which likely represents that patients older than 65 years have lower physical activity levels in general. The weaker effect on daytime physical activity post-discharge may reflect an unexpected attenuated trajectory of recovery once patients leave the hospital. Therefore, it may be important to consider more intensive rehabilitation to bridge hospital to home care to maintain the positive trajectory. This may also help patients achieve higher levels of activity intensity at home, beyond that observed in our study.

Several limitations to this study must be acknowledged. This is a single center study conducted within a small group of older adult, predominantly African American patients, which limits generalizability. Being a prospective cohort study, it can be used to define associations but not causality. Without baseline pre-hospitalization actigraphy data, it is difficult to know how the observed sleep and activity patterns differ from the patients’ habitual patterns. Although our patients are medical rather than surgical, previous studies have used scheduled operations for pre- and postoperative actigraphy monitoring to show decreased nighttime sleep duration and efficiency and increased daytime sleep during the acute phase of recovery in the hospital compared to baseline [42, 43]. Furthermore, the assumed sleep and wake periods based on self-reported wake and bed times may not be accurate representations of the actual sleep and wake periods. Additionally patients wore actigraphy monitors only on their wrists, rather than on the thigh or waist, limiting the amount of movement data we could collect. Finally, we had to exclude many patients who did not wear the actigraphy monitor for at least one in-hospital and one post-discharge night. It is possible there is a difference in sleep and activity patterns between those who did and did not wear the actigraphy monitor. Because we used only one in-hospital and one post-discharge night, not all days and nights used in the analysis are consecutive, which may have affected our results. There were more nights than days recorded because recordings started on the nights following enrollment and discharge for in-hospital and post-discharge periods, respectively. When patients did remove the actigraphy monitor, it was more likely to be during the day such that we had data from the previous night but not the following day resulting in more nights recorded.

Similar to critically ill patients, our results suggest that general medicine patients continue to experience sleep disruption post-discharge. Furthermore, they may even show a slower trajectory to recovery once they are discharged. These findings are important in the context of posthospital syndrome. Continued disturbance to sleep and activity patterns may be an important, and modifiable, contributor to the increased vulnerability after discharge from the hospital. Interventions focused on limiting sleep disturbance in the hospital and emphasizing the importance of sleep after discharge may be able to improve patient recovery and reduce readmissions.

Funding

We acknowledge funding from the National Heart, Lung, and Blood Institute (NHLBI) (5R25HL116372 and 1K24HL136859), Society for Hospital Medicine Student Hospitalist Scholar Grant, the Pritzker School of Medicine Scholarship and Discovery program, and National Center for Advancing Translational Sciences (NCATS) (UL1 TR000430).

Conflict of interest statement. None declared.