Physical Activity and Sleep Patterns in Hemodialysis Patients in a Suburban Environment

It has been shown that hemodialysis (HD) patients are less active than their healthy counterparts [1]. Living a sedentary lifestyle is associated with poor health outcomes. In an observational study conducted using the National Health and Nutrition Examination Survey by Beddhu et al. [2], it was found that taking part even in a low-intensity activity, for example, walking, had a positive impact on survival. It is important to learn more about the physical activity of patients on HD. The effect of interventions on lifestyle to improve outcomes and quality of life may be determined only when an objective measure of patients' activity outside of the health care provider's purview is available [3]. Traditionally, physicians are dependent on subjective patient reports during their time away from the dialysis clinic. In recent years, the availability and adoption of wearable activity trackers have increased. Having patients use commercially available activity trackers provides an objective measurement of how active they are in free-living conditions. These activity trackers have been validated against the gold standard of physical activity measurement, the Actigraph, a triaxle accelerometer [4]. In addition, commercially available activity trackers are easily available, easy to use, and inexpensive [5].

In the study by Han et al. [6,] which evaluated the interdialytic activity of HD patients, it was found that dialysis patients walked on average 8,454 steps with no difference in physical activity levels on dialysis and non-dialysis days. The authors also found that patients on HD had irregular sleep habits and this was more prominent in patients on earlier dialysis shifts. The population studied by Han et al. [6] lived in an urban area: New York City. These patients may be unique and different from those living in suburban areas and it is important to determine if the activity levels exhibited are characteristic of other HD populations.

The objectives of this study were to (1) assess physical activity levels and sleep of HD patients living in a suburban environment and (2) determine if providing feedback on activity during dialysis treatments will have an impact on physical activity levels.

The activity and sleep data of 29 chronic HD patients were collected using the Fitbit® Flex™ (Fitbit, San Francisco, CA, USA) device over a course of 5 weeks. Participants were randomly assigned to 2 groups after enrollment. The ‘feedback' group (n = 15) received a report of activity and sleep data in the week leading to the date of each HD treatment, while the control group (n = 14) did not. The human activity profile (HAP) and a questionnaire on patient experience using the device and attitude toward physical activity were administered at the beginning and end of the study period. The study was approved by Western IRB (protocol No. 20152105).

HD patients treated in 2 out-patient facilities located in Baton Rouge, LA of the Fresenius Kidney Care, were recruited to participate in this study. Patient recruitment occurred on a rolling basis from February 2016 to August 2016. Baton Rouge is a city located on the Mississippi River, with a population of approximately 440,000 residents.

Patients were eligible to participate if they were receiving HD 3 times a week, on HD for more than 3 months, and between 18 and 75 years. Patients were required to have the ability to walk without assistance or assistive devices to ensure that the device is able to track activity. Patients were excluded if they had unstable health (e.g. acute infections, congestive heart failure (CHF) NYHA class 4 and/or unstable angina), were hospitalized within 3 months before enrollment for non-access-related reasons, or were cognitively impaired. The clasp of the device contains trace amounts of nickel, so patients with a known nickel allergy were ineligible to participate. Additionally, patients who had previously worn activity tracking devices were excluded, so the total effect of providing feedback could be observed.

Thirty one participants were enrolled in the study. Two of them died during the study period; their data were not included in the analysis.

All participants were equipped with the Fitbit® Flex™ tracking bracelet and were instructed to wear the bracelet at all times, even during activities like bathing, as it is waterproof. The Fitbit® Flex™ tracks activity parameters (steps taken, distance traveled) and sleep duration and quality (minutes asleep, total time in bed). A Fitbit® user account was created and the device was configured for each subject's age, height, weight and gender. Patients did not have access to their accounts. The device was worn on the non-vascular access arm, with the device settings configured to reflect if this was the dominant or nondominant hand. Data were downloaded from the device to the user account during each HD treatment. Participants were asked to keep a daily sleep log, in which they recorded the times they went to bed and the times they woke up. Sleep start and end times were entered into the user account by the research coordinators in order to calculate sleep duration and quality. The devices were charged during each HD treatment. Activity and sleep data were exported via Fitbit® Premium for analysis.

Based on their average daily step counts, participants were separated into 3 categories. Participants were considered sedentary, fairly active, or active if they walked less than 5,000 steps, between 5,000 and 10,000 steps, or more than 10,000 steps, respectively [7].

The National Institute of Health (NIH) recommends at least 7 h (420 min) of sleep per night [8]. Sleep efficiency (in %) was calculated as 100 times the ratio of sleep duration to total time in bed. A sleep efficiency of 85% or above is considered sleep of good quality [9]. Based on sleep duration and sleep efficiency, patients were categorized into 3 groups: poor, intermediate, and good sleep quality. Patients who slept <420 min with a sleep efficiency of <85% were considered to have poor sleep quality. Patients who slept ≥420 min with a sleep efficiency of <85% or slept <420 min with a sleep efficiency of ≥85% were considered to have intermediate sleep quality. Patients who slept ≥420 min with a sleep efficiency of ≥85% were considered to have good sleep quality.

The Physical Activity Questionnaire was developed to capture subject attitudes toward their physical activity, and was administered at the end of the study period. Statements such as ‘I was able to incorporate this device into my daily activities' and ‘I desire to continue wearing this device to track my own activity' were queried of the patients.

Laboratory measurements were done at Spectra Laboratories (New Jersey, USA). For study purposes, clinical and laboratory data were manually obtained from the participants' electronic health record. In addition to collecting test results from routine monthly blood tests, pre-albumin and C-reactive protein (CRP) were also tested. Blood samples were collected before the start of a mid-week dialysis session.

Baseline (first week in the study) demographics, anthropometrics, comorbidities, treatment-related parameters, activity parameters and sleep parameters were described by mean and SD for continuous variables and frequency distribution for categorical variables. T tests was used to test the statistical significance of the difference between the feedback and control groups for continuous variables; Fisher's exact test was used for categorical variables. Paired t tests were also performed to test the differences among the daily average steps on HD days, non-HD days, and Sundays. Sundays were excluded from non-HD days.

In order to assess the effect of HD scheduling on sleep quality, we also performed paired t tests to compare the sleep duration and sleep efficiency between the night after HD treatment and the night before HD treatment, night after HD treatment and night between 2 non-HD days, and night before HD treatment and night between 2 non-HD days per shift.

Statistical analysis was performed with SAS version 9.4 and Rx64 3.2.0.

Demographics, anthropometrics, treatment-related parameters and laboratory parameters are presented in table 1. On average, participants were 52 years, with a body mass index (BMI) of 33.7 kg/m², and 167 cm tall. The study cohort comprised of 41.4% male and 72.4% black; 58.6% of the participants were diabetic and 17.2% had CHF. Average HD treatment time was 238 min with an average eKt/V of 1.5, and dialysis vintage was 4.0 years. Average levels of pre-albumin, serum albumin, CRP and hemoglobin were 30.0 mg/dl, 3.9 g/dl, 15.6 mg/l, and 11.0 g/dl, respectively.

Patients received treatments at the HD clinic during 3 different shifts, namely, shift 1 from 6 a.m. to 10 a.m., and shift 2 from 10 a.m. to 2 p.m., shift 3 from 2 p.m. to 6 p.m. Many of the patients enrolled were scheduled to receive HD treatments on the first shift (n = 21). The remaining patients who scheduled to receive HD treatments during shifts 2 and 3 were 5 and 3, respectively.

On average, patients slept 370.5 min and had a sleep efficiency score of 83.3%. The patients were categorized to have had poor, intermediate and good sleep quality. Good sleep quality meant they had a sleep efficiency of 85% or greater and slept at least 420 min or more; poor sleep quality meant that they had a sleep efficiency of less than 85% and did not sleep for at least 420 min. The intermediate sleep group was able to fulfill either sleep duration or sleep efficiency criteria [8,9]. Forty-five percent (n = 11) had poor sleep, 38% (n = 13) had intermediate sleep quality and 17% (n = 5) had good sleep quality (fig. 1).

There was no significant difference in sleep efficiency with regard to the different shifts, but there was a difference in sleep duration in relation to shift. As shown in table 2, there was a significant difference between the number of minutes slept the night before an HD treatment and the night after HD treatment for those patients dialyzed on the first shift (284 vs. 420 min, p < 0.0001), as well as, the night between 2 non-HD days (284 vs. 414 min, p < 0.001). For patients on shifts 2 and 3, there was no significant difference in sleep duration or sleep efficiency between the night before HD treatment and the night after HD treatment (fig. 2A, B).

On average, patients walked 5,291 steps each day. There was no difference in the number of steps walked by the feedback vs. the control group. For the purpose of our analysis, all data acquired during the study were pooled. Based on Centers for Disease Control and Prevention (CDC) physical activity recommendations, patients were stratified by the activity level with 45% (n = 13) classified as sedentary, 52% (n = 15) as fairly active and 3% (n = 1) as active [7 ](fig. 3).

There was a significant difference in the number of steps taken on dialysis days and non-dialysis days (p < 0.05). On average, patients walked 1,822 steps less on dialysis days compared to non-dialysis days. There was no significant difference between the number of steps taken on dialysis days and Sundays. When categorized by activity level, it was shown that there was no difference in the number of steps taken by an active patient on any day of the week. The fairly active group is driving the difference we see between non-dialysis days, dialysis days and Sundays. In this group, there is no difference in the number of steps taken on dialysis days and Sundays, but there is a significant difference between dialysis days and non-dialysis days (p < 0.05). The sedentary group took significantly less steps on HD days vs. non-dialysis days (fig. 4A-D).

At the end of the 5-week study period, participants were given a questionnaire to determine how well they liked the device. They were asked as to how much they agreed with the statements on the questionnaire. They could rank their level of agreement in 5 categories: ‘not at all', ‘somewhat', ‘moderately', ‘definitely', or ‘most definitely'. Over 90% stated they were able to incorporate the device into their daily activities. More than half the patients felt that they did walk more than usual during the study period and felt that they wanted to continue wearing the activity tracker (table 3).

The main findings of our study were that the dialysis patients living in a suburban environment walked less than the recommended 10,000 steps per day and often walked significantly less on dialysis days compared to non-dialysis days. This is consistent with the literature indicating that dialysis patients are not as active as the general population [1,10]. In addition, 83% of the patients followed were found to have poor or intermediate sleep quality, confirming findings from previous reports [11].

A majority of the patients slept less than the NIH recommended 420 min and had a sleep efficiency of less than 85%. A systematic review by Fonesca et al. [12] found that 50-80% of HD patients suffered from sleep-disordered breathing. There is also a high rate of periodic limb movement disorder and restless leg syndrome. Any of these sleep disorders can contribute to poor sleep quality in dialysis patients.

Twenty-one of the 29 patients followed were dialyzed on the first shift, between the hours of 6 a.m. and 10 a.m. When the sleep patterns of these patients were evaluated, there was a significant difference between the number of minutes slept the night before dialysis compared to the night after dialysis and the nights between 2 non-dialysis days. This is consistent with previous findings from a similar analysis by Han et al. [6]. The early start time of HD treatment may contribute to the abnormal sleep patterns observed, since patients need to wake up before their scheduled treatment to get ready, for example, showering, brushing teeth, eating breakfast, as well as, travel to the dialysis clinic.

It has been found that the Fitbit® Flex™ overestimates sleep duration and quality due to its limited ability to sense when the wearer is awake [13]. However, the device used does correlate with actual sleep [14]. Polysomnography, the gold-standard for measuring sleep, is time consuming, impractical on a routine basis, and expensive. The Fitbit® Flex™ can be used to measure sleep, as long as the user is aware that the data collected point to an overestimation of sleep duration and quality. Consequently, our measurements of sleep duration and quality, which are already low, may have been overestimated [15].

The patients studied in the Baton Rouge cohort walked significantly less than their New York City counterparts (5,291 vs. 8,454 steps, p < 0.0001). The patients in Baton Rouge were younger, had a higher BMI and had a higher prevalence of diabetes, while the patients in New York City had a higher prevalence of CHF and longer HD vintage. When analyzing the activity patterns between the 2 cohorts, it was also found that the New York City cohort walked approximately the same number of steps during the week and had a significant drop in activity on Sundays, while in Baton Rouge, there was no difference in the number of steps walked between Sundays and other non-dialysis days. Baton Rouge participants walked significantly less on HD days than what was reported in New York City patients (4,166 vs. 9,408 steps, p < 0.05). This difference may be due to the different modes of transportation taken to HD treatments, for example, driving vs. public transportation. HD patients in a suburban environment are less active than patients residing in an urban population. A number of factors may contribute to the lower level of activity in suburban vs. urban areas, for example, the availability and use of public transportation, design of neighborhoods, and access to amenities such as shops and parks.

Many of these factors, which may contribute to urban residents walking more than their suburban counterparts, may be measured and compiled into a walk score. This score is calculated by walkscore.com, a website that evaluates how walkable an area is based on parameters such as presence of sidewalks, proximity of stores, and availability of public transportation. Walk scores range from 0 to 100 and has been shown to be a valid measure of walkability [16]. New York City has more walkable neighborhoods than Baton Rouge. Using patient zip codes to calculate Walk Scores, a preliminary analysis of the association of walkability and activity level was conducted. Higher walk scores were associated with higher physical activity levels. More research into the effect of walkability on physical activity in dialysis patients needs to be performed.

This study conducted in Baton Rouge was a follow-up study based on the observations found in a New York City population [6]. As such, there are similar limitations, namely, the small sample size and the short duration of the study. In addition, in studies of this nature, there may be an inherent selection bias. Patients who were more interested in learning about their activity and sleep habits may have chosen to participate in the study over patients who were less interested. In future studies, it would be better to enroll a larger number of patients and follow them for a longer period of time. This will allow for a better evaluation of sleep and activity in dialysis patients.

The population studied in Baton Rouge was very different than the population studied in New York City. Patients had higher prevalence of diabetes and were more overweight. They also walked less and had a different pattern of activity than the group in New York City. The dialysis patients in Baton Rouge walked significantly less on dialysis days than on non-dialysis days but rarely walked the recommended number of steps per day. This leaves a lot of potential for intervention. The patients studied slept poorly, but this was more pronounced the night before dialysis for patients on the first shift. Further research is needed on how the use of activity and sleep monitors in dialysis patients can improve care and the development of targeted interventions.

Peter Kotanko holds stock in Fresenius Medical Care. The remaining authors declared no competing interest.