Sleep Disturbance and Quality of Life in Rheumatoid Arthritis: Prospective mHealth Study

Abstract

Background: Sleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies.

Objective: This mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL.

Methods: The quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social).

Results: Of the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants' scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β=1.11, 95% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF.

Conclusions: Optimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA.

Keywords: HRQoL; QoL; WHOQoL-BREF; fatigue; health-related quality of life; mobile health; mobile phone; mood; pain; quality of life; rheumatoid arthritis; sleep; sleep disturbance; sleep efficiency.

Figure 1

Directed acyclic graph for the relationship among sleep, health-related quality of life (HRQoL), pain, mood, and fatigue. The likelihood of reporting a particular level of health-related quality of life at days 10, 20, or 30 (HRQoL_tk) is directly predicted by sleep (Sleep_tj) in the previous 10 days (green arrow) as well as the effect of Sleep_tj acting through pain (Pain_tj), mood (Mood_tj), and fatigue (Fatigue_tj) in the previous 10 days (black dashed lines). Pain, fatigue, and mood increase the likelihood of each other (black solid lines). The relationship may be confounded by covariates measured at baseline including age, sex, and disease severity (Covariates_tb; orange arrows).

Figure 2

Examples of individual participants’ actigraphy assessed daily total sleep time showing average, intraindividual SD (iSD), and autocorrelation scores over 10 days. In this figure, each panel plots the daily total sleep time for 2 selected participants over 10 days. The 10-day average sleep time is shown as a straight line. In all, 2 measures of variability of total sleep time across the 10-day period were calculated, the iSD and the autocorrelation. The individual panels show the following: (A) shows 2 participants with similar average iSD and autocorrelation scores, (B) shows 2 participants with different average but similar iSD and autocorrelation scores, and (C) shows 2 participants with similar average and autocorrelation scores. The higher iSD score of participant F reflects the higher amplitude of fluctuations in total sleep time when compared with the low amplitude of fluctuation in the total sleep time of participant E. (D) shows 2 participants with similar average scores. The autocorrelation score of participant H toward −1 reflects the fluctuation in total sleep time, whereas the autocorrelation score of participant G toward 1 reflects the day-to-day stability in total sleep time despite a decrease over the period of observation.

Figure 3

Plot of individual participant World Health Organization Quality of Life-Brief (WHOQoL-BREF) domain scores across 30 days. The blue line is the cohort mean score, the black dashed line is the mean score for healthy individuals.

Figure 4

Correlation between objective (actigraph) and subjective (Consensus Sleep Diary [CSD]) measured time taken to fall asleep (A), total time asleep (B), and sleep efficiency (C).