Relationships between rest-activity rhythms, cancer-related fatigue, and quality of life: Results from a...

Circadian rest-activity rhythms, or a person's consistency in their daily activity and rest patterns, can be quantified using various parametric and non-parametric methods. However, these methods are seldom applied to understand symptoms in cancer survivorship. This study examined associations between various rest-activity rhythm parameters and cancer-related fatigue and quality of life. Adult cancer survivors (n = 31) were enrolled in a 12-week randomized clinical trial of individualized nutrition counseling with or without time-restricted eating. Participants wore actigraphy watches continuously for 3-7 days at baseline, 6 weeks, and 12 weeks. Rest-activity parameters were derived using cosinor models, traditional non-parametric methods, and probabilistic hidden Markov models (HMMs). Participants completed the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) questionnaire, including physical, social, emotional, and functional well-being subscales; fatigue; and a quality-of-life total score. Mixed models revealed that stronger rest-activity rhythms were generally associated with lower fatigue and greater well-being in several dimensions. Specifically, intradaily variability and the HMM-derived rhythm index were positively associated with emotional well-being (p = 0.040 and p = 0.037, respectively, medium effect sizes). Earlier daily activity peaks, identified through both parametric and non-parametric methods, were linked to greater emotional well-being (e.g. cosinor peak time and emotional well-being, p = 0.006, large effect size; start hour of the most active 10 hours, p = 0.004, large effect size). There was no effect of group (time-restricted eating vs. control) on any of the rest-activity parameters. Findings suggest that rest-activity rhythm parameters, including those derived from novel HMMs, may serve as useful biomarkers of fatigue and various dimensions of well-being, supporting further research in larger samples and potential clinical application.