Circadian rhythms measured by actigraphy during oncological treatments: a systematic review

Effect of a Single Light-intensity Walking Session on Sleep Quality of Patients with Bladder Cancer: A Randomized Cross-Over Study

OBJECTIVES

Exercise has been recommended to enhance sleep. However, there is a paucity of studies investigating the relationships between exercise and sleep problems in patients with bladder cancer. The authors explored the effects of a single bout of light-intensity walking on the sleep quality of patients with bladder cancer who have sleep disorders.

DATA SOURCES

A total of 14 patients with bladder cancer with sleep disorders were recruited for this trial. The participants were randomly assigned to the walking or control condition in a cross-over design to explore the effects of a single light-intensity walking session on objectively measured sleep quality. A two-way repeated measures analysis of variance and a nonparametric permutation test were used to examine intervention effects. Twelve participants (85.7%) completed the trial. A significant group × time interaction for sleep latency (P = .023) was identified. The pairwise comparison showed significant results (P = .012) for the difference between the post-test sleep latency and the pre-test. No significant group × time interactions were observed for the remaining seven sleep parameters. Additionally, only the main effects of time on length of awakening and time in bed were significant (P < .001).

CONCLUSION

A single bout of light-intensity walking has a positive effect on shortening the sleep latency of patients with bladder cancer who have sleep disorders.

IMPLICATIONS FOR NURSING PRACTICE

Oncology nurses can encourage patients with bladder cancer to exercise, even light-intensity walking, which may improve sleep quality.

Understanding Sleep Disturbances in Prostate Cancer-A Scientometric Analysis of Sleep Assessment, Aetiology, and Its Impact on Quality of Life

Prostate cancer is the most commonly diagnosed cancer in the United Kingdom. While androgen-deprivation therapy is the most common treatment for prostate cancer, patients undergoing this treatment typically experience side effects in terms of sleep disturbances. However, the relation between prostate cancer and sleep and the way in which sleep interventions may benefit oncological patients is underinvestigated in the literature. The current study aims to review in a data-driven approach the existing literature on the field of prostate cancer and sleep to identify impactful documents and major thematic domains. To do so, a sample of 1547 documents was downloaded from Scopus, and a document co-citation analysis was conducted on CiteSpace software. In the literature, 12 main research domains were identified as well as 26 impactful documents. Research domains were examined regarding the link between prostate cancer and sleep, by taking into account variations in hormonal levels. A major gap in the literature was identified in the lack of use of objective assessment of sleep quality in patients with prostate cancer.

Circadian disruption and cancer- and treatment-related symptoms

Cancer patients experience a number of co-occurring side- and late-effects due to cancer and its treatment including fatigue, sleep difficulties, depressive symptoms, and cognitive impairment. These symptoms can impair quality of life and may persist long after treatment completion. Furthermore, they may exacerbate each other's intensity and development over time. The co-occurrence and interdependent nature of these symptoms suggests a possible shared underlying mechanism. Thus far, hypothesized mechanisms that have been purported to underlie these symptoms include disruptions to the immune and endocrine systems. Recently circadian rhythm disruption has emerged as a related pathophysiological mechanism underlying cancer- and cancer-treatment related symptoms. Circadian rhythms are endogenous biobehavioral cycles lasting approximately 24 hours in humans and generated by the circadian master clock - the hypothalamic suprachiasmatic nucleus. The suprachiasmatic nucleus orchestrates rhythmicity in a wide range of bodily functions including hormone levels, body temperature, immune response, and rest-activity behaviors. In this review, we describe four common approaches to the measurement of circadian rhythms, highlight key research findings on the presence of circadian disruption in cancer patients, and provide a review of the literature on associations between circadian rhythm disruption and cancer- and treatment-related symptoms. Implications for future research and interventions will be discussed.

Actigraphy-measured rest-activity circadian rhythm disruption in patients with advanced cancer: a scoping review

PURPOSE

Patients with later-stage cancer have been reported to demonstrate more disrupted rest-activity circadian rhythms (RACR) than those with earlier-stage cancer, but consolidated evidence of this is lacking. The aim of this review was to examine and map the existing evidence on actigraphy-measured RACR in terms of their (1) pattern, (2) prevalence, (3) related factors, and (4) outcomes in advanced cancer patients.

METHODS

A comprehensive scoping review was conducted using Arksey and O'Malley's framework. A literature search was performed using nine databases: MEDLINE, Embase, PsycINFO, CINAHL, British Nursing Index, Cochrane Library, Scopus, Web of Science, and SINTA.

RESULTS

Twenty-one studies were included in the review. Compared with the healthy population, advanced cancer patients were more likely to display weaker RACR, manifesting as lower activity levels during the day, more frequent and longer daytime naps, and fragmented nighttime sleep. The prevalence of RACR disruption among advanced cancer patients ranged from 31.3 to 54.9%. It was found to be linked to the presence of physical and psychological symptoms (fatigue, appetite loss, pain, dyspnoea, sleep disturbance, depression, and anxiety), chemotherapy, male sex, and also predict the lower quality of life and survival.

CONCLUSION

Disruption of the RACR is prevalent in advanced cancer patients and is associated with a set of physical and psychological symptoms. It was also found to be a predictor of the quality of life and survival among these patients. These results indicate the importance of interventions to restabilise the disrupted RACR among advance cancer patients to improve their health outcomes.

Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study

Background

Pancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity.

Objective

The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning.

Methods

In this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves.

Results

Between February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875.

Conclusions

Machine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics.