Comparison of Accelerometer-Derived Physical Activity Levels Between Individuals with and Without Cancer: A UK Biobank Study

McDonald, Laura; Oğuz, Mustafa; Carroll, Robert; Thakkar, Pratik K.; Yang, Fei; Dhalwani, Nafeesa N.; Cox, Andrew T.; Merinopoulou, Evie; Malcolm, Bill; Mehmud, Faisal; Ramagopalan, Sreeram

doi:10.2217/fon-2019-0443

Cited by 9 publications

(9 citation statements)

References 26 publications

(25 reference statements)

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“…These data have subsequently been used in combination with the UK Biobank's extensive data on disease and health outcomes [11]. These included studies into: functional fitness, measured through walking pace [12] or grip strength [13]; cardio vascular disease [14,15] and heart failure [16]; cancer generally [17] and specifically breast cancer [18]; mental health [19] including psychiatric conditions [20,21] and depression and anxiety [22]; mortality generally [23,24], but also concentrating on volume and intensity of activity [25,26]; sleep quality [27,28] and the relationship of sleep to morbidity outcomes [6,29]; and circadian rhythms [30,31]. Outside of health, a range of studies have been reported that correlate these objectively measured physical activity levels with other observations and outcomes such as environmental characteristics [32], travel [33], and activity [34,35].…”

Section: Introductionmentioning

confidence: 99%

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort

Clark

Lomax

Morris

et al. 2021

Sensors

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Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: “Active 9 to 5”, “Active”, “Morning Movers”, “Get up and Active”, “Live for the Weekend”, “Moderates”, “Leisurely 9 to 5”, “Sedate” and “Inactive”. These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.

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Section: Introductionmentioning

confidence: 99%

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort

Clark

Lomax

Morris

et al. 2021

Sensors

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“…The PASE questionnaire has previously been used in younger patients with cancer [13,14]. Patients with cancer are often debilitated and therefore have lower levels of physical activity compared with age-matched healthy individuals [15]. Consequently, their physical activity levels may be more comparable to the elderly.…”

Section: Introductionmentioning

confidence: 99%

Physical activity in patients with cancer: self-report versus accelerometer assessments

Douma

Beaufort

Kampshoff

et al. 2019

Support Care Cancer

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Purpose The level of daily physical activity in patients with cancer is frequently assessed by questionnaires, such as the Physical Activity Scale for the Elderly (PASE). Objective assessments, with for example accelerometers, may be a good alternative. The aim of this study was to investigate the agreement between the PASE questionnaire and accelerometer-assessed physical activity in a large group of patients with different types of cancer. Methods Baseline accelerometer and PASE questionnaire data of 403 participants from the REACT (Resistance and Endurance Exercise After Chemotherapy, n = 227), the EXIST (Exercise Intervention After Stem-Cell Transplantation, n = 74), and NET-QUBIC (NEtherlands QUality of Life And Biomedical Cohort Studies In Cancer, n = 102) studies were available for the current analyses. Physical activity was assessed by the PASE questionnaire (total score) and accelerometers (total minutes per day > 100 counts). Linear mixed models regression analysis was used to assess the agreement between the PASE questionnaire and accelerometer-assessed physical activity. Results The mean (SD) PASE score was 95.9 (75.1) points and mean (SD) time in physical activity measured with the accelerometer was 256.6 (78.8) min per day. The agreement between the PASE score and the accelerometer data was significant, but poor (standardized regression coefficient (B) = 0.36, 95%CI = 0.27; 0.44, p < 0.01).

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“… 42 43 We hypothesise that the deterioration of physical function in patients with cancer is from age-related and cancer treatment-accelerated declines in aerobic capacity, 11 ambulatory activity, 12 13 and muscle strength and mass. 14 15 Supporting this hypothesis is the observation that patients with cancer have a lower aerobic capacity, 44 ambulatory activity, 45 muscle strength 46 and muscle mass, 47 compared with matched control participants who do not have a history of cancer.…”

Section: Discussionmentioning

confidence: 98%

Physical activity in older adults with metastatic gastrointestinal cancer: a pilot and feasibility study

Brown

Brighton

Campbell

et al. 2022

BMJ Open Sport Exerc Med

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ObjectivesThis study determined the feasibility of delivering a 12-week structured physical activity programme during chemotherapy to older adults recently diagnosed with metastatic gastrointestinal (GI) cancer.MethodsThis study used a single-cohort design. Older adults (aged ≥65 years) diagnosed with metastatic oesophageal, gastric, pancreatic or colorectal cancer who planned to initiate chemotherapy were enrolled. The physical activity programme included a combination of aerobic, flexibility, strength and balance modalities delivered by a certified cancer exercise trainer during chemotherapy infusion appointments, then translated and sustained at home by participants. The co-primary endpoints included: (1) accrual of 20 participants in 12 months and (2) physical activity adherence of ≥50%.ResultsBetween March and October 2018, 29 participants were screened, and 20 were enrolled within 12 months (recruitment rate: 69% (90% CI: 55% to 83%); p<0.001), meeting the first co-primary endpoint. The median age of participants was 73.3 years (IQR: 69.3–77.2). At week 12, 67% (90% CI: 48% to 85%) of participants adhered to ≥50% of the prescribed physical activity (p=0.079 (statistically significant)), meeting the second co-primary endpoint. From baseline to week 12, accelerometer-measured light-intensity and moderate-intensity to vigorous-intensity physical activity increased by 307.4 (95% CI: 152.6 to 462.2; p<0.001) and 25.0 min per week (95% CI: 9.9 to 40.1; p=0.001), respectively. There were no serious or unexpected adverse events. The median overall survival was 16.2 months (8.4–22.4).ConclusionThese results establish the feasibility of a larger scale randomised controlled trial that enrols older adults with metastatic GI cancer and delivers a structured physical activity programme during chemotherapy.Trial registration numberNCT03331406.

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Comparison of Accelerometer-Derived Physical Activity Levels Between Individuals with and Without Cancer: A UK Biobank Study

Cited by 9 publications

References 26 publications

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort

Physical activity in patients with cancer: self-report versus accelerometer assessments

Physical activity in older adults with metastatic gastrointestinal cancer: a pilot and feasibility study

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