A recent study (1) found that a high level of cardiorespiratory fitness (CRF) was associated with an increased risk for localized prostate cancer. The reasons for this are unknown. The researchers speculate that perhaps this group was more likely to undergo preventative screening or detection. However, higher CRF still showed a 32% decreased risk of cancer specific death for lung, colorectal, or prostate cancers; or 68% decreased risk of death from cardiovascular disease (CVD). Note: some cancer treatments can be toxic to the heart.
From: Midlife Cardiorespiratory Fitness, Incident Cancer, and Survival After Cancer in Men: The Cooper Center Longitudinal Study.
Figure Legend: Cardiorespiratory Fitness (CRF) and Risk of Incident Lung, Colorectal, and Prostate Cancer. The low CRF group is the referent group relative to moderate and high fitness. The error bars for moderate and high fitness represent the 95% confidence limits. Adjusted for age, examination year, body mass index, smoking, total cholesterol level, systolic blood pressure, diabetes mellitus, and fasting glucose level.
The authors distinguish CRF from physical activity (I believe research data for both could be provided by valid and reliable activity trackers):
“Cardiorespiratory fitness is also highly reproducible and objectively assessed via incremental exercise tolerance testing compared with physical activity, which is largely determined by self-report questionnaires [and/or activity trackers?]. A prior study demonstrated that CRF is be a more potent marker of mortality than physical activity. As such, given the current study findings and prior evidence, we contend that measurement of CRF should be used more frequently in the cancer prevention setting.”
I agree. Furthermore, I would like to see physical activity, CRF, or aerobic capacity assessed when the cancer diagnosis process begins. How beneficial would it be to tie fitness to an actual biopsy tissue specimen? It’s interesting that CRF in the Cooper Clinic Longitudinal Study was assessed by the duration of performance achieved on a maximal treadmill test (2). Then, based on subjects’ performance time, maximal oxygen uptake (VO2max) and maximal METs achieved were estimated, not measured. If estimates can be used to assess CRF then it’s possible that some activity trackers could also be used. Granted, screening patients before a CRF test is recommended, but some activity tracking data may already provide an adequate assessment of CRF. A few devices already assess VO2max using heart rate, and with acceptable errors (for field measurements) in the 6-7% range (11, 12). Stratifying data from activity trackers may be an important part of sorting its value: data for showing a training effect requires good accuracy; less accurate data is probably acceptable to assess CRF; and, data for tracking physical activity volume (MET-hours per week, etc.) can perhaps be the least precise of these – particularly since current population research using questionnaires tends to overestimate actual physical activity (13).
In discussing limitations of their study the authors mention something I believe may be significant for exercise-oncology research, and which I think validated activity trackers may be able to provide data for:
“CRF was assessed years prior to a diagnosis of lung, colorectal, or prostate cancer or death in men diagnosed as having cancer. Thus, it is not known how changes in CRF and related behaviors, such as physical activity from the initial preventive health care to cancer diagnosis as well as changes in CRF and physical activity after diagnosis, may have had an impact on these current findings.”
I believe that exercise during the time from cancer diagnosis until first treatment will be found to have a positive impact on cancer treatments, treatment side effects, and on survival. Sophisticated activity trackers that also estimate VO2max, or measure heart rate variability (HRV), which is related to CVD, have the potential to provide data in and around the diagnosis/treatment time period. Furthermore, they can provide data across more cancer types by doing it in a more cost-effective manner than mailing out questionnaires or doing a CRF test on every cancer patient. One overlooked benefit of activity trackers is that consumers subsidize the data.
Some useable physical activity data already exists in activity tracking databases but sits there underutilized. Most physical activity data needs standard medical codes to improve its interoperability. Other data could be retooled by correcting METs, which could provide more accurate estimates of energy expenditure (4, 5, 6, 7, 8), population specific intensity levels (9, 10), and might influence adherence to exercise training programs. Regarding METs, an issue for some researchers is that the ‘standard’ MET (3.5 ml oxygen/kg/min) was based on the measurements derived from one 70 kilogram, 40-year-old man (5), and then applied to survey research. Conversely, some activity trackers use ‘standard’ MET values from the Compendium of Physical Activities, which are intended for survey research, to estimate the energy expenditure and exercise intensity for an individual, which the Compendium advises is not its intended purpose.
Besides valid data, another issue activity trackers face is how should data be displayed or reported within an Electronic Health Record (EHR)? Doctors are already over-worked and many complain about the burden of EHRs, adding physical activity data to their workload and expecting them to do something proactive with it (without reimbursement too) is not going to happen. Make physical activity data easy for doctors to accommodate: summarize activity tracker data into an indicator of ‘compliance‘ or ‘non-compliance‘ with recommended physical activity guidelines, and provide that to an EHR. For research, and for the more inquisitive and less time constrained physician, the underlying data supporting a compliance indicator could be accessible via EHR patient portals (e.g. EPIC’s MyChart).
Finally, a new study (3) found the ActiGraph GT3X+ accelerometer not to be very accurate at low and moderate intensity levels. Of the few validation studies done on accelerometer based activity trackers, some were validated against the Actigraph as the criterion measure. However, this study itself also missed an opportunity for better measurement when they estimated Resting Metabolic Rate (RMR) using the Schofield equations rather than measuring it with the Oxycon Mobile system they had – RMR is essentially what 1 MET is. The study’s authors do disclose that they have receive funding support from Bodymedia, which Jawbone recently bought.
There is more to be sorted out in the consumer fitness/activity tracking eco-space. I think devices and apps that produce valid and reliable data can make an impact in exercise-oncology research, particularly in the time periods surrounding diagnosis and treatment.
1. Lakoski, S.G., et al. Midlife Cardiorespiratory Fitness, Incident Cancer, and Survival After Cancer in Men The Cooper Center Longitudinal Study. JAMA Oncol. 2015;1(2):231-237. doi:10.1001/jamaoncol.2015.0226
2. Pollock ML, Bohannon RL, Cooper KH, et al. A comparative analysis of four protocols for maximal treadmill stress testing. Am Heart J. 1976; 92(1):39-46.
3. Kim, Y., Welk G.J. Criterion Validity of Competing Accelerometry-based Activity Monitoring Devices. Med. Sci. Sports Exerc. 2015 Apr 23. [Epub ahead of print]
4. McMurray, R.G., et al. Examining Variations of Resting Metabolic Rate of Adults: A Public Health Perspective. Med. Sci. Sports Exerc., Vol. 46, No. 7, pp. 1352–1358, 2014.
5. Byrne, N., et al. Metabolic equivalent: one size does not fit all. J Appl Physiol 99: 1112–1119, 2005.
6. Kozey, S., et al. Errors in MET Estimates of Physical Activities Using 3.5 ml·kg–1·min–1 as the Baseline Oxygen Consumption. Journal of Physical Activity and Health, 2010, 7, 508-516.
7. Wilms, B., et al. Correction factors for the calculation of metabolic equivalents (MET) in overweight to extremely obese subjects. International Journal of Obesity (2014) 38, 1383–1387.
8. Hall, K., et al. Activity-Related Energy Expenditure in Older Adults: A Call for More Research. Med Sci Sports Exerc 2014 Dec;46(12):2335-40.
9. Blair, C.K., et al. Light-Intensity Activity Attenuates Functional Decline in Older Cancer Survivors. Med Sci Sports Exerc 2014 Jul;46(7):1375-83.
10. Herzig, K-H, et al. Light physical activity determined by a motion sensor decreases insulin resistance, improves lipid homeostasis and reduces visceral fat in high-risk subjects: PreDiabEx study RCT..International Journal of Obesity (2014), 1–8
11. Montgomery, P.G., et al. VALIDATION OF HEART RATE MONITOR–BASED PREDICTIONS OF OXYGEN UPTAKE AND ENERGY EXPENDITURE. Journal of Strength and Conditioning Research 23(5)/1489–1495.
12. Lebouf, SF., et al. Earbud-based sensor for the assessment of energy expenditure, HR, and VO2max. Med Sci Sports Exerc 2014;46(5):1046-52.
13. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. International Journal of Behavioral Nutrition and Physical Activity 2012, 9:103 pgs 1-55.