Lisa Cadmus-Bertram

Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women

INTRODUCTION Direct-to-consumer mHealth devices are a potential asset to behavioral research but rarely tested as intervention tools. This trial examined the accelerometer-based Fitbit tracker and website as a low-touch physical activity intervention. The purpose of this study is to evaluate, within an RCT, the feasibility and preliminary efficacy of integrating the Fitbit tracker and website into a physical activity intervention for postmenopausal women. METHODS Fifty-one inactive, postmenopausal women with BMI ≥25.0 were randomized to a 16-week web-based self-monitoring intervention (n=25) or comparison group (n=26). The Web-Based Tracking Group received a Fitbit, instructional session, and follow-up call at 4 weeks. The comparison group received a standard pedometer. All were asked to perform 150 minutes/week of moderate to vigorous physical activity (MVPA). Physical activity outcomes were measured by the ActiGraph GT3X+ accelerometer. RESULTS Data were collected and analyzed in 2013-2014. Participants were aged 60 (SD=7) years with BMI of 29.2 (3.5) kg/m(2). Relative to baseline, the Web-Based Tracking Group increased MVPA by 62 (108) minutes/week (p<0.01); 10-minute MVPA bouts by 38 (83) minutes/week (p=0.008); and steps by 789 (1,979) (p=0.01), compared to non-significant increases in the Pedometer Group (between-group p=0.11, 0.28, and 0.30, respectively). The Web-Based Tracking Group wore the tracker on 95% of intervention days; 96% reported liking the website and 100% liked the tracker. CONCLUSIONS The Fitbit was well accepted in this sample of women and associated with increased physical activity at 16 weeks. Leveraging direct-to-consumer mHealth technologies aligned with behavior change theories can strengthen physical activity interventions.

Impact of Obesity on Cancer Survivorship and the Potential Relevance of Race and Ethnicity

Evidence that obesity is associated with cancer incidence and mortality is compelling. By contrast, the role of obesity in cancer survival is less well understood. There is inconsistent support for the role of obesity in breast cancer survival, and evidence for other tumor sites is scant. The variability in findings may be due in part to comorbidities associated with obesity itself rather than with cancer, but it is also possible that obesity creates a physiological setting that meaningfully alters cancer treatment efficacy. In addition, the effects of obesity at diagnosis may be distinct from the effects of weight change after diagnosis. Obesity and related comorbid conditions may also increase risk for common adverse treatment effects, including breast cancer-related lymphedema, fatigue, poor health-related quality of life, and worse functional health. Racial and ethnic groups with worse cancer survival outcomes are also the groups for whom obesity and related comorbidities are more prevalent, but findings from the few studies that have addressed these complexities are inconsistent. We outline a broad theoretical framework for future research to clarify the specifics of the biological-social-environmental feedback loop for the combined and independent contributions of race, comorbid conditions, and obesity on cancer survival and adverse treatment effects. If upstream issues related to comorbidities, race, and ethnicity partly explain the purported link between obesity and cancer survival outcomes, these factors should be among those on which interventions are focused to reduce the burden of cancer.

Use of the Fitbit to Measure Adherence to a Physical Activity Intervention Among Overweight or Obese, Postmenopausal Women: Self-Monitoring Trajectory During 16 Weeks

Background Direct-to-consumer trackers and devices have potential to enhance theory-based physical activity interventions by offering a simple and pleasant way to help participants self-monitor their behavior. A secondary benefit of these devices is the opportunity for investigators to objectively track adherence to physical activity goals across weeks or even months, rather than relying on self-report or a small number of accelerometry wear periods. The use of consumer trackers for continuous monitoring of adherence has considerable potential to enhance physical activity research, but few studies have been published in this rapidly developing area. Objective The objective of the study was to assess the trajectory of physical activity adherence across a 16-week self-monitoring intervention, as measured by the Fitbit tracker. Methods Participants were 25 overweight or obese, postmenopausal women enrolled in the intervention arm of a randomized controlled physical activity intervention trial. Each participant received a 16-week technology-based intervention that used the Fitbit physical activity tracker and website. The overall study goal was 150 minutes/week of moderate to vigorous intensity physical activity (MVPA) and 10,000 steps/day; however, goals were set individually for each participant and updated at Week 4 based on progress. Adherence data were collected by the Fitbit and aggregated by Fitabase. Participants also wore an ActiGraph GT3X+ accelerometer for 7 days prior to the intervention and again during Week 16. Results The median participant logged 10 hours or more/day of Fitbit wear on 95% of the 112 intervention days, with no significant decline in wear over the study period. Participants averaged 7540 (SD 2373) steps/day and 82 minutes/week (SD 43) of accumulated “fairly active” and “very active” minutes during the intervention. At Week 4, 80% (20/25) of women chose to maintain/increase their individual MVPA goal and 72% (18/25) of participants chose to maintain/increase their step goal. Physical activity levels were relatively stable after peaking at 3 weeks, with only small declines of 8% for steps (P=.06) and 14% for MVPA (P=.05) by 16 weeks. Conclusions These data indicate that a sophisticated, direct-to-consumer activity tracker encouraged high levels of self-monitoring that were sustained over 16 weeks. Further study is needed to determine how to motivate additional gains in physical activity and evaluate the long-term utility of the Fitbit tracker as part of a strategy for chronic disease prevention. Trial Registration Clinicaltrials.gov NCT01837147; http://clinicaltrials.gov/ct2/show/NCT01837147 (Archived by WebCite at http://www.webcitation.org/6d0VeQpvB)

Metabolism and Breast Cancer Risk: Frontiers in Research and Practice

Fifty years ago the causes of cancer were largely unknown. Since then, it has become clear that a strong relationship exists between obesity and many cancers, particularly postmenopausal breast cancer. A major challenge in understanding the link between obesity and cancer risk has been elucidating the biological basis underlying the association. Although this remains unresolved, the main candidate systems linking adiposity and cancer risk are insulin and the insulin-like growth factor-1 axis, endogenous reproductive hormones, and chronic inflammation. Our purpose is to provide a mechanistic overview of the hypothesized relationship between diet, physical activity, and obesity with breast cancer risk and progression. In addition, we will provide examples of recently funded randomized clinical trials examining metabolic risk factors in relation to breast cancer risk and survival. Additional research is warranted to validate the strength and consistency of the relationships among diet, these biomarkers, and breast cancer risk. As these relationships become clearer, future studies will be needed to develop effective intervention programs to prevent breast cancer and improve cancer prognosis by promoting a healthy lifestyle.

Web-based self-monitoring for weight loss among overweight/obese women at increased risk for breast cancer: the HELP pilot study.

Excess weight and physical inactivity are modifiable risk factors for breast cancer. Training women to use self‐help resources over the internet has potential for reducing intervention costs and enhancing maintenance.

Patterns of Weekday and Weekend Sedentary Behavior Among Older Adults

The purpose of this study was to compare estimates of sedentary time on weekdays vs. weekend days in older adults and determine if these patterns vary by measurement method. Older adults (N = 230, M = 83.5, SD = 6.5 years) living in retirement communities completed a questionnaire about sedentary behavior and wore an ActiGraph accelerometer for seven days. Participants engaged in 9.4 (SD = 1.5) hr per day of accelerometer-measured sedentary time, but self-reported engaging in 11.4 (SD = 4.9) hr per day. Men and older participants had more accelerometer-measured sedentary time than their counterparts. The difference between accelerometer-measured weekday and weekend sedentary time was nonsignificant. However, participants self-reported 1.1 hr per day more sedentary time on weekdays compared with weekend days. Findings suggest self-reported but not accelerometer-measured sedentary time should be investigated separately for weekdays and weekend days, and that self-reports may overestimate sedentary time in older adults.

The Accuracy of Heart Rate Monitoring by Some Wrist-Worn Activity Trackers

The Accuracy of Heart Rate Monitoring by Some Wrist-Worn Activity Trackers Background: Activity trackers may motivate persons to engage in healthy behaviors. They are also used in research and may help manage chronic conditions related to lifestyle (1). New devices have emerged as alternatives to traditional heart rate trackers, which require a separate chest strap. One type is a wrist-worn tracker with a light-emitting diode (LED). It measures the heart rate from tiny changes in skin blood volume by using light reflected from the skin. These new devices are unobtrusive and appropriate for continuous, long-term wear. Although previous studies have shown that they are generally accurate for measuring the number of steps a person takes, less is known about their accuracy when measuring heart rate (2–4). Objective: To determine the accuracy of the heart rate measured by LED-dependent, wrist-worn activity trackers. Methods: We studied 4 commercial, wrist-worn activity trackers that use LEDs to measure the heart rate. Study participants were 40 healthy consenting adults aged 30 to 65 years without cardiovascular conditions. For each participant, we placed 2 trackers on each wrist in random order by right versus left and by proximal versus distal location on the wrist. We then performed electrocardiography on the seated participant and measured the resting heart rate at 1-minute intervals for 10 minutes for each of the 4 trackers. Next, we measured the heart rate at 1-minute intervals for 10 minutes while the participant exercised on a treadmill at 65% of the maximum heart rate, which we calculated as 220 beats/min minus the participants age in years. We used Bland–Altman plots to compare the heart rates measured by electrocardiography with those measured by each of the wrist-worn trackers. We used the MethComp package in R (R Foundation for Statistical Computing) to create a mixed-effects model for linked replicate measurements to summarize these and other comparisons (5). Findings: Participants had a mean age of 49.3 years (SD, 9.5) and a mean body mass index of 25.1 kg/m (SD, 3.9), and 50% were women. The Figure plots individual comparisons among the heart rates measured by the electrocardiograph and trackers, and we summarized the amount of agreement with a statistic known as the limits of agreement (a narrower range is better) (Appendix Table, available at www.annals .org). For participants at rest, the agreement was best for the Fitbit Surge (Fitbit), which had the narrowest limits of agreement ( 5.1 to 4.5 beats/min), worst for the Basis Peak (Basis) ( 17.1 to 22.6 beats/min), and intermediate for the Fitbit Charge (Fitbit) ( 10.5 to 9.2 beats/min) and Mio Fuse (Mio Global) ( 7.8 to 9.9 beats/min). When participants exercised at 65% of the maximum heart rate, the limits of agreement were relatively poor for all the activity trackers (Mio Fuse, 22.5 to 26.0 beats/min; Basis Peak, 27.1 to 29.2 beats/min; Fitbit Surge, 34.8 to 39.0 beats/min; and Fitbit Charge, 41.0 to 36.0 beats/min). The repeatability coefficient determined how close 1 measurement was to another when using the same device in the same study participant under the same conditions (smaller values are better) (Appendix Table). For example, the repeatability coefficient for electrocardiography was 5.3 beats/min at rest and 9.1 beats/min during exercise. In comparison, the repeatability coefficient at rest was 4.2 beats/min for the Fitbit Surge, 9.3 beats/min for the Fitbit Charge, 10.9 beats/min for the Mio Fuse, and 19.3 beats/min for the Basis Peak. During exercise, the repeatability coefficient was 20.2 beats/min for the Basis Peak, 20.6 beats/min for the Fitbit Surge, 21.6 beats/min for the Fitbit Charge, and 23.7 beats/min for the Mio Fuse. Tracker location on the arm did not affect any of these measurements. Discussion: Some of the wrist-worn activity trackers that we studied measured values for heart rate that were similar to those measured by electrocardiography, and some measured similar values when the same device was used to repeat the measurements in the same study participant under the same conditions. However, all of the trackers performed better at rest than during moderately active exercise, performance at rest was better for some trackers than for others, and limited repeatability for each tracker caused more problems than poor agreement between each tracker and electrocardiography. Thus, although wrist-worn trackers may help monitor daily activity, more research is needed before we can confidently conclude that the monitoring feature for heart rate is sufficient to help clinicians advise their patients about health issues and conduct clinical trials that require a high level of accuracy and reliability for heart rate measurement.Background: Activity trackers may motivate persons to engage in healthy behaviors. They are also used in research and may help manage chronic conditions related to lifestyle (1). New devices have emerged as alternatives to traditional heart rate trackers, which require a separate chest strap. One type is a wrist-worn tracker with a light-emitting diode (LED). It measures the heart rate from tiny changes in skin blood volume by using light reflected from the skin. These new devices are unobtrusive and appropriate for continuous, long-term wear. Although previous studies have shown that they are generally accurate for measuring the number of steps a person takes, less is known about their accuracy when measuring heart rate (24). Objective: To determine the accuracy of the heart rate measured by LED-dependent, wrist-worn activity trackers. Methods: We studied 4 commercial, wrist-worn activity trackers that use LEDs to measure heart rate. Study participants were 40 healthy consenting adults aged 30 to 65 years without cardiovascular conditions. For each participant, we placed 2 trackers on each wrist in random order by right versus left and by proximal versus distal location on the wrist. We then performed electrocardiography on the seated participant and measured the resting heart rate at 1-minute intervals for 10 minutes for each of the 4 trackers. Next, we measured the heart rate at 1-minute intervals for 10 minutes while the participant exercised on a treadmill at 65% of the maximum heart rate, which we calculated as 220 beats/min minus the participants age in years. We used BlandAltman plots to compare the heart rates measured by electrocardiography with those measured by each of the wrist-worn trackers. We used the MethComp package in R (R Foundation for Statistical Computing) to create a mixed-effects model for linked replicate measurements to summarize these and other comparisons (5). Findings: Participants had a mean age of 49.3 years (SD, 9.5) and a mean body mass index of 25.1 kg/m2 (SD, 3.9), and 50% were women. The Figure plots individual comparisons among the heart rates measured by electrocardiography and the trackers, and we summarized the amount of agreement with a statistic known as the limits of agreement (a narrower range is better) (Appendix Table). For participants at rest, the agreement was best for the Fitbit Surge (Fitbit), which had the narrowest limits of agreement (5.1 to 4.5 beats/min), worst for the Basis Peak (Basis) (17.1 to 22.6 beats/min), and intermediate for the Fitbit Charge (Fitbit) (10.5 to 9.2 beats/min) and Mio Fuse (Mio Global) (7.8 to 9.9 beats/min). When participants exercised at 65% of the maximum heart rate, the limits of agreement were relatively poor for all the activity trackers (Mio Fuse, 22.5 to 26.0 beats/min; Basis Peak, 27.1 to 29.2 beats/min; Fitbit Surge, 34.8 to 39.0 beats/min; and Fitbit Charge, 41.0 to 36.0 beats/min). The repeatability coefficient determined how close 1 measurement was to another when using the same device in the same study participant under the same conditions (smaller values are better) (Appendix Table). For example, the repeatability coefficient for electrocardiogram was 5.3 beats/min at rest and 9.1 beats/min during exercise. In comparison, the repeatability coefficient at rest was 4.2 beats/min for the Fitbit Surge, 9.3 beats/min for the Fitbit Charge, 10.9 beats/min for the Mio Fuse, and 19.3 beats/min for the Basis Peak. During exercise, the repeatability coefficient was 20.2 beats/min for the Basis Peak, 20.6 beats/min for the Fitbit Surge, 21.6 beats/min for the Fitbit Charge, and 23.7 beats/min for the Mio Fuse. Tracker location on the arm did not affect any of these measurements. Figure. Comparison of heart rates measured by electrocardiography and each tracker, at rest and with moderately intense activity. Each point represents the heart rate measured at the same time by electrocardiography and a tracker. The x-axis describes the mean of the 2 values in beats per minute, and the y-axis describes the difference between the 2 values in beats per minute. Points identified by a unique combination of color and symbol are measurements in a single study participant. On the y-axis, points above 0 indicate that the tracker value was higher than that of electrocardiography and those below 0 indicate that the tracker value was lower. The black horizontal lines indicate the mean difference between the heart rate measured by the tracker and electrocardiography, which is known as the bias. (On this small scale, many of the black lines are so close to the value of 0 on the y-axis that appreciating the direction and size of the bias is difficult. See the Appendix Table for bias values.) The red horizontal lines indicate the limits of agreement and include approximately 95% of the differences. Appendix Table. Summary Measures Comparing Heart Rates Measured by Electrocardiograph Versus Each Tracker and Within Each Tracker* Discussion: Some of the wrist-worn activity trackers that we studied measured values for heart rate that were similar to those measured by electrocardiography, and some measured similar values when the same device was used to repeat the measurements in the same study participant under the same conditions. However, all of the trackers performed better at rest than during moderately active exercise, performance at rest was better for some trackers than for others, and limited repeatability for each tracker caused more problems than poor agreement between each tracker and electrocardiography. Thus, although wrist-worn trackers may help monitor daily activity, more research is needed before we can confidently conclude that the monitoring feature for heart rate is sufficient to help clinicians advise their patients about health issues and conduct clinical trials that require a high level of accuracy and reliability for heart rate measurement.

Accelerometer-derived physical activity and sedentary time by cancer type in the United States

The 2003–2004 and 2005–2006 cycles of the National Health and Nutrition Examination Survey (NHANES) were among the first population-level studies to incorporate objectively measured physical activity and sedentary behavior, allowing for greater understanding of these behaviors. However, there has yet to be a comprehensive examination of these data in cancer survivors, including short- and long-term survivors of all cancer types. Therefore, the purpose of this analysis was to use these data to describe activity behaviors in short- and long-term cancer survivors of various types. A secondary aim was to compare activity patterns of cancer survivors to that of the general population. Cancer survivors (n = 508) and age-matched individuals not diagnosed with cancer (n = 1,016) were identified from a subsample of adults with activity measured by accelerometer. Physical activity and sedentary behavior were summarized across cancer type and demographics; multivariate regression was used to evaluate differences between survivors and those not diagnosed with cancer. On average, cancer survivors were 61.4 (95% CI: 59.6, 63.2) years of age; 57% were female. Physical activity and sedentary behavior patterns varied by cancer diagnosis, demographic variables, and time since diagnosis. Survivors performed 307 min/day of light-intensity physical activity (95% CI: 295, 319), 16 min/day of moderate-vigorous intensity activity (95% CI: 14, 17); only 8% met physical activity recommendations. These individuals also reported 519 (CI: 506, 532) minutes of sedentary time, with 86 (CI: 84, 88) breaks in sedentary behavior per day. Compared to non-cancer survivors, after adjustment for potential confounders, survivors performed less light-intensity activity (P = 0.01), were more sedentary (P = 0.01), and took fewer breaks in sedentary time (P = 0.04), though there were no differences in any other activity variables. These results suggest that cancer survivors are insufficiently active. Relative to adults of similar age not diagnosed with cancer, they engage in more sedentary time with fewer breaks. As such, sedentary behavior and light-intensity activity may be important intervention targets, particularly for those for whom moderate-to-vigorous activity is not well accepted.

Recruitment strategies, design, and participant characteristics in a trial of weight-loss and metformin in breast cancer survivors

Weight loss and metformin are hypothesized to improve breast cancer outcomes; however the joint impacts of these treatments have not been investigated. Reach for Health is a randomized trial using a 2 × 2 factorial design to investigate the effects of weight loss and metformin on biomarkers associated with breast cancer prognosis among overweight/obese postmenopausal breast cancer survivors. This paper describes the trial recruitment strategies, design, and baseline sample characteristics. Participants were randomized in equal numbers to (1) placebo, (2) metformin, (3) weight loss intervention and placebo, or (4) weight-loss intervention and metformin. The lifestyle intervention was a personalized, telephone-based program targeting a 7% weight-loss in the intervention arm. The metformin dose was 1500 mg/day. The duration of the intervention was 6 months. Main outcomes were biomarkers representing 3 metabolic systems putatively related to breast cancer mortality: glucoregulation, inflammation, and sex hormones. Between August 2011 and May 2015, we randomized 333 breast cancer survivors. Mass mailings from the California Cancer Registry were the most successful recruitment strategy with over 25,000 letters sent at a cost of

Predicting Adherence of Adults to a 12-Month Exercise Intervention

191 per randomized participant. At baseline, higher levels of obesity were significantly associated with worse sleep disturbance and impairment scores, lower levels of physical activity and higher levels of sedentary behavior, hypertension, hypercholesterolemia, and lower quality of life (p<0.05 for all). These results illustrate the health burden of obesity. Results of this trial will provide mechanistic data on biological pathways and circulating biomarkers associated with lifestyle and pharmacologic interventions to improve breast cancer prognosis.