obese endometrial and breast cancer survivors using an interactive mobile application

Gynecologic Oncology 137 (2015) 508–515

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Gynecologic Oncology journal homepage: www.elsevier.com/locate/ygyno

Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application☆ Michele L. McCarroll a,c,⁎, Shannon Armbruster a, Rachael J. Pohle-Krauza a,b, Amy M. Lyzen a, Sarah Min c, David W. Nash c, G. Dante Roulette a,c, Stephen J. Andrews a,c, Vivian E. von Gruenigen a,c a b c

Summa Health System, Akron, OH, USA Youngstown State University, Youngstown, OH, USA Northeast Ohio Medical University (NEOMED), Rootstown, OH, USA

H I G H L I G H T S • The purpose of this study was to elicit weight-loss via mobile app lifestyle intervention. • The lifestyle intervention delivered via an app showed significant reductions in body weight. • Significant improvements were noted in the Weight Efficacy Life-Style Questionnaire.

a r t i c l e

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Article history: Received 14 July 2014 Received in revised form 19 December 2014 Accepted 20 December 2014 Available online 11 February 2015 Keywords: Endometrial cancer Lifestyle Weight loss

a b s t r a c t Objective. The study aimed to assess a one-month lifestyle intervention delivered via a web- and mobilebased weight-loss application (app) (LoseIt!) using a healthcare-provider interface. Methods. Early-stage overweight/obese (body mass index [BMI] ≥ 25 kg/m2) cancer survivors (CS) diagnosed in the past three years, and without recurrent disease were enrolled and received exercise and nutrition counseling using the LoseIt! app. Entry and exit quality of life (FACT-G) and Weight Efficacy Lifestyle Questionnaire (WEL) measuring self-efficacy were measured along with anthropometrics, daily food intake, and physical activity (PA) using the app. Results. Mean participant age was 58.4 ± 10.3 years (n = 50). Significant reductions (p b 0.0006) in anthropometrics were noted between pre- and post-intervention weight (105.0 ± 21.8 kg versus 98.6 ± 22.5 kg); BMI (34.9 ± 8.7 kg/m2 versus 33.9 ± 8.4 kg/m2); and waist circumference (108.1 ± 14.9 cm versus 103.7 ± 15.1 cm). A significant improvement in pre- and post-intervention total WEL score was noted (99.38 ± 41.8 versus 120.19 ± 47.1, p = 0.043). No significant differences were noted in FACT-G, macronutrient consumption, and PA patterns. Conclusion. These results indicate that a lifestyle intervention delivered via a web- and mobile-based weightloss app is a feasible option by which to elicit short-term reductions in weight. Though these results parallel the recent survivors of uterine cancer empowered by exercise and healthy diet (SUCCEED) trial, it is notable that they were achieved without encumbering significant cost and barrier-access issues (i.e. time, transportation, weather, parking, etc.). © 2014 Published by Elsevier Inc.

1. Introduction Overweight/obese endometrial (EC) and breast cancer (BC) survivors face numerous co-morbidities that are the leading cause of death

☆ Presentation at meeting: Oral Plenary Presentation Society of Gynecologic Oncology, Tampa, FL March 23, 2014. ⁎ Corresponding author at: Summa Center for Women's Health Research, Summa Health System, 525 East Market Street, Medical Building 2nd Floor, Akron, OH 44304, USA. E-mail address: [email protected] (M.L. McCarroll).

http://dx.doi.org/10.1016/j.ygyno.2014.12.025 0090-8258/© 2014 Published by Elsevier Inc.

that supersedes cancer diagnosis [1,2]. The majority of women in the United States of America (USA) are overweight or obese; therefore, not surprising that the majority of EC and BC survivors are overweight or obese which can interfere with a survivor's recovery and subsequent quality of life (QOL) [2–4]. Correspondingly, the majority of overweight/ obese cancer survivors are not meeting public health exercise and/or nutrition recommendations [2]. There is a critical need to determine what methods of weight-loss and risk factor reduction in this population are most effective. As a result, the strategic plan for the National Institutes of Health (NIH) Obesity Research calls for innovations to

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improve health outcomes in populations affected by obesity using theory based approaches such as Social Cognitive Theory (SCT) and Theory of Planned Behavior (TPB) [5–7]. Recent studies have demonstrated that increased obesity or body mass index (BMI) is associated with decreased survival [8,9]. The National Cancer Institute (NCI) emphasizes the need for interventions including weight, physical activity, nutrition counseling that contribute to survivorship, and methods to improve health outcomes and mortality for cancer survivors [10,11]. Improving body weight in EC and BC survivors may decrease morbidity and have the potential to improve overall survival since relative risk of death for obese EC women with a body mass index (BMI) 30–34 was 2.53, and BMI N40 was 6.25; the highest of all cancers [12,13]. Specifically, the risk of death from cardiovascular disease (CVD) related causes begin to exceed the risk from cancerrelated causes 3.5 years after EC diagnosis [14]. Thus, interventions that address dietary change and increased exercise together are necessary to elicit weight-loss and improve cardiovascular disease risk factors. Lifestyle interventions have been shown to improve wellness, self-efficacy, and QOL in cancer survivors [15–18]. Mobile health (mHealth) applications (aka. “apps”) using web- and/ or a mobile-based app are tools that have the potential to improve effective patient-provider communication, adherence to treatment and selfmanagement, especially in regards to weight-loss. In recent years, mobile phones have become a major conduit for communication and information. The uses of web- and mobile-based apps as a tool for health promotion and weight-loss have been successful in randomized trials [19,20]. The current study aimed to assess the feasibility of delivering a lifestyle intervention focusing on weight-loss using a multi-disciplinary team via popular mHealth app (LoseIt!) which offers both a website and mobile versions for users. The secondary objective was to assess characteristics of EC and BC survivors in regards to nutrient intake, physical activity (PA), self-efficacy, QOL, and correlation to patientprovider contact points. We hypothesized that EC and BC survivors would be willing to engage in a lifestyle program using mHealth technology and improve the principal co-morbidity of weight. 2. Methods 2.1. Study design and patient recruitment This study was a prospective intervention in 50 overweight/obese women with a history of Stage I or II (early) EC and/or BC. The comprehensive lifestyle program with emphasis on nutrition quality, physical activity, and improving eating self-efficacy was delivered using a “beta” healthcare provider version of LoseIt! (Boston, MA), a popular web- and mobile-based app for logging food intake and volitional exercise. The multi-disciplinary team delivering the comprehensive lifestyle program included a gynecologic oncologist (GO), resident physician (RP), research coordinator (RC), registered dietician nutritionist (RDN), and a certified clinical exercise specialist (CES) by the American College of Sports Medicine (ACSM) to elicit weight-loss in patients at a rate of 1.0–2 lb per week. Participants were monitored over the course of 4-weeks through the “beta” healthcare provider version of LoseIt!. This allowed for real-time interface to provide personalized feedback via notifications. Weight change was the primary outcome. Secondary outcomes included the number of motivational patient-provider feedback notifications to the participant in relation to weight-loss, self-efficacy towards weight-loss, minutes spent in physical activity, and nutritional content. Women included in the study were aged 18 to 75 years with histologically confirmed Stage I or II EC or BC within the previous three years and no evidence of recurrent disease. Participants needed access to a smartphone or Internet with unlimited data or Internet connection, body mass index (BMI) ≥ 25 kg/m2, medical clearance from the patient's oncologist, a performance status of 0–2, surgical treatment

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greater than 6-months prior to start of the study, and an endorsed desire to lose weight. Exclusion criteria included: non-English speaking, inability to read the consent form, lack of smartphone or Internet connection, inability to use the LoseIt! app, patients with severe depression, physical or cognitive deficits, pregnancy, plan to become pregnant, breastfeeding, surgical treatment less than 6-months prior to start of the study, and women who participated in a structured weight-loss program in the last 6-months. The cancer registry was used to identify eligible BC patients based upon time of diagnosis and stage of disease. Endometrial cancer patients were identified through the use of ICD-9 codes and eligibility was determined through subsequent chart review. All eligible women were contacted via telephone or in-person in the oncology office, screened using inclusion and exclusion criteria, and provided a baseline research appointment, if interested in participating (Fig. 1). 2.2. Intervention protocol Participants individually met with a member of the research team (RP, RC, or CES) for two-visits, one at baseline and another at exit, four-weeks later. The baseline visit lasted between 30 and 60 min, time depending on patient familiarity with web- or mobile-based apps and time to answer questions. Each woman received log-on information and instructions on how to use LoseIt!. Each participant passed a LoseIt! competency test, which encompassed logging exercise as well as looking up and logging foods from different venues including supermarket items, restaurant items, and home cooked meals. Participants were then instructed to use LoseIt! in order to log daily food choices, daily exercise type and duration, and daily body weight in the morning over the course of the next four, consecutive weeks. The nutritional component of our intervention focused on limiting the daily intake of carbohydrates to less than 70 g per day and increasing fiber intake to 30 g per day, and an approach that has been demonstrated as effective in other weight-loss programs [21]. Nutrition goals were conveyed to participants during the baseline visit. Here, participants were shown nutrition labels to aid their understanding of monitoring carbohydrate and fiber intake. No other restrictions in fat or calories were made. Participants were able to monitor their carbohydrate intake through the LoseIt! app. Women were encouraged to meet standard physical activity guidelines as indicated by the ACSM which emphasizes moderateintensity cardiorespiratory exercise training for ≥ 150 min per week, vigorous exercise ≥40 min per week, and resistance exercises for each of the major muscle groups [22]. Weight-loss goals were established at the baseline visit and included the loss of approximately 1–2 lb/week. The real-time feedback component provided by the multi-disciplinary team was based on the SCT whereas messaging focused on the patient to improve weight-loss self-efficacy by using verbal or typed persuasion, vicarious learning experiences, mastery, and social support [6,23,24]. Participants received motivational patient-provider feedback notifications in response to their individual input in the LoseIt! app for the four weeks study period. A patient-provider feedback notification is defined as a phone call, email message, and/or a push notification which was manually generated based on user input. Push notifications were automatically provided by LoseIt! technology to serve as motivation reminders to log meals at specified times throughout the day and reward compliance. User input regarding declining adherence to entering weight, dietary information, and exercise habits triggered the more frequent delivery of automatic push notifications. Patients were encouraged to log nutrition and exercise in real-time or at least once a day. The daily exercise and nutrition logs were monitored by the multi-disciplinary team (GO, RP, RC, RD, and CES). Participants were also provided a Bluetooth scale (Withings©, Boston, MA) to assist in weight tracking during the intervention period; however, the final analyses were completed from the validated scale in the clinical office (499KL Health O Meter ® Professional Digital Column Scale, Neosho, MO).

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Fig. 1. Study design.

2.3. Measures Patient demographic and clinical data were obtained at baseline. Patient-related outcome measures (PROS) were collected at baseline. Participant's height and weight were measured at baseline and exit in their street clothes, without shoes to the nearest 0.1 kg for weight and nearest 1/8th inch for height (499KL Health O Meter ® Professional Digital Column Scale, Neosho, MO). Waist circumference was obtained to the nearest centimeter (cm) and taken as the average of a series of three measurements, using a spring-loaded tape measure (Gulick Tape Measure, Perform Better). Patient-reported outcomes QOL and self-efficacy were measured by the Functional Assessment of Cancer Therapy-General (FACT-G) and the Weight Efficacy Life-Style Questionnaire (WEL), respectively.

The FACT-G (version 4.0) is a valid and reliable 27-item questionnaire evaluating physical well-being, social/family well-being, emotional well-being, and functional well-being [25]. The FACT-G provides a generic core of questions that are often combined with cancer sitespecific questionnaires [26]. Previous studies have set bench-marks for the FACT-G scores in the general and cancer populations; thus, relevant for our population in this study [27,28]. Self efficacy was measured with the WEL, a validated metric assessing five situational factors: negative emotions, availability, social pressure, physical discomfort, and positive activities [29]. Here, the participant is asked to rate their ability to resist eating (0 = not confident and 9 = very confident) in context of five situational scenarios, one relating to each domain, with a higher score indicative of greater self-control over nutritional decisions. The domain of negative emotions relating to self-efficacy included anxiety, depression,

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failure, and anger. WEL was administered at the baseline and four-week follow up/exit visit.

Table 1 Demographics. Variable

2.4. Feasibility outcomes Outcomes for the study included compliance with daily logging of nutrition and exercise information, recruitment age of the population using the technology, adherence to measurement completion from baseline to exit, and program evaluation. Non-compliance was addressed for women who failed to log for more than three-days in a row by telephoning them to address any logging concerns. Participants were asked to log nutrition and exercise during the four-weeks. Logging for N75% of days during study duration was necessary for continued inclusion. Individuals were removed from the final analysis if they stopped logging or did not log greater than three-weeks. Adherence was measured by attendance at baseline and exit sessions. 2.5. Statistical analysis Demographics and clinical outcomes were assessed. Statistical analyses were performed using SPSS 22.0 software to conduct a paired samples Student's t-test for changes between baseline and exit along with repeated measures analysis of variance (ANOVA) with Geisser– Greenhouse's correction for time points (TP) throughout the intervention and Bonferroni post-hoc analysis. Statistical testing was two-sided with p b 0.05 considered statistically significant. Pearson's productmoment correlations were used to evaluate the association between weight-loss and the number of healthcare provider contact points. 3. Results Baseline variables are presented in Table 1. Mean participant age was 58.4 ± 10.3 years. Forty-four of the fifty participants were white (88.0%), while six were African American (12%). Twenty-six (52%) and 19 (38%) participants had a diagnosis of endometrial and breast cancers, respectively, while five (10%) had history of both cancers. Education level was dispersed with 14 high school graduates (28%), 12 who attended college (24%), 12 with an associate or bachelor's degree (24%), 10 with a master's degree (20%), and two with a doctoral degree (4%). Fifty cancer survivors were enrolled and attended the baseline intervention visit. All patients received instruction on using the Loseit! app for logging exercise and nutrition information. Exit visits and participant adherence rate was 70% with a dropout rate of 30%, leaving n = 35 participants completing the logging and the follow-up visit requirements. There were no significant differences (age, race/ethnicity, and cancer type) at baseline in those participants that did not complete the study. Fig. 2 includes the primary anthropometric changes demonstrating significant reductions between pre- and post-intervention weight (97.3 ± 22.5 kg versus 95.0 ± 22.1 kg; p = 0.000), BMI (36.4 ± 8.1 kg/m2 versus 35.6 ± 8.0 kg/m2; p = 0.0000), and WC (106.6 ± 16.8 cm versus 103.4 ± 17.4 cm; p = 0.0006). In Table 2, secondary outcomes of WEL and FACT-G scores showed differing results. The mean WEL score significantly improved (p = 0.0432) from baseline 99.38 (± 41.8) to exit 120.19 (± 471). Quality of life scores via the FACT-G showed no significant changes (p = 0.152) from baseline 50.47 (±13.3) to exit 44.35 (±19.9). 3.1. Nutrient quality Nutrition data was calculated using the dietary logs from the LoseIt! healthcare provider interface. Weekly consumption of each macronutrient (carbohydrate, fat, protein, and fiber) along with calories were collected at baseline and averaged each week for four-weeks consisting of four recorded TP. Table 3 displays the mean (SD) consumption for

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Participant Demographics (n = 50)

Age (Years) Average (SD) Median Min–max Ethnicity — n (%) Non-Hispanic Hispanic Race — n (%) White Black American Indian/Alaskan Asian Cancer history — n (%) Breast Endometrial Both endometrial and breast Marital status — n (%) Single Married Widowed Divorced Education level — n (%) Junior high High school Some college Associate/bachelor Masters Doctoral Individual income level — n (%) b$40 k $41–60 k N $60 k Employment status — n (%) Full-time Part-time Retired Unemployed Unable to work

58.4 (10.3) 60 39–75 50 (100%) 0 (0%) 44 (88%) 6 (12%) 0 (0%) 0 (0.0%) 26 (52%) 19 (38%) 5 (10%) 5 (10%) 30 (60%) 7 (14%) 8 (16%) 0 (0%) 14 (28%) 12 (24%) 12 (24%) 10 (20%) 2 (4%) 16 (32%) 12 (24%) 22 (44%) 19 (38%) 14 (28%) 15 (30%) 0 (0%) 2 (4%)

macronutrients at each TP. There were no significant differences between the TP in each of the macronutrient categories: carbohydrates p = 0.726; fat p = 0.184; protein p = 0.226; fiber p = 0.281; and calories p = 0.263. However, noted changes in caloric intake were observed from the second TP to the other remaining TPs while carbohydrate, fat, protein, and fiber intake remained relatively steady. There were caloric changes following the restricted carbohydrate approach whereas patients usually reported to be less hungry due to the changes in protein, fiber, and fat intake. 3.2. Physical activity Physical activity data was calculated using the PA logs from the LoseIt! healthcare provider interface. Fig. 3 demonstrates PA minutes and calories participants engaged in at baseline and during the intervention period. Baseline revealed 77.5185 (± 156.6) kcals expended and 22.7 (± 44.0) minutes which indicated that no one was meeting the recommendations for physical activity at the commencement of the study. A significant increase (p = 0.001) in PA was noted from baseline to TP1 in caloric expenditure 1971.8 kcals (± 1105.4) and time 182.3 min (±196.6). No other significant differences (p = 1.00) were noted between TP in calories expended: TP2: 973.0 kcals (± 953.7); TP3: 826.2 kcals (±958.6); and TP4: 632.0 kcals (±909.8) and in time spent doing PA: TP2: 200.2 min (±216.1); TP3: 181.2 min (±244.0); and TP4: 127.0 min (±185.3). However, TP1 and TP2 to TP4 showed a trend towards a significance (p = 0.09) as PA levels were lower than

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Fig. 2. Physical activity (PA) patterns during intervention.

the other TP. The first and second TP were noted to have the highest level of minutes and caloric expenditure followed by TP three which all met the ACSM guidelines for quantity of PA at 150-minutes per week. The last TP of the intervention, PA minutes 127.0 min (±185.3) no longer met the ACSM guidelines for PA quantity recommendations.

3.3. Patient-provider interaction On average, each participant received 15.7 (±13.4) feedback and/or push notifications regarding their progress in logging. A moderate positive correlation between total WEL scores 120.19 (± 47.1) and the amount of healthcare provider feedback and push notifications (r = 0.349, p = 0.382) were noted. While not significant, the result supports the literature whereby the more patient “interactions” from a healthcare provider, the more successful the patient is in weight-loss. No other notable correlations were found.

4. Discussion The majority of EC and BC survivors are overweight/obese and not meeting recommendations for a healthy lifestyle. Treatment for early stage disease only marks the beginning of a journey for these overweight/obese cancer survivors due to the co-morbidities associated with their obesity. New technology and weight-loss interventions have a rich history of success. Previous studies involving technology-based interventions have shown that individually customized messages are

Table 2 Self-efficacy and quality of life measures. Study group Variable

Pre (n = 50)

Post (n = 35)

Mean difference, 95% CI

p-Value

WEL total score Mean (SD) 99.38 (±41.8) 120.19 (±47.1) 19.42 (0.6389, 38.21) 0.0432a FACT-G total score Mean (SD) 50.47 (±13.3) 44.35 (±19.9) −5.968 (−14.25, 2.316) 0.152 Weight Efficacy Lifestyle Questionnaire: WEL. Functional Assessment of Cancer Therapy-General: FACT-G. Confidence Interval: CI. Paired sample t-tests were performed on only those patients that followed-up. a = The mean difference is significant at the .05 level.

more effective than non-customized messages in improving selfefficacy and dietary behaviors [30,31]. As established in this study, mobile health apps can facilitate significant short-term weight-loss, decreased waist circumference, and increased self-efficacy in EC and BC survivors. Our study focused on limiting carbohydrate intake, an approach that is supported by the literature as a means to reduce obesity, risk factors for heart disease, and diabetes [32,33]. Novel interventions such as this study are essential to improve the health of these survivors, especially EC patients that have a history of not seeing their cancer diagnosis as a “sentinel event” for making lifestyle changes [32]. Even with the challenges of EC and BC survivors making lifestyle changes, several randomized controlled trials regarding lifestyle interventions have been successful [33,34]. von Gruenigen et al. (2011) examined the effects of a six-month face-to-face multi-disciplinary lifestyle intervention whereby the group lost significantly (~ 5% from baseline) more weight than the control group. A reduction in body weight of 5% has been shown to decrease medical co-morbidities [35–37]. The present study had an average weight-loss of 6% of initial body weight using a similar multi-disciplinary approach. Social Cognitive Theory was the foundation on how the multidisciplinary team interacted with study participants during the intervention to emphasize eating self-efficacy (SE). The counselor notes in the healthcare provider version of LoseIt! were the central communication points for all GO, RP, RC, RD, and CES providers. Messages were sent to specific providers within each discipline if the patient demonstrated or requested more focus in this arena (e.g. nutrition quality — RD; exercise tips — CES; concerns about fatigue — GO, RP; and technical issues related to logging — RC). The emphasis on personalized feedback received through the app as well as the patient's knowledge that healthcare professionals were watching their nutrition and exercise choices in real-time potentially impacted the participant's SE. Selfefficacy has been shown to be a predictor of behavior change in obese women, particularly weight loss [38]. Rejeski et al. showed the role of SE in weight reduction and the QOL [39]. McCarroll et al. (2013) revealed that an increase of the total WEL score by 4.49 points resulted in a loss of 1 unit of BMI using a face-to-face intervention (p b0.0001) [23]. Self-efficacy is not only important for initial weight-loss but has been shown to be an important factor in long-term weight management [40,41]. Although our study only lasted for four-weeks, interventions focused on weight-loss, SE, and PA will lead to success with continued weight-loss and weight maintenance. This concept is echoed by Basen-Engquist et al. as they investigated 100 EC survivors and

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Table 3 Nutrition quality parameters of food intake. Baseline Variable/statistic Carbohydrates (g) Mean (SD) Fats (g) Mean (SD) Protein (g) Mean (SD) Fiber (g) Mean (SD) Calories (kcals) Mean (SD)

120.629 (±69.3)

Time point

Time point

Time point

Time point

1

2

3

4

p-Valuea

132.6 (±118.7)

124.4 (±102.6)

123.9 (±120.0)

124.0 (±120.3)

0.726

44.1 (±23.4)

63.8 (±69.6)

60.6 (±61.2)

60.2 (±71.3)

58.2 (±60.0)

0.184

55.2 (±26.6)

74.4 (±97.5)

75.4 (±63.8)

69.6 (±73.5)

65.4 (±62.3)

0.226

11.0 (±6.3)

14.9 (±16.0)

12.8 (±12.0)

12.1 (±12.8)

13.3 (±13.6)

0.281

1022.6 (±494.4)

2134.3 (±4373.9)

1323.6 (±1062.5)

1275.9 (±1264.3)

1281.1 (±1130.6)

0.263

Grams = g. Standard Deviation = SD. Kilocalories = kcals. Time points = week in the intervention. Repeated measures analysis of variance (ANOVA) with Geisser–Greenhouse's correction. a = The mean difference is significant at the .05 level.

revealed their morning SE significantly (p b0.001) determined that day's exercise minutes [24]. The QOL outcomes were consistent with those found in the survivors of uterine cancer empowered by exercise and healthy diet (SUCCEED) randomized control trial; whereas, QOL did not change with weightloss outcomes [18,23]. However, several other studies conducting lifestyle interventions in female cancer survivors have reported QOL improvements after a group lifestyle intervention with associated weight-loss [42,43]. More research is needed to identify whether QOL is an indicator for weight-loss in female cancer survivors after a lifestyle intervention. Macronutrient intake was the focus of the dietary component of the intervention versus caloric restriction. The analysis revealed lower carbohydrate intake levels compared to the recommended daily values that recommends 30% of an individual's daily nutrition come from grains [44]. Weight-loss using simply energy restriction has not solved the obesity epidemic [29]. Furthermore, adopting a nutrition plan with variations in dietary macronutrient composition and functional foods are demonstrating success in weight-loss [45]. However, more in-

depth randomized clinical trials with longitudinal outcomes are needed to identify whether various eating plans using a mobile platform are more cost effective with improved PROS than face-to-face interventions. Since being in a weight-loss or exercise program for at least 6-months was an exclusion criterion for the study, most women reported to be sedentary. Physical activity levels peaked in the first week exceeding the recommended guidelines for PA; however, by the end of the fourth week, PA levels were below recommended guidelines for PA. The decline in PA could be the result of the Hawthorne effect in regards to being part of the study and the study nearing the end of completion [46]. Also, Forbes et al. found that the majority of cancer survivors do not meet PA guidelines and indicated that the Theory of Planned Behavior (TPB), accounted for 55% of variance in PA intentions for breast cancer survivors [47]. Also, a recent study by Speed-Andrews et al. indicated that the strongest correlate to PA was planning PA [48]. Thus, in future studies, emphasis on the TPB should be underscored to maintain PA levels throughout the entire intervention. As this was a feasibility study, there are several limitations to the study. Limitations include: short-term intervention period, small

Fig. 3. Physical activity (PA) patterns during intervention.

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sample size, a high standard of compliance for logging (N75%), differences in likability of using technology, ability of using technology, and lack of diversity in the sample size. We also acknowledge that this intervention approach will not be suitable for all cancer survivors and that lack of interest in weight-loss and understanding of current technology may deter some patients. One criticism prior to implementation of the study was that this generation of female EC and BC survivors would not use a mobile app or technology for a lifestyle intervention. Our study refutes this claim as many participants were able to use the technology with ease even for those women needing additional instruction for using the technology. Additionally, the patient has access to the multi-disciplinary team via LoseIt! which can be accessed at any point in time as well as from any device capable of access to the Internet or downloading apps. Lastly, compared to face-to-face methods, it is potentially be more convenient for both participants and providers as the app can be accessed at any point in time. Future studies involving mobile applications to deliver lifestyle interventions should be compared to “face-to-face” interventions for EC and BC survivors for cost comparisons, staff time/effort, and continued improvement of weight-loss. Also, incorporating a lifestyle intervention is a vital component of the Survivorship Care Plan (SCPs) released by the Society for Gynecologic Oncology (SGO) to ensure a cohesive approach to obesity treatment for cancer survivors [49–52]. More robust studies are merited to determine the sustainability of this intervention on weight-loss, health, QOL, SE, and ultimately to decrease co-morbidities to improve long-term survivorship of women with obesity-driven cancers. Conflict of interest statement None of the authors have any conflict of interest to disclose regarding the manuscript.

Funding support Summa Foundation. FitNow, Inc. provided the healthcare provider interface of the Lose It! application for the study. References [1] Weiderpass E, Persson I, Adami HO, Magnusson C, Lindgren A, Baron JA. Body size in different periods of life, diabetes mellitus, hypertension, and risk of postmenopausal endometrial cancer (Sweden). Cancer Causes Control 2000;11(2): 185–92. [2] Azrad M, Demark-Wahnefried W. The association between adiposity and breast cancer recurrence and survival: a review of the recent literature. Curr Nutr Rep Mar 2014;3(1):9–15. [3] Statistics related to overweight and obesity. Weight-control information network. U.S. Department of Health and Human Services, National Institutes of Health. http://www.win.niddk.nih.gov. [4] The National Cancer Institute. An Annual Plan and Budget Proposal for Fiscal Year. Available at http://plan.cancer.gov/; 2016. (Accessed June 7, 2009). [5] National Institutes of Health (NIH) Obesity Research. Retrieved on October 1, 2011. Last Modified: 3/30/2011 http://www.obesityresearch.nih.gov/About/strategicplan.aspx. [6] Bandura A. Social Foundations of Thought and Action. Englewood Cliffs: PrenticeHall; 1986. [7] Ajzen Icek. The theory of planned behavior. Organ Behav Hum Decis Process 1991; 50(2):179–211. [8] Arem H, Irwin ML. Obesity and endometrial cancer survival: a systematic review. Int J Obes May 2013;37(5):634–9 (Epub 2012 Jun 19). [9] Chia VM, Newcomb PA, Trentham-Dietz A, Hampton JM. Obesity, diabetes, and other factors in relation to survival after endometrial cancer diagnosis. Int J Gynecol Cancer 2007 Mar-Apr;17(2):441–6. http://dx.doi.org/10.1016/j.ygyno. 2012.04.013 (Epub 2012 Apr 13.). [10] National Cancer Institute. Research-Tested Intervention Program (RTIPs). Moving science into programs for people. Available at http://rtips.cancer.gov/rtips/. (accessed October 28, 2010). [11] The National Cancer Institute. Dialogue on Disparities. Available at http://www.cancer.gov/aboutnci/organization/crchd/crchd-blog. (Accessed March, 2015). [12] Weiderpass E, Persson I, Adami HO, Magnusson C, Lindgren A, Baron JA. Body size in different periods of life, diabetes mellitus, hypertension, and risk of postmenopausal endometrial cancer (Sweden). Cancer Causes Control 2000;11(2):185–92 (PMID: 10710204). [13] Hassan MK, Joshi AV, Madhavan SS, Amonkar MM. Obesity and health-related quality of life: a cross-sectional analysis of the US population. Int J Obes Relat

Metab Disord 2003;27(10):1227–32 (PMID: 14513071). [14] Ward KK, Shah NR, Saenz CC, McHale MT, Alvarez EA, Plaxe SC. Cardiovascular disease is the leading cause of death among endometrial cancer patients. Gynecol Oncol Aug 2012;126(2):176–9 (PMID: 22507532). [15] von Gruenigen VE, Courneya KS, Gibbons HE, Kavanagh MB, Waggoner SE, Lerner E. Feasibility and effectiveness of a lifestyle intervention program in obese endometrial cancer patients: a randomized trial. Gynecol Oncol 2008;109(1):19–26. [16] von Gruenigen VE, Waggoner SE, Frasure HE, Kavanagh MB, Janata JW, Rose PG, Courneya KS, Lerner E. Lifestyle challenges in endometrial cancer survivorship. Obstet Gynecol 2011;117(1):93–100. [17] Kavanagh M, Lerner E, Frasure H, Waggoner S, von Gruenigen V. Short-term effects of nutrition and exercise intervention on nutrient intake and body weight in obese endometrial cancer survivors: a pilot study. J Am Diet Assoc 2006;106(83):A8. [18] von Gruenigen V, Frasure H, Kavanagh MB, Janata J, Waggoner S, Rose P, Lerner E, Courneya KS. Survivors of uterine cancer empowered by exercise and healthy diet (SUCCEED): a randomized controlled trial. Gynecol Oncol Jun 2012;125(3):699–704. [19] Carter MC, Burley VJ, Nykjaer C, Cade Janet Elizabeth. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res Apr 2013;15(4):e32. [20] van Genugten L, van Empelen P, Boon B, Borsboom G, Visscher T, Oenema A. Results from an online computer-tailored weight management intervention for overweight adults: randomized controlled trial. J Med Internet Res Mar-Apr 2012;14(2):e44. [21] Dieli-Conwright CM, Sullivan-Halley J, Patel A, Press M, Malone K, Ursin G, Burkman R, Strom B, Simon M, Weiss L, Marchbanks P, Folger S, Spirtas R, Deapen D, Bernstein L. Does hormone therapy counter the beneficial effects of physical activity on breast cancer risk in postmenopausal women? Cancer Causes Control Mar 2011;22(3): 515–22. [22] Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, Nieman DC, Swain DP. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc July 2011;43(7):1334–59. [23] McCarroll ML, Armbruster S, Frasure HE, Gothard MD, Gil KM, Kavanagh MB, Waggoner S, von Gruenigen VE. Self-efficacy, quality of life, and weight loss in overweight/obese endometrial cancer survivors (SUCCEED): a randomized controlled trial. Gynecol Oncol Feb 2014;132(2):397–402. [24] Basen-Engquist K, Carmack CL, Li Y, Brown J, Jhingran A, Hughes DC, Perkins HY, Scruggs S, Harrison C, Baum G, Bodurka DC, Waters A. Social-cognitive theory predictors of exercise behavior in endometrial cancer survivors. Health Psychol February 25 2013. http://dx.doi.org/10.1037/a0031712 (Advance online publication). [25] Cella DF, Tulsky DS, Gray G, et al. The functional assessment of cancer therapy (FACT) scale: development and validation of the general measure. J Clin Oncol 1993;11(3):570–9. [26] Brucker PS, Yost K, Cashy J, et al. General population and cancer patient norms for the functional assessment of cancer therapy- general (FACT-G). Eval Health Prof 2005;2:192–211. [27] Janda M, DiSipio T, Hurst C, et al. The Queensland cancer risk study: general population norms for the functional assessment of cancer therapy-general (FACT-G). PsychoOncology 2009;18:606–16. [28] Webster K, Cella D, Yost K. The functional assessment of chronic illness therapy (FACIT) measurement system: properties, applications, and interpretation. Health Qual Life Outcomes 2003;1:79. [29] Clark MM, Abrams DB, Niaura RS. Self-efficacy in weight management. J Consult Clin Psychol 1991;59(5):739–44. [30] Kroeze W, Werkman A, Brug J. A systematic review of randomized trials on the effectiveness of computer-tailored education on physical activity and dietary behaviors. Ann Behav Med 2006;31(3):205–23. [31] Neville LM, O'Hara B, Milat AJ. Computer-tailored dietary behavior change interventions: a systematic review. Health Educ Res 2009;24(4):699–720. [32] von Gruenigen VE, Gil KM, Frasure HE, et al. Complementary medicine use, diet and exercise in endometrial cancer survivors. J Cancer Integr Med 2005;3:3–18. [33] Taubes Gary. Why We Get Fat. New York: Anchor Books–Random House, Inc.; 2011. [34] Smits A, Lopes A, Das N, Bekkers R, Galaal K. The impact of BMI on quality of life in obese endometrial cancer survivors: does size matter? Gynecol Oncol Jan 2014; 132(1):137–41. http://dx.doi.org/10.1016/j.ygyno.2013.11.018. [35] American Obesity Association. AOA fact sheets, women and obesity. http://www. obesity.org/subs/fastfacts/obesitywomen.html. [36] United States Department of Health and Human Services. The surgeons general's call to action to prevent and decrease overweight and obesity. http://www. surgeongeneral.gov/topics/obesity/calltoaction/fact_advice.html. [37] Weight Control Information Network: National Institute of Diabetes and Digestive Kidney Diseases (NIDDK). Healthy eating & physical activity across your lifespan, better health and you. http://win.niddk.nih.gov/publications/forlife.html. [38] Rogers LQ, Matevey C, Hopkins-Price P, Shah P, Dunnington G, Courneya KS. Exploring social cognitive theory constructs for promoting exercise among breast cancer patients. Cancer Nurs 2004;27(6):462–73. [39] Rejeski WJ, Mihalko SL, Ambrosius WT, Bearon LB, McClelland JW. Weight loss and self-regulatory eating efficacy in older adults: the cooperative lifestyle intervention program. J Gerontol B Psychol Sci Soc Sci 2011;66(3):279–86. [40] Rock C, McEligot A, Flatt S, Sobo E, Wilfey D, Jones V, Hollenbach K, Marx R. Eating pathology and obesity in women at risk for breast cancer recurrence. Int J Eat Disord 2000;27:172–9 (63). [41] Angelino AF, Treisman GJ. Major depression and demoralization in cancer patients: diagnostic and treatment considerations. Support Care Cancer 2001;9(5):344–9. [42] von Gruenigen VE, Gibbons HE, Kavanagh MB, Janata JW, Lerner E, Courneya KS. A randomized trial of a lifestyle intervention in obese endometrial cancer survivors:

M.L. McCarroll et al. / Gynecologic Oncology 137 (2015) 508–515

[43]

[44] [45] [46]

[47]

[48]

quality of life outcomes and mediators of behavior change. Health Qual Life Outcomes Feb 25 2009;7:17. Befort CA, Klemp JR, Austin HL, Perri MG, Schmitz KH, Sullivan DK, Fabian CJ. Outcomes of a weight loss intervention among rural breast cancer survivors. Breast Cancer Res Treat Apr 2012;132(2):631–9. Choose my plate. United States Department of Agriculture. Retrieved June 22, 2014 http://www.choosemyplate.gov/. Andersen CJ, Fernandez ML. Dietary strategies to reduce metabolic syndrome. Rev Endocr Metab Disord Sep 2013;14(3):241–54. McCarney R, Warner J, Iliffe S, van Haselen R, Griffin M, Fisher P. The Hawthorne effect: a randomised, controlled trial. BMC Med Res Methodol 2007;7: 30. Forbes CC, Blanchard CM, Mummery WK, Courneya KS. A comparison of physical activity correlates across breast, prostate and colorectal cancer survivors in Nova Scotia, Canada. Support Care Cancer Apr 2014;22(4):891–903. Speed-Andrews AE, Rhodes RE, Blanchard CM, Culos-Reed SN, Friedenreich CM, Belanger LJ, Courneya KS. Medical, demographic and social cognitive correlates of

[49]

[50]

[51]

[52]

515

physical activity in a population-based sample of colorectal cancer survivors. Eur J Cancer Care 2012;21:187–96. Nicolaije KA, Ezendam NP, Vos MC, Pijnenborg JM, van de Poll-Franse LV, Kruitwagen RF. Oncology providers' evaluation of the use of an automatically generated cancer survivorship care plan: longitudinal results from the ROGY Care trial. J Cancer Surviv Dec 2013;20. van de Poll-Franse LV, Nicolaije KA, Vos MC, Pijnenborg JM, Boll D, Husson O, et al. The impact of a cancer Survivorship Care Plan on gynecological cancer patient and health care provider reported outcomes (ROGY Care): study protocol for a pragmatic cluster randomized controlled trial. Trials Dec 5 2011;12:256. Forsythe LP, Parry C, Alfano CM, Kent EE, Leach CR, Haggstrom DA, et al. Use of survivorship care plans in the United States: associations with survivorship care. J Natl Cancer Inst Oct 16 2013;105(20):1579–87. Salani R. Survivorship planning in gynecologic cancer patients. Gynecol Oncol Aug 2013; 130(2):389–97. http://dx.doi.org/10.1016/j.ygyno.2013.05.022 (Epub 2013 May 23).