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- Copyright
- © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
- ISSN
- 1869-6716
- eISSN
- 1613-9860
- DOI
- 10.1093/tbm/iby002
- Publisher site
- See Article on Publisher Site
Abstract
Abstract Breast cancer survivors are at risk for poor health, with physical activity a possible treatment. Little research has examined how technology might promote breast cancer survivor physical activity or health. The aim of this study is to investigate the feasibility of employing a commercially available mobile health application- and social media-based health education intervention to improve breast cancer survivor physical activity or health. Ten breast cancer survivors (X̅ age = 45.80 ± 10.23 years; X̅ weight = 79.51 ± 20.85 kg) participated in this 10-week single-group pilot study from 2015 to 2016. Participants downloaded the MapMyFitness application, documented all physical activity with MapMyFitness, and were enrolled in a Social Cognitive Theory-based, Facebook-delivered health education intervention. Objectively measured physical activity, weight or body composition, cardiovascular fitness, psychosocial constructs, and quality of life indices were measured at baseline and 10 weeks. Intervention use and acceptability was evaluated during and following the intervention. Descriptive statistics were calculated for all study outcomes, with qualitative analyses performed regarding use and acceptability. At postintervention, average daily moderate-to-vigorous physical activity and steps increased by 2.6 min and 1,657, respectively, with notable decreases in weight (2.4 kg) and body fat percentage (2.3%). Physical activity–related social support and ability to engage in social roles or activity demonstrated the greatest improvements among all psychosocial and quality of life indices, respectively. Participants enjoyed the feedback and tracking features of MapMyFitness, with most finding the Facebook component helpful. All participants recommended the intervention for future use. Physical activity interventions combining commercially available mobile health applications and theoretically based social media–delivered health interventions may promote certain physiological, psychosocial, and quality of life outcomes among breast cancer survivors. Larger samples and randomized studies are warranted. Implications Practice: A combined smartphone application and social media–delivered health behavior change intervention may promote improved physiological, psychosocial, and quality of life outcomes among breast cancer survivors. Policy: Stakeholders within the medical community may seek to fund projects which attempt to utilize technology in the delivery of cost-effective and low-burden health behavior change interventions among clinical populations of which are integrated into the population’s lifestyle and align with the shift from a reactive healthcare approach to a preventive healthcare approach. Research: Future research should include a larger sample size and randomization against standard care or another form of technology-based treatment (e.g., internet-based intervention only). INTRODUCTION In 2017, approximately 260,000 women are expected to be diagnosed with invasive or in situ forms of breast cancer [1]. Fortunately, given improved treatment options, many women diagnosed with breast cancer survive. Recent figures state that 3.1 million individuals in the USA are currently living as breast cancer survivors—the majority of whom are women [1, 2]. However, breast cancer survivors often demonstrate lower quality of life (i.e., higher rates of depression or anxiety; decreased physical functioning), poorer physical health, and greater fatigue than women never diagnosed with breast cancer [3, 4]. Lifestyle changes such as increased physical activity participation have been recommended as important components of postbreast cancer treatment to decrease cancer-related symptomology (e.g., fatigue) and increase quality of life [4–10]. Given the omnipresence of mobile devices (e.g., smartphones and tablets), mobile device health applications may be a feasible manner to promote improved health and quality of life among breast cancer survivors. Presently, 165,000 mobile device health applications are available via the Apple App Store (for iOS-based smartphones or tablets) and Google Play (for Android-based smartphones or tablets), with 36 per cent focused on fitness and 17 and 12 per cent focused on stress and diet/nutrition, respectively [11]. Many mobile device health applications seek to improve user’s self-regulation of specific health behaviors to promote overall health. To date, several large scale, methodologically rigorous studies have been completed among nonclinical, but sedentary and/or overweight/obese samples regarding the use of mobile device health applications in the promotion of physical activity and other health outcomes, with positive or null findings observed [12–15]. Specifically, Carter et al. [12] and Harries et al. [13] indicated that researcher-developed mobile device health applications facilitated weight loss and increased walking behavior, respectively, whereas other studies observed no difference in the effectiveness of mobile device health application–based interventions in promoting physical activity or other positive health outcomes versus control/comparison [14,15]. The most recent meta-analysis regarding the effectiveness of mobile device health applications in promoting physical activity and weight loss confirms findings of the previously outlined studies. Specifically, this review indicated mobile device health application–based interventions to facilitate significant improvements in weight and body composition while promoting positive but nonsignificant increases in physical activity versus comparison/control [16]. Yet, the preceding meta-analysis and the latest narrative review [17] on mobile device health application–based interventions indicated a paucity of literature on cancer survivors—much less breast cancer survivors. Of the known literature employing a mobile device health application–based intervention among breast cancer survivors, Quintiliani and colleagues [18] found the use of a mobile device health application to track physical activity and dietary behaviors feasible in the health promotion of breast cancer survivors when combined with four motivational interview sessions. However, these authors suggested that future studies use accelerometry to assess physical activity and employ intervention components which provide health-related educational content to participants. Regardless of whether the preceding literature was conducted in nonclinical or clinical samples, four methodological limitations are worth noting. First, few studies used any theoretical framework in the development and implementation of the mobile device health application–based intervention—a significant limitation given theory’s ability to improve intervention effectiveness [19, 20]. Indeed, theories such as the Social Cognitive Theory [21, 22] allow researchers to systematically target and improve psychosocial determinants of physical activity and other health behaviors among participants, which contributes to enhanced intervention effectiveness. Second, no mobile device health application–based study has included quality of life as a variable of interest, despite the literature highlighting the need to promote quality of life among breast cancer survivors [4–10]. Third, preceding studies have not examined the possibility of integrating a theoretically backed, social media–delivered health education intervention as part of an intervention employing a mobile device health application. Notably, reviews have indicated that social media–delivered health interventions show promise for improving health [23]. Not only does social media promote social support among individuals going through similar life situations (e.g., breast cancer survivors), but social media can also facilitate the effective delivery of information—characteristics which researchers posit as making integration of a social media component within health interventions a viable manner by which to promote behavior change and improved health outcomes [23, 24]. As individuals accessing the internet most often due so to log on to social media [25], the use of a combined mobile device health application-based and theoretically backed, social media-delivered health education intervention might prove effective in the promotion of breast cancer survivors’ physical activity and health. Finally, most previous mobile device health application–based interventions have employed researcher-developed smartphone applications—as opposed to commercially available smartphone applications—which limits the generalizability of these studies. Given the preceding limitations, the current study investigated the feasibility of using a commercially available mobile device health application, MapMyFitness, employed in conjunction with a Social Cognitive Theory–based, Facebook-delivered health education intervention to improve physical activity and health in breast cancer survivors over 10 weeks. Specifically, the research examined this novel intervention methodology for the following feasibility outcomes: intervention use and acceptability; physiological changes (weight, body composition, cardiovascular fitness, physical activity, and energy expenditure); social cognitive theory–related psychosocial changes (self-efficacy, social support, enjoyment, barriers, and outcome expectancy); and quality of life changes (anxiety, depression, physical functioning, pain, and sleep). The preceding outcomes were deemed important to allow the researchers to discern if the proposed multifaceted intervention approach might represent a viable option for future health interventions among breast cancer survivors. METHODS Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed when drafting this manuscript [26]. Study design The current study was a 10-week feasibility study conducted from 2015 to 2016 including pre–post evaluations of physiological, psychosocial, and quality of life outcomes, with a midpoint survey and one postintervention survey assessing the use or acceptability of the intervention. All procedures performed with the participants were in accordance with the ethical standards of the institution and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards [27]. University approval and informed consent was obtained prior to any testing. Recruitment and inclusion/exclusion criteria Breast cancer survivors were recruited via posted flyers in the University’s Cancer Hospital and surrounding medical buildings, University-wide mass emails, online postings, and word of mouth. Interested breast cancer survivors then contacted the researchers for screening against the following inclusion criteria: (a) females of any race/ethnicity; (b) ≥21 years old; (c) previously diagnosed with stage 0-III breast cancer; (d) completed breast cancer treatment between 3 months and 5 years earlier with no recurrence; (e) Android or Apple smartphone owner; and (f) willing to complete the Physical Activity Readiness Questionnaire [28]. Exclusion criteria included the following: (a) currently undergoing breast cancer treatment; and (b) having any contraindications to physical activity engagement (e.g., medical condition and pacemaker implant) which might interfere with study participation as determined by the Physical Activity Readiness Questionnaire. Participants who successfully completed all study procedures received a $200 gift card. Measures Demographic/clinical variables The following variables were self-report in nature: age, race/ethnicity, place of birth, education level, health insurance type, marital status, employment status, annual income, breast cancer diagnosis stage, treatment type, months since diagnosis, Tamoxifen use, and months in remission. Use/Acceptability Assessed via midpoint and postintervention surveys which examined frequency/duration of MapMyFitness use, positive/negative features of MapMyFitness and application enjoyment, technical problems associated with MapMyFitness, and whether the breast cancer survivors found the Facebook-delivered health education intervention helpful. This survey contained a mix of opened-ended and Likert-type survey responses. Physical activity levels/energy expenditure Assessed via Actigraph GT3X+ accelerometers and evaluated as average daily minutes of sedentary behavior, light physical activity, and moderate-to-vigorous physical activity in addition to average daily energy expenditure in kilocalories. The Actigraph GT3X+ accelerometer has been validated among adults in free-living conditions [29]. Participants wore the accelerometer for 7 days (ensuring collection of at least two weekdays and one weekend day) as suggested for the field-based accelerometer research [30], with data analyzed using empirically based activity count cut points (sedentary behavior: 0–100; light physical activity: 101–2295; moderate-to-vigorous physical activity: ≥2296) [31]. Days in which the participant(s) had less than 10 hr/day of valid wear time were excluded from the analyses using wear time validation [30]. Anthropometry, body composition, and cardiovascular fitness Trained research assistants measured height to the nearest half-centimeter using a Seca stadiometer (Seca, Hamburg, Germany) after which weight and body fat percentage was evaluated via bioelectrical impedance using the Tanita BC-558 IRONMAN® Segmental Body Composition Monitor (Tanita, Tokyo, Japan) digital weight scale. Bioelectrical impedance has been validated among adults [32]. Finally, cardiovascular fitness was evaluated using the YMCA 3-min Step Test, with evaluation of heart rate occurring for 1 min immediately following the test via palpation of the radial artery [33]. Psychosocial variables Self-efficacy, social support, enjoyment, barriers, and outcome expectancy were assessed at baseline and postintervention using psychometrically sound questionnaires. Specifically, self-efficacy was examined by a 9-question measure developed by Rodgers and colleagues [34] wherein breast cancer survivors rated how confident they feel in certain exercise situations (e.g., “…exercise when you feel discomfort” or “…exercise when you lack energy”) on a percentage scale (0%: not confident at all to 100%: extremely confident in 10% increments). Social support was assessed using a 5-question measure adapted from the Patient-Centered Assessment and Counseling for Exercise questionnaire, with breast cancer survivors rating on a 5-point Likert-type scale (1: almost never to 5: almost always) how often significant others support/encourage them to be physically active [35]. Physical activity enjoyment was measured with a 5-question measure developed by Harter [36] wherein breast cancer survivors noted agreement with statements such as “Engaging in physical activity is the thing I like to do best” on a 5-point Likert-type scale (1: strongly disagree to 5: strongly agree). A 14-question measure evaluated participant’s physical activity barriers, with breast cancer survivors asked to rate the degree of agreement between their own barriers and hypothetical barriers on a 4-point Likert-type scale (1: strongly disagree to 4: strongly agree) [37]. Finally, outcome expectancy was assessed with a 9-question measure developed by Trost and colleagues [38] using a 5-point Likert scale (1: strongly disagree to 5: strongly agree), to evaluate breast cancer survivors’ agreement with responses originating from the stem “If I was to exercise on most days it would….” Sample responses were “give me more energy” and “help to control my weight.” Quality of life The Patient Reported Outcome Measurement Information System [39] was used in the assessment of physical functioning, anxiety, depression, fatigue, sleep, ability to participate in social roles/activities, and pain, with all but one response option comprised of choices presented on a 5-point Likert-type scale. To assess physical functioning, the survey employed items which asked breast cancer survivors about current physical abilities and how health limits these abilities (e.g., “Are you able to get in and out of bed?”). Breast cancer survivors then reported the degree of difficulty (i.e., “without any difficulty” to “unable to do”). The anxiety, depression, and ability to participate in social roles/activities scales required a 7-day recall of symptom frequency ranging from “never” to “always.” Notably, fatigue, sleep, and pain were also assessed via a 7-day recall of symptomology ranging in frequency from “not at all” to “very much”. Finally, overall sleep quality was assessed on a 5-point Likert-type scale ranging from “very poor” to “very good” while overall pain intensity was evaluated on a 0 to 10 scale (i.e., 0 = no pain to 10 = worst pain imaginable). The Patient Reported Outcome Measurement Information System has demonstrated acceptable measurement properties in clinical populations [40] and has been administered in cancer populations [41]. Procedures As stated above, interested breast cancer survivors contacted the researchers after which researchers screened against inclusion criteria. Eligible breast cancer survivors were then scheduled for baseline testing which included a battery of surveys to assess demographic/clinical characteristics, psychosocial constructs, and quality of life indices. Following completion of these surveys, breast cancer survivors completed height, weight, and body composition measurements, with cardiovascular fitness evaluated thereafter. To conclude baseline testing, breast cancer survivors were instructed on how to wear the accelerometer and use/access MapMyFitness and the study’s Facebook page, with breast cancer survivors encouraged to ask questions during the training session and at any time throughout the course of the intervention. Following baseline testing, accelerometers were then worn over the next 7 days above the right hip to assess habitual physical activity participation. Over the next 10 weeks, researchers posted twice-weekly Social Cognitive Theory-based health education tips (see Appendix) on the Facebook page geared toward improving physical activity-related self-efficacy, outcome expectancy, social support, and enjoyment while reducing physical activity-related barriers. For example, health education tips which sought to increase self-efficacy most often provided breast cancer survivors with empirically based facts regarding physical activity which not only facilitated increased confidence for incorporation of physical activity into their daily routine, but also served to decrease barriers (e.g., informing breast cancer survivors that incorporating three 10-min bouts of physical activity into their day five or more times weekly is enough to meet physical activity recommendations). Other health education tips presented facts regarding the potential beneficial outcomes (e.g., improved mood/quality of life, decreased weight/body fat percentage) of incorporating more physical activity into one’s lifestyle (i.e., increasing outcome expectancy) and encouraging ways to make physical activity participation more fun and/or social (e.g., promoting improved physical activity enjoyment and social support by encouraging breast cancer survivors to try several different physical activities with friends/family). Researchers encouraged breast cancer survivors to post and comment on the page as much or as little as they desired and to support one another toward individual health goals as they used MapMyFitness to self-regulate progress toward their goal(s), with constant reminders for the breast cancer survivors to contact the researchers with any questions/concerns. A midpoint survey was conducted regarding the use/acceptability of the intervention. Postintervention testing included all outcome assessments outlined previously in addition to a comprehensive postintervention use/acceptability survey. Following postintervention testing, breast cancer survivors again wore the accelerometer for a 7-day period. Data analysis Open-ended use/acceptability survey responses were transcribed verbatim and then thematically analyzed for commonalities by two researchers (Z.P. and N.Z.) [42]. Discrepancies between these analyses were to be resolved by a third researcher (J.E.L.). Notably, however, 100 per cent inter-rater agreement was observed when analyzing the responses to the two brief open-ended use/acceptability survey questions. Likert-type use/acceptability survey responses and all other quantitative data were descriptively analyzed using frequencies, means, and standard deviations, with all statistical analyses completed in SPSS 23.0 (IBM, Inc., Armonk, NY). No inferential statistics were performed given the small sample size and the pilot nature of the study. RESULTS A total of 12 breast cancer survivors were screened, with two found ineligible for participation as both were greater than 5 years postbreast cancer treatment. Thus, 10 breast cancer survivors (X̅age = 45.80 ± 10.23 years; X̅weight = 79.51 ± 20.85 kg) initially enrolled and began the study’s intervention, with the sample comprised of nine Caucasian breast cancer survivors and one Asian breast cancer survivor. Four breast cancer survivors were diagnosed with stage II breast cancer, followed in frequency by stage I (n = 3), stage III (n = 2), and stage 0 (n = 1). Four breast cancer survivors were still taking Tamoxifen. Average time in remission was 34.5 ± 25.2 months. Complete baseline demographic/clinical information is presented in Table 1. Notably, two breast cancer survivors were unable to finish the intervention due to changes in health status unrelated to the study. No missing data were present in the current study. Table 1 | Participant baseline demographic and clinical characteristics Demographic characteristics (N = 10) Averages (Mean ± SD) Frequencies (Counts) Age (years) 45.8 ± 10.2 Race/ethnicity • Caucasian 9 • Asian 1 Educational status • Some college/technical school 2 • College graduate 4 • Graduate school 4 Health insurance • Private 10 Employment status • Full time 8 • Part time 1 • Housewife 1 Marital status • Married 9 • Separated/divorced 1 Annual income (USD) • $50,000–74,999 2 • $75,000–99,999 1 • $100,000 or more 7 Clinical characteristics (N = 10) Time in remission 34.5 ± 25.2 Diagnosed breast cancer stage • Stage 0 1 • Stage 1 3 • Stage 2 4 • Stage 3 2 Treatment type • Surgery only 3 • Surgery + radiation 1 • Surgery + chemo 3 • Surgery + radiation + chemo 3 Tamoxifen use • Yes 4 • No 6 Follow-up care in past 12 months • Yes 9 • No 1 Clinical breast exam frequency • 0 times yearly 1 • Every 3–6 months 4 • Every 6–12 months 2 • Once yearly 3 Comorbidities • Yes 0 • No 10 Demographic characteristics (N = 10) Averages (Mean ± SD) Frequencies (Counts) Age (years) 45.8 ± 10.2 Race/ethnicity • Caucasian 9 • Asian 1 Educational status • Some college/technical school 2 • College graduate 4 • Graduate school 4 Health insurance • Private 10 Employment status • Full time 8 • Part time 1 • Housewife 1 Marital status • Married 9 • Separated/divorced 1 Annual income (USD) • $50,000–74,999 2 • $75,000–99,999 1 • $100,000 or more 7 Clinical characteristics (N = 10) Time in remission 34.5 ± 25.2 Diagnosed breast cancer stage • Stage 0 1 • Stage 1 3 • Stage 2 4 • Stage 3 2 Treatment type • Surgery only 3 • Surgery + radiation 1 • Surgery + chemo 3 • Surgery + radiation + chemo 3 Tamoxifen use • Yes 4 • No 6 Follow-up care in past 12 months • Yes 9 • No 1 Clinical breast exam frequency • 0 times yearly 1 • Every 3–6 months 4 • Every 6–12 months 2 • Once yearly 3 Comorbidities • Yes 0 • No 10 View Large Table 1 | Participant baseline demographic and clinical characteristics Demographic characteristics (N = 10) Averages (Mean ± SD) Frequencies (Counts) Age (years) 45.8 ± 10.2 Race/ethnicity • Caucasian 9 • Asian 1 Educational status • Some college/technical school 2 • College graduate 4 • Graduate school 4 Health insurance • Private 10 Employment status • Full time 8 • Part time 1 • Housewife 1 Marital status • Married 9 • Separated/divorced 1 Annual income (USD) • $50,000–74,999 2 • $75,000–99,999 1 • $100,000 or more 7 Clinical characteristics (N = 10) Time in remission 34.5 ± 25.2 Diagnosed breast cancer stage • Stage 0 1 • Stage 1 3 • Stage 2 4 • Stage 3 2 Treatment type • Surgery only 3 • Surgery + radiation 1 • Surgery + chemo 3 • Surgery + radiation + chemo 3 Tamoxifen use • Yes 4 • No 6 Follow-up care in past 12 months • Yes 9 • No 1 Clinical breast exam frequency • 0 times yearly 1 • Every 3–6 months 4 • Every 6–12 months 2 • Once yearly 3 Comorbidities • Yes 0 • No 10 Demographic characteristics (N = 10) Averages (Mean ± SD) Frequencies (Counts) Age (years) 45.8 ± 10.2 Race/ethnicity • Caucasian 9 • Asian 1 Educational status • Some college/technical school 2 • College graduate 4 • Graduate school 4 Health insurance • Private 10 Employment status • Full time 8 • Part time 1 • Housewife 1 Marital status • Married 9 • Separated/divorced 1 Annual income (USD) • $50,000–74,999 2 • $75,000–99,999 1 • $100,000 or more 7 Clinical characteristics (N = 10) Time in remission 34.5 ± 25.2 Diagnosed breast cancer stage • Stage 0 1 • Stage 1 3 • Stage 2 4 • Stage 3 2 Treatment type • Surgery only 3 • Surgery + radiation 1 • Surgery + chemo 3 • Surgery + radiation + chemo 3 Tamoxifen use • Yes 4 • No 6 Follow-up care in past 12 months • Yes 9 • No 1 Clinical breast exam frequency • 0 times yearly 1 • Every 3–6 months 4 • Every 6–12 months 2 • Once yearly 3 Comorbidities • Yes 0 • No 10 View Large Use/acceptability At midpoint, the average frequency breast cancer survivors reported using MapMyFitness per week was 3.75 times, with a slight increase in weekly frequency of use seen at postintervention (4.34 times). Average duration of weekly MapMyFitness use contrasted trends observed for weekly use frequency as breast cancer survivors reported using MapMyFitness for 39.7 min at midpoint and 35 min at postintervention. Notably, however, breast cancer survivors reported becoming more familiar and efficient at using the application. Breast cancer survivors stated positive features of MapMyFitness to be the encouraging prompts during physical activity, the ability to track caloric burn, convenience of the application’s tracking abilities, and the ability to keep an electronic record of workouts on the application. Nonetheless, the breast cancer survivors reported that it was sometimes difficult to find certain exercises on the application and that the application did not track strength exercises well. That said, four of the eight breast cancer survivors who completed the intervention remarked not perceiving any negative feature of MapMyFitness. Over the course of the intervention, only one breast cancer survivor reported any technical problem with MapMyFitness which came while trying to track distance with the application’s GPS function. In spite of this small technical difficulty, all breast cancer survivors recommended the use of the application in future interventions. As for breast cancer survivors’ use of the Facebook group wherein the twice-weekly social cognitive theory-related health education tips were provided, breast cancer survivors contributed 16 unique posts to this page. Of these 16 posts, 11 were statements regarding the workout(s) the breast cancer survivor(s) completed, four posts were uploads of MapMyFitness GPS tracking data showing the distance the breast cancer survivor(s) walked, ran, and/or biked, and one post was a picture of a breast cancer survivor paddle-boarding. Moreover, of the eight breast cancer survivors who completed the study, an average of 7.4 ± 0.9 of these ladies read each post (range = five to eight breast cancer survivors per posting). Finally, despite one breast cancer survivor not finding the Social Cognitive Theory-based health education tips helpful, all breast cancer survivors recommended the combined MapMyFitness and Facebook intervention for use in the future. Physiological outcomes Increases were observed for average daily moderate-to-vigorous physical activity duration (2.6-min increase), with decreased average daily light physical activity and sedentary behavior seen as well. Further, average daily step counts increased by 1,657 while daily activity-related energy expenditure increased by 87 kcal. Additionally, average weight loss during the 10-week intervention was 2.4 kg, with a concomitant decrease in average body fat percentage noted (2.3% decrease). However, no change was seen for cardiovascular fitness as assessed by post-Step Test heart rate. Table 2 presents full pre–post measurements for the preceding physiological characteristics. Table 2 | Baseline and postintervention physiological variable descriptive statistics Baseline Postintervention Average activity-related daily EE (calories) 421.0 ± 204.0 507.6 ± 191.9 Average daily steps 4,930 ± 1,376 6,587 ± 1,229 Average daily MVPA duration (min) 26.8 ± 13.8 29.4 ± 22.5 Average daily LPA duration (min) 94.9 ± 44.8 86.7 ± 64.7 Average daily SB duration (min) 493.7 ± 176.0 381.0 ± 265.3 Weight (kg) 79.5 ± 20.8 77.2 ± 21.7 Body fat percentage (%) 38.7 ± 8.4 36.3 ± 8.6 Step test (heart rate) 105.6 ± 23.2 105.6 ± 25.6 Baseline Postintervention Average activity-related daily EE (calories) 421.0 ± 204.0 507.6 ± 191.9 Average daily steps 4,930 ± 1,376 6,587 ± 1,229 Average daily MVPA duration (min) 26.8 ± 13.8 29.4 ± 22.5 Average daily LPA duration (min) 94.9 ± 44.8 86.7 ± 64.7 Average daily SB duration (min) 493.7 ± 176.0 381.0 ± 265.3 Weight (kg) 79.5 ± 20.8 77.2 ± 21.7 Body fat percentage (%) 38.7 ± 8.4 36.3 ± 8.6 Step test (heart rate) 105.6 ± 23.2 105.6 ± 25.6 All values Mean ± Standard Deviation. EE energy expenditure; LPA Light physical activity; MVPA moderate-to-vigorous physical activity; SB sedentary behavior. View Large Table 2 | Baseline and postintervention physiological variable descriptive statistics Baseline Postintervention Average activity-related daily EE (calories) 421.0 ± 204.0 507.6 ± 191.9 Average daily steps 4,930 ± 1,376 6,587 ± 1,229 Average daily MVPA duration (min) 26.8 ± 13.8 29.4 ± 22.5 Average daily LPA duration (min) 94.9 ± 44.8 86.7 ± 64.7 Average daily SB duration (min) 493.7 ± 176.0 381.0 ± 265.3 Weight (kg) 79.5 ± 20.8 77.2 ± 21.7 Body fat percentage (%) 38.7 ± 8.4 36.3 ± 8.6 Step test (heart rate) 105.6 ± 23.2 105.6 ± 25.6 Baseline Postintervention Average activity-related daily EE (calories) 421.0 ± 204.0 507.6 ± 191.9 Average daily steps 4,930 ± 1,376 6,587 ± 1,229 Average daily MVPA duration (min) 26.8 ± 13.8 29.4 ± 22.5 Average daily LPA duration (min) 94.9 ± 44.8 86.7 ± 64.7 Average daily SB duration (min) 493.7 ± 176.0 381.0 ± 265.3 Weight (kg) 79.5 ± 20.8 77.2 ± 21.7 Body fat percentage (%) 38.7 ± 8.4 36.3 ± 8.6 Step test (heart rate) 105.6 ± 23.2 105.6 ± 25.6 All values Mean ± Standard Deviation. EE energy expenditure; LPA Light physical activity; MVPA moderate-to-vigorous physical activity; SB sedentary behavior. View Large Psychosocial outcomes Improved physical activity-related self-efficacy (3% increase), social support (0.56-point increase), and enjoyment (0.15-point increase) were observed. No change was seen for physical activity-related barriers or outcome expectancy as most breast cancer survivors in the sample did not perceive many physical activity barriers and had neutral to positive outcome expectancy for physical activity participation prior to the intervention. Table 3 presents the pre–post measurements for all psychosocial variables. Table 3 | Baseline and postintervention psychosocial variable descriptive statistics Baseline Postintervention Self-efficacya 72.89 ± 29.70 75.28 ± 25.74 Social supportb 2.82 ± 0.92 3.38 ± 1.24 Enjoymentb 3.18 ± 0.90 3.33 ± 0.80 Barriersc 1.99 ± 0.26 1.97 ± 0.33 Outcome expectancyb 3.91 ± 0.47 3.90 ± 0.45 Baseline Postintervention Self-efficacya 72.89 ± 29.70 75.28 ± 25.74 Social supportb 2.82 ± 0.92 3.38 ± 1.24 Enjoymentb 3.18 ± 0.90 3.33 ± 0.80 Barriersc 1.99 ± 0.26 1.97 ± 0.33 Outcome expectancyb 3.91 ± 0.47 3.90 ± 0.45 All values Mean±Standard Deviation. aEvaluated on a percentage confidence scale from 0% (Not confident at all) to 100% (Extremely confident). bEvaluated on 5-point Likert-type scale. cEvaluated on 4-point Likert-type scale. View Large Table 3 | Baseline and postintervention psychosocial variable descriptive statistics Baseline Postintervention Self-efficacya 72.89 ± 29.70 75.28 ± 25.74 Social supportb 2.82 ± 0.92 3.38 ± 1.24 Enjoymentb 3.18 ± 0.90 3.33 ± 0.80 Barriersc 1.99 ± 0.26 1.97 ± 0.33 Outcome expectancyb 3.91 ± 0.47 3.90 ± 0.45 Baseline Postintervention Self-efficacya 72.89 ± 29.70 75.28 ± 25.74 Social supportb 2.82 ± 0.92 3.38 ± 1.24 Enjoymentb 3.18 ± 0.90 3.33 ± 0.80 Barriersc 1.99 ± 0.26 1.97 ± 0.33 Outcome expectancyb 3.91 ± 0.47 3.90 ± 0.45 All values Mean±Standard Deviation. aEvaluated on a percentage confidence scale from 0% (Not confident at all) to 100% (Extremely confident). bEvaluated on 5-point Likert-type scale. cEvaluated on 4-point Likert-type scale. View Large Quality of life indices The largest improvement among all quality of life indices was a 0.62-point decrease in perceived limitations inhibiting the ability of breast cancer survivors from engaging in social roles/activities. This decrease was equivalent to breast cancer survivors perceiving that they were “sometimes” limited from engaging in social roles/or activities to “rarely” or “never” limited—perhaps resulting from the decreased pain intensity perceived by the breast cancer survivors (0.29-point decrease). Breast cancer survivors also reported improved physical functioning (0.16-point decrease; signifying fewer limitations) and decreased anxiety (0.25-point decrease), depression (0.17-point decrease), and sleep disturbances (0.19-point decrease). Notably, no marked changes were seen for indices of fatigue. Table 4 presents complete information on pre–post measurements of quality of life indices. Table 4 | Baseline and postintervention quality of life variable descriptive statistics Baseline Postintervention Physical functioninga 1.22 ± 0.58 1.06 ± 0.12 Anxietya 1.69 ± 0.54 1.44 ± 0.53 Depressiona 1.33 ± 0.41 1.16 ± 0.19 Fatiguea 2.22 ± 1.07 2.19 ± 1.23 Sleep qualitya 4.00 ± 0.50 3.88 ± 0.64 Sleep disturbancesa 2.52 ± 0.29 2.33 ± 0.44 Social roles/activities limitationsa 2.00 ± 0.94 1.38 ± 0.38 Pain limitationsa 1.47 ± 0.59 1.47 ± 0.65 Pain intensityb 1.67 ± 1.22 1.38 ± 2.07 Baseline Postintervention Physical functioninga 1.22 ± 0.58 1.06 ± 0.12 Anxietya 1.69 ± 0.54 1.44 ± 0.53 Depressiona 1.33 ± 0.41 1.16 ± 0.19 Fatiguea 2.22 ± 1.07 2.19 ± 1.23 Sleep qualitya 4.00 ± 0.50 3.88 ± 0.64 Sleep disturbancesa 2.52 ± 0.29 2.33 ± 0.44 Social roles/activities limitationsa 2.00 ± 0.94 1.38 ± 0.38 Pain limitationsa 1.47 ± 0.59 1.47 ± 0.65 Pain intensityb 1.67 ± 1.22 1.38 ± 2.07 All values Mean ± Standard Deviation. aEvaluated on a 5-point Likert-type scale. bEvaluated on a scale from 0 (no pain) to 10 (worst pain imaginable). View Large Table 4 | Baseline and postintervention quality of life variable descriptive statistics Baseline Postintervention Physical functioninga 1.22 ± 0.58 1.06 ± 0.12 Anxietya 1.69 ± 0.54 1.44 ± 0.53 Depressiona 1.33 ± 0.41 1.16 ± 0.19 Fatiguea 2.22 ± 1.07 2.19 ± 1.23 Sleep qualitya 4.00 ± 0.50 3.88 ± 0.64 Sleep disturbancesa 2.52 ± 0.29 2.33 ± 0.44 Social roles/activities limitationsa 2.00 ± 0.94 1.38 ± 0.38 Pain limitationsa 1.47 ± 0.59 1.47 ± 0.65 Pain intensityb 1.67 ± 1.22 1.38 ± 2.07 Baseline Postintervention Physical functioninga 1.22 ± 0.58 1.06 ± 0.12 Anxietya 1.69 ± 0.54 1.44 ± 0.53 Depressiona 1.33 ± 0.41 1.16 ± 0.19 Fatiguea 2.22 ± 1.07 2.19 ± 1.23 Sleep qualitya 4.00 ± 0.50 3.88 ± 0.64 Sleep disturbancesa 2.52 ± 0.29 2.33 ± 0.44 Social roles/activities limitationsa 2.00 ± 0.94 1.38 ± 0.38 Pain limitationsa 1.47 ± 0.59 1.47 ± 0.65 Pain intensityb 1.67 ± 1.22 1.38 ± 2.07 All values Mean ± Standard Deviation. aEvaluated on a 5-point Likert-type scale. bEvaluated on a scale from 0 (no pain) to 10 (worst pain imaginable). View Large DISCUSSION As increased physical activity has been shown to decrease postbreast cancer treatment symptomology (e.g., fatigue) and promote quality of life [4–10], innovative and practical interventions are needed to aid breast cancer survivors in self-regulating physical activity behaviors. Given the multitude of commercially available mobile device health applications and the large number of individuals who currently use Facebook, a combined mobile device health application- and social media-based health education intervention may show promise in promoting breast cancer survivors’ physical activity and health. This study was the first known study to employ this type of combined intervention among breast cancer survivors. Findings suggested that a combined MapMyFitness- and Facebook-based theoretically backed health education intervention was feasible among this population and capable of promoting the improvement of several physiological, psychosocial, and quality of life outcomes over the course of 10 weeks. Regarding the use/acceptability of the intervention, breast cancer survivors were overwhelmingly positive. In fact, half of the breast cancer survivors reported no negative feature of the MapMyFitness application at study’s end, with seven out of the eight participants who finished the study liking the Social Cognitive Theory-based Facebook-delivered health education tip component of the intervention and all recommending the combined intervention for future use. Frequently, participants reported liking the self-regulatory and feedback aspects of MapMyFitness (e.g., the day-by-day physical activity diary which facilitated goal setting and the encouraging voice which was heard from the application when exercising, respectively). Indeed, technology-based intervention research has shown self-regulation and feedback to be beneficial in promoting physical activity [43]—perhaps explaining the positive findings of the current study. Of the breast cancer survivors reporting any problems with MapMyFitness, these problems centered around the application’s inability to adequately track strength exercises and difficulty finding certain physical activity choices to log with the application—both representing future improvements needed to applications of this type. Notable changes in physical activity and physiological outcomes included increased daily moderate-to-vigorous physical activity and steps and decreased daily sedentary behavior, body weight, and body fat percentage. A recent review indicated physical activity to be inversely associated with breast cancer recurrence [44]. As such, the fact breast cancer survivors increased daily moderate-to-vigorous physical activity and steps (approximate three-minute and 1,700-step increases, respectively) while decreasing engagement in sedentary behavior is promising. Additionally, over the course of the intervention, breast cancer survivors lost nearly 2.5 kg and 2.5 per cent body fat—a positive finding as research has suggested body weight and body fat percentage status can negatively associate with breast cancer prognosis following treatment [45]. Notably, cues and goal priming have been cited as possible intervention techniques by which to promote health behavior change [46, 47]. Therefore, the act of inputting, tracking, and setting physical activity–related goals with MapMyFitness in addition to receiving Facebook notifications twice weekly when a new health education tip (i.e., a health-related cue) was posted may have contributed to the beneficial physical activity- and physiology-related changes observed. Improvements in psychosocial and quality of life outcomes were also found as well. Social support had the largest increase among psychosocial variables—likely a result of the Facebook support and interaction the breast cancer survivors received throughout the study on the Facebook page which were ancillary to using MapMyFitness. Indeed, social support has been observed to be significantly correlated with physical activity in breast cancer survivors [48]. However, a recent review [23] regarding the ability of social media–based health interventions found mixed results regarding this technology’s ability to improve social support. Yet, the studies included in the preceding review were not conducted among breast cancer survivors, a population more close-knit resulting from their past breast cancer diagnosis. To elaborate, many breast cancer survivors in this study knew one another from various breast cancer survivor support groups which therefore might explain the improvements in physical activity–related social support from baseline to postintervention—congruent with research stating social media–based health interventions may be most effective at increasing social support and other health outcomes when participants know one another [49]. Among quality of life outcomes, participants reported marked improvements in the ability to participate in social roles and activities. Improvements in the preceding variable mirrored, and are likely attributable to, the improvements observed for physical functioning and pain intensity. Indeed, literature has indicated moderately active female breast cancer survivors who maintained physical activity levels within 3.5 MET-hr/week of prediagnosis levels during the first year postdiagnosis had significantly better physical functioning than those who did not maintain prediagnosis physical activity levels [50], with other literature noting the beneficial effects of increased physical activity on pain among breast cancer survivors [51]. Notably, depression and anxiety also decreased over the course of the intervention—possibly due to increased physical activity. However, this phenomenon deserves more investigation as literature on the affect physical activity has on depression and anxiety symptomology in breast cancer survivors is mixed, as some studies have found physical activity to not significantly decrease this symptomology [3], with still other studies indicating physical activity to have a marginally significant positive relationship [10] or significant, albeit indirect, beneficial relationship with this symptomology [4, 7]. Strengths of this study include the investigation of a population understudied with regard to physical activity interventions, breast cancer survivors; the combined mobile device health application–based and social media–delivered health education intervention; the use of the Social Cognitive Theory in the development and implementation of the intervention; and the assessment of physiological, psychosocial, and quality of life outcomes. The preceding strengths and associated findings of this study have real-world implications as it pertains to the prevention of breast cancer recurrence among breast cancer survivors. For example, healthcare is currently moving from a reactive paradigm (i.e., treatment of disease following occurrence) to a preventive/proactive paradigm (i.e., preventing disease occurrence or recurrence in at-risk individuals). Aligned with this paradigm shift, researchers have forwarded the concept of “systems medicine” [52]. Systems medicine employs big data acquired using modern technology (e.g., online social media groups, mobile device health applications, and wearable technology) in the collection and storage of patient health behavior data (e.g., daily step counts/energy expenditure, sleep habits) to facilitate effective health treatment and delivery [53,54]. With this in mind, health professionals could create social media–based support groups for breast cancer survivors on which health professionals could provide healthcare support and education to this population—possibly improving recovery outcomes. These social media–based support groups may also promote improved health outcomes by way of social modeling. Indeed, much like in the current study, a social media–based group where breast cancer survivors post about tough workout(s) they complete and/or upload GPS tracking data and pictures of their physical activity may increase other breast cancer survivors’ perceptions of social support and, further, their confidence in being about to participate in physical activity (i.e., increased self-efficacy). Moreover, by integrating technology such as mobile device health applications (e.g., MapMyFitness) to monitor health metrics such as habitual physical activity and energy expenditure, health professionals could provide personalized and timely feedback to breast cancer survivors regarding potential changes needed to these health behaviors. This approach would be cost- and time-efficient for both parties as this technology-based healthcare method does not require travel to a clinic on the part of breast cancer survivors nor scheduling considerations on the part of health professionals—reducing overall burden. Finally, as increased burden on health intervention participants can decrease health intervention effectiveness due to reductions in participant adherence [55], there is potential for the aforementioned low-burden technology-based health intervention approach to result in higher intervention adherence and better health outcomes. Nonetheless, while this approach is feasible, researchers have noted that patients will have to think critically about the trade-off between maintaining the privacy of personal information regarding health behaviors (e.g., habitual physical activity) collected via technology such as mobile device health applications and the potential benefit sharing this information with health professionals may facilitate [56]. Despite the notable future implications of this study, limitations are present. First, the study did not include a control group, impeding any ability to draw initial intervention effectiveness conclusions. Second, the study’s sample size rendered inferential statistics ill-advised. Although the study sought only to provide a descriptive analysis, future studies should include a randomized-controlled study design and a larger sample to address the preceding two limitations. Third, although the current study provided breast cancer survivors with practical Social Cognitive Theory-based health education tips, the intervention did not provide participants with a structured workout program or any nutrition education component—possibly limiting intervention effectiveness. Inclusion of a fitness program and nutrition education component should be considered in future investigations as research indicates combined physical activity and nutritional interventions are often more effective than interventions focusing exclusively on one of the preceding behaviors [57]. Finally, the fact MapMyFitness required breast cancer survivors to open the application to begin tracking or logging physical activity may have increased participants’ perceived intervention burden and led to lower utilization of the application. Lower application use may have limited the ability of this application to promote self-regulation of physical activity behaviors. Future studies may consider the use of smartwatches and these devices’ associated smartphone applications given these devices’ “always on” functionality and thus no requirement on the part of participants to initiate the tracking or logging of physical activity behaviors. Combined with a social media-based health education intervention such as the one employed in the current study, smartwatches may hold great promise in promoting participation in health behaviors among clinical populations such as breast cancer survivors. CONCLUSIONS Currently, the shift from reactive healthcare (i.e., treating diseases/conditions after onset) to preventive/proactive healthcare (i.e., treating diseases/conditions prior to onset or recurrence) is placing mobile device health applications at the forefront of a healthcare revolution. Indeed, employing commercially available apps such as MapMyFitness in a population such as breast cancer survivors may aid in preventing breast cancer recurrence as individuals begin to learn how to self-regulate engagement in health behaviors such as physical activity—similar to recent recommendations made by the President’s Cancer Panel Report on Connected Health regarding the promotion of physical activity among cancer survivors [58]. In combination with a theoretically based, social media–based health education intervention such as the one employed successfully in the current study, health professionals have a high-tech blueprint by which to promote the patient health which is low burden for both parties. Findings from the current study indicate the acceptability and feasibility of a technology-based intervention of this type. Acknowledgments: This research was funded by the University of Minnesota’s Multicultural Research Award. Ethical Approval: All procedures performed with the participants were in accordance with the ethical standards of the institution and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Notably, this article does not contain any studies with animals performed by any of the authors. Compliance with Ethical Standards: Conflict of Interest: Zachary Pope, Nan Zeng, Jung Eun Lee, and Zan Gao have no conflict of interest to report. Authors’ Contributions: While conducting this study, Zachary Pope played a role in developing the idea, conducting data collection and analysis, and writing the manuscript. Jung Eun Lee played a role in conducting data collection and editing the manuscript. Nan Zeng played a role in data collection/analysis. Zan Gao oversaw the study and edited the manuscript. Primary Data: This manuscript represents results of original work. Specifically, (i) the findings of this study have not been previously published and the manuscript is not being simultaneously submitted elsewhere; (ii) the data have not been previously reported elsewhere; (iii) the authors have full control of all primary data and agree to allow the Translational Behavioral Medicine to review our data if requested; (iv) the authors have declared all funding sources; (v) the authors have declared any actual or potential conflicts of interest; (vi) the authors have followed all ethical standards regarding the treatment of human experimental participants in addition to following proper informed consent procedures; and (vii) the authors have included all necessary acknowledgements. If accepted for publication, the manuscript will not be published elsewhere. Informed Consent: University approval and informed consent was obtained prior to any testing. APPENDIX: TWICE-WEEKLY TIPS The following tips were provided twice a week (once on Monday and once on Thursday) for 10 weeks to all study participants. Psychosocial constructs targeted with each posting are listed in bold italics following each tip. Week 1 Monday: Did you know the American College of Sports Medicine states that the recommended 30 min a day of physical activity can be accumulated in 3 ten-minute bouts?! If you have an extra ten minutes, try going for a brisk walk or bike ride. Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Decreasing Barriers. Thursday: Taking the stairs instead of the elevator is a great way to interject more physical activity into your day! Trying doing so today! Social-cognitive belief(s) targeted: Promoting Self-Efficacy. Week 2 Monday: Did you know that burning an extra 100 calories per day or reducing calorie intake by 100 calories a day can result in weight loss of over 10 pounds in one year?! This can be as simple as a one mile walk after dinner or foregoing a can of soda in exchange for water during the day. Baby steps! Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Improving Outcome Expectancy, Decreasing Barriers. Thursday: Travelling and not confident about your ability to work out? Try one of the two things. First, ask the hotel if they have a fitness room or exclusive access to a nearby gym. Note the hours of operation for either and build at least 30 min into each day of your vacation to get a short workout in. Second, if you find the hotel does not have a fitness center or access to a nearby gym, explore nearby walking trails or maybe even walk laps around the hotel for the hotel for 30 min. Still want to lift? Use your luggage as weights for resistance training. Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Decreasing Barriers. Week 3 Monday: Social support is crucial to continued physical activity engagement. Thus, try to find a good friend willing to go “sweat it out” with you two or three times per week. Although your friend may not be able to join you for every workout, she/he may be able to provide company on the days where you are lacking the motivation to get to the gym. Social-cognitive belief(s) targeted: Enhancing Social Support. Thursday: School work or your job stressing you out? Lucky for you, physical activity releases stress-reducing hormones such as endorphins into the bloodstream even during short 10-min bouts of exercise. All the more reason to set aside a little time each day to be physically active! Social-cognitive belief(s) targeted: Improving Outcome Expectancy. Week 4 Monday: We schedule dentist appointments, haircuts, and meals. Why not schedule physical activity into your day? Make exercise a part of your daily schedule at a time where physical activity can help you get ready for the day (for morning exercisers) or unwind from the day (for evening exercisers). Treat this scheduled exercise as important and much needed “you” time—allowing you to be better for those around you. Furthermore, you can be confident that you are going to get your workout in when viewing it in this manner. Social-cognitive belief(s) targeted: Promoting Self-Efficacy. Thursday: Worried about your motivation to exercise in the morning or that you will forget your exercise clothes as you head out the door? Place your workout clothes/shoes in front of the door you exit each morning. In this manner, you will have to move the clothes prior to opening the door, acting as a reminder to be a little more physically active during the day or to not skip the gym in the evening. Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Decreasing Barriers. Week 5 Monday: Setting realistic, yet challenging goals can be a great way to sustain motivation to remain physically active. For example, set the goal of increasing the distance you walk by one-quarter mile each week until you reach three miles. At three miles, consider a walk-run pattern wherein you walk for 1 min and then jog for 1 min. You can repeat this pattern for a designated amount of time (e.g., 30 min) or for a certain distance (e.g., 3 miles). As it gets easier, gradually introduce more jogging and less walking. Social-cognitive belief(s) targeted: Promoting Self-Efficacy. Thursday: Blisters and chaffing caused by the wrong workout clothes/shoes can be a serious threat to continued participation in physical activity. Thus, consider an investment in proper (and good-looking) athletic clothes and shoes. Doing so may just help increase your motivation to be physically active while also decreasing the likelihood of experiencing painful skin irritation! Social-cognitive belief(s) targeted: Decreasing Barriers, Improving Outcome Expectancy. Week 6 Monday: Again, social support is important to physical activity participation. If you are engaging in a new physical activity program, perhaps tell your family and close friends about your new program. Stating your plans out loud not only increases the likelihood that you will continue this physical activity program, but your family and friends will surely ask about it at some point in the future meaning you may be held accountable for sticking to this program! Social-cognitive belief(s) targeted: Enhancing Social Support. Thursday: Water is vital. Although recommendations put forth, many numerous health organizations such as the Centers for Disease Control and Prevention and the American College of Sports Medicine state 6–8 cups a day is needed, this does not always hold true for all individuals. Therefore, if you are not confident in your ability to drink enough water, buy a good water bottle that can be used at the gym and during the day and drink each time you feel thirsty. Furthermore, drinking consistently throughout the day can help decrease your appetite and improve digestion, helping you lose weight. Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Improving Outcome Expectancy. Week 7 Monday: One of the best ways to ensure you continue to participate in a physical activity program is finding an activity you enjoy. If you are an individual who prefers to workout alone, perhaps swimming, running, or biking suits you the best. For individuals who prefer to workout with others, consider group exercise classes such as yoga or step aerobics such as Zumba or dancing. Additionally, do not be afraid to mix and match different types of exercise! If you are going to sweat you might as well be doing something that interests you! Social-cognitive belief(s) targeted: Increasing Enjoyment, Enhancing Social Support. Thursday: Face it, you have put in the time in the gym and, perhaps, even lost a little weight in the process. Consider a monthly reward. This reward can be anything from the purchase of that one shirt that you have been dying to add to your wardrobe to a night out with your significant other. Yet, whatever the reward is, make sure that it does not derail your quest for better health and participation in physical activity. Social-cognitive belief(s) targeted: Improving Outcome Expectancy. Week 8 Monday: Did you know that physical activity has been linked to greater feelings of well-being? Well, it has! Physical activity, even in bouts as short as 10 min can increase “good” hormones within the body such as endorphins. Indeed, release of these hormones on a regular basis as a result of continued physical activity participation has been found to lower likelihood of diseases such as depression and increase self-esteem. Social-cognitive belief(s) targeted: Improving Outcome Expectancy. Thursday: Sitting at a desk all day is not healthy. Consider setting your watch or phone to beep every 30 min during the day at which point you HAVE to get up and go for a 5 min walk or engage in some light stretching. Not only will this give your body a much needed boost, it might also give your mind the break in concentration it needs and allow you to be more productive while doing homework or completing work for your job. Social-cognitive belief(s) targeted: Improving Outcome Expectancy. Week 9 Monday: Ensure you do not pull a muscle during your workout. Start each exercise session with some light exercise such as jumping jacks, brisk walking, or light biking/weight lifting. This will allow you to heat your core temperature up to a point wherein physical activity engagement does not pose much risk to your body. Social-cognitive belief(s) targeted: Decreasing Barriers. Thursday: Static stretching prior to exercise may actually decrease workout performance and, due to the fact the muscles are not warm, is not increasing flexibility. However, engaging in static stretching after engaging in physical activity is one of the best ways to increase flexibility and may even help with delayed-onset muscle soreness. Ensure your stretching routine is sufficient to stretch all major muscles of the upper and lower body. Furthermore, only stretch to the point of slight discomfort, not pain, and hold the stretch for 20 s. Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Improving Outcome Expectancy. Week 10 Monday: Sleep may not seem important to physical activity, but it is. Not getting enough sleep is a sure-fire way to experience decreases in motivation for engaging in physical activity. Therefore, aim for 6 to 8 hr of sleep each night. Moreover, try to cut out all screen time in the 10 to 15 min prior to going to sleep as watching TV or using your computer/smartphone to cruise social media or read the news decreases the body’s ability to produce melatonin, a key sleep hormone. Finally, consider removing any TV from the bedroom and/or not playing music while you sleep as this background noise can actually decrease sleep quality. Making these small changes can go a long way in helping you feel more rested and ready to engage in physical activity (and life) the next day! Social-cognitive belief(s) targeted: Promoting Self-Efficacy, Decreasing Barriers. Thursday: Crank those tunes! If music is something you know will get you motivated to engage in your workout, consider investing in some athletic-oriented headphones that are sweat resistant and capable of staying in the ear during exercise. Keeping an up-to-date playlist of your favorite songs will allow you to have a better workout. This is especially true of exercisers preferring to exercise indoors. However, if exercising outdoors, consider leaving the headphones at home and enjoying the scenery as use of headphones while exercising outdoors can put you in danger. Social-cognitive belief(s) targeted: Increasing Enjoyment. References 1. American Cancer Society . How Common Is Breast Cancer? 2017 ; Available from https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html. Accessibility verified on February 8, 2017 . 2. Howlader N , Noone A , Krapcho M et al. SEER Cancer Statistics Review . Bethesda, MD : National Cancer Institute ; 2016 . Available from https://seer.cancer.gov/csr/1975_2013/. Accessibility verified on February 8, 2017 . 3. Jadoon NA , Munir W , Shahzad MA , Choudhry ZS . Assessment of depression and anxiety in adult cancer outpatients: A cross-sectional study . bmc Cancer . 2010 ; 10 : 594 . doi: 10.1186/1471-2407-10-594 Google Scholar Crossref Search ADS PubMed 4. Phillips S , McAuley E . Physical activity, quality of life, and survivorship in breast cancer survivors: a brief review . CML – Breast Cancer . 2012 ; 24 ( 3 ): 77 – 84 . 5. Harrison SA , Hayes SC , Newman B . Age-related differences in exercise and quality of life among breast cancer survivors . Med Sci Sports Exerc . 2010 ; 42 ( 1 ): 67 – 74 . Google Scholar Crossref Search ADS PubMed 6. Mack DE , Meldrum LS , Wilson PM , Sabiston CM . Physical activity and psychological health in breast cancer survivors: An application of basic psychological needs theory . Appl Psychol Health Well Being . 2013 ; 5 ( 3 ): 369 – 388 . Google Scholar Crossref Search ADS PubMed 7. Phillips SM , McAuley E . Physical activity and fatigue in breast cancer survivors: A panel model examining the role of self-efficacy and depression . Cancer Epidemiol Biomarkers Prev . 2013 ; 22 ( 5 ): 773 – 781 . Google Scholar Crossref Search ADS PubMed 8. Phillips SM , McAuley E . Physical activity and quality of life in breast cancer survivors: The role of self-efficacy and health status . Psychooncology . 2014 ; 23 ( 1 ): 27 – 34 . Google Scholar Crossref Search ADS PubMed 9. Smith AW , Alfano CM , Reeve BB et al. Race/ethnicity, physical activity, and quality of life in breast cancer survivors . Cancer Epidemiol Biomarkers Prev . 2009 ; 18 ( 2 ): 656 – 663 . Google Scholar Crossref Search ADS PubMed 10. Taylor DL , Nichols JF , Pakiz B , Bardwell WA , Flatt SW , Rock CL . Relationships between cardiorespiratory fitness, physical activity, and psychosocial variables in overweight and obese breast cancer survivors . Int j Behav Med . 2010 ; 17 ( 4 ): 264 – 270 . Google Scholar Crossref Search ADS PubMed 11. Intercontinental Marketing Services Institue for Healthcare Informatics . Patient Adoption of mHealth . 2015 . Available from http://www.imshealth.com/en/thought-leadership/ims-institute/reports/patient-adoption-of-mhealth. Accessibility veriefed on February 21, 2017 . 12. Carter M , Burley V , Nykjaer C , Cade J . Adherence to a smartphone application for weight loss compared to website and paper diary: Pilot randomized controlled trial . J Med Internet Res . 2013 ; 15 ( 4 ). doi: 10.2196/jmir.2283 . 13. Harries T , Eslambolchilar P , Stride C , Rettie R , Walton S . Walking in the wild-Using an always-on smartphone application to increase physical activity . Human Compu . 2013 ; 8120 : 19 – 36 . doi: 10.1007/978-3-642-40498-6_2. 14. Duncan M , Vandelanotte C , Kolt G et al. Effectiveness of web- and mobile phone-based intervention to promote physical activity and healthy eating in middle-aged males: Randomized controlled trial of the ManUp study . J Med Internet Res . 2014 ; 16 ( 6 ). doi: 10.2196/jmir.3107 . 15. Laing BY , Mangione CM , Tseng CH et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: A randomized, controlled trial . Ann Intern Med . 2014 ; 161 ( 10 Suppl ): S5 – 12 . Google Scholar Crossref Search ADS PubMed 16. Flores Mateo G , Granado-Font E , Ferré-Grau C , Montaña-Carreras X . Mobile phone apps to promote weight loss and increase physical activity: A systematic review and meta-analysis . J Med Internet Res . 2015 ; 17 ( 11 ): e253 . doi: 10.2196/jmir.4836 . Google Scholar Crossref Search ADS PubMed 17. Coughlin S , Whitehead M , Sheats J , Mastromonico J , Smith S . A review of smartphone applications for promoting physical activity . Jacobs J Community Med . 2016 ; 2 ( 1 ). PMCID: PMC4811195. 18. Quintiliani L , Mann D , Puputti M , Quinn E , Bowen D . Pilot and feasibility test of a mobile health-supported behavioral counseling intervention for weight management among breast cancer survivors . J Med Internet Res Cancer . 2016 ; 2 ( 1 ). doi: 10.2196/cancer.5305 . 19. Brug J , Oenema A , Ferreira I . Theory, evidence and Intervention Mapping to improve behavior nutrition and physical activity interventions . Int J Behav Nutr Phys Act . 2005 ; 2 ( 1 ): 2 . Google Scholar Crossref Search ADS PubMed 20. Patten M . The role of theory in research . In: Patten M , ed. Understanding Research Methods: An Overview of the Essentials . 9 th ed. Glendale, CA : Pyrczak Publishing ; 2014 : 27 – 29 . 21. Bandura A . Self-efficacy: Toward a unifying theory of behavioral change . Psychol Rev . 1977 ; 84 ( 2 ): 191 – 215 . Google Scholar Crossref Search ADS PubMed 22. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory . Englewood Cliffs, NJ : Prentice Hall ; 1986 . 23. Maher CA , Lewis LK , Ferrar K , Marshall S , De Bourdeaudhuij I , Vandelanotte C . Are health behavior change interventions that use online social networks effective? A systematic review . J Med Internet Res . 2014 ; 16 ( 2 ): doi: 10.2196/jmir.2952 . 24. Nakhasi A , Shen AX , Passarella RJ , Appel LJ , Anderson CA . Online social networks that connect users to physical activity partners: a review and descriptive analysis . J Med Internet Res . 2014 ; 16 ( 6 ): e153 .doi: 10.2196/jmir.2674 . Google Scholar Crossref Search ADS PubMed 25. Perrin A . Social media usage: 2005–2015 . 2015 ; Available from http://www.pewinternet.org/2015/10/08/2015/Social-Networking-Usage-2005–2015/. Accessibility verified on February 28, 2017 . 26. von Elm E , Altman DG , Egger M , Pocock SJ , Gøtzsche PC , Vandenbroucke JP ; STROBE Initiative . The Strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies . Ann Intern Med . 2007 ; 147 ( 8 ): 573 – 577 . Google Scholar Crossref Search ADS PubMed 27. World Medical Association . World medical association declaration of Helsinki: ethical principles for medical research involving human subjects . JAMA . 2008 ; 1 – 5 . 28. Canadian Society for Exerise Physiology . PAR-Q Forms. 2016 ; Available from http://www.csep.ca/view.asp?ccid=517. Accessibility verfied on March 1, 2017 . 29. Kaminsky LA , Ozemek C . A comparison of the Actigraph GT1M and GT3X accelerometers under standardized and free-living conditions . Physiol Meas . 2012 ; 33 ( 11 ): 1869 – 1876 . Google Scholar Crossref Search ADS PubMed 30. Trost SG , McIver KL , Pate RR . Conducting accelerometer-based activity assessments in field-based research . Med Sci Sports Exerc . 2005 ; 37 ( 11 Suppl ): S531 – S543 . Google Scholar Crossref Search ADS PubMed 31. Graf DL , Pratt LV , Hester CN , Short KR . Playing active video games increases energy expenditure in children . Pediatrics . 2009 ; 124 ( 2 ): 534 – 540 . Google Scholar Crossref Search ADS PubMed 32. Aandstad A , Holtberget K , Hageberg R , Holme I , Anderssen SA . Validity and reliability of bioelectrical impedance analysis and skinfold thickness in predicting body fat in military personnel . Mil Med . 2014 ; 179 ( 2 ): 208 – 217 . Google Scholar Crossref Search ADS PubMed 33. Golding L , Meyers C , Sinning W. Y’s Way to Physical Fitness: The Complete Guide to Fitness Testing and Instruction . 4 th ed. Champaign, IL : Human Kinetics ; 1998 . 34. Rodgers W , Wilson P , Hall C , Fraser S , Murray T . Evidence for a multidimensional self-efficacy for exercise scale . Res Q Exercise Sport . 2008 ; 79 ( 2 ): 222 – 234 . Google Scholar Crossref Search ADS 35. Carlson JA , Sallis JF , Wagner N et al. Brief physical activity-related psychosocial measures: reliability and construct validity . J Phys Act Health . 2012 ; 9 ( 8 ): 1178 – 1186 . Google Scholar Crossref Search ADS PubMed 36. Harter S . Effectance motivation reconsidered: Toward a developmental model . Human Dev . 1978 ; 21 : 34 – 64 . Google Scholar Crossref Search ADS 37. Sechrist KR , Walker SN , Pender NJ . Development and psychometric evaluation of the exercise benefits/barriers scale . Res Nurs Health . 1987 ; 10 ( 6 ): 357 – 365 . Google Scholar Crossref Search ADS PubMed 38. Trost SG , Pate RR , Saunders R , Ward DS , Dowda M , Felton G . A prospective study of the determinants of physical activity in rural fifth-grade children . Prev Med . 1997 ; 26 ( 2 ): 257 – 263 . Google Scholar Crossref Search ADS PubMed 39. National Institutes of Health . Patient Reported Outcome Measurement Information System: Instrument Details . 2014 ; Available from http://www.nihpromis.org/measures/instrumentdetails. Accessibility verified on March 1, 2017 . 40. Lin FJ , Pickard AS , Krishnan JA et al. ; CONCERT Consortium . Measuring health-related quality of life in chronic obstructive pulmonary disease: Properties of the EQ-5D-5L and PROMIS-43 short form . BMC Med Res Methodol . 2014 ; 14 (78) . doi: 10.1186/1471-2288-14-78. Google Scholar Crossref Search ADS PubMed 41. Yost KJ , Eton DT , Garcia SF , Cella D . Minimally important differences were estimated for six Patient-Reported Outcomes Measurement Information System-Cancer scales in advanced-stage cancer patients . J Clin Epidemiol . 2011 ; 64 ( 5 ): 507 – 516 . Google Scholar Crossref Search ADS PubMed 42. Cresswell J. Qualitative Inquiry and Reserach Design: Choosing Among Five Approaches . 2 nd ed. Thousand Oaks, CA : Sage ; 2007 . 43. Van Dyck D , Plaete J , Cardon G , Crombez G , De Bourdeaudhuij I . Effectiveness of the self-regulation eHealth intervention ‘MyPlan1.0.’ on physical activity levels of recently retired Belgian adults: a randomized controlled trial . Health Educ Res . 2016 ; 31 ( 5 ): 653 – 664 . Google Scholar Crossref Search ADS PubMed 44. Dieli-Conwright CM , Lee K , Kiwata JL . Reducing the risk of breast cancer recurrence: An evaluation of the effects and mechanisms of diet and exercise . Curr Breast Cancer Rep . 2016 ; 8 ( 3 ): 139 – 150 . Google Scholar Crossref Search ADS PubMed 45. Demark-Wahnefried W , Campbell KL , Hayes SC . Weight management and its role in breast cancer rehabilitation . Cancer . 2012 ; 118 ( 8 Suppl ): 2277 – 2287 . Google Scholar Crossref Search ADS PubMed 46. Ohtomo S . Exposure to diet priming images as cues to reduce the influence of unhealthy eating habits . Appetite . 2017 ; 109 : 83 – 92 . Google Scholar Crossref Search ADS PubMed 47. Papies EK . Health goal priming as a situated intervention tool: How to benefit from nonconscious motivational routes to health behaviour . Health Psychol Rev . 2016 ; 10 ( 4 ): 408 – 424 . Google Scholar Crossref Search ADS PubMed 48. Kampshoff CS , Stacey F , Short CE et al. Demographic, clinical, psychosocial, and environmental correlates of objectively assessed physical activity among breast cancer survivors . Support Care Cancer . 2016 ; 24 ( 8 ): 3333 – 3342 . Google Scholar Crossref Search ADS PubMed 49. Foster D , Linehan C , Kirman B , Lawson S , James G. Motivating Physical Activity At Work: Using Persuasive Social Media for Competitive Step Counting . Presented at: 14th International Academic MindTrek Conference : Envisioning Future Media Environments ; 2010 . 50. Voskuil DW , van Nes JG , Junggeburt JM , van de Velde CJ , van Leeuwen FE , de Haes JC . Maintenance of physical activity and body weight in relation to subsequent quality of life in postmenopausal breast cancer patients . Ann Oncol . 2010 ; 21 ( 10 ): 2094 – 2101 . Google Scholar Crossref Search ADS PubMed 51. Forsythe LP , Alfano CM , George SM et al. Pain in long-term breast cancer survivors: the role of body mass index, physical activity, and sedentary behavior . Breast Cancer Res Treat . 2013 ; 137 ( 2 ): 617 – 630 . Google Scholar Crossref Search ADS PubMed 52. TechAmerica Foundation . Demystifying Big Data: A Practical Guide to Transforming the Business of Government . 2012 ; Available from https://www-304.ibm.com/industries/publicsector/fileserve?contentid=239170. Accessibility verified on March 1, 2017 . 53. Flores M , Glusman G , Brogaard K , Price ND , Hood L . P4 medicine: How systems medicine will transform the healthcare sector and society . Per Med . 2013 ; 10 ( 6 ): 565 – 576 . Google Scholar Crossref Search ADS PubMed 54. Hood L , Balling R , Auffray C . Revolutionizing medicine in the 21st century through systems approaches . Biotechnol j . 2012 ; 7 ( 8 ): 992 – 1001 . Google Scholar Crossref Search ADS PubMed 55. Matthews CE , Hagströmer M , Pober DM , Bowles HR . Best practices for using physical activity monitors in population-based research . Med Sci Sports Exerc . 2012 ; 44 ( 1 Suppl 1 ): S68 – S76 . Google Scholar Crossref Search ADS PubMed 56. Kvedar J . The privacy trade-offs . The Internet of Healthy Things . Boston, MA : Partners HealthCare Connected Health ; 2015 . 57. Kohl H , III Murray T . Overweight and obesity . In: Kohl H , III Murray T , eds. Foundations of Physical Activity and Public Health . Champaign, IL : Human Kinetics ; 2012 : 95 – 117 . 58. President’s Cancer Panel . Improving Cancer-Related Outcomes With Connected Health: A Report to the President of the United States From the President’s Cancer Panel . 2016 ; Available from https://prescancerpanel.cancer.gov/report/connectedhealth. Accessibility verified on October 3, 2017 . © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Journal
Translational Behavioral Medicine – Oxford University Press
Published: Jan 1, 2019