Perceived Influence of Wearable Fitness Trackers on Eating Disorder Symptoms in a Clinical Transdiagnostic Binge Eating and Restrictive Eating Sample

Perceived Influence of Wearable Fitness Trackers on Eating Disorder Symptoms in a Clinical Transdiagnostic Binge Eating and Restrictive Eating Sample | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Perceived Influence of Wearable Fitness Trackers on Eating Disorder Symptoms in a Clinical Transdiagnostic Binge Eating and Restrictive Eating Sample Olivia Wons, Elizabeth Lampe, Anna Gabrielle Patarinski, Katherine Schaumberg, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1627345/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Wearable fitness trackers are an increasingly popular tool for measuring physical activity (PA) due their accuracy and momentary data collection abilities. Despite the benefits of using wearable fitness trackers, there is limited research in the eating disorder (ED) field using wearable fitness trackers to measure PA in the context of EDs. Wearable fitness trackers are often underused in ED research because there is limited known about whether wearable fitness trackers negatively or positively impact PA engagement and ED symptoms in individuals with EDs. The current study aimed to assess the perceived impact wearable fitness trackers have on PA engagement and ED symptoms over a 12-week CBT treatment for 30 individuals with EDs that presented to treatment engaging or not engaging in maladaptive exercise. Participants in the maladaptive exercise group ( n = 17) and non-maladaptive exercise group ( n = 13) wore a fitness tracker for 12 weeks and completed questionnaires assessing participants’ perceptions of the fitness trackers’ influence on ED symptoms and PA engagement throughout treatment. Results demonstrated a small percentage of individuals perceived the fitness tracker influenced ED behaviors or PA engagement, and there were mixed results on whether participants positively or negatively perceived the fitness tracker influenced them to engage in ED behaviors or PA engagement. Although preliminary, these results demonstrate the need to continue using objective measurements of PA via wearable fitness trackers to further our understanding of the positive and negative effects of fitness trackers on clinical ED samples. maladaptive exercise adaptive exercise eating disorders wearable fitness trackers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Physical activity (PA) in the context of eating disorders (EDs) can be either maladaptive (i.e., used to compensate for binge eating or feels driven to avoid negative consequences of not exercising such as weight gain) or adaptive (i.e., neither driven, nor compensatory). The inclusion of fitness trackers in research studies may be a useful methodology in understanding objective characteristics of PA in EDs (e.g., duration, frequency, intensity), but such methods are underutilized in research studies to-date due to concerns about participant reactivity to trackers. The current study aims to improve understanding of the perceived impact of fitness trackers on ED symptoms and PA engagement in an ED population in order to evaluate safety and potential efficacy of use for research studies. Use of Fitness Trackers in Extant Research There is a growing literature base examining PA in the context of eating disorders EDs. To date, most research on PA in ED populations has relied on retrospective self-report measures of PA frequency and duration (Bezzina et al., 2019). These retrospective self-report measures are subject to several biases, such as recall bias (e.g. forgetting ) and social desirability bias (e.g. over/underreporting) (Bezzina et al., 2019). Other literature has used ecological momentary assessment (EMA) to examine PA in EDs. While EMA reporting reduces recall errors, it is still subject to social desirability bias. Extant literature supports these concerns; self-report measurements of PA have been found to be inaccurate both in ED groups and in the general adult population (Bezzina et al., 2019; Mathisen et al., 2018; Prince et al., 2008). Objective measurement of PA via sensors (e.g., accelerometry) shows great promise for more accurate measurement of PA engagement in ED populations. Sensor-based assessment tools (e.g., wearable fitness trackers) use objective measurement, more accurately measure the amount of PA, and increase the credibility and validity of research findings (Grosser et al., 2020; Mathisen et al., 2018). Despite these strong benefits of objective PA measurement, few studies have objectively measured the amount of PA cross sectionally (Carr, Lydecker, White, & Grilo, 2019; Mathisen et al., 2018). There are two main reasons why the ED field tends to under-use objective measures of PA in research assessment. First, many characteristics of both maladaptive and adaptive exercise are cognitive in nature, and prior research suggests these cognitive components of exercise are more clinically relevant in defining maladaptive exercise as compared to exercise frequency and duration (Coniglio, Davis, Sun, Loureiro, & Selby, 2021). For example, wearable fitness trackers cannot measure a sense of being compelled or driven to continue exercise or the intended reason for exercise. As a result, researchers cannot presently depend on wearable fitness trackers to distinguish adaptive from maladaptive exercise, though they can use these devices to gain objective data about other important exercise features such as duration and intensity. Second, researchers and clinicians are often concerned about the potential negative effects wearable fitness trackers could have on the development and maintenance of ED symptoms (Simpson & Mazzeo, 2017). Thus, the use of fitness trackers in ED research has remained limited. Despite concerns about reactivity to sensor measurement of PA among individuals with EDs, little research has specifically examined this phenomenon. To date, only three studies have tested the effects of a wearable fitness trackers on ED symptoms in non-clinical populations, with mixed results. Two were observational studies of university students or young adults which found fitness tracker devices and apps (e.g., Fitbit) were associated with ED symptomatology (e.g., rates of binge eating or purging) and higher rates of body dissatisfaction (Honary, Bell, Clinch, Wild, & McNaney, 2019; Simpson & Mazzeo, 2017). The third study assigned half their sample of university students to wear fitness trackers and found those that were assigned to wear fitness trackers were more likely to report exercising to support their health and fitness and were less likely to report engagement in ED behaviors (e.g., dietary restraint, binge eating) (Gittus et al., 2020). A major limitation of all the studies described above is that none of them included individuals with a diagnosed ED. Despite the valuable contributions of the aforementioned studies, it remains unknown how using wearable fitness trackers impacts individuals with EDs, specifically. Patient Perceptions of Influence of Fitness Trackers on ED Behaviors In addition to the field’s limited understanding of the relationship between wearable fitness trackers, ED symptoms, and PA engagement, no study has assessed the perceived impact of fitness trackers on ED symptoms and PA engagement over an extended period (e.g., >10 days), nor throughout the course of an ED treatment. Importantly, there could be increased reactivity during the first few days of wear as compared to longer-term wear of fitness trackers. Assessing the perceived impact of a fitness tracker on PA engagement and ED symptoms during an ED treatment will allow for the opportunity to further understand the perceived impact of the fitness tracker at the start of treatment when ED symptoms are likely more severe or during treatment when ED symptoms are likely subsiding. Influence of Maladaptive Exercise on Fitness Tracker Reactivity To date, research has also not assessed the perceived influence of fitness trackers on PA engagement and ED symptoms separately in individuals with EDs that do and do not engage in maladaptive exercise. It is possible the perceived impact of fitness trackers may be different among those with and without maladaptive exercise as these groups tend to show different symptom presentations prior to treatment (Levallius, Collin, & Birgegård, 2017; Shroff et al., 2006; Welch, Birgegård, Parling, & Ghaderi, 2011). Assessing the perceived impact of fitness trackers on PA engagement and ED symptoms during an ED treatment may reveal whether specific ED symptoms, like maladaptive exercise, predict whether an individual perceives the fitness tracker to positively or negatively impact their ED symptoms and/or PA engagement. Current Study The current study aimed to characterize participants’ perception of the fitness tracker's influence on ED symptoms across a 12-week CBT treatment for individuals with an ED characterized by both clinically significant binge eating and restrictive eating. There were two aims of the current study and each aim consisted of two parts. Aim 1a was to describe session-by-session perception of the fitness trackers influence on overall PA and maladaptive PA among individuals who did and did not engage in maladaptive exercise at pre-treatment. For Aim 1b, we aimed to examine the moderating role of pre-treatment maladaptive exercise engagement on the perceived influence of the fitness tracker on overall PA engagement across treatment. Due to the potentially greater perceived impact of fitness trackers on PA engagement among individuals currently engaging in maladaptive exercise, we hypothesized the individuals that entered treatment engaging in maladaptive exercise would experience greater reductions of the perceived influence of the fitness tracker on PA engagement during treatment compared to those that did not engage in maladaptive exercise at the start of treatment. Aim 2a was to characterize session-by-session perception of the fitness trackers influence on ED symptoms among individuals who did and did not engage in maladaptive exercise at pre-treatment. For Aim 2b, we aimed to examine the moderating role of pre-treatment maladaptive exercise engagement on the perceived influence of the fitness tracker on ED symptoms across treatment. We had no specific directional hypotheses for whether the perceived influence of the fitness tracker on ED symptoms would differ throughout the course of treatment between those that did and did not engage in maladaptive exercise at the start of treatment. Methods Participants and Recruitment Participants ( n = 30) were recruited via referrals from internal and external health care clinics, flyers, newspapers, radio, and online postings including social media campaigns from May 2020 to June 2021. Sample characteristics are presented in Table 1. Participants were recruited to participate in a larger parent study (ClinicalTrials.gov Identifier: NCT04126694; R43 MH121205 ) designed to test the feasibility and acceptability of a novel sensor-based smartphone application when used as an adjunct to CBT for adult patients with transdiagnostic binge eating and restrictive eating pathology. Participants were included if they were 18-65 years of age, experienced clinically significant binge eating (defined as 12 or more objectively large binge eating episodes in the past 3 months), engaged in clinically significant dietary restriction (defined as 3 or more episodes of fasting for 5 or more waking hours per week in the last 4 weeks), had a body mass index (BMI) 17.5-35, and were willing and able to use a wearable fitness tracker every day for 12 weeks. Participants were excluded if they were receiving other treatment for an ED, were receiving structured behavioral weight loss treatment (e.g., Weight Watchers), or required immediate treatment for medical complications as a result of ED symptoms. Procedure All assessments were completed virtually due to the COVID-19 pandemic. Data for the current study was collected at each assessment point and weekly with the therapy sessions. Participants completed an initial phone screening, a baseline assessment, mid-treatment assessments after session four and session eight, and a post-treatment assessment after session 12. Participants also completed a weekly pre-session questionnaire before every therapy session. Informed consent was obtained by a study assessor from each participant at the baseline assessment. At the baseline assessment, participants were given the Mi Smart Band, a wearable fitness tracker, and they were instructed to wear the tracker throughout the entirety of the 12-week treatment. When participants noticed the battery was running low on the fitness trackers (approximately every twenty days), they were instructed to charge the tracker either when showering or during a time when they were not exercising or sleeping. The current study was conducted in compliance with the Institutional Review Board. Measures Eating Disorder Examination (EDE). The EDE is a semi-structured clinician-administered interview (Cooper & Fairburn, 1987). The current study used the EDE for diagnostic purposes and to assess for adaptive and maladaptive exercise at the baseline assessment. The study team modified the EDE to separately assess adaptive and maladaptive exercise. In the modified EDE, assessors asked questions generated by our research team that separately assess the duration and frequency of solely adaptive exercise episodes and the duration and frequency of all maladaptive exercise episodes, which included driven exercise only episodes, compensatory exercise only episodes, and driven and compensatory exercise episodes. MiFit Smart Band. The MiFit smart band is a wearable fitness tracker that collects daily step count and heart rate. The corresponding MiFit app recorded and compiled this data. Participants had access to the MiFit app on their phone and occasionally had to open the app to sync data for study purposes, but they were instructed by research personnel to not look at their data throughout treatment. Participants wore the MiFit smart band during the 12-week study. Perceived Fitness Tracker Reactivity and Influence on ED Symptoms. Participants’ perceived influence of the fitness tracker on PA engagement and ED symptoms were assessed through two measures developed for the purposes of this study. This included a 3-item weekly pre-session questionnaire and a 5-item mid-treatment and post-treatment questionnaire measuring two constructs: 1) perceived fitness tracker reactivity and 2) the perception of the fitness trackers influence on ED symptoms. The specific items used to measure perceived fitness tracker reactivity (Gittus et al., 2020; Maher, Ryan, Ambrosi, & Edney, 2017) and perception of influence on ED symptoms (Gittus et al., 2020; Honary et al., 2019; Simpson & Mazzeo, 2017) were based on previous work that similarly assessed these constructs. Perceived fitness tracker reactivity was defined as the degree to which wearing a fitness tracker influenced overall PA or maladaptive PA engagement. The primary question asked to assess perceived fitness tracker reactivity was the following: “Did the fitness tracker motivate you to engage in physical activity this week?” If participants responded yes, they were prompted with two follow-up questions: 1) “If yes, did the fitness tracker motivate you to engage in exercise that was driven or compelled?” and 2) “If yes, did the fitness tracker motivate you to engage in exercise that was to compensate or make up for a binge eating episode?” Perceived fitness tracker reactivity was measured weekly via the patient’s pre-session questionnaire, at both mid-treatment assessments, and the post-treatment assessment. Perception of fitness tracker influence on ED symptoms was defined as the degree to which participants perceived wearing a fitness tracker influenced whether they engage in ED symptoms. The perception of the fitness trackers influence on ED symptoms was assessed using two questions: 1) “Over the last four weeks, did the fitness tracker influence you to binge eat or not to binge eat?” and 2) “Over the last four weeks, did the fitness tracker influence you to restrict or not restrict your food intake?” Participants had three response options for each question: 1) “Yes, the fitness tracker influenced me to engage in the ED behavior,” 2) “Yes, the fitness tracker influenced me to not engage in the ED behavior,” and 3) “No, the fitness tracker did not influence me to engage or not engage in the ED behavior.” Perceived influence on ED symptoms was only measured at the two mid-treatment assessments and post-treatment assessment, not at the baseline assessment or during the weekly pre-session questionnaires. Statistical Analyses Results were summarized in graphs to demonstrate the percentage of individuals that perceived the fitness tracker influenced them to engage in PA and ED behaviors on a session-by-session basis. Three two-way repeated measures ANOVAs were run to demonstrate the perceived impact that fitness trackers had on 1) PA, 2) binge eating, and 3) dietary restriction at three time points across treatment (e.g., session four, session eight, and session 12) between individuals in the maladaptive and non-maladaptive exercise groups. For all repeated measures ANOVAs, we used partial η² cutoffs to determine small (0.01-0.05), medium (0.06-0.13), and large (≥ 0.14) effect sizes. Results Sample Characteristics Sample characteristics are presented in Table 1. For all results, participants were divided in two groups: 1) those that engaged maladaptive PA at the start of treatment and 2) those that only engaged in non-maladaptive PA or no PA at pre-treatment. No participant reported a serious injury or illness that limited their ability to engage in PA during treatment. Aim 1a: Characterization of Session-by-Session Perception of Fitness Tracker Influence on PA Engagement Figure 1 demonstrates the percentage of individuals that reported the fitness tracker motivated them to engage in PA on a session-by-session basis. Both the maladaptive and non-maladaptive exercise groups demonstrated similar patterns for the percentage of individuals that reported feeling like the fitness tracker motivated them to engage in PA during treatment. There was also considerable variability in both the maladaptive and non-maladaptive exercise groups from session-to-session with the percentage of individuals that reported feeling like the fitness tracker motivated them to engage in PA during treatment. From visual inspection, both the maladaptive and non-maladaptive exercise group demonstrated reduction trends in the percentage of individuals that reported feeling like the fitness tracker motivated them to engage in PA during treatment. Figure 2 displays the percentage of individuals in the maladaptive exercise group that reported the fitness tracker motivated them to engage in driven or compensatory PA on a session-by-session basis. No individuals in the non-maladaptive exercise group reported they perceived the fitness tracker influenced them to engage in maladaptive exercise. At session one, 18% of individuals in the maladaptive exercise group reported they perceived the fitness tracker influenced them to engage in driven exercise but at session 12, 0% of individuals in the maladaptive exercise group they perceived the fitness tracker influenced them to engage in driven exercise. A similar pattern existed with compensatory exercise; 24% of individuals in the maladaptive exercise group reported they perceived the fitness tracker influenced them to engage in compensatory exercise at session one1 but at session 12, 0% of individuals in the maladaptive exercise group they perceived the fitness tracker influenced them to engage in compensatory exercise. Aim 1b: The Moderating Role of Pre-Treatment Maladaptive Exercise Engagement on the Perceived Influence of the Fitness Tracker on Overall PA Engagement Across Treatment Although not significant, there was a moderate interaction effect demonstrating there was a greater reduction in the number of individuals in the maladaptive exercise group that perceived the fitness tracker influenced them to engage in PA during treatment compared to the non-maladaptive exercise group ( F (2) = 1.76, p = 0.18, η 2 = 0.07). Aim 2a: Characterization of Fitness Tracker Influence on ED Behaviors Across Treatment Figure 3 demonstrates the percentage of individuals that reported the fitness tracker influenced them to not binge eat. Across both groups, no participants reported the fitness tracker influenced them to binge eat therefore, the results shown in Figure 3 only demonstrate the percentage of individuals that responded the fitness tracker influenced them to not binge eat. The percentage of individuals that responded the fitness tracker influenced them to not binge eat at the two mid-treatment and post-treatment assessments for the maladaptive exercise group was 13%, 7%, and 18% respectively and 8%, 0% and 0% respectively for the non-maladaptive exercise group. Figure 4 demonstrates the percentage of individuals that reported the fitness tracker influenced them to restrict or not restrict their eating . The percentage of individuals that responded the fitness tracker influenced them to not restrict at the session four, eight, and 12 assessments for the maladaptive exercise group was 0%, 7%, and 9% respectively and 8%, 0% and 0% respectively for the non-maladaptive exercise group. Only individuals in the maladaptive exercise group reported perceiving that the fitness tracker influenced them to restrict. The percentage of individuals that responded the fitness tracker influenced them to restrict at the two mid-treatment and post-treatment assessments for the maladaptive exercise group was 12%, 0%, and 9% respectively. Aim 2b: The Moderating Role of Pre-Treatment Maladaptive Exercise Engagement on the Perceived Influence of the Fitness Tracker on ED Symptoms Across Treatment There were no significant results when comparing the influence of the fitness tracker on binge eating between the maladaptive non-maladaptive exercise groups across treatment ( F (2)= 0.97, p = 0.39, η 2 = 0.04). Although not significant, there was a moderate interaction effect demonstrating more individuals in the maladaptive exercise group perceived the fitness tracker influenced them to not restrict their food intake compared to the non-maladaptive exercise group across treatment ( F (2) = 2.00, p = 0.15, η 2 = 0.09). Discussion The current study assessed the perceived influence wearable fitness trackers have on PA and ED symptoms in 30 treatment seeking individuals with an ED characterized by recurrent binge eating and restrictive eating over a 12-week CBT treatment. About half of the total sample reported they perceived the fitness tracker influenced them to engage in PA, which is comparable to what similar studies in non-clinical ED samples have found (Laranjo et al., 2021 ). Patterns of responses seemed to demonstrate the perceived influence the fitness tracker had on motivating individuals to engage in PA varied session-to-session and seemed to have similar variation patterns for both the maladaptive and non-maladaptive exercise groups. It is possible the introduction of specific session content (e.g., introduction to weighing, overvaluation of weight and shape) influenced some of the session-to-session variability. Specifically, certain session content (e.g., addressing shape and weight concerns during session nine and ten) may have explained why the percentages were low for both groups in one week and high for both groups the next week. Within the maladaptive exercise group, a relatively small percentage of individuals reported perceiving the fitness trackers influenced them to engage in maladaptive PA. Response patterns demonstrated there was variability session-to-session for the perceived impact on both compensatory and driven exercise but, ultimately, by the end of treatment no individuals reported perceiving the fitness tracker was motivating them to engage in maladaptive exercise. It is possible the participants’ relationship with the fitness tracker changed or they didn’t feel impacted because maladaptive exercise was no longer an ED symptom by the end of treatment. This suggests fitness trackers may not interfere with one’s ability to stop engaging in maladaptive exercise during an ED treatment. In line with our hypothesis, the maladaptive exercise group demonstrated a larger decrease in the perceived impact of the fitness tracker on PA engagement compared to the non-maladaptive group. This result may be due to the maladaptive exercise group reducing their amount of maladaptive PA over treatment, which would impact their amount of overall PA. If they were engaging in less overall PA as treatment continued, then there may have been less opportunities where they felt the watch was influencing PA engagement. Given some individuals with EDs seem to experience positive effects while others experience negative effects from fitness trackers on overall PA engagement and maladaptive PA, it is difficult to draw conclusions on whether fitness trackers are beneficial or harmful for individuals with EDs, which is in line with the mixed literature on the impact of fitness trackers in non-clinical ED populations (Gittus et al., 2020 ; Honary et al., 2019 ; Laranjo et al., 2021 ; Simpson & Mazzeo, 2017 ). It seems possible that with caution and proper psychoeducation, fitness trackers may be able to benefit some individuals with EDs and help promote individuals receiving CBT-E to engage in greater levels of overall adaptive PA. Further, general trends demonstrated the individuals perceived the fitness trackers influenced them to engage in physical activity more towards the beginning of treatment and gradually decreased over the 12-week treatment. This trend may show that treatment had no impact on perceptions of the influence of the fitness tracker but rather participants habituated to wearing the fitness tracker and felt less influenced by it as time progressed. Future research should assess habituation to fitness trackers in an ED sample over an extended period (i.e., < 12 weeks), replicate these findings, and assess the perceived impact of fitness trackers on maladaptive PA, adaptive PA, and overall PA separately across treatment. Additionally, a small proportion of individuals reported they perceived the fitness tracker had a positive impact on ED symptoms (i.e., influenced them not to engage in ED behaviors) while another small proportion of individuals reported they perceived the fitness tracker had a negative impact on ED symptoms (i.e., influenced them to engage in ED behaviors). It is possible that for a small number of individuals, the fitness trackers were helpful in encouraging adaptive behavior change, like limiting binge eating. This supports previous work in a non-clinical ED sample, which found users of fitness trackers were less likely to report engagement in ED behaviors compared to individuals who didn’t use fitness trackers (Gittus et al., 2020 ). Given this encouraging finding, future research should aim to replicate these findings in a larger study. Doing so may help the field better understand if there are certain factors that can help us predict whether an individual with an ED will experience positive or negative effects from a fitness tracker. Despite the novel findings from the current study, there are many limitations to note. First, the sample was small, mostly White, and most individuals identified as female. Further, the study’s inclusion and exclusion criteria for binge eating and restrictive eating was a limiting factor. Notably, the non-maladaptive exercise group included a few individuals not engaging in any exercise, which makes it complicated to interpret the perceived influence of the fitness tracker on PA in both groups. Additionally, participants were instructed to not look at the app associated with the fitness tracker, so the results cannot be generalized to typical consumer fitness tracker use. If individuals had used fitness trackers and a corresponding app that was more invasive and sent notifications to the participant regularly, the results may have differed and may have given more insight to researchers and clinicians on when and how to integrate fitness trackers and apps into their work with ED patients. Lastly, the data in the current study was collected during the COVID-19 pandemic, which may be a limitation as some research has suggested many adults did not engage in their typical PA routine (e.g., working out in fitness center, attending in-person fitness classes) (Alomari, Khabour, & Alzoubi, 2020; Zheng et al., 2020). Given these limitations, results should be interpreted with caution as the results may not be generalizable. Thus, future studies should replicate these findings when COVID 19 restrictions are limited or non-existent, in a larger sample with equal sample sizes, and in a more diverse and representative sample of race and sex. Overall, some individuals with EDs perceive fitness trackers have an impact on PA engagement or ED symptoms. Although preliminary, the findings suggest there are mixed perceptions on the impact fitness trackers have on individuals with EDs. For a small number of individuals with EDs, it appears they perceive the fitness trackers influence them at times to engage in ED behaviors. Contrarily, for some, fitness trackers may be beneficial in promoting PA that is adaptive, minimizing maladaptive exercise, and reducing binge eating and dietary restriction. Given these results are preliminary, continuing to use objective measurements of PA via wearable fitness trackers is necessary to further our understanding of the positive and negative effects of fitness trackers on clinical ED samples. Declarations The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of Mental Health [ClinicalTrials.gov Identifier: NCT04126694; R43 MH121205]. The authors have no relevant financial or non-financial interests to disclose. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bezzina, L., Touyz, S., Young, S., Foroughi, N., Clemes, S., Meyer, C., . . . Hay, P. (2019). Accuracy of self-reported physical activity in patients with anorexia nervosa: links with clinical features. Journal of eating disorders, 7 (1), 28. doi:10.1186/s40337-019-0258-y Carr, M. M., Lydecker, J. A., White, M. A., & Grilo, C. M. (2019). Examining physical activity and correlates in adults with healthy weight, overweight/obesity, or binge-eating disorder. 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Tables Table 1 Descriptives and Sample Characteristics of the total sample and separately of the non-maladaptive and maladaptive exercise groups Total Sample Non-Maladaptive Exercise Group Maladaptive Exercise Group Welch’s Test M SD M SD M SD F df p Baseline Descriptives Age 37.10 12.19 37.92 13.47 36.47 11.51 2.83 1 0.76 BMI 29.52 5.20 31.26 4.62 28.19 5.36 0.10 1 0.10 n % n % n % Male 3 10.00% 2 15.38% 1 5.88% -- - -- Female 27 90.00% 11 84.62% 16 94.12% -- - -- White 24 80.00% 10 76.92% 13 76.47% -- - -- Black 3 10.00% 2 15.38% 2 11.76% -- - -- Asian 2 6.66% 1 7.69% 1 5.88% -- - -- More than one race 1 3.33% 0 0.00% 1 5.88% -- - -- Hispanic/Latino/Latina 2 6.66% 1 7.69% 1 5.88% -- - -- Diagnostic Presentation Bulimia Nervosa 20 66.67% 6 46.15% 14 82.35% -- - -- Binge Eating Disorder 10 33.33% 7 53.85% 3 17.65% -- - -- Exercise Descriptives Participants only engaging in adaptive exercise at pre-treatment 11 36.67% 11 84.62% 0 0% -- -- -- Participants not engaging in any exercise at pre-treatment 2 6.67% 2 15.38% 0 0% -- -- -- Participants engaging in at least one episode of maladaptive exercise one month prior to treatment 17 56.67% 0 0% 17 100% -- -- -- Treatment Retention Dropout from Treatment 4 13.33% 1 7.69% 3 17.65% -- - -- Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2022 Reviews received at journal 09 May, 2022 Reviewers invited by journal 07 May, 2022 Editor assigned by journal 05 May, 2022 First submitted to journal 05 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Wons","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCQbGhx8qmEFMxgMMDMxEaWE2ljgDUUm0FjYJ3jZStPBL9xhISM6zzueXSD5wgKHCOrGBkBbJOWcMDAq3pVvO7DmWcIDhTDphLQY3cjckSG47bGBwvMfgAGPbYeK0HOCdc9jA/jD/hwOM/4jTsrGBtwFoC3sPwwHGBiK0SM45/5lZ4li6gcSZYwYHEo6lGxPUwi/dlv7zQ421Af+M5IcPgAxZglpQQQJpykfBKBgFo2AU4AIAAsVCLCMozJkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1701-5490","institution":"Drexel University","correspondingAuthor":true,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Wons","suffix":""},{"id":104308739,"identity":"3907c1fa-1081-42f3-b7a2-974b9deaf078","order_by":1,"name":"Elizabeth Lampe","email":"","orcid":"","institution":"Drexel University","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Lampe","suffix":""},{"id":104308740,"identity":"3d132209-fc0d-4051-886c-120aec2cadf6","order_by":2,"name":"Anna Gabrielle Patarinski","email":"","orcid":"","institution":"Drexel University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Gabrielle","lastName":"Patarinski","suffix":""},{"id":104308741,"identity":"89d2a29a-0c66-4de0-86dc-db7ae1011f40","order_by":3,"name":"Katherine Schaumberg","email":"","orcid":"","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Schaumberg","suffix":""},{"id":104308742,"identity":"9b41fee9-7cd6-456d-99a4-7066b6917626","order_by":4,"name":"Meghan Butryn","email":"","orcid":"","institution":"Drexel University","correspondingAuthor":false,"prefix":"","firstName":"Meghan","middleName":"","lastName":"Butryn","suffix":""},{"id":104308743,"identity":"25ef1bc6-8e93-4c42-8a05-a88b853744d2","order_by":5,"name":"Adrienne S Juarascio","email":"","orcid":"","institution":"Drexel University","correspondingAuthor":false,"prefix":"","firstName":"Adrienne","middleName":"S","lastName":"Juarascio","suffix":""}],"badges":[],"createdAt":"2022-05-05 20:02:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1627345/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1627345/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21392740,"identity":"4d2a9a11-eca5-4c59-846e-9442d24944d1","added_by":"auto","created_at":"2022-05-12 14:52:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42735,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of individuals in the maladaptive and non-maladaptive exercise groups that reported they perceived the fitness tracker motivated them to engage in physical activity over the course of treatment\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1627345/v1/bb3fc118eca2f3baa95b9c2f.png"},{"id":21392739,"identity":"1bbac574-2083-4576-8c30-7c106f522753","added_by":"auto","created_at":"2022-05-12 14:52:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":327265,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of individuals in the maladaptive exercise group that reported the fitness tracker motivated them to engage in compensatory exercise and/or driven exercise over the course of treatment\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1627345/v1/62e80252c1bbf368072e4fbe.png"},{"id":21392738,"identity":"67f0b721-e945-49b9-b470-e8b01279ba27","added_by":"auto","created_at":"2022-05-12 14:52:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33976,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of individuals in the maladaptive and non-maladaptive exercise groups that reported they perceived the fitness tracker influenced them to binge eat over the course of treatment\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1627345/v1/3e1682d6bc5c05c91aa818f7.png"},{"id":21392737,"identity":"dcc05870-10f0-46aa-980f-11c6b41900c9","added_by":"auto","created_at":"2022-05-12 14:52:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":278780,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of individuals in the maladaptive and non-maladaptive exercise groups that reported they perceived the fitness tracker influenced them to restrict their eating or not restrict their eating over the course of treatment\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1627345/v1/aae90cc91dadd57b6bcc1367.png"},{"id":21392741,"identity":"d89903a8-1ae0-4d4c-b066-810971f89dc4","added_by":"auto","created_at":"2022-05-12 14:52:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":488308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1627345/v1/579f0192-59fc-4ec4-a5cd-2a05430a0195.pdf"}],"financialInterests":"","formattedTitle":"Perceived Influence of Wearable Fitness Trackers on Eating Disorder Symptoms in a Clinical Transdiagnostic Binge Eating and Restrictive Eating Sample","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysical activity (PA) in the context of eating disorders (EDs) can be either maladaptive (i.e., used to compensate for binge eating or feels driven to avoid negative consequences of not exercising such as weight gain) or adaptive (i.e., neither driven, nor compensatory). The inclusion of fitness trackers in research studies may be a useful methodology in understanding objective characteristics of PA in EDs (e.g., duration, frequency, intensity), but such methods are underutilized in research studies to-date due to concerns about participant reactivity to trackers. The current study aims to improve understanding of \u0026nbsp;the perceived impact of fitness trackers on ED symptoms and PA engagement in an ED population in order to evaluate safety and potential efficacy of use for research studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Fitness Trackers in Extant Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is a growing literature base examining PA in the context of eating disorders EDs. To date, most research on PA in ED populations has relied on retrospective self-report measures of PA frequency and duration\u0026nbsp;(Bezzina et al., 2019). These retrospective self-report measures are subject to several biases, such as recall bias (e.g. forgetting ) and social desirability bias (e.g. over/underreporting)\u0026nbsp;(Bezzina et al., 2019). Other literature has used ecological momentary assessment (EMA) to examine PA in EDs. While EMA reporting reduces recall errors, it is still subject to social desirability bias. Extant literature supports these concerns; self-report measurements of PA have been found to be inaccurate both in ED groups and in the general adult population\u0026nbsp;(Bezzina et al., 2019; Mathisen et al., 2018; Prince et al., 2008).\u003c/p\u003e\n\u003cp\u003eObjective measurement of PA via sensors (e.g., accelerometry) shows great promise for more accurate measurement of PA engagement in ED populations. Sensor-based assessment tools (e.g., wearable fitness trackers) use objective measurement, more accurately measure the amount of PA, and increase the credibility and validity of research findings\u0026nbsp;(Grosser et al., 2020; Mathisen et al., 2018). Despite these strong benefits of objective PA measurement, few studies have objectively measured the amount of PA cross sectionally\u0026nbsp;(Carr, Lydecker, White, \u0026amp; Grilo, 2019; Mathisen et al., 2018). There are two main reasons why the ED field tends to under-use objective measures of PA in research assessment. First, many characteristics of both maladaptive and adaptive exercise are cognitive in nature, and prior research suggests these cognitive components of exercise are more clinically relevant in defining maladaptive exercise as compared to exercise frequency and duration\u0026nbsp;(Coniglio, Davis, Sun, Loureiro, \u0026amp; Selby, 2021). For example, wearable fitness trackers cannot measure a sense of being compelled or driven to continue exercise or the intended reason for exercise. As a result, researchers cannot presently depend on wearable fitness trackers to distinguish adaptive from maladaptive exercise, though they can use these devices to gain objective data about other important exercise features such as duration and intensity. Second, researchers and clinicians are often concerned about the potential negative effects wearable fitness trackers could have on the development and maintenance of ED symptoms\u0026nbsp;(Simpson \u0026amp; Mazzeo, 2017). Thus, the use of fitness trackers in ED research has remained limited.\u003c/p\u003e\n\u003cp\u003eDespite concerns about reactivity to sensor measurement of PA among individuals with EDs, little research has specifically examined this phenomenon. To date, only three studies have tested the effects of a wearable fitness trackers on ED symptoms in non-clinical populations, with mixed results. Two were observational studies of university students or young adults which found fitness tracker devices and apps (e.g., Fitbit) were associated with ED symptomatology (e.g., rates of binge eating or purging) and higher rates of body dissatisfaction\u0026nbsp;(Honary, Bell, Clinch, Wild, \u0026amp; McNaney, 2019; Simpson \u0026amp; Mazzeo, 2017). The third study assigned half their sample of university students to wear fitness trackers and found those that were assigned to wear fitness trackers were more likely to report exercising to support their health and fitness and were less likely to report engagement in ED behaviors (e.g., dietary restraint, binge eating)\u0026nbsp;(Gittus et al., 2020). A major limitation of all the studies described above is that none of them included individuals with a diagnosed ED. Despite the valuable contributions of the aforementioned studies, it remains unknown how using wearable fitness trackers impacts individuals with EDs, specifically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Perceptions of Influence of Fitness Trackers on ED Behaviors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the field’s limited understanding of the relationship between wearable fitness trackers, ED symptoms, and PA engagement, no study has assessed the perceived impact of fitness trackers on ED symptoms and PA engagement over an extended period (e.g., \u0026gt;10 days), nor throughout the course of an ED treatment. Importantly, there could be increased reactivity during the first few days of wear as compared to longer-term wear of fitness trackers. Assessing the perceived impact of a fitness tracker on PA engagement and ED symptoms during an ED treatment will allow for the opportunity to further understand the perceived impact of the fitness tracker at the start of treatment when ED symptoms are likely more severe or during treatment when ED symptoms are likely subsiding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of Maladaptive Exercise on Fitness Tracker Reactivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo date, research has also not assessed the perceived influence of fitness trackers on PA engagement and ED symptoms separately in individuals with EDs that do and do not engage in maladaptive exercise. It is possible the perceived impact of fitness trackers may be different among those with and without maladaptive exercise as these groups tend to show different symptom presentations prior to treatment\u0026nbsp;(Levallius, Collin, \u0026amp; Birgegård, 2017; Shroff et al., 2006; Welch, Birgegård, Parling, \u0026amp; Ghaderi, 2011). Assessing the perceived impact of fitness trackers on PA engagement and ED symptoms during an ED treatment may reveal whether specific ED symptoms, like maladaptive exercise, predict whether an individual perceives the fitness tracker to positively or negatively impact their ED symptoms and/or PA engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study aimed to characterize participants’ perception of the fitness tracker's influence on ED symptoms across a 12-week CBT treatment for individuals with an ED characterized by both clinically significant binge eating and restrictive eating. There were two aims of the current study and each aim consisted of two parts. Aim 1a was to describe session-by-session perception of the fitness trackers influence on overall PA and maladaptive PA among individuals who did and did not engage in maladaptive exercise at pre-treatment. For Aim 1b, we aimed to examine the moderating role of pre-treatment maladaptive exercise engagement on the perceived influence of the fitness tracker on overall PA engagement across treatment. Due to the potentially greater perceived impact of fitness trackers on PA engagement among individuals currently engaging in maladaptive exercise, we hypothesized the individuals that entered treatment engaging in maladaptive exercise would experience greater reductions of the perceived influence of the fitness tracker on PA engagement during treatment compared to those that did not engage in maladaptive exercise at the start of treatment. Aim 2a was to characterize session-by-session perception of the fitness trackers influence on ED symptoms among individuals who did and did not engage in maladaptive exercise at pre-treatment. For Aim 2b, we aimed to examine the moderating role of pre-treatment maladaptive exercise engagement on the perceived influence of the fitness tracker on ED symptoms across treatment. We had no specific directional hypotheses for whether the perceived influence of the fitness tracker on ED symptoms would differ throughout the course of treatment between those that did and did not engage in maladaptive exercise at the start of treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants and Recruitment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants\u0026nbsp;(\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 30) were recruited via referrals from internal and external health care clinics, flyers, newspapers, radio, and online postings including social media campaigns from May 2020 to June 2021. Sample characteristics are presented in Table 1. Participants were recruited to participate in a larger parent study (ClinicalTrials.gov Identifier: NCT04126694;\u0026nbsp;R43 MH121205\u003cstrong\u003e)\u003c/strong\u003e designed to test the feasibility and acceptability of a novel sensor-based smartphone application when used as an adjunct to CBT for adult patients with transdiagnostic binge eating and restrictive eating pathology.\u0026nbsp;\u0026nbsp;Participants were included if they were\u0026nbsp;18-65 years of age, experienced clinically significant binge eating (defined as 12 or more objectively large binge eating episodes in the past 3 months), engaged in clinically significant dietary restriction (defined as 3 or more episodes of fasting for 5 or more waking hours per week in the last 4 weeks), had a body mass index (BMI) 17.5-35, and were willing and able to use a wearable fitness tracker every day for 12 weeks.\u0026nbsp;Participants were excluded if they\u0026nbsp;were receiving other treatment for an ED, were receiving structured behavioral weight loss treatment (e.g., Weight Watchers), or required immediate treatment for medical complications as a result of ED symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll assessments were completed virtually due to the COVID-19 pandemic.\u0026nbsp;Data for the current study was collected at each assessment point and weekly with\u0026nbsp;the\u0026nbsp;therapy sessions.\u0026nbsp;Participants completed an initial phone screening, a baseline assessment, mid-treatment assessments after session\u0026nbsp;four\u0026nbsp;and session\u0026nbsp;eight,\u0026nbsp;and a post-treatment assessment after session\u0026nbsp;12. Participants also completed a weekly pre-session questionnaire before every therapy session. Informed consent was obtained by a study assessor from each participant at the baseline assessment. At the baseline assessment, participants were given the Mi Smart Band, a wearable fitness tracker, and they were instructed to wear the tracker throughout the entirety of the\u0026nbsp;12-week treatment. When participants noticed the battery was running low on the fitness trackers (approximately every\u0026nbsp;twenty\u0026nbsp;days), they were instructed to charge the tracker either when showering or during a time when they were not exercising or sleeping. The current study was conducted in compliance with the Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEating Disorder Examination (EDE).\u003c/em\u003e\u003c/strong\u003eThe EDE is a semi-structured clinician-administered interview\u0026nbsp;(Cooper \u0026amp; Fairburn, 1987). The current study used the EDE for diagnostic purposes and to assess for adaptive and maladaptive exercise at the baseline assessment.\u0026nbsp;\u0026nbsp;The study team modified the EDE to separately assess adaptive and maladaptive exercise. In the modified EDE,\u0026nbsp;assessors asked questions generated by our research team that separately assess the duration and frequency of solely adaptive exercise episodes and the duration and frequency of all maladaptive exercise episodes, which included driven exercise only episodes, compensatory exercise only episodes, and driven and compensatory exercise episodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMiFit\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Smart Band.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe\u0026nbsp;MiFit\u0026nbsp;smart band is a wearable fitness tracker that collects daily step count and heart rate. The corresponding MiFit app recorded and compiled this data. Participants had access to the MiFit app on their phone and occasionally had to open the app to sync data for study purposes, but they were instructed by research personnel to not look at their data throughout treatment. \u0026nbsp; Participants wore the\u0026nbsp;MiFit\u0026nbsp;smart band during the 12-week study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePerceived Fitness Tracker Reactivity and Influence on ED Symptoms.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eParticipants’ perceived influence of the fitness tracker on PA engagement and ED symptoms were assessed through two measures developed for the purposes of this study. This included a\u0026nbsp;3-item weekly pre-session questionnaire and a\u0026nbsp;5-item mid-treatment and post-treatment questionnaire measuring two constructs: 1) perceived fitness tracker reactivity and 2) the perception of the fitness trackers influence on ED symptoms. The specific items used to measure perceived fitness tracker reactivity (Gittus et al., 2020; Maher, Ryan, Ambrosi, \u0026amp; Edney, 2017) and perception of influence on ED symptoms (Gittus et al., 2020; Honary et al., 2019; Simpson \u0026amp; Mazzeo, 2017)\u0026nbsp;were based\u0026nbsp;on previous work that similarly assessed these constructs.\u003c/p\u003e\n\u003cp\u003ePerceived fitness tracker reactivity was defined as the degree to which wearing a fitness tracker influenced overall PA or maladaptive PA engagement.\u0026nbsp;The primary question asked to assess perceived fitness tracker reactivity was the following: “Did the fitness tracker motivate you to engage in physical activity this week?” If participants responded yes, they were prompted with two follow-up questions: 1) “If yes, did the fitness tracker motivate you to engage in exercise that was driven or compelled?” and 2) “If yes, did the fitness tracker motivate you to engage in exercise that was to compensate or make up for a binge eating episode?” Perceived fitness tracker reactivity was measured weekly via the patient’s pre-session questionnaire, at both mid-treatment assessments, and the post-treatment assessment.\u003c/p\u003e\n\u003cp\u003ePerception of fitness tracker influence on ED symptoms was defined as the degree to which participants perceived wearing a fitness tracker influenced whether they engage in ED symptoms. The perception of the fitness trackers influence on ED symptoms was assessed using two questions: 1) “Over the last four weeks, did the fitness tracker influence you to binge eat or not to binge eat?” and 2) “Over the last four weeks, did the fitness tracker influence you to restrict or not restrict your food intake?” Participants had three response options for each question: 1) “Yes, the fitness tracker influenced me to engage in the ED behavior,” 2) “Yes, the fitness tracker influenced me to \u003cu\u003enot\u0026nbsp;\u003c/u\u003eengage in the ED behavior,” and 3) “No, the fitness tracker did not influence me to engage or not engage in the ED behavior.” Perceived influence on ED symptoms was only measured at the two mid-treatment assessments and post-treatment assessment, not at the baseline assessment or during the weekly pre-session questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults were summarized in graphs to demonstrate the percentage of individuals that perceived the fitness tracker influenced them to engage in PA and ED behaviors on a session-by-session basis. Three two-way repeated measures ANOVAs were run to demonstrate the perceived impact that fitness trackers had on 1) PA, 2) binge eating, and 3) dietary restriction at three time points across treatment (e.g., session four, session eight, and session 12) between individuals in the maladaptive and non-maladaptive exercise groups. For all repeated measures ANOVAs, we used partial η² cutoffs to determine small (0.01-0.05), medium (0.06-0.13), and large (≥ 0.14) effect sizes.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample characteristics are presented in Table 1. For all results, participants were divided in two groups: 1) those that engaged maladaptive PA at the start of treatment and 2) those that only engaged in non-maladaptive PA or no PA at pre-treatment.\u0026nbsp;No participant reported a serious injury or illness that limited their ability to engage in PA during treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim 1a: Characterization of Session-by-Session Perception of Fitness Tracker Influence on PA Engagement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 demonstrates the percentage of individuals that reported the fitness tracker motivated them to engage in PA on a session-by-session basis. Both the maladaptive and non-maladaptive exercise groups demonstrated similar patterns for the percentage of individuals that reported feeling like the fitness tracker motivated them to engage in PA during treatment. \u0026nbsp;There was also considerable variability in both the maladaptive and non-maladaptive exercise groups from session-to-session with the percentage of individuals that reported feeling like the fitness tracker motivated them to engage in PA during treatment. From visual inspection, both the maladaptive and non-maladaptive exercise group demonstrated reduction trends in the percentage of individuals that reported feeling like the fitness tracker motivated them to engage in PA during treatment.\u003c/p\u003e\n\u003cp\u003eFigure 2 displays the percentage of individuals in the maladaptive exercise group that reported the fitness tracker motivated them to engage in driven or compensatory PA on a session-by-session basis. No individuals in the non-maladaptive exercise group reported they perceived the fitness tracker influenced them to engage in maladaptive exercise. At session one, 18% of individuals in the maladaptive exercise group reported they perceived the fitness tracker influenced them to engage in driven exercise but at session 12, 0% of individuals in the maladaptive exercise group they perceived the fitness tracker influenced them to engage in driven exercise. A similar pattern existed with compensatory exercise; 24% of individuals in the maladaptive exercise group reported they perceived the fitness tracker influenced them to engage in compensatory exercise at session one1 but at session 12, 0% of individuals in the maladaptive exercise group they perceived the fitness tracker influenced them to engage in compensatory exercise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim 1b:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe Moderating Role of Pre-Treatment Maladaptive Exercise Engagement on the Perceived Influence of the Fitness Tracker on Overall PA Engagement Across Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough not significant, there was a moderate interaction effect demonstrating there was a greater reduction in the number of individuals in the maladaptive exercise group that perceived the fitness tracker influenced them to engage in PA during treatment compared to the non-maladaptive exercise group (\u003cem\u003eF\u003c/em\u003e(2) = 1.76, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.18, η\u003csup\u003e2\u003c/sup\u003e = 0.07).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim 2a: Characterization of Fitness Tracker Influence on ED Behaviors Across Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 demonstrates the percentage of individuals that reported the fitness tracker influenced them to \u003cem\u003enot\u003c/em\u003e binge eat. Across both groups, no participants reported the fitness tracker influenced them to binge eat therefore, the results shown in Figure 3 only demonstrate the percentage of individuals that responded the fitness tracker influenced them to \u003cu\u003enot\u0026nbsp;\u003c/u\u003ebinge eat. The percentage of individuals that responded the fitness tracker influenced them to \u003cu\u003enot\u0026nbsp;\u003c/u\u003ebinge eat at the two mid-treatment and post-treatment assessments for the maladaptive exercise group was 13%, 7%, and 18% respectively and 8%, 0% and 0% respectively for the non-maladaptive exercise group.\u003c/p\u003e\n\u003cp\u003eFigure 4 demonstrates the percentage of individuals that reported the fitness tracker influenced them to \u003cem\u003erestrict or not restrict their eating\u003c/em\u003e. The percentage of individuals that responded the fitness tracker influenced them to \u003cem\u003enot\u003c/em\u003e restrict at the session four, eight, and 12 assessments for the maladaptive exercise group was 0%, 7%, and 9% respectively and 8%, 0% and 0% respectively for the non-maladaptive exercise group. Only individuals in the maladaptive exercise group reported perceiving that the fitness tracker influenced them to restrict. The percentage of individuals that responded the fitness tracker influenced them to restrict at the two mid-treatment and post-treatment assessments for the maladaptive exercise group was 12%, 0%, and 9% respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim 2b: The Moderating Role of Pre-Treatment Maladaptive Exercise Engagement on the Perceived Influence of the Fitness Tracker on ED Symptoms Across Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no significant results when comparing the influence of the fitness tracker on binge eating between the maladaptive non-maladaptive exercise groups across treatment (\u003cem\u003eF\u003c/em\u003e(2)= \u0026nbsp;0.97, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.39, η\u003csup\u003e2\u003c/sup\u003e = 0.04). Although not significant, there was a moderate interaction effect demonstrating more individuals in the maladaptive exercise group perceived the fitness tracker influenced them to not restrict their food intake compared to the non-maladaptive exercise group across treatment (\u003cem\u003eF\u003c/em\u003e(2) = 2.00, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.15, η\u003csup\u003e2\u003c/sup\u003e = 0.09).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study assessed the perceived influence wearable fitness trackers have on PA and ED symptoms in 30 treatment seeking individuals with an ED characterized by recurrent binge eating and restrictive eating over a 12-week CBT treatment. About half of the total sample reported they perceived the fitness tracker influenced them to engage in PA, which is comparable to what similar studies in non-clinical ED samples have found (Laranjo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Patterns of responses seemed to demonstrate the perceived influence the fitness tracker had on motivating individuals to engage in PA varied session-to-session and seemed to have similar variation patterns for both the maladaptive and non-maladaptive exercise groups. It is possible the introduction of specific session content (e.g., introduction to weighing, overvaluation of weight and shape) influenced some of the session-to-session variability. Specifically, certain session content (e.g., addressing shape and weight concerns during session nine and ten) may have explained why the percentages were low for both groups in one week and high for both groups the next week.\u003c/p\u003e \u003cp\u003eWithin the maladaptive exercise group, a relatively small percentage of individuals reported perceiving the fitness trackers influenced them to engage in maladaptive PA. Response patterns demonstrated there was variability session-to-session for the perceived impact on both compensatory and driven exercise but, ultimately, by the end of treatment no individuals reported perceiving the fitness tracker was motivating them to engage in maladaptive exercise. It is possible the participants\u0026rsquo; relationship with the fitness tracker changed or they didn\u0026rsquo;t feel impacted because maladaptive exercise was no longer an ED symptom by the end of treatment. This suggests fitness trackers may not interfere with one\u0026rsquo;s ability to stop engaging in maladaptive exercise during an ED treatment.\u003c/p\u003e \u003cp\u003eIn line with our hypothesis, the maladaptive exercise group demonstrated a larger decrease in the perceived impact of the fitness tracker on PA engagement compared to the non-maladaptive group. This result may be due to the maladaptive exercise group reducing their amount of maladaptive PA over treatment, which would impact their amount of overall PA. If they were engaging in less overall PA as treatment continued, then there may have been less opportunities where they felt the watch was influencing PA engagement.\u003c/p\u003e \u003cp\u003eGiven some individuals with EDs seem to experience positive effects while others experience negative effects from fitness trackers on overall PA engagement and maladaptive PA, it is difficult to draw conclusions on whether fitness trackers are beneficial or harmful for individuals with EDs, which is in line with the mixed literature on the impact of fitness trackers in non-clinical ED populations (Gittus et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Honary et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Laranjo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Simpson \u0026amp; Mazzeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It seems possible that with caution and proper psychoeducation, fitness trackers may be able to benefit some individuals with EDs and help promote individuals receiving CBT-E to engage in greater levels of overall adaptive PA. Further, general trends demonstrated the individuals perceived the fitness trackers influenced them to engage in physical activity more towards the beginning of treatment and gradually decreased over the 12-week treatment. This trend may show that treatment had no impact on perceptions of the influence of the fitness tracker but rather participants habituated to wearing the fitness tracker and felt less influenced by it as time progressed. Future research should assess habituation to fitness trackers in an ED sample over an extended period (i.e., \u0026lt;\u0026thinsp;12 weeks), replicate these findings, and assess the perceived impact of fitness trackers on maladaptive PA, adaptive PA, and overall PA separately across treatment.\u003c/p\u003e \u003cp\u003eAdditionally, a small proportion of individuals reported they perceived the fitness tracker had a positive impact on ED symptoms (i.e., influenced them \u003cem\u003enot\u003c/em\u003e to engage in ED behaviors) while another small proportion of individuals reported they perceived the fitness tracker had a negative impact on ED symptoms (i.e., influenced them to engage in ED behaviors). It is possible that for a small number of individuals, the fitness trackers were helpful in encouraging adaptive behavior change, like limiting binge eating. This supports previous work in a non-clinical ED sample, which found users of fitness trackers were less likely to report engagement in ED behaviors compared to individuals who didn\u0026rsquo;t use fitness trackers (Gittus et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given this encouraging finding, future research should aim to replicate these findings in a larger study. Doing so may help the field better understand if there are certain factors that can help us predict whether an individual with an ED will experience positive or negative effects from a fitness tracker.\u003c/p\u003e \u003cp\u003eDespite the novel findings from the current study, there are many limitations to note.\u003c/p\u003e \u003cp\u003eFirst, the sample was small, mostly White, and most individuals identified as female. Further, the study\u0026rsquo;s inclusion and exclusion criteria for binge eating and restrictive eating was a limiting factor. Notably, the non-maladaptive exercise group included a few individuals not engaging in any exercise, which makes it complicated to interpret the perceived influence of the fitness tracker on PA in both groups. Additionally, participants were instructed to not look at the app associated with the fitness tracker, so the results cannot be generalized to typical consumer fitness tracker use. If individuals had used fitness trackers and a corresponding app that was more invasive and sent notifications to the participant regularly, the results may have differed and may have given more insight to researchers and clinicians on when and how to integrate fitness trackers and apps into their work with ED patients. Lastly, the data in the current study was collected during the COVID-19 pandemic, which may be a limitation as some research has suggested many adults did not engage in their typical PA routine (e.g., working out in fitness center, attending in-person fitness classes) (Alomari, Khabour, \u0026amp; Alzoubi, 2020; Zheng et al., 2020). Given these limitations, results should be interpreted with caution as the results may not be generalizable. Thus, future studies should replicate these findings when COVID 19 restrictions are limited or non-existent, in a larger sample with equal sample sizes, and in a more diverse and representative sample of race and sex.\u003c/p\u003e \u003cp\u003eOverall, some individuals with EDs perceive fitness trackers have an impact on PA engagement or ED symptoms. Although preliminary, the findings suggest there are mixed perceptions on the impact fitness trackers have on individuals with EDs. For a small number of individuals with EDs, it appears they perceive the fitness trackers influence them at times to engage in ED behaviors. Contrarily, for some, fitness trackers may be beneficial in promoting PA that is adaptive, minimizing maladaptive exercise, and reducing binge eating and dietary restriction. Given these results are preliminary, continuing to use objective measurements of PA via wearable fitness trackers is necessary to further our understanding of the positive and negative effects of fitness trackers on clinical ED samples.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of Mental Health [ClinicalTrials.gov Identifier: NCT04126694; R43 MH121205].\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBezzina, L., Touyz, S., Young, S., Foroughi, N., Clemes, S., Meyer, C., . . . Hay, P. (2019). Accuracy of self-reported physical activity in patients with anorexia nervosa: links with clinical features. \u003cem\u003eJournal of eating disorders, 7\u003c/em\u003e(1), 28. doi:10.1186/s40337-019-0258-y\u003c/li\u003e\n \u003cli\u003eCarr, M. M., Lydecker, J. A., White, M. A., \u0026amp; Grilo, C. M. (2019). Examining physical activity and correlates in adults with healthy weight, overweight/obesity, or binge-eating disorder. \u003cem\u003eInternational Journal of Eating Disorders, 52\u003c/em\u003e(2), 159-165. doi:10.1002/eat.23003\u003c/li\u003e\n \u003cli\u003eConiglio, K. A., Davis, L., Sun, J., Loureiro, N., \u0026amp; Selby, E. A. (2021). Detecting pathological exercise in college men: An investigation using latent profile analysis. \u003cem\u003eJournal of American College Health\u003c/em\u003e, 1-5.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCooper, Z., \u0026amp; Fairburn, C. (1987). The eating disorder examination: A semi‐structured interview for the assessment of the specific psychopathology of eating disorders. \u003cem\u003eInternational Journal of Eating Disorders, 6\u003c/em\u003e(1), 1-8.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGittus, M., Fuller-Tyszkiewicz, M., Brown, H. E., Richardson, B., Fassnacht, D. B., Lennard, G. R., . . . Krug, I. (2020). Are Fitbits implicated in body image concerns and disordered eating in women? \u003cem\u003eHealth psychology : official journal of the Division of Health Psychology, American Psychological Association\u003c/em\u003e. doi:10.1037/hea0000881\u003c/li\u003e\n \u003cli\u003eGrosser, J., Hofmann, T., Stengel, A., Zeeck, A., Winter, S., Correll, C. U., \u0026amp; Haas, V. (2020). Psychological and nutritional correlates of objectively assessed physical activity in patients with anorexia nervosa. \u003cem\u003eEur Eat Disord Rev, 28\u003c/em\u003e(5), 559-570. doi:10.1002/erv.2756\u003c/li\u003e\n \u003cli\u003eHonary, M., Bell, B. T., Clinch, S., Wild, S. E., \u0026amp; McNaney, R. (2019). Understanding the Role of Healthy Eating and Fitness Mobile Apps in the Formation of Maladaptive Eating and Exercise Behaviors in Young People. \u003cem\u003eJMIR mHealth and uHealth, 7\u003c/em\u003e(6), e14239.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLaranjo, L., Ding, D., Heleno, B., Kocaballi, B., Quiroz, J. C., Tong, H. L., . . . Bates, D. W. (2021). Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression. \u003cem\u003eBritish Journal of Sports Medicine, 55\u003c/em\u003e(8), 422. doi:10.1136/bjsports-2020-102892\u003c/li\u003e\n \u003cli\u003eLevallius, J., Collin, C., \u0026amp; Birgeg\u0026aring;rd, A. (2017). Now you see it, Now you don\u0026rsquo;t: compulsive exercise in adolescents with an eating disorder. \u003cem\u003eJournal of eating disorders, 5\u003c/em\u003e(1), 1-9.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMathisen, T. F., Rosenvinge, J. H., Friborg, O., Pettersen, G., Stensrud, T., Hansen, B. H., . . . Sundgot-Borgen, J. (2018). Body composition and physical fitness in women with bulimia nervosa or binge-eating disorder. \u003cem\u003eInternational Journal of Eating Disorders, 51\u003c/em\u003e(4), 331-342. doi:10.1002/eat.22841\u003c/li\u003e\n \u003cli\u003ePrince, S. A., Adamo, K. B., Hamel, M. E., Hardt, J., Connor Gorber, S., \u0026amp; Tremblay, M. (2008). A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. \u003cem\u003eInt J Behav Nutr Phys Act, 5\u003c/em\u003e, 56. doi:10.1186/1479-5868-5-56\u003c/li\u003e\n \u003cli\u003eShroff, H., Reba, L., Thornton, L. M., Tozzi, F., Klump, K. L., Berrettini, W. H., . . . Fichter, M. M. (2006). Features associated with excessive exercise in women with eating disorders. \u003cem\u003eInternational Journal of Eating Disorders, 39\u003c/em\u003e(6), 454-461.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSimpson, C. C., \u0026amp; Mazzeo, S. E. (2017). Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. \u003cem\u003eEating behaviors, 26\u003c/em\u003e, 89-92.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWelch, E., Birgeg\u0026aring;rd, A., Parling, T., \u0026amp; Ghaderi, A. (2011). Eating disorder examination questionnaire and clinical impairment assessment questionnaire: general population and clinical norms for young adult women in Sweden. \u003cem\u003eBehaviour Research and Therapy, 49\u003c/em\u003e(2), 85-91. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptives and Sample Characteristics of the total sample and separately of the non-maladaptive and maladaptive exercise groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal Sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-Maladaptive Exercise Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMaladaptive Exercise Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eWelch\u0026rsquo;s Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eF\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline Descriptives\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than one race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic/Latino/Latina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDiagnostic Presentation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulimia Nervosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinge Eating Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eExercise Descriptives\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants only engaging in adaptive exercise at pre-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants not engaging in any exercise at pre-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants engaging in at least one episode of maladaptive exercise one month prior to treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTreatment Retention\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDropout from Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"maladaptive exercise, adaptive exercise, eating disorders, wearable fitness trackers","lastPublishedDoi":"10.21203/rs.3.rs-1627345/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1627345/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWearable fitness trackers are an increasingly popular tool for measuring physical activity (PA) due their accuracy and momentary data collection abilities. Despite the benefits of using wearable fitness trackers, there is limited research in the eating disorder (ED) field using wearable fitness trackers to measure PA in the context of EDs. Wearable fitness trackers are often underused in ED research because there is limited known about whether wearable fitness trackers negatively or positively impact PA engagement and ED symptoms in individuals with EDs. The current study aimed to assess the perceived impact wearable fitness trackers have on PA engagement and ED symptoms over a 12-week CBT treatment for 30 individuals with EDs that presented to treatment engaging or not engaging in maladaptive exercise. Participants in the maladaptive exercise group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17) and non-maladaptive exercise group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13) wore a fitness tracker for 12 weeks and completed questionnaires assessing participants\u0026rsquo; perceptions of the fitness trackers\u0026rsquo; influence on ED symptoms and PA engagement throughout treatment. Results demonstrated a small percentage of individuals perceived the fitness tracker influenced ED behaviors or PA engagement, and there were mixed results on whether participants positively or negatively perceived the fitness tracker influenced them to engage in ED behaviors or PA engagement. Although preliminary, these results demonstrate the need to continue using objective measurements of PA via wearable fitness trackers to further our understanding of the positive and negative effects of fitness trackers on clinical ED samples.\u003c/p\u003e","manuscriptTitle":"Perceived Influence of Wearable Fitness Trackers on Eating Disorder Symptoms in a Clinical Transdiagnostic Binge Eating and Restrictive Eating Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-12 14:52:32","doi":"10.21203/rs.3.rs-1627345/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-05-17T10:53:52+00:00","index":0,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-05-09T11:39:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-08T01:15:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-06T02:56:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","date":"2022-05-05T16:00:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ff39b6bf-695a-4884-a923-19c73c79cbda","owner":[],"postedDate":"May 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-01T13:44:41+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-12 14:52:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1627345","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1627345","identity":"rs-1627345","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}