- Email: [email protected]
Binge eating predicts adherence to digital self-monitoring during behavioral weight loss
Eating Behaviors 39 (2020) 101448
Contents lists available at ScienceDirect
Eating Behaviors journal homepage: www.elsevier.com/locate/eatbeh
Binge eating predicts adherence to digital self-monitoring during behavioral weight loss Mary K. Martinelli *, Laura D’Adamo, Meghan L. Butryn Department of Psychology, Drexel University, Philadelphia, PA, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: Obesity Binge eating Self-monitoring Behavioral weight loss Treatment adherence
Objective: Consistent self-monitoring of dietary intake, weight, and physical activity predicts better outcomes during behavioral weight loss, but the factors that influence self-monitoring adherence are not well understood. This study attempted to fill gaps in the existing literature by examining whether pre-treatment eating behaviors predict adherence to digital self-monitoring during a behavioral weight loss program. Method: Participants (N = 77) reported on binge eating, uncontrolled eating, and emotional eating at baseline, and were instructed to self-monitor their food intake, weight, and physical activity using digital devices (food logging app, Fitbit, and wireless “smart” scale) throughout the 12-week treatment. Adherence to self-monitoring was assessed using data captured from these devices. Results: Greater baseline binge eating severity predicted greater adherence to self-monitoring of weight (ρ = 0.25, p = .03) and eating (ρ = 0.25, p = .03), but not self-monitoring of physical activity. Uncontrolled eating and emotional eating did not significantly predict self-monitoring adherence. Conclusions: In contrast to previous research, this study found that participants with greater pre-treatment binge eating severity had better adherence to self-monitoring of eating, and for the first time established a relationship between binge eating severity and digital self-monitoring of weight in behavioral weight loss. Individuals with greater pre-treatment binge eating may exhibit characteristics, such as motivation or rigidity, that are beneficial during the initial period of weight loss. Future studies should determine if there are features of analogue versus digital self-monitoring that may explain this pattern of findings, and examine these associations longitudinally.
1. Introduction While consistent self-monitoring of dietary intake, weight, and physical activity predicts better outcomes during behavioral weight loss interventions (Burke, Wang, & Sevick, 2011; Butryn et al., 2019; Carels, Selensky, Rossi, Solar, & Hlavka, 2017; Harvey, Krukowski, Priest, & West, 2019), the factors that influence self-monitoring adherence are not well understood. Problematic eating behaviors, such as sub-clinical binge eating, uncontrolled eating, and emotional eating, are common in individuals with obesity seeking weight loss treatment (Konttinen, van Strien, Mannisto, Jousilahti, & Haukkala, 2019; Marcus & Wildes, 2014; Rohrer, Vickers-Douglas, & Stroebel, 2009). It is unknown if these be haviors predict self-monitoring adherence. A sense of shame or avoid ance related to these problematic eating behaviors is common (Mason & Lewis, 2015; Wong & Qian, 2016), which might impede self-monitoring adherence. Alternatively, individuals who engage in problematic eating behaviors might feel more motivated to adhere to treatment strategies to
reduce the distress caused by these behaviors, or to control their eating following periods of overeating or weight gain (Smith, Williamson, Bray, & Ryan, 1999). In one previous study, Ariel and Perri (2016) found that greater baseline binge eating severity predicted poorer adherence to food log ging during behavioral weight loss. In contrast, Kong et al. (2012) found that binge eating, emotional eating, and uncontrolled eating were un related to the number of food logs completed during weight loss treat ment. These mixed findings make it difficult to draw firm conclusions about the relationship between eating behaviors and self-monitoring of eating. Extant studies also relied exclusively on written food logging. Digital food logging may be more convenient, accessible, and discreet than written records. Moreover, prior research has not examined whether problematic eating behaviors predict self-monitoring of weight or physical activity. Given that self-monitoring of eating, weight, and physical activity have been shown to independently predict weight loss outcomes (Butryn et al., 2019), identifying pretreatment predictors of
* Corresponding author at: 3141 Chestnut St., Stratton Hall 119, Philadelphia, PA 19104, USA. E-mail address: [email protected] (M.K. Martinelli). https://doi.org/10.1016/j.eatbeh.2020.101448 Received 1 April 2020; Received in revised form 20 October 2020; Accepted 25 October 2020 Available online 29 October 2020 1471-0153/© 2020 Elsevier Ltd. All rights reserved.
M.K. Martinelli et al.
Eating Behaviors 39 (2020) 101448
self-monitoring may be helpful in promoting self-monitoring adherence. The current study examined the relationship between baseline eating behaviors and adherence to digital self-monitoring of eating, physical activity, and weight among adults enrolled in a group-based behavioral weight loss program. We hypothesized that eating behaviors would significantly predict self-monitoring adherence during the 12-week treatment program.
unlikely that participants would restrict below 800 cal. Thus, it was assumed that days with less than 800 cal likely represented incomplete self-monitoring records. Adherence to physical activity self-monitoring was determined by the percentage of days with ≥500 steps were tracked with the Fitbit (Korinek et al., 2018; Meyer, Wasmann, Heuten, El Ali, & Boll, 2017). Percent adherence to weight self-monitoring was calculated based on adherence to the varied self-weighing prescription (described above).
2. Method
2.3. Data analysis
2.1. Participants and procedures
Data were analyzed in R and SPSS version 25 and statistical level was set at ∝ = 0.05. Associations between eating behavior and age, BMI, sex, and race were examined using independent samples t-tests for categor ical variables and Pearson correlation for continuous variables, and subsequent analyses controlled for characteristics that were significantly related to eating behavior. Race was dichotomized for analyses (White = 1, other racial groups = 0) given that there was a general pattern of White participants scoring higher on baseline measures of eating behavior. Additionally White participants made up the largest racial category in the sample (41%), and if race was examined with multiple dichotomized variables (e.g., a dummy coded variable for each race) this would have limited the statistical power needed to detect relationships for the primary aim. The self-monitoring adherence variables were nonnormally distributed (e.g., negatively skewed), causing violations of assumptions for parametric models. Thus, non-parametric tests (Spearman correlations) were used to assess relationships between eating behavior and self-monitoring adherence. If participants dis continued treatment (n = 10), device data were retained only until the date that they discontinued treatment. Therefore, only completers (N = 77) were used in the present analyses. One participant was missing BES data and was excluded from those analyses.
This study is a secondary analysis of data collected for a clinical trial (R21DK112741) focused on digital data sharing and supportive accountability following face-to-face behavioral weight loss treatment. Eligible participants were 18–70 years old with a body mass index (BMI) between 25 and 45 kg/m2, capable of engaging in physical activity, and had a smartphone. Participants who currently met diagnostic criteria for binge eating disorder (assessed via clinical interview) were excluded, as were those who had a medical or psychiatric condition that may constitute a risk during the intervention, had recent weight loss (>5%), or used weight-affecting medication. The parent study received Institutional Review Board approval and was registered at www.clinicaltrials.gov (NCT03337139). Detailed study design, recruitment, and intervention methods are reported else where (Butryn et al., 2019). Briefly, the parent study consisted of an orientation session followed by a 12-week group-based behavioral weight loss treatment. The present study analyzed data from this 12week period only, which was considered to be the weight loss phase. At the start of treatment, participants were provided digital devices and directed to use them to self-monitor their diet, physical activity, and weight, and review and reflect on their data throughout treatment. The Fitbit app was used for dietary self-monitoring, and also synced with physical activity and weight device data, allowing participants to track progress toward goals using one application. Participants were instruc ted to log all dietary intake in the app as soon as possible after eating. A wrist-worn physical activity sensor (Fitbit Flex), was provided to par ticipants to measure physical activity daily. Participants were instructed to weigh themselves on the Yunmai Smart Scale weekly during the first 10 weeks of treatment and daily during the final 2 weeks, an approach commonly used in behavioral weight loss (Burke et al., 2011).
3. Results Table 1 displays baseline participant characteristics and descriptive statistics for eating behaviors and self-monitoring adherence. Eating behaviors significantly differed by race, with White participants reporting significantly higher BE (t = 3.66, p < .001), UE (t = 2.47, p = .02), and EE (t = 3.04, p = .003) compared to participants of other racial Table 1 Participant characteristics.
2.2. Measures
M (SD) or n (%)
Eating behaviors were measured at baseline. Binge eating (BE) severity was assessed using the 16-item Binge Eating Scale (BES; Gor mally, Black, Daston, & Rardin, 1982). Items are rated on a 3- or 4-point scale. Total scores range from 0 to 46, with higher scores reflecting greater BE severity. The Three-Factor Eating Questionnaire (TFEQ-R18; Karlsson, Persson, Sjostrom, & Sullivan, 2000) was used to assess un controlled eating (UE) (excessive appetite and cravings, eating in response to external cues; 9 items) and emotional eating (EE) (eating in response to negative mood; 3 items). Items are rated on a 4-point scale and subscale scores are transformed to a 0–100 scale with higher scores indicating greater uncontrolled or emotional eating. Adherence to self-monitoring during treatment was assessed using data collected from the digital devices. Self-monitoring data were accessed after the completion of treatment via a remote, web-based research portal that captured data from the devices. The full 12-week treatment period was used to compute percent adherence. Adherence to dietary self-monitoring was calculated as the percentage of days with ≥800 cal logged (Patel, Hopkins, Brooks, & Bennett, 2019; Shay, Sei bert, Watts, Sbrocco, & Pagliara, 2009). The treatment program advised participants not to eat less than 1200 cal per day. Therefore 800 cal was chosen as the threshold for adherence because it was suspected that even if some participants truly consumed less than 1200 cal, it would be
Demographic/anthropometric factors BMI (kg/m2) Age (years) Gender (n, % female)
34.86 (4.75) 50.77 (13.39) 62 (80.52%)
Race American Indian/Native Alaskan Asian Native Hawaiian or other Pacific Islander Black/African American White/Caucasian Other or more than one race
1 (1.30%) 2 (2.60%) 0 (0%) 28 (36.36%) 41 (53.25%) 5 (6.49%)
Ethnicity Hispanic/Latina Not Hispanic/Latina
3 (3.90%) 74 (96.10%)
Eating behaviors Binge eating Uncontrolled eating Emotional eating
13.19 (7.58) 40.10 (18.56) 55.19 (24.05)
Self-monitoring % adherence Eating Weight Physical activity
82.51 (17.60) 85.55 (18.67) 93.64 (13.92)
Note. BMI = body mass index. 2
Eating Behaviors 39 (2020) 101448
M.K. Martinelli et al.
groups. Age was negatively associated with UE (r = − 0.29, p < .001). Age was not related to BE (r = − 0.15, p = .21) or EE (r = − 0.19, p = .10). Eating behaviors did not differ by sex (BE: t = 3.66, p = .10; UE: t = 3.66, p = .10; EE: t = 3.66, p = .10) and were not associated with baseline BMI (BE: r = 0.04, p = .74; UE: r = − 0.0004, p = .997; EE: r = 0.20, p = .08). Eating behaviors were significantly intercorrelated: BE and UE (r = 0.68, p < .001), BE and EE (r = 0.68, p < .001), EE and UE (r = 0.67, p < .001). Associations between baseline anthropometric and demographic vari ables (BMI, age, sex, and race) and the self-monitoring adherence vari ables were examined, and no significant relationships were observed (p’s > 0.05). Spearman correlations controlling for race revealed that greater BE severity at baseline predicted greater adherence to self-monitoring of weight (ρ = 0.25, p = .03), and eating (ρ = 0.25, p = .03). BE severity did not predict adherence to physical activity self-monitoring (ρ = 0.08, p = .50). Spearman correlations controlling for race and age indicated that UE at baseline did not predict adherence to self-monitoring of weight (ρ = 0.01, p = .94), eating (ρ = 0.01, p = .92), or physical activity (ρ = − 0.005, p = .97). Similarly, EE at baseline, controlling for race, did not predict adherence to self-monitoring of weight (ρ = 0.15, p = .190), eating (ρ = 0.10, p = .41), or physical activity (ρ = − 0.03, p = .79).
maintain high adherence relatively easily throughout treatment, regardless of particular barriers or motivators for this type of selfmonitoring. This study has several strengths and limitations. The strengths of this study include rigorous measurement of self-monitoring adherence using digital devices rather than analogue methods, inclusion of weight and physical activity self-monitoring adherence, and multifaceted mea surement of eating behaviors. The findings from this study were limited to the first 3 months of weight loss and self-monitoring adherence during this phase of treatment was high, and it is possible, but perhaps unlikely, that some participants may have restricted below the 800 cal threshold for eating self-monitoring adherence. It is also possible that some par ticipants may have exceeded the target self-weighing threshold or engaged in excessive weighing, however this was not examined in this study. It should also be noted that self-monitoring data was concealed from coaches during this treatment period to avoid potential contami nation of future randomized conditions. Although general encourage ment to self-monitor was provided during group sessions, this was done so without reference to participant adherence. Lastly, given the emphasis of digital self-monitoring in this study, participants who selfselected for participation may have been more comfortable with or interested in using digital self-monitoring than the general population. The current study found that higher binge eating severity at baseline was associated with greater adherence to digital self-monitoring of weight and eating, but not physical activity, during a 12-week behav ioral weight loss intervention. Future studies should determine if there are psychological mechanisms (e.g., motivation) or features of digital self-monitoring that may explain this pattern of findings. Given the link between self-monitoring adherence and treatment outcomes (Butryn et al., 2019), future work examining these associations over multiple timepoints and in relation to weight loss outcomes is also warranted.
4. Discussion The current study was designed to examine the relationship between eating behaviors and adherence to digital self-monitoring of eating, weight, and physical activity during a 12-week behavioral weight loss intervention. This is the first study with findings to suggest that greater baseline binge eating severity is associated with better adherence to eating and weight self-monitoring; of note, even the most severe binge eating behaviors were subclinical, as individuals who met diagnostic criteria were not eligible for treatment. Given that self-monitoring is a cornerstone of treatment for both obesity and binge eating, one expla nation for these findings is that eating and weight self-monitoring may have been perceived as particularly helpful to those with greater eating problems. It is also possible that those with greater pretreatment eating severity differ from other individuals in meaningful ways, such as higher motivation to adhere to treatment or greater tendency to rigidly adopt weight-related behaviors initially following periods of overeating or weight gain (Smith et al., 1999). However, it is unclear if this pattern persists over time; two studies that found no relationship (Kong et al., 2012) or a negative association between binge eating and selfmonitoring of eating (Ariel & Perri, 2016) were conducted over a 6month period, compared to 3 months in the current study, raising the possibility that those with greater binge eating severity may adhere strictly to treatment initially, but are less successful at maintaining adherence as treatment progresses. Lastly, the finding that binge eating was related to eating and weight self-monitoring while uncontrolled and emotional eating were not suggests that, while the three constructs overlap, the BES may capture factors uniquely predictive of adherence. For instance, the BES includes items assessing cognitive features of binge eating, such as weight concerns and high dieting standards, which are hypothesized to motivate a pattern of strict dieting followed by periods of overeating (Gormally et al., 1982). To our knowledge, the current study was the first to examine eating behaviors as predictors of physical activity tracking during behavioral weight loss. The findings from this study do not support a relationship between these eating behaviors and physical activity self-monitoring. It is possible that physical activity tracking and eating behavior are more distally related to one another as compared to weight and dietary selfmonitoring and eating behavior, or that physical activity selfmonitoring does not provide information that is as emotionally salient or reinforcing as weight and dietary self-monitoring information. Another possible explanation is that physical activity self-monitoring in this study was overall very high, possibly due to the passive nature of physical activity tracking, meaning that most participants were able to
Funding This research was funded by National Institute of Diabetes and Digestive and Kidney Diseases (Grant: R21DK112741) to M.L.B. CRediT authorship contribution statement Mary K. Martinelli: Conceptualization, Methodology, Formal anal ysis, Investigation, Data curation, Visualization, Writing – original draft, Writing - review & editing. Laura D’Adamo: Writing – original draft, Writing - review & editing. Meghan L. Butryn: Writing - review & editing, Supervision, Funding acquisition. Declaration of competing interest All authors declare that they have no conflicts of interest. References Ariel, A. H., & Perri, M. G. (2016). Effect of dose of behavioral treatment for obesity on binge eating severity. Eating Behaviors, 22, 55–61. https://doi.org/10.1016/j. eatbeh.2016.03.032. Burke, L. E., Wang, J., & Sevick, M. A. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111 (1), 92–102. https://doi.org/10.1016/j.jada.2010.10.008. Butryn, M. L., Godfrey, K. M., Martinelli, M. K., Roberts, S. R., Forman, E. M., & Zhang, F. (2019). Digital self-monitoring: Does adherence or association with outcomes differ by self-monitoring target? Obesity Science & Practice.. https://doi.org/10.1002/ osp4.391. Carels, R. A., Selensky, J. C., Rossi, J., Solar, C., & Hlavka, R. (2017). A novel steppedcare approach to weight loss: The role of self-monitoring and health literacy in treatment outcomes. Eating Behaviors, 26, 76–82. https://doi.org/10.1016/j. eatbeh.2017.01.009. Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47–55. https://doi.org/ 10.1016/0306-4603(82)90024-7.
3
M.K. Martinelli et al.
Eating Behaviors 39 (2020) 101448 Mason, T. B., & Lewis, R. J. (2015). Assessing the roles of impulsivity, food-related cognitions, BMI, and demographics in the dual pathway model of binge eating among men and women. Eating Behaviors, 18, 151–155. https://doi.org/10.1016/j. eatbeh.2015.05.015. Meyer, J., Wasmann, M., Heuten, W., El Ali, A., & Boll, S. C. (2017). Identification and classification of usage patterns in long-term activity tracking. Paper presented at the proceedings of the 2017 CHI conference on human factors in computing systems. Patel, M. L., Hopkins, C. M., Brooks, T. L., & Bennett, G. G. (2019). Comparing selfmonitoring strategies for weight loss in a smartphone app: Randomized controlled trial. JMIR mHealth and uHealth, 7(2), Article e12209. https://doi.org/10.2196/ 12209. Rohrer, J. E., Vickers-Douglas, K. S., & Stroebel, R. J. (2009). Uncontrolled eating and obesity in adult primary care patients. Obesity Research & Clinical Practice, 3(2), I–II. https://doi.org/10.1016/j.orcp.2009.01.004. Shay, L. E., Seibert, D., Watts, D., Sbrocco, T., & Pagliara, C. (2009). Adherence and weight loss outcomes associated with food-exercise diary preference in a military weight management program. Eating Behaviors, 10(4), 220–227. https://doi.org/ 10.1016/j.eatbeh.2009.07.004. Smith, C. F., Williamson, D. A., Bray, G. A., & Ryan, D. H. (1999). Flexible vs. rigid dieting strategies: Relationship with adverse behavioral outcomes. Appetite, 32(3), 295–305. https://doi.org/10.1006/appe.1998.0204. Wong, M., & Qian, M. (2016). The role of shame in emotional eating. Eating Behaviors, 23, 41–47. https://doi.org/10.1016/j.eatbeh.2016.07.004.
Harvey, J., Krukowski, R., Priest, J., & West, D. (2019). Log often, lose more: Electronic dietary self-monitoring for weight loss. Obesity (Silver Spring), 27(3), 380–384. https://doi.org/10.1002/oby.22382. Karlsson, J., Persson, L. O., Sjostrom, L., & Sullivan, M. (2000). Psychometric properties and factor structure of the three-factor eating questionnaire (TFEQ) in obese men and women. Results from the Swedish obese subjects (SOS) study. International Journal of Obesity and Related Metabolic Disorders, 24(12), 1715–1725. https://doi. org/10.1038/sj.ijo.0801442. Kong, A., Beresford, S. A., Imayama, I., Duggan, C., Alfano, C. M., Foster-Schubert, K. E., … McTiernan, A. (2012). Adoption of diet-related self-monitoring behaviors varies by race/ethnicity, education, and baseline binge eating score among overweight-toobese postmenopausal women in a 12-month dietary weight loss intervention. Nutrition Research, 32(4), 260–265. https://doi.org/10.1016/j.nutres.2012.03.001. Konttinen, H., van Strien, T., Mannisto, S., Jousilahti, P., & Haukkala, A. (2019). Depression, emotional eating and long-term weight changes: A population-based prospective study. The International Journal of Behavioral Nutrition and Physical Activity, 16(1), 28. https://doi.org/10.1186/s12966-019-0791-8. Korinek, E. V., Phatak, S. S., Martin, C. A., Freigoun, M. T., Rivera, D. E., Adams, M. A., … Hekler, E. B. (2018). Adaptive step goals and rewards: A longitudinal growth model of daily steps for a smartphone-based walking intervention. Journal of Behavioral Medicine, 41(1), 74–86. https://doi.org/10.1007/s10865-017-9878-3. Marcus, M. D., & Wildes, J. E. (2014). Disordered eating in obese individuals. Current Opinion in Psychiatry, 27(6), 443–447. https://doi.org/10.1097/ YCO.0000000000000103.
4
Contents lists available at ScienceDirect
Eating Behaviors journal homepage: www.elsevier.com/locate/eatbeh
Binge eating predicts adherence to digital self-monitoring during behavioral weight loss Mary K. Martinelli *, Laura D’Adamo, Meghan L. Butryn Department of Psychology, Drexel University, Philadelphia, PA, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: Obesity Binge eating Self-monitoring Behavioral weight loss Treatment adherence
Objective: Consistent self-monitoring of dietary intake, weight, and physical activity predicts better outcomes during behavioral weight loss, but the factors that influence self-monitoring adherence are not well understood. This study attempted to fill gaps in the existing literature by examining whether pre-treatment eating behaviors predict adherence to digital self-monitoring during a behavioral weight loss program. Method: Participants (N = 77) reported on binge eating, uncontrolled eating, and emotional eating at baseline, and were instructed to self-monitor their food intake, weight, and physical activity using digital devices (food logging app, Fitbit, and wireless “smart” scale) throughout the 12-week treatment. Adherence to self-monitoring was assessed using data captured from these devices. Results: Greater baseline binge eating severity predicted greater adherence to self-monitoring of weight (ρ = 0.25, p = .03) and eating (ρ = 0.25, p = .03), but not self-monitoring of physical activity. Uncontrolled eating and emotional eating did not significantly predict self-monitoring adherence. Conclusions: In contrast to previous research, this study found that participants with greater pre-treatment binge eating severity had better adherence to self-monitoring of eating, and for the first time established a relationship between binge eating severity and digital self-monitoring of weight in behavioral weight loss. Individuals with greater pre-treatment binge eating may exhibit characteristics, such as motivation or rigidity, that are beneficial during the initial period of weight loss. Future studies should determine if there are features of analogue versus digital self-monitoring that may explain this pattern of findings, and examine these associations longitudinally.
1. Introduction While consistent self-monitoring of dietary intake, weight, and physical activity predicts better outcomes during behavioral weight loss interventions (Burke, Wang, & Sevick, 2011; Butryn et al., 2019; Carels, Selensky, Rossi, Solar, & Hlavka, 2017; Harvey, Krukowski, Priest, & West, 2019), the factors that influence self-monitoring adherence are not well understood. Problematic eating behaviors, such as sub-clinical binge eating, uncontrolled eating, and emotional eating, are common in individuals with obesity seeking weight loss treatment (Konttinen, van Strien, Mannisto, Jousilahti, & Haukkala, 2019; Marcus & Wildes, 2014; Rohrer, Vickers-Douglas, & Stroebel, 2009). It is unknown if these be haviors predict self-monitoring adherence. A sense of shame or avoid ance related to these problematic eating behaviors is common (Mason & Lewis, 2015; Wong & Qian, 2016), which might impede self-monitoring adherence. Alternatively, individuals who engage in problematic eating behaviors might feel more motivated to adhere to treatment strategies to
reduce the distress caused by these behaviors, or to control their eating following periods of overeating or weight gain (Smith, Williamson, Bray, & Ryan, 1999). In one previous study, Ariel and Perri (2016) found that greater baseline binge eating severity predicted poorer adherence to food log ging during behavioral weight loss. In contrast, Kong et al. (2012) found that binge eating, emotional eating, and uncontrolled eating were un related to the number of food logs completed during weight loss treat ment. These mixed findings make it difficult to draw firm conclusions about the relationship between eating behaviors and self-monitoring of eating. Extant studies also relied exclusively on written food logging. Digital food logging may be more convenient, accessible, and discreet than written records. Moreover, prior research has not examined whether problematic eating behaviors predict self-monitoring of weight or physical activity. Given that self-monitoring of eating, weight, and physical activity have been shown to independently predict weight loss outcomes (Butryn et al., 2019), identifying pretreatment predictors of
* Corresponding author at: 3141 Chestnut St., Stratton Hall 119, Philadelphia, PA 19104, USA. E-mail address: [email protected] (M.K. Martinelli). https://doi.org/10.1016/j.eatbeh.2020.101448 Received 1 April 2020; Received in revised form 20 October 2020; Accepted 25 October 2020 Available online 29 October 2020 1471-0153/© 2020 Elsevier Ltd. All rights reserved.
M.K. Martinelli et al.
Eating Behaviors 39 (2020) 101448
self-monitoring may be helpful in promoting self-monitoring adherence. The current study examined the relationship between baseline eating behaviors and adherence to digital self-monitoring of eating, physical activity, and weight among adults enrolled in a group-based behavioral weight loss program. We hypothesized that eating behaviors would significantly predict self-monitoring adherence during the 12-week treatment program.
unlikely that participants would restrict below 800 cal. Thus, it was assumed that days with less than 800 cal likely represented incomplete self-monitoring records. Adherence to physical activity self-monitoring was determined by the percentage of days with ≥500 steps were tracked with the Fitbit (Korinek et al., 2018; Meyer, Wasmann, Heuten, El Ali, & Boll, 2017). Percent adherence to weight self-monitoring was calculated based on adherence to the varied self-weighing prescription (described above).
2. Method
2.3. Data analysis
2.1. Participants and procedures
Data were analyzed in R and SPSS version 25 and statistical level was set at ∝ = 0.05. Associations between eating behavior and age, BMI, sex, and race were examined using independent samples t-tests for categor ical variables and Pearson correlation for continuous variables, and subsequent analyses controlled for characteristics that were significantly related to eating behavior. Race was dichotomized for analyses (White = 1, other racial groups = 0) given that there was a general pattern of White participants scoring higher on baseline measures of eating behavior. Additionally White participants made up the largest racial category in the sample (41%), and if race was examined with multiple dichotomized variables (e.g., a dummy coded variable for each race) this would have limited the statistical power needed to detect relationships for the primary aim. The self-monitoring adherence variables were nonnormally distributed (e.g., negatively skewed), causing violations of assumptions for parametric models. Thus, non-parametric tests (Spearman correlations) were used to assess relationships between eating behavior and self-monitoring adherence. If participants dis continued treatment (n = 10), device data were retained only until the date that they discontinued treatment. Therefore, only completers (N = 77) were used in the present analyses. One participant was missing BES data and was excluded from those analyses.
This study is a secondary analysis of data collected for a clinical trial (R21DK112741) focused on digital data sharing and supportive accountability following face-to-face behavioral weight loss treatment. Eligible participants were 18–70 years old with a body mass index (BMI) between 25 and 45 kg/m2, capable of engaging in physical activity, and had a smartphone. Participants who currently met diagnostic criteria for binge eating disorder (assessed via clinical interview) were excluded, as were those who had a medical or psychiatric condition that may constitute a risk during the intervention, had recent weight loss (>5%), or used weight-affecting medication. The parent study received Institutional Review Board approval and was registered at www.clinicaltrials.gov (NCT03337139). Detailed study design, recruitment, and intervention methods are reported else where (Butryn et al., 2019). Briefly, the parent study consisted of an orientation session followed by a 12-week group-based behavioral weight loss treatment. The present study analyzed data from this 12week period only, which was considered to be the weight loss phase. At the start of treatment, participants were provided digital devices and directed to use them to self-monitor their diet, physical activity, and weight, and review and reflect on their data throughout treatment. The Fitbit app was used for dietary self-monitoring, and also synced with physical activity and weight device data, allowing participants to track progress toward goals using one application. Participants were instruc ted to log all dietary intake in the app as soon as possible after eating. A wrist-worn physical activity sensor (Fitbit Flex), was provided to par ticipants to measure physical activity daily. Participants were instructed to weigh themselves on the Yunmai Smart Scale weekly during the first 10 weeks of treatment and daily during the final 2 weeks, an approach commonly used in behavioral weight loss (Burke et al., 2011).
3. Results Table 1 displays baseline participant characteristics and descriptive statistics for eating behaviors and self-monitoring adherence. Eating behaviors significantly differed by race, with White participants reporting significantly higher BE (t = 3.66, p < .001), UE (t = 2.47, p = .02), and EE (t = 3.04, p = .003) compared to participants of other racial Table 1 Participant characteristics.
2.2. Measures
M (SD) or n (%)
Eating behaviors were measured at baseline. Binge eating (BE) severity was assessed using the 16-item Binge Eating Scale (BES; Gor mally, Black, Daston, & Rardin, 1982). Items are rated on a 3- or 4-point scale. Total scores range from 0 to 46, with higher scores reflecting greater BE severity. The Three-Factor Eating Questionnaire (TFEQ-R18; Karlsson, Persson, Sjostrom, & Sullivan, 2000) was used to assess un controlled eating (UE) (excessive appetite and cravings, eating in response to external cues; 9 items) and emotional eating (EE) (eating in response to negative mood; 3 items). Items are rated on a 4-point scale and subscale scores are transformed to a 0–100 scale with higher scores indicating greater uncontrolled or emotional eating. Adherence to self-monitoring during treatment was assessed using data collected from the digital devices. Self-monitoring data were accessed after the completion of treatment via a remote, web-based research portal that captured data from the devices. The full 12-week treatment period was used to compute percent adherence. Adherence to dietary self-monitoring was calculated as the percentage of days with ≥800 cal logged (Patel, Hopkins, Brooks, & Bennett, 2019; Shay, Sei bert, Watts, Sbrocco, & Pagliara, 2009). The treatment program advised participants not to eat less than 1200 cal per day. Therefore 800 cal was chosen as the threshold for adherence because it was suspected that even if some participants truly consumed less than 1200 cal, it would be
Demographic/anthropometric factors BMI (kg/m2) Age (years) Gender (n, % female)
34.86 (4.75) 50.77 (13.39) 62 (80.52%)
Race American Indian/Native Alaskan Asian Native Hawaiian or other Pacific Islander Black/African American White/Caucasian Other or more than one race
1 (1.30%) 2 (2.60%) 0 (0%) 28 (36.36%) 41 (53.25%) 5 (6.49%)
Ethnicity Hispanic/Latina Not Hispanic/Latina
3 (3.90%) 74 (96.10%)
Eating behaviors Binge eating Uncontrolled eating Emotional eating
13.19 (7.58) 40.10 (18.56) 55.19 (24.05)
Self-monitoring % adherence Eating Weight Physical activity
82.51 (17.60) 85.55 (18.67) 93.64 (13.92)
Note. BMI = body mass index. 2
Eating Behaviors 39 (2020) 101448
M.K. Martinelli et al.
groups. Age was negatively associated with UE (r = − 0.29, p < .001). Age was not related to BE (r = − 0.15, p = .21) or EE (r = − 0.19, p = .10). Eating behaviors did not differ by sex (BE: t = 3.66, p = .10; UE: t = 3.66, p = .10; EE: t = 3.66, p = .10) and were not associated with baseline BMI (BE: r = 0.04, p = .74; UE: r = − 0.0004, p = .997; EE: r = 0.20, p = .08). Eating behaviors were significantly intercorrelated: BE and UE (r = 0.68, p < .001), BE and EE (r = 0.68, p < .001), EE and UE (r = 0.67, p < .001). Associations between baseline anthropometric and demographic vari ables (BMI, age, sex, and race) and the self-monitoring adherence vari ables were examined, and no significant relationships were observed (p’s > 0.05). Spearman correlations controlling for race revealed that greater BE severity at baseline predicted greater adherence to self-monitoring of weight (ρ = 0.25, p = .03), and eating (ρ = 0.25, p = .03). BE severity did not predict adherence to physical activity self-monitoring (ρ = 0.08, p = .50). Spearman correlations controlling for race and age indicated that UE at baseline did not predict adherence to self-monitoring of weight (ρ = 0.01, p = .94), eating (ρ = 0.01, p = .92), or physical activity (ρ = − 0.005, p = .97). Similarly, EE at baseline, controlling for race, did not predict adherence to self-monitoring of weight (ρ = 0.15, p = .190), eating (ρ = 0.10, p = .41), or physical activity (ρ = − 0.03, p = .79).
maintain high adherence relatively easily throughout treatment, regardless of particular barriers or motivators for this type of selfmonitoring. This study has several strengths and limitations. The strengths of this study include rigorous measurement of self-monitoring adherence using digital devices rather than analogue methods, inclusion of weight and physical activity self-monitoring adherence, and multifaceted mea surement of eating behaviors. The findings from this study were limited to the first 3 months of weight loss and self-monitoring adherence during this phase of treatment was high, and it is possible, but perhaps unlikely, that some participants may have restricted below the 800 cal threshold for eating self-monitoring adherence. It is also possible that some par ticipants may have exceeded the target self-weighing threshold or engaged in excessive weighing, however this was not examined in this study. It should also be noted that self-monitoring data was concealed from coaches during this treatment period to avoid potential contami nation of future randomized conditions. Although general encourage ment to self-monitor was provided during group sessions, this was done so without reference to participant adherence. Lastly, given the emphasis of digital self-monitoring in this study, participants who selfselected for participation may have been more comfortable with or interested in using digital self-monitoring than the general population. The current study found that higher binge eating severity at baseline was associated with greater adherence to digital self-monitoring of weight and eating, but not physical activity, during a 12-week behav ioral weight loss intervention. Future studies should determine if there are psychological mechanisms (e.g., motivation) or features of digital self-monitoring that may explain this pattern of findings. Given the link between self-monitoring adherence and treatment outcomes (Butryn et al., 2019), future work examining these associations over multiple timepoints and in relation to weight loss outcomes is also warranted.
4. Discussion The current study was designed to examine the relationship between eating behaviors and adherence to digital self-monitoring of eating, weight, and physical activity during a 12-week behavioral weight loss intervention. This is the first study with findings to suggest that greater baseline binge eating severity is associated with better adherence to eating and weight self-monitoring; of note, even the most severe binge eating behaviors were subclinical, as individuals who met diagnostic criteria were not eligible for treatment. Given that self-monitoring is a cornerstone of treatment for both obesity and binge eating, one expla nation for these findings is that eating and weight self-monitoring may have been perceived as particularly helpful to those with greater eating problems. It is also possible that those with greater pretreatment eating severity differ from other individuals in meaningful ways, such as higher motivation to adhere to treatment or greater tendency to rigidly adopt weight-related behaviors initially following periods of overeating or weight gain (Smith et al., 1999). However, it is unclear if this pattern persists over time; two studies that found no relationship (Kong et al., 2012) or a negative association between binge eating and selfmonitoring of eating (Ariel & Perri, 2016) were conducted over a 6month period, compared to 3 months in the current study, raising the possibility that those with greater binge eating severity may adhere strictly to treatment initially, but are less successful at maintaining adherence as treatment progresses. Lastly, the finding that binge eating was related to eating and weight self-monitoring while uncontrolled and emotional eating were not suggests that, while the three constructs overlap, the BES may capture factors uniquely predictive of adherence. For instance, the BES includes items assessing cognitive features of binge eating, such as weight concerns and high dieting standards, which are hypothesized to motivate a pattern of strict dieting followed by periods of overeating (Gormally et al., 1982). To our knowledge, the current study was the first to examine eating behaviors as predictors of physical activity tracking during behavioral weight loss. The findings from this study do not support a relationship between these eating behaviors and physical activity self-monitoring. It is possible that physical activity tracking and eating behavior are more distally related to one another as compared to weight and dietary selfmonitoring and eating behavior, or that physical activity selfmonitoring does not provide information that is as emotionally salient or reinforcing as weight and dietary self-monitoring information. Another possible explanation is that physical activity self-monitoring in this study was overall very high, possibly due to the passive nature of physical activity tracking, meaning that most participants were able to
Funding This research was funded by National Institute of Diabetes and Digestive and Kidney Diseases (Grant: R21DK112741) to M.L.B. CRediT authorship contribution statement Mary K. Martinelli: Conceptualization, Methodology, Formal anal ysis, Investigation, Data curation, Visualization, Writing – original draft, Writing - review & editing. Laura D’Adamo: Writing – original draft, Writing - review & editing. Meghan L. Butryn: Writing - review & editing, Supervision, Funding acquisition. Declaration of competing interest All authors declare that they have no conflicts of interest. References Ariel, A. H., & Perri, M. G. (2016). Effect of dose of behavioral treatment for obesity on binge eating severity. Eating Behaviors, 22, 55–61. https://doi.org/10.1016/j. eatbeh.2016.03.032. Burke, L. E., Wang, J., & Sevick, M. A. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111 (1), 92–102. https://doi.org/10.1016/j.jada.2010.10.008. Butryn, M. L., Godfrey, K. M., Martinelli, M. K., Roberts, S. R., Forman, E. M., & Zhang, F. (2019). Digital self-monitoring: Does adherence or association with outcomes differ by self-monitoring target? Obesity Science & Practice.. https://doi.org/10.1002/ osp4.391. Carels, R. A., Selensky, J. C., Rossi, J., Solar, C., & Hlavka, R. (2017). A novel steppedcare approach to weight loss: The role of self-monitoring and health literacy in treatment outcomes. Eating Behaviors, 26, 76–82. https://doi.org/10.1016/j. eatbeh.2017.01.009. Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47–55. https://doi.org/ 10.1016/0306-4603(82)90024-7.
3
M.K. Martinelli et al.
Eating Behaviors 39 (2020) 101448 Mason, T. B., & Lewis, R. J. (2015). Assessing the roles of impulsivity, food-related cognitions, BMI, and demographics in the dual pathway model of binge eating among men and women. Eating Behaviors, 18, 151–155. https://doi.org/10.1016/j. eatbeh.2015.05.015. Meyer, J., Wasmann, M., Heuten, W., El Ali, A., & Boll, S. C. (2017). Identification and classification of usage patterns in long-term activity tracking. Paper presented at the proceedings of the 2017 CHI conference on human factors in computing systems. Patel, M. L., Hopkins, C. M., Brooks, T. L., & Bennett, G. G. (2019). Comparing selfmonitoring strategies for weight loss in a smartphone app: Randomized controlled trial. JMIR mHealth and uHealth, 7(2), Article e12209. https://doi.org/10.2196/ 12209. Rohrer, J. E., Vickers-Douglas, K. S., & Stroebel, R. J. (2009). Uncontrolled eating and obesity in adult primary care patients. Obesity Research & Clinical Practice, 3(2), I–II. https://doi.org/10.1016/j.orcp.2009.01.004. Shay, L. E., Seibert, D., Watts, D., Sbrocco, T., & Pagliara, C. (2009). Adherence and weight loss outcomes associated with food-exercise diary preference in a military weight management program. Eating Behaviors, 10(4), 220–227. https://doi.org/ 10.1016/j.eatbeh.2009.07.004. Smith, C. F., Williamson, D. A., Bray, G. A., & Ryan, D. H. (1999). Flexible vs. rigid dieting strategies: Relationship with adverse behavioral outcomes. Appetite, 32(3), 295–305. https://doi.org/10.1006/appe.1998.0204. Wong, M., & Qian, M. (2016). The role of shame in emotional eating. Eating Behaviors, 23, 41–47. https://doi.org/10.1016/j.eatbeh.2016.07.004.
Harvey, J., Krukowski, R., Priest, J., & West, D. (2019). Log often, lose more: Electronic dietary self-monitoring for weight loss. Obesity (Silver Spring), 27(3), 380–384. https://doi.org/10.1002/oby.22382. Karlsson, J., Persson, L. O., Sjostrom, L., & Sullivan, M. (2000). Psychometric properties and factor structure of the three-factor eating questionnaire (TFEQ) in obese men and women. Results from the Swedish obese subjects (SOS) study. International Journal of Obesity and Related Metabolic Disorders, 24(12), 1715–1725. https://doi. org/10.1038/sj.ijo.0801442. Kong, A., Beresford, S. A., Imayama, I., Duggan, C., Alfano, C. M., Foster-Schubert, K. E., … McTiernan, A. (2012). Adoption of diet-related self-monitoring behaviors varies by race/ethnicity, education, and baseline binge eating score among overweight-toobese postmenopausal women in a 12-month dietary weight loss intervention. Nutrition Research, 32(4), 260–265. https://doi.org/10.1016/j.nutres.2012.03.001. Konttinen, H., van Strien, T., Mannisto, S., Jousilahti, P., & Haukkala, A. (2019). Depression, emotional eating and long-term weight changes: A population-based prospective study. The International Journal of Behavioral Nutrition and Physical Activity, 16(1), 28. https://doi.org/10.1186/s12966-019-0791-8. Korinek, E. V., Phatak, S. S., Martin, C. A., Freigoun, M. T., Rivera, D. E., Adams, M. A., … Hekler, E. B. (2018). Adaptive step goals and rewards: A longitudinal growth model of daily steps for a smartphone-based walking intervention. Journal of Behavioral Medicine, 41(1), 74–86. https://doi.org/10.1007/s10865-017-9878-3. Marcus, M. D., & Wildes, J. E. (2014). Disordered eating in obese individuals. Current Opinion in Psychiatry, 27(6), 443–447. https://doi.org/10.1097/ YCO.0000000000000103.
4