Examining Physical Activity and Affect Using Objective Measures: A Pilot Study of Anorexia Nervosa

Authors:
Trisha M. Karr, Ph.D., Brian Cook, Ph.D., Christie Zunker, Ph.D., Li Cao, M.S., Ross D. Crosby, Ph.D., Stephen A. Wonderlich, Ph.D., & James E. Mitchell, M.D.

Affiliation: Neuropsychiatric Research Institute, 120 8th Street, Fargo, ND 58103

Corresponding Author:
Dr. Christie Zunker, 260 River Valley Rd, Atlanta, GA 30328
christiezunker@hotmail.com
205-821-1499

ABSTRACT
This pilot study used accelerometers and ecological momentary assessment (EMA) to objectively examine physical activity and affect among women suffering from anorexia nervosa (AN). Nine women with AN wore ActiGraphTM accelerometers and completed EMA recordings across seven days. Mixed-effects linear models revealed temporal associations between physical activity and affect within the same day, within the same hour, and within the next hour. Momentary measurement of physical activity and positive affect revealed reciprocal effects, in that physical activity enhanced positive affect, which in turn, facilitated further activity. Findings reflect the utility of objective assessment measures in real time for the link between physical activity and affect among women with AN. The implementation of a tailored physical activity program, coordinated by trained clinical and sports professionals, may be a valuable asset for the treatment of AN.

Keywords: accelerometer, physical activity, anorexia nervosa, ecological momentary assessment, affect

Examining Physical Activity and Affect Using Objective Measures:
A Pilot Study of Anorexia Nervosa

Anorexia nervosa (AN) is characterized by minimal body weight through food restriction or purging methods, and often includes fear of weight gain and disturbance in one’s perception of body shape or size (American Psychiatric Association [APA], 2013). While exercise is often used as a weight-related compensatory behavior, understanding of physical activity and exercise patterns in AN are still unclear. The largest study to date has determined the prevalence of exercise as a means for individuals with AN to achieve and maintain low body weight ranges from 37.4% in individuals with bingeing with or without purging sub-type of AN to 54.5% of individuals with purging only sub-type of AN (Shroff et al., 2006). However, prevalence estimates such as these are typically determined through biased sampling methods in retrospective, cross-sectional, or case history designs using invalidated self-report exercise measures and have not addressed psychological characteristics that may explain why the exercise was undertaken (Cook, 2016; Hausenblas, Cook, & Chittester, 2008).

Much of the current understanding regarding possible etiologies and pathophysiology of AN has been obtained from studies utilizing research designs that limit causal inferences about the role of such factors at the momentary or daily level. For example, negative affectivity has been shown to predict the onset of eating disorder behavior and to predict a negative course for AN (Stice, 2002; Strober, Freeman, & Morrell, 1997). Thus, negative affect may have both etiologic and maintenance significance for AN, but it is unclear how negative affect causally relates to AN at the momentary level.

Similarly, stressful life events have been found to be elevated in AN participants in the time period before the onset of the disorder (Bulik, 2003), yet it is unclear if such life events persist after onset and if so, how they may relate to ongoing AN symptomatology on a daily basis. Given that the daily assessment of individuals with AN in the natural environment is limited within current research evidence (Engel et al., 2005; Haynos & Fruzzetti, 2011), it is possible that momentary variables serve to maintain AN symptoms. Ecological momentary assessment (EMA) provides an innovative approach for assessing the experience of stress, among women with AN, in a momentary fashion.

Ecological Momentary Assessment (EMA) methodologies have recently begun to elucidate the momentary relationships among physical activity and key factors related to AN. Specifically, EMA methods allow for a more rigorous examination of the functional role of behaviors and affect that may play a key role in the etiology of AN (Engel et al., 2005; Haynos & Fruzzetti, 2011). For example, an EMA study of individuals with high levels of depression revealed that physical activity produced increased positive mood (Kanning & Schlicht, 2010). EMA has also been used to examine the relationship between exercise and cognitions among individuals suffering from eating disorders. Furthermore, individuals with AN who also reported engaging in driven exercise reported a steeper rise in negative affect prior to an eating binge, a steeper rise in negative affect prior to purging, and a faster fall in negative affect after purging than in individuals with AN whom did not exercise (Cook et al., 2014).

While these studies have suggested associations that may account for the functional role of physical activity and provide insights into how activity may be used in AN, they have relied on self-reports of behavior rather than objective assessments. Therefore, physical activity monitors that obtain data objectively used in conjunction with assessments of psychological characteristics in real-time may further elucidate the functional relationship of physical activity and/or exercise in AN. Simply stated, real time continuous measures of physical activity behaviors that parallel the temporal sequence of EMA assessments of AN are needed to better understand the functional role of physical activity in AN. Physical activity, negative affect, and rigid behavioral patterns are important issues among individuals with AN, yet little is known about these relationships. The purpose of this pilot study was to examine associations between physical activity and affect among a group of women with AN, using objective assessments employing ActiGraphTM accelerometers and EMA. The researchers hypothesized that temporal associations would be found between physical activity and affect across multiple levels: within the same day, within the same hour, within the next hour, and within the next day. More specifically, the researchers anticipated increased positive affect and decreased negative affect following bouts of physical activity.

METHODS

Study participants

For this pilot study, participant recruitment involved mailings to professionals who treat patients with emotional problems, as well as fliers distributed within the community (i.e., local health clubs, inpatient treatment facilities, college campuses). Participants met specific inclusion criteria: a) female, b) aged 18 and older, and c) criteria for AN according to the DSM-5 (APA, 2013), either restricting or binge-purge type, or met criteria for sub-clinical AN. Participants were excluded based on the following criteria: a) presence of active psychosis; b) currently pregnant; c) inability to read English; d) history of bariatric surgery or other relevant gastrointestinal surgery; e) medical instability; f) inpatient or partial hospitalization (current or within past 6 weeks); and g) recently initiated or discontinued psychotherapy or drug therapy (< 6 weeks). Based on the exclusion criteria, one individual was screened out of the study, resulting in a sample of 9 participants. The final sample included women between 22-33 years of age (mean = 26.38, standard deviation = 4.81) with a mean body mass index of 17.46 kg/m2 (standard deviation = .88).

Measures
ActiGraphTM accelerometer. ActiGraphTM (MTI model GT3X; Manufacturing Technology, Fort Walton Beach, FL, USA) activity monitors were used for the objective assessment of participants’ activity levels (i.e., light, moderate, vigorous intensity) and energy expenditure. Participants were instructed to wear the activity monitors on their right hip consistently over the 1-week period (excluding showering, swimming, and sleeping) for the collection of motion data. Exercise bouts and intensity levels, for the 6 time-blocks across 7 days/week of capture, were identified based on the algorithm defined by Freedson and colleagues (1998). ActiLife Version 6 software (2010) was used for all data analyses. Objective physical activity monitors have demonstrated efficacy and validity in previous eating disorder research. (Alberti et al., 2013; Bratland-Sanda et al., 2011; El Ghoch et al., 2013).

Structured clinical interviews. Two clinical interviews were used in order to assess eating disorder psychopathology. The Eating Disorders Examination (EDE; Fairburn & Cooper, 1993), was used as the primary measure of eating disorder psychopathology. The EDE contains frequency measures of binge eating and compensatory behaviors and categorizes eating disorder symptomatology across four subscales (restraint, eating concerns, shape concerns, weight concerns). The Structured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-I/P; First, Spitzer, Gibbon, & Williams, 1995), was included in the study in order to identify Axis I psychiatric disorders. Additional questions were added to the SCID-I/P screening module for the assessment of all medical and psychiatric treatments during the past six months, including outpatient psychotherapy, medication treatments, and hospitalizations. Trained Master’s level clinical staff administered the SCID-I/P and the EDE to all participants.

Ecological momentary assessment (EMA). Affect was assessed in real time by the International PANAS Short Form (I-PANAS-SF; Thompson, 2007), an efficient 10-item measure of both positive and negative affect, which has shown acceptable internal consistency across all items (α ≥ .72). Five items were selected for positive affect: alert, attentive, determined, inspired, and active, and five items were chosen for negative affect: afraid, ashamed, nervous, upset, and hostile. Cronbach’s α values for the current study were .84 for positive affect and .89 for negative affect. Stress was also assessed via EMA across eight categories: Work-Related Problems, Family Concerns, Personal Relationship Problems, Financial Problems, School-Related Problems, Appearance Concerns, Other Stressful Experiences, and No Stressful Experiences. These items were rated as yes/no responses.

Procedure
Institutional review boards approved the study protocol. Initially, trained research assistants conducted telephone screenings to determine if participants met preliminary DSM-5 diagnostic criteria for AN (APA, 2013). Next, a brief informational meeting with the principal investigator was scheduled in order for the participants to learn more about the study, as well as completing the measurement of participant height and weight and explanation of participant informed consent. Following the information meeting, a screening visit with trained clinical staff was scheduled in order to determine medical and psychological stability. This meeting involved: a) a brief physical, b) the collection of blood samples, c) structured clinical interviews, and d) the completion of the questionnaires.

Participants who met eligibility criteria were then given instructions on how to use the ActiGraphTM equipment for the measurement of physical activity and the PalmTop™ computers for EMA. Participants were prompted to use the PalmTop™ computers when signaled in order to record their affect, eating behaviors, physical activity, and AN symptoms. Signal-contingent recordings were generated by semi-random signals multiple times each day during the 1-week interval; 6 time blocks were developed for signaling between 8:00 A.M. and 10:00 P.M. Principal investigators trained all participants on how to define eating and physical activity events relative to participants’ habits reported during the initial interview process. Participants were also instructed to wear the ActiGraphTM device routinely over the 1-week period.

Following a 24-hour practice assessment period, participants returned to meet with research assistants for a quality assurance visit regarding use of the equipment. Data during the practice period were reviewed with participants to ensure understanding of how to use the equipment, but these data were not used within analyses. Data collection then took place over the subsequent 1-week period. After the completion of data collection, participants met with the principal investigator for a brief follow-up visit for debriefing and return of the equipment, and each participant provided data from the PalmTop™ computers and the ActigraphTM equipment. Participants were compensated with $100 for the week of data collection and earned a $50 bonus for a minimum rate of 80% compliance within 45 minutes of the signaled prompts on the handheld computers.

Statistical Analysis
Mixed effect models were used for repeated EMA measures in this study. Mixed model analysis allows a wide variety of correlation configurations within each subject. A first-order autoregressive covariance structure (AR1) was used to take into account of serial correlations. Analyses were conducted in Statistical Package for the Social Science (SPSS) 19 and were based on all available data. Missing data were not imputed and mixed linear model uses available data on a subject when the missing data is random.

RESULTS

Participant Compliance
Participants responded to the signaled prompts with an average compliance of 87.4%, ranging from 72.3% to 98.8%. Participants also complied with the ActigraphTM equipment, showing an average of 218 minutes of total physical activity each day during the data collection period. Physical activity was categorized as moderate-vigorous level of intensity (25 average total minutes) or light level of intensity (193 average total minutes) based on the ratio of counts per minute (Freedson et al., 1998). Due to the varied minutes of physical activity across levels of intensity, the researchers chose to sum the minutes of physical activity and to use the total minutes as our variable of interest for all further analyses.

Association of Physical Activity & Affect Within Same Day/Next Day

Mixed-effects linear models were conducted in order to examine the association between physical activity and affect within the same day as well as the following day. Participants reported varied average scores for positive affect (PA), negative affect (NA), and stress (Table 1). Data were aggregated across repeated assessments within days so that mean NA, PA, and stress scores could be calculated for each participant for each day of data collection. Variability in scores was calculated with mean squared successive difference (MSSD) statistics to determine the average degree of variability in affect over time. MSSD values symbolized the variation in PA (PA-MSSD), NA (NA-MSSD), and stress (Stress-MSSD) each day in relation to the squared difference across successive time points and the distance between the measured time points. A significant association was found for total minutes of physical activity predicting PA-MSSD; more minutes of total physical activity predicted less variability around the daily mean for positive affect within the same day [F = 4.093, p = .05; β = -.031]. Total minutes of physical activity significantly predicted Stress-MSSD; more minutes of total physical activity predicted less variability around the daily mean for stress within the same day [F = 6.506, p < .014; β = -.005]. That is, physical activity predicted less variability for the average level of daily stress. Significant effects were not found for total minutes of physical activity predicting variability in daily negative affect (NA-MSSD; p < .541). Stress-MSSD predicted total minutes of physical activity; thus, less variability around the daily mean for stress predicted more minutes of total activity within the same day [F = 7.447, p < .008; β = -20.204]. No significant effects were found for NA-MSSD (p < .542) or PA-MSSD (p < .066) predicting total minutes of physical activity within the same day. For the next day, no significant effects were found for affect predicting activity (NA-MSSD, p < .954; PA-MSSD, p < .365; Stress-MSSD, p < .727), or for activity predicting affect (NA-MSSD, p < .946; PA-MSSD, p < .338; Stress-MSSD, p < .819).

Table 1

Association of Physical Activity & Affect Within the Same Hour

Mixed-effects linear models were run to investigate the relation between physical activity and affect within the same hour. Total minutes of physical activity significantly predicted current level of positive affect (PA level); more minutes of physical activity predicted greater current PA level [F = 6.992, p < .01; β = .038]. Significant effects for outcomes during the same hour were not found for physical activity predicting current level of negative affect (NA level; p = .257) or current stress level (p = .883). Within the same hour, current level of positive affect (PA level) significantly predicted physical activity; greater PA level predicted more minutes of activity [F = 6.733, p < .01; β = .594]. Significant effects were not found for current level of negative affect (NA level; p < .305) or current level of stress predicting physical activity (p < .883).

Association of Physical Activity & Affect Within the Next Hour

Mixed-effects linear models were performed to examine the association between physical activity and affect within the next hour. Total minutes of physical activity significantly predicted PA level; more minutes of activity predicted greater PA level within the next hour [F = 6.792, p < .01; β = .044]. Significant effects for activity predicting affect during the next hour were not found for NA level (p < .06) or Stress level (p < .270). Level of positive affect (PA level) significantly predicted total minutes of physical activity; greater PA level predicted more minutes of total physical activity within the next hour [F = 15.676, p < .01; β = 1.003]. Significant effects were not found for NA level (p < .242) or Stress level (p < .867), predicting minutes of physical activity within the next hour.

DISCUSSION
The purpose of this pilot study was to examine the utility of objective assessments for the measurement of physical activity and affect among participants with AN, using ActigraphTM activity monitors and EMA. In line with other research showing the value of objective measures of physical activity (Alberti et al., 2013; Bratland-Sanda et al., 2011), and affect (Engel et al.,
2005; 2013; Haynos & Fruzzetti, 2011), it was predicted that temporal associations would be found for physical activity and affect among women with AN. This hypothesis was partially supported as associations between physical activity and affect were found across multiple levels: within the same day, within the same hour, and within the next hour.

Within the same day, total physical activity was associated with affective stability. More minutes of total physical activity predicted less variability in the average level of positive affect and the average level of stress. These findings suggest that physical activity may influence the stability of affect, and support previous findings showing that physical activity facilitated calmness (Kanning & Schlicht, 2010). Such implications of physical activity in relation to positive affect are noteworthy given difficulties with affect regulation among women with AN. Furthermore, the result of enhanced positive affect following engagement in physical activity may promote well-being and enhanced quality of life for women with AN (Kanning & Schlicht, 2010). In addition, less variability in the average level of stress predicted more minutes of total physical activity, suggesting that stability in stress level may influence engagement in physical activity. Therefore, individuals with AN who are physically active, may experience stability of positive affect and stress, and thus, be more likely to continue such behaviors. Nonetheless, total minutes of physical activity was not associated with affect reported during the next day, implying that the temporal effects of physical activity are limited to daily affect.

Physical activity was also associated with affect at the momentary level, within the same hour and the next hour. Within the same hour, more minutes of total physical activity predicted a greater average level of positive affect intensity. For the next hour, individuals with greater positive affect were likely to engage in more total minutes of physical activity. These findings support previous research using EMA (Engel et al., 2013), in that individuals with AN reported increased positive affect prior to physical activity. Additionally, results from the present study showed that individuals with AN who engaged in physical activity were also likely to experience greater positive affect following activity. Thus, it may be implied that this physical activity was purposeful behavior in an attempt to regulate affect. That is, reciprocal effects may be present for the association between positive affect and physical activity, in that activity enhances affect, which in turn, facilitates further activity (Schwerdtfeger, Eberhardt, Chmitorz, & Schaller, 2010).

Interestingly, negative affect was not associated with total minutes of physical activity in the current study. Similar findings have been described by Schwerdtfeger, Eberhardt, and Chmitorz (2008), who used ecological methods for daily monitoring of bodily movement and found that movement did not influence negative affect. In a follow-up study, Schwerdtfeger and colleagues (2010) proposed that bodily movement does not simply facilitate mood repair, but that the association between negative affect and movement may be more complex. Future studies that examine types of negative affect, the degree of affect, or characteristics of bodily movement, such as intensity level, may provide valuable information for clarifying the connection between negative affect and physical activity.

The current study reflects the strengths of physical activity research using objective assessments. First, this study used ActigraphTM accelerometers for a reliable measurement of physical activity among a unique sample of women, for which standardized physical activity guidelines are yet to be defined. Second, this study used EMA, a well-validated and psychometrically established method of assessment. Interestingly, the real-time assessment of physical activity in this study revealed low levels of vigorous physical activity among women suffering from AN, a finding also reported by Alberti and colleagues (2013). One explanation is that women in this study simply engaged in fewer minutes of daily vigorous activity in order to manage affect. Future research is necessary in order to better understand the typical frequency and duration of vigorous physical activity among women suffering from AN. Third, the objective assessments provided data collection in real-time to reduce the likelihood of self-report bias in the retrospective recall of participants’ ratings of affect and physical activity. However, despite the use of EMA methods, it is possible that self-report biases may have influenced participants’ responses during the real-time data collection. Also, the researchers study is limited in their inability to determine temporal order for physical activity and affect within the same hour. An additional limitation of the study is the small sample size. Future replication studies using objective measures with larger samples of women with AN are necessary in order to further illustrate temporal associations between physical activity and affect.

CONCLUSIONS
The findings of the present study highlight the value of physical activity for affective stability among women with AN. Given increasing evidence of emotion regulation deficits in AN (Schwerdtfeger et al., 2008), individuals with AN may obtain particularly strong affect related reward from physical activity. Although standard health guidelines for physical activity among individuals with AN are yet to be identified, previous studies (Hausenblas et al., 2008; Ng, Ng, & Wong, 2013), have shown that activity can be safely implemented during treatment for individuals with AN and that continued activity post-treatment has further potential health benefits for these individuals.

APPLICATION IN SPORT
Safe physical activity practices, determined by clinical and sports professionals, alongside treatment programs that focus on strengthened emotion awareness and regulation, may provide a valuable combination for advanced treatment approaches for individuals with AN (Ng et al., 2013). In addition, there may be benefits to focusing more on improving functional fitness, enhancing health, and setting personalized goals (e.g., participating in a weekly yoga class, running a 5k) instead of focusing on weight, appearance, and competing against others; adults who compare themselves with others may develop negative perceptions of their own athletic ability and may increase the risk of avoiding engaging in various sports or physical activities (Zunker et al., 2014). It may be useful to encourage women with AN to include mindfulness exercises and avoid comparing themselves with others as part of their treatment.

In summary, the current study adds to previous research findings noting the utility of objective measures by identifying temporal associations between physical activity and affect among women with AN. More specifically, this study highlights connections between increased engagement in physical activity and emotional stability for positive affect and stress among women with AN. Additionally, greater momentary positive affect was reported prior to and following physical activity. Future research studies on the association between physical activity and positive affect should include evaluation of quality of life among women with AN. Implementation of safe physical activity in the absence of medical contraindications, determined by clinical professionals, may be a valuable asset for advancing treatment approaches and promoting well-being for individuals with AN (Cook et al., 2016; Ng et al., 2013).

ACKNOWLEDGEMENTS
Funding source – this study was funded, in part, by a Young Investigator Grant from The National Eating Disorders Association. All authors declare no conflicts of interest.

REFERENCES
1. Alberti, M., Galvani, C., El Ghoch, M., Capelli, C., Lanza, M., Calugi, S., & Dalle Grave, R. (2013). Assessment of physical activity in anorexia nervosa and treatment outcome. Medicine & Science in Sports & Exercise, 45, 1643-1648.

2. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, D.C.: Author.

3. Bratland-Sanda, S., Martinsen, E. W., Rosenvinge, J. H., Rø, Ø., Hoffart, A., & Sundgot-Borge, J. (2011). Exercise dependence score in patients with longstanding eating disorders and controls: The importance of affect regulation and physical activity intensity. European Eating Disorders Review, 19(3), 249-55.

4. Bulik, C. M. (2003). Risk factors for anorexia nervosa. In Annenberg Report on Adolescent Eating Disorders.

5. Cook, B. (2016). The assessment of physical activity and exercise in eating disorders. In Wade, T. (Eds). Encyclopedia of Feeding and Eating Disorders. (pp 1-6). Singapore; Springer Science+Business Media.

6. Cook, B., Wonderlich, S., Crosby, R., Engel, S., Mitchell, J., Crow, S.,…& Simonich, H. (2014).
Ecological momentary assessment of compulsive exercise intensity and affect in anorexia nervosa. Paper presentation at the annual meeting of the Eating Disorders Research Society, San Diego, CA.

7. Cook, B., Wonderlich, S. A., Mitchell, J. E., Thompson, R., Sherman, R., & McCallum, K. (2016). Exercise in eating disorders treatment: Systematic review and proposal of guidelines. Medicine & Science in Sport & Exercise, 48, 1408-14.

8. El Ghoch, M., Calugi, S., Pellegrini, M., Milanese, C., Busacchi, M., Battistini, N.C.,… & Dalle Grave, R. (2013). Measured physical activity in anorexia nervosa: Features and treatment outcome. International Journal of Eating Disorders, 46, 709-712.

9. Engel, S. G., Wonderlich, S. A., Crosby, R. D., Mitchell, J. E., Crow, S., Peterson, C. B.,… & Gordon, K. H. (2013). The role of affect in the maintenance of anorexia nervosa: Evidence from a naturalistic assessment of momentary behaviors and emotion. Journal of Abnormal Psychology, 122, 709-719.

10. Engel, S. G., Wonderlich, S. A., Crosby, R. D., Wright, T. L., Mitchell, J. E., Crow, S. J., & Venegoni, E. E. (2005). A Study of patients with anorexia nervosa using ecologic momentary assessment. International Journal of Eating Disorders, 38, 335–339.

11.Fairburn, C. G., & Cooper, Z. (1993). The Eating Disorder Examination, 12th ed. In Fairburn, C. B., Wilson, G. T (Eds.), Binge eating: Nature, assessment, and treatment. (pp. 317–360). New York, Guilford.

12. First, M., Spitzer, R., Gibbon, M., & Williams, J., (1995). Structured clinical interview for DSM-IV axis I disorders: Patient edition (SCID-I/P). New York: Biometrics.

13. Freedson, P. S., Melanson, E., & Sirard, J. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine & Science in Sports & Exercise, 30, 777-781.

14. Hausenblas, H. A., Cook, B. J., & Chittester, N. I. (2008). Can exercise treat eating disorders? Exercise and Sport Sciences Reviews, 36, 43-47.

15. Haynos, A. F., & Fruzzetti, A. E. (2011). Anorexia nervosa as a disorder of emotion dysregulation: Evidence and treatment implications. Clinical Psychology Science and Practice, 18, 183-202.

16. Kanning, M., & Schlicht, W. (2010). Be active and become happy: An ecological momentary assessment of physical activity and mood. Journal of Sport & Exercise Psychology, 32, 253-261.

17. Ng, L. W. C., Ng, D. P., & Wong, W. P. (2013). Is supervised exercise training safe in patients with anorexia nervosa?: A meta-analysis. Physiotherapy, 99, 1–11.

18. Schwerdtfeger, A., Eberhardt, R., & Chmitorz, A. (2008). Gibt es einen Zusammenhang zwischen Bewegungsaktivität und psychischem Befinden im Alltag? Eine Methodenillustration zum ambulanten Monitoring in der Gesundheitspsychologie [Is there an association between everyday-life physical activity and psychological wellbeing? – A methodological study using ambulatory monitoring]. Zeitschrift für Gesundheitspsychologie, 16, 2-11.

19. Schwerdtfeger, A., Eberhardt, R., Chmitorz, A., & Schaller, E. (2010). Momentary affect predicts bodily movement in daily life: An ambulatory monitoring study. Journal of Sport & Exercise Psychology, 32, 674-693.

20. Shroff, H., Reba, L., Thornton, L. M., Tozzi, F., Klump, K., Berrettini, W.H.,… & Bulik, C. M. (2006). Features associated with excessive exercise in women with eating disorders. International Journal of Eating Disorders, 39, 454-461.

21. Stice, E. (2002). Risk and maintenance factors for eating pathology: A meta-analytic review. Psychological Bulletin, 128, 825-848.

22. Strober, M., Freeman, R., & Morrell, W. (1997). The long-term course of severe anorexia nervosa in adolescents: Survival analysis of recovery, relapse, and outcome predictors over 10-15 years in a prospective study. International Journal of Eating Disorders, 22, 339-360.

23. Thompson, E. (2007). Development and validation of an internationally reliable short-form of the Positive and Negative Affect Schedule (PANAS). Journal of Cross-Cultural Psychology, 38, 227–242.

24. Zunker, C., Karr, T., Sherman, R. T., Thompson, R. A., Cao, L., Crosby, R. D., & Mitchell, J. E. (2014). Perceptions of running performance: The role of clothing fit. The Sport Journal. Retrieved from https://thesportjournal.org/article/perceptions-of-running-performance-the-role-of-clothing-fit/

Print Friendly, PDF & Email