This item is only available as the following downloads:
1 PREVENTING THE FRESHMAN 15: THE EFFECT OF LIFESTYLE INTERVENTION ON FRESHMAN YEAR WEIGHT GAIN By KATHRYN ROSS MIDDLETON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013
2 2013 Kathry n Ross Middleton
3 T o my parents, Denise and Gary Ross, for their love and encouragement over the years, and to my husband, Harris Middleton, for always providing support and cups of coffee to keep me going
4 ACKNOWLEDGMENTS I would like to thank my mentor, Dr. M ichael G. Perri for his support and guidance in the compilation and completion of this dissertation. I would also like to thank the members of my supervisory committee, Dr. Patricia Durning, Dr. David Janicke, Dr. Anne Mathews, and Dr Michael Daniels, for their time and assistance. Additionally, I would like to thank my fellow graduate students and colleagues in the UF Weight Management Lab for their continued support and encouragement. I would particularly like to thank Valerie Hoove r, M.S., for her invaluable assistance during the second year on the Gainesville Roller Rebels for their unending encouragement. This research was supported by gr ant R18HL73326 from the National Heart, Lung, and Blood Institute, and a PHHP Research Day grant from the College of Public Health and Health Professions, University of Florida.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 The Obesity Epidemic ................................ ................................ ....................... 11 Weight Gain Prevention Programs ................................ ................................ .... 13 Key Times for Weight Gain ................................ ................................ ............... 19 Weight Gain During the Freshman Year of College ................................ .......... 20 Previous Interventions in College Students ................................ ...................... 24 Current Study ................................ ................................ ................................ .... 27 2 METHODS ................................ ................................ ................................ .............. 30 Participants ................................ ................................ ................................ ....... 30 Recruitment. ................................ ................................ ................................ ..... 31 Intervention ................................ ................................ ................................ ....... 32 Measures ................................ ................................ ................................ .......... 35 Statistical Analysis ................................ ................................ ............................ 39 3 RESULTS ................................ ................................ ................................ ............... 42 Participants ................................ ................................ ................................ ....... 42 Participation and Ad herence ................................ ................................ ............ 43 Primary Aim ................................ ................................ ................................ ...... 43 Secondary Aims ................................ ................................ ............................... 44 Tertiary Aim ................................ ................................ ................................ ...... 46 Exploratory Aims ................................ ................................ .............................. 47 4 DISCUSSION ................................ ................................ ................................ ......... 57 Primary Aim ................................ ................................ ................................ ...... 57 Secondary Aims ................................ ................................ ............................... 59 Tertiary Aim ................................ ................................ ................................ ...... 62 Exploratory Aims ................................ ................................ .............................. 62 Limitations ................................ ................................ ................................ ........ 64 Strengths ................................ ................................ ................................ .......... 65
6 Future Directions ................................ ................................ .............................. 65 Conclusion ................................ ................................ ................................ ........ 66 APPENDIX A PLAN OF SESSIONS ................................ ................................ ............................. 68 B MEASURES ................................ ................................ ................................ ............ 69 REFERENCE LI S T ................................ ................................ ................................ ........ 91 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 99
7 LIST OF TABLES Table page 1 1 Summary of studies examining freshman year weight gain ................................ 29 3 1 Mean SE* for weight, BMI, caloric intake, and physical activity at baseline, post test, and follow up, by treatment group. ................................ ...................... 50 3 2 Mean SE at baseline, post test and follow up for exploratory outcomes. ........ 51
8 LIST OF FIGURES Figure page 3 1 CONSORT flow diagram. ................................ ................................ ................... 52 3 2 Mean SE change in weight by treatment group from baseline to post test and follow up. ................................ ................................ ................................ ..... 53 3 3 Mean SE change in caloric intake by treatment group, from baseline to post test and follow up. ................................ ................................ ...................... 54 3 4 Mean SE change in physical activity (square root transformed) by treatment group, from baseline to post test and follow up. ................................ 55 3 5 SE change in weight by treatment group from baseline to post test and follow up. ............................. 56
9 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PREVENTING THE FRESHMAN 15: THE EFFECT OF LIFESTYLE INTERVENTION ON FRESHMAN YEAR WEIGHT GAIN By Kathryn Ross Middleton August 2013 Chair: Michael G. Perri Major: Psychology Prevention of initial excess weight gain has received increased focus as a method for addressing the current obesity epidemic. Due to the slow, incremental nature of weight gain experienced by most individuals, p revention studies have encountered difficulties assessing program impact An emerging strategy has been to conduct weight gain prevention interventions with individuals at high risk for gaining excess weight such as college freshmen. Thus, the current study was a randomized con trolled trial investigating the effect of an innovative, short term lifestyle interv ention on weight gain in freshma n college students. Participants included 95 students (mean SD BMI = 26.80 6.45 kg/m 2 ) randomized to a five session, four week treatment or to wait list control We hypothesized that there would be a significant difference in weight between groups at immediate post test and end of semester follow up. The hypothesized group by time interaction was not significant p = .393, suggesting that there were no statistically significant differences in weight between groups at post test or follow up Non significant trends in the data suggested however, that while participants in the control group tended to maintain their ba seline weight, participants in the intervention group initially lost weight from baseline to post test (mean SE change 2.07 2.52 kg)
10 but then regained weight from post test to follow up (1.05 2.42 kg) such that change from baseline to follow up was only 1.02 2.61 kg In terms of caloric intake and physical activity again there were no significant group by time interactions ( ps = 763 and 259, respectively). Significantly more participants in the treatment group decreased their caloric intake by at least 200 kcal/day from baseline to post test compared to participants in the control group, z = 1.96, p = .050, which suggests that participants were responding to intervention Further, participants who were overweight or obese in the treatment group lost a mean of 4.22 2.53 kg from baseline to post test demonstrating that these participants may benefit more from intervention than normal weight participants Future studies should investigate lengthening the intervention to enhance effectiveness an d given the wide variability in response to treatment, increasing recruitment to improve statistical power.
11 CHAPTER 1 INTRODUCTION The Obesity Epidemic A s of 2010, 36% of adults in the United States were obese (defined as a body 2 ; Flegal, Carroll, Kit, & Ogden, 201 2 ). This large prevalence of obesity has led to growing public health concern as excess weight is asso ciated with adverse health outcomes and decreased life expectancy (Haslam & James, 2005) Obesity is associated with 5 of the 10 leading causes of death (National Heart, Lung, and Blood Association, 1998) and may soon pass smoking as the most preventable c ause of disease and death (Jia & Lubetkin, 2010) While there is a significant genetic contribution to body weight (Bouchard & Perusse, 1993) substantial environmental and behavioral influences, including increased portion sizes, food availability, fast food consumption and high fat diets have been increasing concordant with the prevalence of obesity (Hill & Peters, 1998) There has been a st eady increase in food dollars spent outside of the home, from an aver age of $1,664 per family in 1991 to $2,698 per family in 2008 ( U.S. Department of Labor 2001, 2008 ), which is p articularly concer n ing given that individuals who frequently consume food o utside of the home (e.g., in fast food restaurants) have been found to have higher BMIs and caloric intakes than those who do not (Bowman & Vinyard, 2004; Jeffery & French, 1998; Schmidt et al., 2005) Portion sizes for foods eaten both inside and outside of the home have further increased substantially; from 1977 to 1996, energy intake of salty snacks increased by 93 kcal, soft drinks by 49 kcal, hamburgers by 97 kcal, F rench fries by 68 kcal, and Mexican food by 133 kcal (Nielsen & Popkin, 2003)
12 Since th e 1980s, there has been increased focus on interventions to help overweight and obese individuals lose weight. These programs have overall been successful, with participants achieving, on average, clinically significant weight losses of 8 10% of their init ial body weight ( Butryn, Webb, & Wadden, 2011 ) Despite this success, however, long term maintenance of lost weight remains an issue ( Butryn, Webb, & Wadden, 2011; Jeffery et al., 2000; Perri, 1998) I ndividuals who are initially successful at weight loss typically regain one third to one half of this lost weight within a year after initial intervention ends, and generally return to their baseline weight within 3 5 years (Institute of Medicine, 1995) Accordingly, researchers have argued that obesity must b e treated as a chronic condition using a continual care model of treatment (Perri & Corsica, 2002 ; Perri, Nezu, & Viegener, 1992 ) This model would necessitate increases in cost as interventions expand to include more sessions and increased opportunities f or provider contact over longer periods of time. These increases in cost, coupled with moderate long term effectiveness indicate that behavioral interventions alone may not be the solution to decreasing the population wide obesity epidemic. As individuals are generally unsuccessful at maintaining lost weight, the key to addressing the population wide obesity epidemic may be preventing individuals from initially gaining excess weight. Primary level interventions (e.g., prevention programs) generally have a m uch wider reach, increased cost effectiveness, and a heightened ability to influence population wide change relative to tertiary level interventions (that treat individuals with an existing health condition). While increasing research has focused on preven ting weight regain after initial weight loss, there has been limited
13 research on the prevention of initial excess weight gain in adults. The following sections review the research that has been done to date in the area of weight gain prevention. Weight Gai n Prevention Programs The Pound of Prevention pilot study (POP; Forster, Jeffery, Schmid, & Kramer, 1988) was one of the first randomized controlled trials aimed at the prevention of adult weight gain. During this pilot study, 211 participants were randomized to either an intervention group or a no contact control group. Participants in the intervention group received a monthly mailing including a newsletter focused on weight management techniques and a postage paid postcard. Investigators requ ested that participants record their current weight and strategies used to control their weight on this postcard and mail it back to the researchers. Participants were also given the option to attend a four session educational weight management course half way through the intervention year. Finally, financial incentives were used with this intervention group; participants consented to have $10 withdrawn from their checking account monthly that they could receive back at the end of the study, with interest, i f they remained at or below their baseline weight. Alternatively, participants could withdraw this money earlier if still at or below their baseline weight at the time of withdrawal. After adjusting for height, participants in the intervention group lost significantly more weight than participants in the control group (mean SE = 1.0 0.3 kg and 0.1 0.3 kg for participants in the intervention group and the control group, respectively). Results of this study demonstrated differential treatment effect s by gender. Men in the intervention group lost significantly more weight than men in the control group during the year of study (mean SE = 2.1 0.7 kg and 0.6 0.6 kg for the intervention and control groups, respectively); however, there was no sign ificant treatment effect for
14 women (mean SE = 0.5 0.4 kg and 0.1 0.3 kg in the intervention and control groups, respectively). Interestingly, the weight changes observed in the control group of the POP pilot study do not reflect the 0.5 0.9 kg gained per year by the average adult (Forster et al., 1988; Garn et al., 1976; Lewis et al., 2000) which may have led to the difficulty in finding significant group differences, especially in women. One potential issue may have been recruitment; t he study sample was recruited from the Minnesota Heart Health Program, and these participants may have been more health con scious than the average person. This health consciousness may have led individuals in both groups to be more aware of their weight than the a verage individual, and thus to actively work on weight maintenance with or without intervention A follow up study to the POP pilot study examined the effect of this one year educational intervention plus the additional effect of financial incentives thr ough a lottery condition on weight gain in a sample of 1226 men and women (Jeffery & French, 1997) Participants were randomized either to a no contact control condition, an education plus monthly newsletter/postcard condition (as with the original POP stu dy design, this also included optional semiannual classes on nutrition and exercise), or an education plus lottery condition that included the same content as the education condition but added a lottery component as a financial incentive. Participants in t he lottery condition were offered the chance to win $100 each month if their postcard was returned. Unlike the results of the POP pilot study, there was no significant difference between groups in terms of weight change during the year of the interventio n (Jeffery & French, 1997) Nevertheless, weight change trends were in the hypothesized direction
15 (mean SE weight change = 0.9 0.4, 0.3 0.5, and 0.1 0.6 kg, respectively, in the control, education, and education + lottery conditions). The intervent ion additionally demonstrated impact on health behaviors; participants in the intervention groups significantly increased their self monitoring of weight as compared to control participants. No other significant behavioral changes were found between groups A three year follow up of the second POP study (Jeffery & French, 1999) found no significant differences between the control and intervention groups in terms of weight change over the 3 year period (mean SE = 1.8 0.3 kg in the control condition, 1. 6 0.5 kg in the education condition, and 1.5 0.5 kg in the education + lottery condition). The researchers did find, however, that participants in the intervention group were more educing calories, increasing exercise, increasing fruit and vegetable intake, decreasing fat intake, watching portion sizes, and cutting out sweets and junk food) compared to participants in the control group Taken together, the results from these trial s have demonstrated that education interventions alone may not be sufficient to prevent weight gain in adults. Apart from the original pilot, these studies did not yield significant differences in weight between groups over time. This may have b e e n due, in part, to the slow, incremental change in body weight experienced by most individuals. In all but the original POP pilot, weight gain in the control group reflected national trends of 0.5 0.9 kg of weight gain per year (Garn et al., 1976; Lewis et al., 2 000) This small change, while clinically significant in the long term, can lead to small effect sizes in prevention programs and make observing statistically significant changes difficult (Hill, Wyatt, Reed, & Peters, 2003) To reach
16 sufficient power to d etect a significant difference, studies must have an extremely large sample, or follow a group of subjects for a long time (e.g., over several years). As a result, attempts to study weight gain prevention in adults may require a new approach instead focus ing on times when individuals tend to gain a large amount of weight. Healthy Lifestyle Project (WHLP; Simkin Silverman, Wing, Boraz, & Kuller, 2003) As women tend to gain significant amounts of weight during menopause (Wing, Matthews, Kuller, Meilahn, & Plantinga, 1991) this population was chosen to help establish a successful model of weight gain prevention. Noting that previous low intensity, educational interventions (e.g., the POP studies) were largely unsuccessful, the WHLP researchers proposed an intervention based on the intensive lifestyle programs used for weight management in obese populations. The researchers randomized 535 women aged 44 to 50 to either a lifestyle intervent ion group that received a 5 year lifestyle program or to an assessment only control group. The lifestyle intervention consisted of 15 g roup meetings over 20 weeks followed by several 6 offered over the following 5 years. Initially participants were encouraged to lose small losing weight to offset expected weight gains. After 6 months, participants in the intervention group had los t an average of 4.8 kg, 95% CI 5.4, 4.3, which was significantly greater than the 0.22 kg, 95% CI 0.59, 0.15, of weight change experienced in the control group (Simkin Silverman et al., 1995)
17 Normal weight women in the intervention group experienced a mean ( SD) weight change of 4.1 3.1 kg, while overweight women in this group experienced a mean ( SD) weight change of 5.3 4.9 kg and obese women experienced a mean ( SD) weight change of 7.4 7.2 kg (Simkin Silverman et al., 2003). Women in the intervention group reported significant increases in physical activity (mean SD = 383 1086 kcal per day) and significant decreases in daily calories consumed (mean SD = 249 480 kcal per day). After five years, researchers found that the mean ( SD) weight change in the intervention group was 0.1 5.2 kg while the mean weight change in the con trol group was 2.4 4.9 kg (Simkin Silverman et al., 2003). Further, 55% of participants in the intervention group were at or below their baseline weight compared to 26% of participants in the control group. This study demonstrated that promoting weight l oss to buffer future weight gains may be a viable method of weight gain prevention. Exploratory analyses of intervention effects further demonstrated that long term adherence to physical activity and a low fat eating pattern was associated with improved we ight maintenance. Recently, Gokee LaRose, Tate, Gorin and Wing (2010) conducted a pilot study that assessed the use of a 2.3 4.5 kg weight loss as a method to buffer future weight weight gain. For this study, researchers randomized 52 young adults aged 18 were asked to make daily changes in their caloric intake equal to appr oximately 200 calories and to increase steps by 2,000 steps per day. Participants in the large changes
18 group were asked to follow guidelines similar to those of traditional behavioral interventions; specifically, they were asked to keep daily food diaries, cut 500 1,000 calories from daily intake, and exercise at least 5 days per week (for a total of 250/minutes per week). Participants in both groups were asked to monitor their weight daily and compare this weight to their baseline weight to monitor program progress. Participants in the large changes group lost significantly more weight after 8 weeks (mean weight change SD = 3.2 2.5 kg for those in the large changes group, compared to 0.68 1.5 kg for participant s in the small changes group p < .001) and 16 weeks ( 3.5 3.1 kg in the large changes group compared to 1.5 1.8 kg in the small changes group). One limitation to this study, however, was the lack of a control group including participants who were recruited but randomized to a contact cont rol condition ( e.g. a similar contact schedule but with sessions focused on unrelated health education sessions) Participants who were interested in taking part in this study may have had higher motivation and self efficacy for weight management, which m ay have led them to be successful at weight gain prevention despite intervention. The effectiveness of this study for women who are interested in preventing weight gain but not given access to either of the two interventions is therefore unknown. Given t he aforementioned research, future studies of weight gain prevention should focus on times when individuals tend to gain large amounts of weight. Weight loss at this time could be used as a buffer for future weight gain, or skills learned at this time (suc h as those learned in the small changes approach) could be used to help individuals prevent incremental weight gain. Additionally, the effectiveness of weight loss as a buffer for future weight gain suggests that individuals should be taught not
19 only how t o maintain weight, but further how to lose weight. Behavioral mastery of weight loss techniques can be demonstrated during the primary intervention through Key Times for Weight Gain Rates of weight gain are generally h ighest among young adults, stabilize around middle age, and then decrease in old age (Sheehan, DuBrava, DeChello, & Fang, 2003) For many individuals, young adulthood represents a pivotal time in development and behavior change. This age is associated with changes ranging from increases in independence and moving out of the parental home to shifting social groups and relationship statuses. These changes may have substantial impact on weight; epidemiological researchers have found an increase in obesity inci dence during the transition from adolescence to adulthood (Gordon Larsen, Adair, Nelson, & Popkin, 2004; Must, Gortmaker, & Dietz, 1994) This increase in body weight is accompanied and likely affected by unhealthy behavioral changes. During this transiti on, many individuals demonstrate decreases in physical activity accompanied by significant increases in sedentary behaviors such as weekly television/video viewing and computer/video game use (Gordon Larsen, Nelson, & Popkin, 2004) Researchers have also f ound an increased consumption of fast food and other detrimental eating behaviors, such as skipping breakfast, in this population (Niemeier, Raynor, Rogers, & Wing, 2006) Thus, this transitional period represents a time where researchers could intervene t o mitigate increases in unhealthy behaviors. College freshman constitute a subpopulation within this group that has been shown to be at ngly finding is a real and significant weight
20 gain experienced during the freshman year of college. While there is disagreement about how much weight freshman gain, the literature generally supports the existence of a 0.9 1.8 kg weight gain during the fi rst semester of college (Anderson, Shapiro, & Lundgren, 2003; Hovell, Mewborn, Randle, & Fowler Johnson, 1985; Levitsky, Halbmaier, & Mrdjenovic, 2004; Lloyd Richardson, Bailey, Fava, & Wing, 2009) Studies looking at the phenomenon of freshman year weight gain, in addition to studies looking at the dietary and activity changes that occur in college students, are reviewed in the following sections. Weight Gain During the Freshman Year of College The transition to college presents unique changes in lifestyle behaviors for many young adults. Moving from the parental home to a college campus often represents an environmental upheaval; students change from family cooked meals to all you can eat cafeteria meals with friends, and may cease participation in sports after leaving their high school teams ( Butler, Black, Blue, & Gretebeck, 2004) In line with these changes, weight gain during the freshman year of college has been well documented (A nderson et al., 2003; Butler et al., 2004; Hajhosseini et al., 2006; Hove ll et al., 1985; Levitsky et al., 2004; Lloyd Richardson et al., 2009) The existing literature examining freshman year weight gain is presented in Table 1 1. Of the ten studies, seven found significant weight gains during the first semester o f freshman year of college (Anderson et al., 2003; Butler et al., 2004; Hajhosseini et al., 2006; Hovell et al., 1985; Levitsky et al., 2004; Lloyd Richardson et al., 2009; Racette et al., 2005), two studies reported weight gains but did not perform statisti cal tests (Hoffman, Policastro, Quick, & Lee, 2006; Megel, 1994), and one did not find significant weight gain during the freshman year of college (Graham & Jones, 2002).
21 One major limitation of the current literature on freshman year weight gain is the h igh rate of attrition in these studies. Participant loss to follow up ranged from 12% to 69%, and averaged 37% for the studies that reported attrition (Anderson et al., 2003; Butler et al., 2004; Graham et al., 2002; Hoffman et al., 2006; Hovell et al., 19 85; Levitsky et al., 2004; Lloyd Richardson et al., 2009; Megel, 1994). Two studies did not provide attrition information and only listed participants who had full data (Hajhosseini et al., 2006; Racette et al., 2005). Moreover, all but one of these studie s used completers only analysis (the study by Lloyd Richardson et al. modeled missing data using a maximum likelihood approach). Using only participants with full data assumes that participants who drop out are from the same population as those who do not drop out; however, previous research has demonstrated that participants who drop out of research studies tend to have more adverse health outcomes (Molenberghs & Kenward, 2007) Since this assumption was likely not met in any of these studies, the statisti cal inferences may not be valid. The repetition of significant findings over several studies, however, provides evidence that there may be a true significant mean weight gain during the freshman year of college. Additionally, if those who drop out tend to have poorer health outcomes (and thus may have gained more weight) the true mean weight gain may be higher than what is currently reported in the literature. Despite slight variations in mean weight gain by study and methodological challenges due to attri tion, the aforementioned studies consistently document a tendency for freshman to gain weight during their first year of college. Consequently, researchers have investigated the eating and exercise habits of college students as a contributing factor to fre shman year weight gain.
22 College students have been found to have suboptimal nutrition and activity habits, which are key areas where interventions could improve health related behaviors. Over several studies, a large majority (over three quarters) of un dergraduate students reported eating less than 5 servings of fruit and vegetables per day (Anding, Suminski, and Boss, 2001; Huang et al., 2003; Lowry et al., 2000). On average, participants in these studies did not meet national physical activity recommen dations and female students were significantly less likely to report regularly engaging in exercise than male students (Anding et al., 2001; Huang et al., 2003; Lowry et al., 2000). Investigating changes reported by freshman due to relocation to college, B utler and colleagues (2004) found that freshman reported a significant decrease in occupational and sports related activity and a significant increase in percentage fat and alcohol consumed. Two studies investigated the association between behavioral chan ges and freshman year weight gain. The first found an association between freshman year weight gain and the nu you can fat foods (Levitsky et al., 2004). The second found that women who gained weight during the freshman year of college were more likely to consume alcohol, use maladaptive coping behaviors, consume caffeine, and eat foods in low fiber, and were less likely to eat vegetables and a void high cholesterol foods (Adams & Rini, 2007). One potential contributor to poor eating and exercise behaviors may be inadequate health knowledge. In general, high school students and college freshman have been found to have poor nutritional knowledge (Matvienko, Lewis, & Schafer, 2001; Schwartz, 1975) For example, one study found that 25% of female high school
23 students could not explain the origin of dietary energy, and only 43% recognized that dietary fat is more energy dense than either carbohydrate or protein (Searles, Terry, & Amos, 1986) Additionally, the ability to apply nutrition knowledge to influence body weight was poor in this sample, as only 22% of these students knew how many calories must be expended to lose one pound of fat. Possibly as a result of this poor health knowledge, college students have been shown to use suboptimal methods to control their weight. In a representative national sample of college students, approximately half of those trying to lose weight reported usin g exercise, and one third reported dieting (Lowry et al., 2000). Only 53.8% of females and 40.9% of males reported using both exercise and diet for weight control. Further, one in seven female students who were trying to lose weight reported using (Lowry et al., 2000) Using data from the Youth Risk Behavior Survey, Serdula et al. (1993) found that 49% adolescent females who were trying to lose weight reported regularly skipping meals. This method of weight control is unhealthy and particularly ineffective as skipping meals has been shown to be inversely related to body weight in young adults (Boutelle, Neumark Sztainer, Story, & Resnick, 2002) and eating regular meals has been associated with succ essful prevention of weight regain after weight loss ( Klem, Wing, McGuire, Seagle, & Hill, 1997) A study by Levitsky et al. (2004) found that recent dieting was related to weight gain during the freshman year of co llege. While Levitsky interpreted this f inding to mean that dieting is ineffective for weight management, the findings by Lowry et al. (2000) and Serdula et al. (1993) demonstrate that instead these students may be employing
24 particularly ineffective dieting techniques. Thus, a short term interve ntion introducing effective weight management techniques may be especially beneficial in this population. Overall, these studies have demonstrated that college students are prone to gaining significant amounts of weight during their first year at college. Moreover, research regarding health knowledge in college students has demonstrate d a clear need for education programs in this population. This is a need that may not be met by current university offerings; Lowry et al. (2000) found that while nearly half of all undergraduate participants were trying to lose weight, only 1 in 3 reported getting information about either healthy diet or physical activity from their college or university. The following sections will focus on previous interventions focused on increasing health knowledge, promoting healthy nutrition and exercise behaviors, and/or preventing weight gain in college students. Previous Interventions in College Students Matvienko, Lewis, and Schafer (2001) conducted one of the first interventions fo cusing on the prevention of freshman year weight gain. For this study, freshman college students were randomized to either a nutrition course designed to increase education. M ean weight changes were not significantly different between the intervention and control group; however, weight change trends were observed in the hypothesized direction ( 0.2 kg vs. 1.8 kg at 4 months for the intervention and control groups, respectively, and 0.0 kg vs. 3.2 kg at one year). The lack of statistical significance between groups may have been due to a lack of power, as this study had a small sample size (total N = 40, intervention group n = 21 and control group n = 19).
25 Despite not finding a significant difference between groups in terms of body weight, this study did find significant differences in daily caloric consumption by group, with participants in the intervention group experiencing a significant decrease in calories consumed compared to the control group ( 326 kcal/day vs. 73 kcal/day from baseline to 4 months, respectively). When looking at intervention effects by BMI category, 2 ) in the intervention had a mea n body weight change of 1.4 kg whereas those in the control group gained, on average, 9.2 kg. Thus, this intervention was beneficial to individuals at higher risk for weight gain. Levitsky, Garay, Nausbaum, Neighbors, and DeValle (2006) demonstrated feas ibility for weight body weight. Through two studies conducted during fall semesters, freshman college students were given body weight scales and asked to monitor their weight daily. Partic ipants were further asked to submit this information to the researchers, who provided regression researchers visually demonstrated if weight was increasing, decreasing, or staying stable), and prov ided the daily caloric equivalent for these changes. Individuals in the control group gained significantly more weight than individuals in the intervention group for both studies (mean SD weight change = 3.1 0.5 kg and 2.0 .7 kg for the control group s, compared to 0.1 1.0 kg and 0.8 .6 kg for the intervention groups). Further, individuals in the intervention group did not experience significant weight change during the semester.
26 Other researchers have investigated the prevention of weight gain i n young adults through seminar based intervention programs. Hivert, Langlois, Berard, Cuerrier, and Carpentier (2007) designed a weight gain prevention program for 115 first and second year students in the Faculty of Medicine at a Canadian university. This program involved small group seminars that met biweekly for the first fall semester and then monthly for the next year and a half (excluding small breaks during the summer semester). In total, participants randomized to the seminar group attended twenty t hree 45 minute sessions. T he first set of sessions involved education on nutrition and physical activity and the remaining seminars focused on group discussion, problem solving, goal setting, and self monitoring strategies. Participants in the intervention group had gained significantly less weight than participants in the control group at 12 months ( 0.2 0.4 kg vs. 1.2 0.5 kg, respectively) and at 2 years (0.1 0.6 kg vs. 0.6 0.5 kg respectively). While participants in the intervention group did no t see any significant changes in major health indicators, participants in the control group experienced significant increases in total cholesterol, cholesterol/HDL cholesterol ratio, plasma triglycerides, and LDL cholesterol. No significant differences wer e found in caloric or macronutrient intake between the groups at 2 years (possibly due to low power following high attrition ); however, participants in the intervention group significantly decreased their alcohol consumption as compared to participants in the control group. Further, participants in the control group experienced decreases in physical activity over the course of the intervention while the intervention group maintained their high level of baseline activity.
27 One difficulty noted in the study by Hivert et al. (2007) concerned the retention of participants. The intervention group experienced considerable difficulties with attendance; nearly half of participants in this study attended less than 60% of the sessions, and attendance at sessions was pa rticularly low during the second year of the intervention. With two years of sessions and follow ups, this intervention may have decrease its feasibility in a wider community o r university setting. Taken together, these studies demonstrate the difficulty showing significant effects in weight gain prevention studies. On a positive note, these studies demonstrate preliminary evidence that it is possible to improve the health behav iors of college students. The current study extend ed the literature in this area by using components of an established, effective weight management program to improve the eating and exercise behaviors of college students during the first semester of the freshman year. Current Study The current study assesse d the effects of an innovative short term lifestyle intervention on weight change in freshmen during their first semester at college. The intervention consisted of five 90 minute sessions delivered over four weeks, which focused on increasing nutritional and physical activity knowledge while improving behavioral management skills such as self monitoring, goal setting, and problem hypotheses were as follows: Aim 1 : To assess semester of college. Hypothesis 1 : Participants in the intervention group would experience significantly less weight gain at post test and follow up compared to participants in the contr ol group.
28 Aim 2 : To assess the effect of the intervention on change in caloric intake and levels of physical activity. Hypothesis 2a : Participants in the intervention group would have significantly greater reduction s in calories at post test and follow up when compared to participants in the control group. Hypothesis 2b : Participants in the intervention group would experience significantly greater increase s in physical activity (measured in weekly MET minutes) at post test and follow up compared to partic ipants in the control group. Aim 3: To assess the impact of intervention amongst overweight and obese individuals. Hypothesis 3: Participants who were in the treatment group would experience significantly less w eight gain at post test and follow up compared to overweight or obese participants in the control group. Exploratory a ims: This study had several exploratory aims; (a), to assess the effect of intervention on a measure of eating disorder attitudes, (b) to investigate the impact of intervention on eating habits and weight self efficacy; and (c) to investigate change in problem solving skills and nutritional knowledge.
29 Table 1 1. Summary of studies examining freshman year weight g ain Authors Follow up time Population Attrition Weight Change Significance Hovell et al. (1985) 12 months 164 freshman women 25% University women gained a mean of .33 kg/month, community match controls gained a mean of .01 kg/month < .001* Megel et al. (1994) 7 months 105 freshman women 51% Mean weight change = 1.4 kg n.a. Graham & Jones (2002) 7 months 81 freshman men and women 40% Mean weight change = 0.7 kg ns Anderson et al. (2003) 3 months 192 freshman men and women 30% Mean weight gain of 1.3 kg < .01 Butler et al. (2004) 5 months 82 freshman women 34% Mean weight gain of 0.7 kg .014 Levitsky et al. (2004) 12 weeks 68 freshman men and women 12% Mean SD weight gain of 1.9 2.4 kg < .01 Racette et al. (2005) 3.5 years 204 freshman men and women n.a. Mean SD weight gain of 2.5 5.3 kg < .001 Hajhosseini et al. (2006) 4 months 27 freshman men and women n.a. Mean SE weight gain of 1.4 0.3 kg .001 Hoffman et al. (2006) 7 months 217 freshman men and women 69% Mean SD weight change of 1.3 4.0 kg n.a. Lloyd Richardson et al. (2009) 8 months 912 freshman men and women 59% Mean weight gain = 3.5 kg, 95% CI 2.8 4.6 kg < .001 Note: all significance tests were within group with the exception of Hovell et al., which was between groups
30 CHAPTER 2 METHODS Participants Participants in the current study were 95 first year female undergraduate students recruited from the University of the Florida. An original sample size of 90 was selected to provide a power of .80 (at an alpha level of .05) for detect ing a 1.5 2.5 kg mean difference in weight change between the intervention and control groups at follow up. As several different studies investigating freshman year weight gain have found varying levels of weight gain (likely depending partly on varied d emographic composition of into SAS, which estimated the total number of participants nec essary to achieve a and colleagues (2004) and Graham and Jones (2002) studies were used; to find a significant difference between .7 kg 1.5 kg, the current study wo uld need a total N of Based on a mean SD weight gain of 1.9 2.4 for women during the first 12 weeks of freshman year, the required N would be 54. To assess mean differe nces that range power analysis (again at power = .80 and alpha = .05) for means of 1.5 and SD of 1.5, 2.0, and 2.5. This analysis resulted in estimated Ns of 34, 58, and 90, re spectively. As standard deviations in the previous literature were generally twice the mean weight change, the estimate for the weight change ( SD) of 1.5 2.5 kg was chosen, for a total sample size of 90 participants.
31 The current study was li mited to female students following previous research demonstrating that behavioral interventions involving physical activity have differential effects on men and women (Calfas et al., 2000; Donnelly, et al., 2003). Exclusionary criteria for the current study includ ed BMIs of less than 22 (to avoid participant weight loss to a BMI of below normal range), the presence of an eating disorder or significantly disordered eating patterns, medical conditions that affect weight, current medications that affect weight, seriou s infectious diseases, or pregnancy (or stated intent to become pregnant during the following year). Individuals who were unwilling to give informed consent or were unwilling to accept randomization into the treatment groups were further excluded from the current study. Finally o nly participants who complete d baseline assessment measures (including questionnaires and 3 24 hour recalls) were eligible to be randomized for the study. Recruitment To achieve the proposed sample size of 90, initial recruitment goals were 130 female undergraduate students (assuming a 15% drop out/withdrawal rate between recruitment and group start and a 15% attrition rate between group start and the end of the fall se mester). Participants were recruited from the University of Florida campus, primarily through flyers placed around campus and in student mailboxes, in person recruitment on campus, in person presentations given at start of the year university events and cl asses, and an ad in The Independent Florida Alligator, a student newspaper distributed on campus. Prospective participants were instructed to call a local telephone number to get more information about the study and, if interested, to be screened for bas ic eligibility criteria (e.g., freshman status, BMI, and history of medical disorders/medications that
32 may affect weight). After this telephone screening, individuals interested in participation were asked to meet for an assessment visit. At this visit, po tential participants were asked to read and sign a consent form, after which the project director or a trained research assistant measured and record ed their height and weight. Participants were then asked to fill out questionnaires with information about their demographics, eating habits and dietary patterns (including disordered eating patterns), physical activity, and general health. At the end of this screening visit, participants were given log in information and a schedule to fill out 3 online dietary recalls (two on a week day and one on a weekend day). Participants were informed that study participation was contingent on the completion of these recalls and questionnaires. All of the above measures were repeated at immediate post test (October) and th e end of the semester (December). Intervention The current int ervention consisted of a five session weight gain prevention program delivered over four weeks, and focused on decreasing caloric intake and increasing physical activity, implementing self moni toring of weight, and improving self regulatory skills (see Appendix A for session outlines). Intervention groups of 8 10 participants were led by doctoral level graduate students in clinical psychology, and co led by either graduate students in clinical p sychology or trained undergraduate research assistants. Initially, participants were taught self regulatory skills that could be used to lose weight, including goal setting and self monitoring, and asked to decrease caloric intake to 1,200 kcal/day. Partic ipants were encouraged to meet weight loss goals ( i.e ., 1.4 2.3 kg over the course of the four week intervention) to demonstrate mastery of these weight regulation skills To
33 assess success in chan ging caloric intake, participants were asked to keep daily food records. Traditional paper or log based food records would have required individuals to carefully measure foods (e.g., using measuring spoons and cups) and look up caloric values in reference books. This method of self monitoring may have been too burdensome for participants who were juggling class schedules, jobs, recreational activities, and social commitments. Thus, the current intervention focused on the use of online food records, through FitDay.com or through another recording software of application such as Lose It or Daily Burn). Participants were required to use a recording service that allowed them to see the calories in food as they were adding them to the record and additionally that allowed for food records to be printed and brought to group (for group activities, interventionist monitoring, and data collection to assess program adherence). After mastering skills to lose weight, participants were taught the Stoplight Diet as a method to increase nutritional quality and lower their dietary fat intake. The Stoplight Diet focuse d on improving the quality of food consumed by increasing fruits, and ve fat, low nutrient, high were encouraged to track their total number on to their daily food logs for the remainder of the program. Introducing the Stoplight Diet was envisioned as a method to reduce participant burden from food records following the end of intervention. In the long term, daily food records that include d the calories of all foods and drinks consumed may have been too burdensome for participants.
34 Specifically, the cost ( significant amounts of time) might have outweigh ed the perceived benefits, as many participants in this intervention would likely not be tryin g to lose significant amounts of weight. The Stoplight Diet was thus used as a method to help participants maintain favorable changes in dietary intake long term without the use of daily food records. In terms of physical activity, participants were encou raged to increase their daily activity levels to at least 30 minutes/day of moderate intensity physical activity. For participants who are already at this level or who reach ed this level during the intervention, this goal was raised to one hour per day of moderate intensity physical activity. This goal was based on work by Jakicic and Otto (2005) who found that an hour of physical activity per day was associated with long term weight maintenance in women. After the end of the five session, four week inter vention, participants were encouraged to continue to weigh themselves daily and to complete self monitoring records. Further, participants were given a goal sheet ex plaining success at long t erm maintenance, and created action plans for when weight deviate d from of a maintenance participants created two sets of planned behavioral changes for weight gains > 0.9 kg and > 2.3 kg above weight at week four respectively ). Participants were also encouraged to call or email their group leade r if experiencing any difficulties with their weight management. Participants randomized to the control group were contacted for assessments at post test and follow up but received no further contact during the fall semester. These
35 wait list control parti cipants then receive d the five session, four week intervention during the first month of the spring semester. Measures In addition to height and weight measured by project staff, p articipants were asked to complete several questionnaires either on paper o r through SurveyMonkey.com (an online questionnaire tool). As an eligibility requirement, participants were required to complete three online 24 hour recalls (Subar et al., 2007) on two non consecutive week days and one weekend day. The measures used in this study are listed below (see Appendix B for the full forms). The forms in Appendix B reflect the content but not format of the final forms as they were available electronically for participants usi ng SurveyMo nkey.com during the second wave of the study All measures (excluding height) were given at baseline ( August/ September ), immediate post test (October) and follow up (December); height was measured only at baseline. Height and W eight Height was measured t o the nearest .0 1 c m using a digital stadio meter and standardized protocol. Body w eight was measured to the nearest 0.1kg using a calibrated digital scale. Participants were weighed in light indoor clothing and without shoes. To assure stability of weight measurements, visits were scheduled early in the morning, and participants were instructed to fast for 1 0 hours before their vi sit (starting the night before), avoiding both food and liquid (including water) consumption. Height and weight were used to calc ulate body mass index. Caloric I ntake Caloric intake was assessed using the online Automated Self Administered Dietary Recall (Subar et al., 2007). The ASA 24 use d pass recall
36 system, and was fully self administered through the National C ancer Instit During completion of the ASA 24, participants cho se the foods and drinks they consume d from the USDA Food and Nutrient Database, select ed portion sizes from were le d through a and snacks eaten between meals (Zimmerman et al., 2009). At each assessment point, participants were asked to complete three 24 hour recalls: two reflecting consumption on weekdays and one reflecting consumption on weekend days. Physical A ctivity The International Physical Activity Questionnaire (IPAQ; Booth, 2000) was used to assess baseline physical activity and change s in activity over time While several versions of this questionnaire exist, the self administered long form was used in the current study. This form ha d advantages over the short form in a college student population, as it specifically include d time spent biking and walking for transportation each day, items which may be particularly pertinent to freshman who live on campus and use biking and walking as primary modes of transportation. The self administered long form IPAQ has been found to have good test r .81 across 12 countries; Craig et al., 2003) and has been validated against activity report physical activity log & Sjstrm, 2007). Overall, the IPAQ (and other self report measures of physical activity) has been shown to be more valid for vigorous intensity physical activity than for moderate intensity, as the latter tends to accumulate throughout the day and not in planned sessions (Hagstrmer et al 2007).
37 Eating D isorder A ttitudes Disordered eating patterns and eating disorder symptomatology were measured using the Eating Attitudes Test (EAT; Garner & Garfinkel, 1979; Garner, Olmsted, Bohr, & Garfinkel, 1982) As the short form EAT 26 has been shown to be highly predictive of the long form EAT 40, r = .98 (Garner et al., 1982) the short form was used to reduce participant burden. Researchers have established a cut off score of 20, which has been found to have a true positive rate of 84.9% for individuals clinically diagnosed with anorexia nervosa (Garner et al., 1982); this cut off was used to screen out potential participants who demonstrated disordered eating behaviors at baseline. Despite previous studies t hat have demonstrated that weight gain prevention interventions were not associated with increases in eating disorder symptomatology (Jeffery & French, 1999; Klem, Wing, Simkin Silverman, & Kuller, 1997). ), there was a fear that some intervention component s (e.g., the 1,200 kcal/day intake goal) might lead to increased disordered eating. Thus, we included a measure of eating disorder attitudes to use both as a screening measure and to monitor changes in disordered eating over the course of the intervention. Eating H abits The Three Factor Eating Questionnaire (TFEQ) was used to assess eating habits (Stunkard & Messick, 1985). The TFEQ was a 51 dietary restraint, disinhibition, and hunger ratings. The reliability and v alidity of the TFEQ has been widely documented (Gorman & Allison, 1995). Researchers found that the TFEQ has high test retest reliability in a college student sample, ranging from .80 to .93, and good criterion validity to assess binge eating (Stunkard & M essick, 1985).
38 Problem S olving S kills Problem solving skills were assessed using the Social Problem Solving Inventory Revised (SPSI Olivares, 2002). The SPSI R was a 52 item, self report measure that assesse d problem orientation, skills at rational problem solving, impulsivity/carelessness style, and avoidance style. The internal consistency of the SPSI R has been shown to range from of .76 to .92 and researchers have demons trated sound test retest reliability Olivares, 2002). Further, researchers have found that the SPSI R is able to detect changes in skills due to clinical training in problem solving (Nezu, Nezu, Friedman, Faddis, & Houts, 1998). Weight S elf E fficacy efficacy for weight management was assessed using the Weight Efficacy Lifestyle Questionnaire (WEL; Clark, Abrams, Niaura, Eaton, & Rossi, 1991). This 20 question measure assess ed weigh t self efficacy across five domains: availability, negative emotions, physical discomfort, positive, activities, and social pressure. This measure was found to have a stable five factor structure, acceptable internal consistency, and a test retest reliabil ity of .92 (Clark et al., 1991; Fontaine & Cheskin, 1997). Nutrition K nowledge Nutrition knowledge was assessed using the Nutrition Knowledge Questionnaire (Parmenter & Wardle, 1999). The Nut rition Knowledge Questionnaire was a 50 item questionnaire that cover ed (a) knowledge of recommendations for increasing and decreasing intake of different food groups, (b) nutrient knowledge, (c) food choice, and (d) beliefs about which foods are associated with particular diseases. Only the first two
39 sections (covering knowledge of recommendations and nutrient knowledge) were used for the current study. This measure has been found to be reliable and have acceptabl e internal consistency (Cronbach 0.97 ; Parmenter & Wardle, 1999). Further, nutrition students were found to have significantly higher scores than students in other disciplines, demonstrating good construct validity (Parmenter & Wardle, 1999). As this questionnaire was created in the United Kingdom, colloquial ver nacular (e.g., were replaced with the phrases that were more easily Adherence Adherence to intervention strategies was assessed using weekly records of ca loric intake, physical activity and body weight The number of total days of completed food records (defined as days with at least two meals recorded) was counted f or each participant. Ad herence to the intervention was assessed for the intervention group in terms of attendance at group meetings which was recorded at each meeting by the group leader or co leader. Statistical A nalysis Baseline differences between treatment groups were assessed using t tests. Any baseline differences were controlled by en tering these variables into the following analyses as covariates. Results of this study were assessed using an intent to treat approach, thus data from all participants randomized to either the intervention or control groups were used in the final model. For the primary outcome, missing data at post test and follow up were multiply imputed (Rubin, 1976) by SAS proc MI. Missing data in this study could not be assumed to be missing at random (MAR), an assumption of the multiple impu tation
40 process; however, simulation studies have demonstrated that even under not missing at random (NMAR) conditions, multiple imputation produces less biased estimate s than other commonly used procedures such as mean imputation, last observation carried forward, or hot deck imputation (Schafer & Graham, 2002; Tang, Song, Belin, & Untzer, 2005). Missing data from both the treatment and control group were estimated from the distribution of control group data, assuming that participants who did not return f rom the treatment group had similar outcomes to those in the control group. For secondary outcomes, missing data were handled under a MAR approach, using SAS PROC MIXED. All analyses were completed in SAS version 9.2 for Windows XP Professional (SAS Instit ute, 2008). Primary A im The primary aim was analyzed using a repeated measures ANOVA (using SAS PROC MIXED) to examine change in weight from baseline to post test and follow up by treatment group. We hypothesized that there would be a significant interact ion between treatment group and time. Planned contrasts were used to assess differences between groups in body weight at post test and follow up. Change in weight from baseline to post test, from post test to follow up, and from baseline to follow up were assessed by group. Secondary A im In order to assess the impact of the intervention on behavioral changes, two repeated measures ANOVAs (using SAS PROC MIXED) were used to assess change in caloric intake and physical activity by treatment group. The main ef fects for time and treatment were evaluated, as well as the interaction effects of treatment group and time for each outcome.
41 Tertiary Aim To assess treatment effect within participants who were overweight or obese at baseline, these participants (with BM an identical model to that used for the primary aim. Exploratory A nalyses To assess clinical significance in weight gain between groups, a chi square was used to assess whether significantly more particip ants in the treatment group attained a 1.5 kg weight loss from baseline to post test compared to the control group. Similarly, two chi square analyses were used to assess 1) whether participants in the treatment group were significantly more likely to dec rease caloric intake by 200 kcal/day compared to the control group, and 2) whether participants in the treatment group were significantly more likely to increase or maintain baseline levels of physical activity compared to participants in the control group To assess the effect of intervention on disordered eating behaviors, changes in EAT score by group were assessed at post test and follow up using a similar repeated measures ANOVA to the secondary aim analyses Changes by treatment group in weight loss s elf efficacy (using the Weight Loss Efficacy questionnaire) and eating habits (as measured by the Three Factor Eating Questionnaire) were similarly assessed with repeated measures ANOVAs Finally, as a manipulation check, change in nutritional knowledge an d problem solving skills from baseline to post test were assessed using similar models.
42 CHAPTER 3 RESULTS Participants Three hundred and sixty four participants completed initial phone screens for the current study. Out of these participants, 168 attended an in person assessment visit and signed an informed consent form (Figure 3 1 shows participant flow through assessment and randomization). These participants were recruited over two waves, in August/September 2010 and August/September 2011. After completion of baseline measures, 95 participants were randomized into the treatment (n = 47) or control (n = 48) groups. See Fi gure 3 1 for a CONSORT diagram detailing participant randomization and follow up. At baseline, the mean ( SD) age of participants was 18.53 .35, the mean weight was 69.41 12.57 kg, and mean BMI was 26.80 6.45 kg/m 2 Using WHO categories ( World Healt h Organization, 1995 ), 40 (42.1%) of participants were normal weight, 44 (46.3%) were overweight, and 11 (11.6%) were obese. In terms of race/ethnicity, 23 (24.2%) participants self identified as African American, 41 (43.2%) as Caucasian, 21 (22.1%) as His panic, 5 (5.3%) as Asian, and 5 (5.3%) reported multiple race/ethnicity categories. There were no differences at baseline between groups for weight, p = .691, age, p = .430, race/ethnicity, p = .618, physical activity, p = .203, or caloric intake, p = .448 At baseline 61.1% of participants reported eating in on campus, all you can eat style dining halls less than 3 times per week, 9.5% reported 3 5 times per week, 9.5% reported 6 10 times per week, and 20.0% reported 11+ times per week (overall mean SD dining hall meals consumed per week was 4.59 6.65). Participants reported eating
43 in their dorm rooms 9.02 6.66 meals per week, with 20% reporting less than 3 meals eaten in dorm rooms per week, 16.8% reporting 3 5 meals per week, 27.4% reporting 6 10 t imes per week, and 35.8% reporting 11+ meals per week. Finally, participants reported eating 3.85 3.50 meals per week at fast food establishments; 41.1% reported eating meals at fast food establishments less than 3 times per week, 37.9% reported 3 5 meal s per week, 15.8% reported 6 10 meals per week, and 5.3% reported 11+ meals per week Participation and Adherence Participants randomized to the treatment group attended a mean SD of 3.77 1.73 sessions (75.32 34.69 percent of sessions) Of the 47 participants randomized to this group, 42 attended at least one treatment session. For these 42 participants, mean SD attendance was 4.19 1.17 (out of 5 potential sessions, equaling an average percent of 84.29 24.01 sessions attended), and these part icipants completed an average of 14.57 10.86 food records (range: 0 28). Primary Aim A mixed model with an autoregressive correlation structure provided the best fit for the weight change data. There were no significant main effects for treatment gro up or time on change in weight p s = .364 and .535, respectively. The hypothesized interaction between treatment group and time was further not significant, p = .393. See Table 3 1 for means and standard error estimates for treatment and control group participants at baseline, post test, and follow up. Although there was not a significant interaction, we explored group differences at each time, and change over time by group as an exploratory analysis to better understand trends in the data and to make suggestions for future studies. There were
44 no significant differences in weight by treatment group at either post test or follow up, ps = .155 and .443, respectively. From baseline to post test participants in the treatment group experienced a non signifi cant mean ( SE) change of 2.07 2.52 kg, p = .412. From post test to follow up, participants in the treatment group further experienced a non significant mean change of 1.05 2.42, p = .668, for a total non significant change from baseline to follow up of 1.02 2.61, p = .697. Participants in the control group experienced a non significant change of 0.47 2.5 0 p = .849, from baseline to post test, and 0.61 2.4 0 p = .803, from post test to follow up, for a total of 0.14 2.48, p = .952 from bas eline to follow up. Figure 3 2 demonstrates change in weight by group over the course of the intervention and follow up period. A sensitivity analysis was run using only the 42 participants in the treatment group who attended at least one treatment session ; however, the pattern of results from this analysis was identical to the pattern demonstrated in the above analysis including all 47 treatment group participants. Secondary Aims For the secondary aims, two mixed models were run to assess the effect of t reatment on differences in a) caloric intake and b) physical activity at post test and follow up. Caloric I ntake A mixed model with an autoregressive covariance structure provided the best fit for caloric activity data. There were no significant main effe cts for time or treatment group, ps = .625 and .636, respectively. Further, the hypothesized time by treatment interaction was not significant, p = .763. As an exploratory measure, we investigated group differences in caloric intake by time and changes in caloric intake over time by
45 group. There was not a significant difference between participants in the treatment group and the control group in terms of caloric intake at post test, mean SE difference in change = 89.05 43.58 kcal, p = .111, or at fol low up, mean SE difference in change = 2.89 75.50, p = .971. See Figure 3 3 for changes over time by group in caloric intake, and Table 3 1 for mean and standard error estimates by group and time point. Physical Activity A mixed model with an autoregressive covariance structure provided the best fit to the physical activity data (as measured by the IPAQ). Due to positive skew, the outcome for total physical activity (measured by weekly METs) was transformed using the square root transformation. There was not a significant main effect for treatment group, p = .733, and the hypothesized interaction for treatment group and time was further not significant, p = .259. There was a significant main effect for time, however, such that participants expe rienced a general decrease in physical activity over time, F (2,253) = 3.38, p = .036. See Table 3 1 for means and standard error estimates for treatment and control group participants at baseline, post test, and follow up. Despite not finding the hypothe sized interaction, we examined change in physical activity between groups and over time by group on an exploratory basis to help further understand the data. There were no significant differences between the treatment group and control group at post test o r follow up, ps = .917 and .456, respectively (see Figure 3 4 ) Participants in the treatment group did not experience significant changes in physical activity from baseline to follow up, mean SE change = 19.29 39.45 MET minute s/week, p = .485. Partic ipants in the control group, however, experienced a
46 significant decrease in physical activity from baseline to follow up, mean S E change = 334.21 37.02, p = .003 Since the focus of physical activity for the current intervention was increasing minutes spent in moderate intensity activity, particularly focused on walking, an additional mixed model using weekly METs from walking was run on an exploratory basis Again, the variable for weekly METs from walking was transformed using a square root transform ation due to positive skew. For weekly METs from walking, a similar pattern emerged such that participants in the treatment group did not experience a significant change in weekly MET minute s from walking from baseline to follow up, mean SE change = 13. 38 19.45, p = .408, while participants in the control group again experienced a significant decrease during this time, mean SE = 190.97 18.24, p = .001. Although the changes from baseline to post test were not significant for either group for total METs or walking METs Figure 3 4 demonstrates that the trend was for participants in the intervention group to maintain their baseline levels of total activity though post test, and then experience a slight decrease from post test to follow up. Participants in the control group meanwhile experience d a seemingly linear decrease from baseline to follow up. Tertiary Aim For the tertiary aim, we investigated the treatment by time interaction within p articipants who were overweight or obese at baseline (baseline BMI no significant main effects for treatment group or time, ps = .873 and .227, and the treatment by time interaction was further not significant, p = .400. As demonstrated i n Figure 3 5, participants in the intervention group experienced a non significant mean
47 SE change of 4.22 2.53 kg from baseline to post test, and a non significant change of 0.17 2.75 kg from post test to follow up, leading to an overall non signifi cant change of 4.05 3.16 kg from baseline to follow up. Participants in the control group, meanwhile, experienced a non significant change of 0.29 1.92 kg from baseline to post test, 0.37 1.75 kg from post test to follow up, and overall experienced a non significant change of 0.08 1.94 kg from baseline to follow up. Exploratory Aims Clinically Significant Weight C hange Participants were dichotomized by weight change, such that proportion of participants who achieved eline to post test could be compared by treatment group. There was no significant differences between the treatment and control group in this proportion, p = .844. Clin ically Significant Decrease in C alories Participants were dichotomized by change in calo ric intake, such that the compared by group. Significantly more participants in the treatment group (47.0%) decreased their daily calories by at least 200 kcal compared to t he control group (31.7%) z = 1.96, p = .050. Increased or M aintai ned Levels of Physical A ctivity Participants were dichotomized by change in physical activity from baseline to post test, such that the proportion of participants who increased or maintained their physical activity could be compared by treatment group. There was no significant difference between the proportion of participants in the treatment (46.8%) and control
48 group (39.6%) who increased or maintained their physical activity from baseline t o post test, p = .477. Disordered E ating An additional exploratory aim included assessing change in disordered eating (using the EAT total score) over time, by treatment group. Due to positive skew, the total EAT score was transformed by logarithmic transformation prior to analysis. A mixed model with an autoregressive covariance structure found no main effects for treatment group or time, ps = .432 and .414, respectively, or for the treatment group by time interaction, p = .320, suggesting that there were no significant differences in disordered eating over time by treatment groups. Mean change and standard error estimates for EAT scores over time are available in Table 3 2 Eating H abits Next, using data from the Three Factor Eating Questionnaire, we found no main effect s for treatment group or time on restraint, p s = 268 and .334, respectively. Further, the group by time interaction was not significant, p = 318 In terms of disinhibition, there were no significant main effect s for treatment group or time, p s = 080 and .346, respectively an d there was not a significant treatment group by time interaction, p = 194 In terms of hunger, there were no significant main effects for treatment group or time, ps = 616 and 586 and the interaction between treatment group and time was not significan t, p = 983 Post hoc exploratory analyses demonstrated that participants in the treatment group reported significantly lower disinhibition at post test compared to the control group, t (242) = 2.02, p = .044; likely resulting from a non significant trend for individuals in the treatment group to experience decreases in disinhibition from baseline to post
49 test, t (242) = 1.78, p = .076. Further, there was a non significant trend for participants in the t reatment group to experience decreases in disinhibition from baseline to follow up, t (242) = 1.86, p = .065. There were no significant changes from baseline to post test or baseline to follow up in disinhibition for participants in the control group, ps = 500 and .424, respectively. Problem Solving Skills While there was a significant group effect for total problem solving skills by treatment group (using total SPSI standardized scores), F (1,178) = 7.65, p = .006, such that participants in the control grou p had significantly lower problem solving scores than those in the intervention group, there was not a significant main effect for time, p = .458, and the hypothesized group by time interaction was not significant, p = .833. Weight Loss Self Efficacy The re was a trend for weight loss self efficacy, measured by scores on the WEL, to improve over time across all participants F (2,160) = 2.82, p = .063; however, there was not a significant main effect for treatment group, p = .135 and the interaction betwee n treatment group and time was not significant, p = .474. Mean scores ( SE) for total weight loss efficacy by treatment group at each time point are available in Table 3 2. Nutrition Knowledge Finally, we investigated change in nutrition knowledge (as measured by the Nutrition Knowledge Questionnaire) by treatment group over the course of the intervention and follow up. There were no significant main effects for time or group, ps = .642 and .934 respectively, and the group by time interaction was not significant, p =.921.
50 Table 3 1. Mean SE for weight, BMI, caloric intake, and p hy sical a ctivity a t b aseline, post test, and follow up, by treatment g roup. Treatment Control Outcome M SE M SE Weight (kg) Baseline 68.89 1.84 69.92 1.82 Post test 66.81 1.70 70.39 1.72 Follow up 67.86 1.85 69.78 1.68 BMI (kg/m 2 ) Baseline 26.93 0.95 26.67 0.94 Post test 26.20 0.95 26.86 0.94 Follow up 26.66 1.01 26.65 0.96 Caloric Intake (kcal) Baseline 1558.51 74.22 1587.24 63.87 Post test 1473.54 70.46 1562.59 66.81 Follow up 1537.62 83.67 1534.73 81.33 Physical Activity (MET minute/week) Baseline 4100.71 17.58 5348.17 17.47 Post test 4146.96 19.02 4064.60 19.02 Follow up 3557.55 21.61 3008.52 19.55 SE used as standard deviations for each mean were not available
51 Table 3 2. Mean SE at baseline, post test and follow up for exploratory o utcomes. Treatment Control M SE M SE Disordered Eating Baseline 2.34 0.10 2.53 0.09 Post test 2.38 0.10 2.39 0.10 Follow up 2.41 0.10 2.49 0.10 Problem Solving Baseline 114.07 2.07 109.65 2.10 Post test 113.38 2.90 105.89 3.05 Follow up 111.78 2.35 106.74 2.39 Weight Loss Efficacy Baseline 132.21 4.16 127.52 4.12 Post test 131.61 4.4 120.32 4.3 0 Follow up 136.28 4.58 132.07 4.37 Restraint Baseline 11.39 0.65 11.70 0.64 Post test 12.89 0.73 11.15 0.69 Follow up 11.18 0.76 10.72 0.70 Disinhibition Baseline 6.98 0.50 6.71 0.49 Post test 5.67 0.54 7.20 a 0.53 Follow up 5.57 0.57 6.59 0.54 Hunger Baseline 5.79 0.51 6.11 0.51 Post test 5.83 0.54 6.05 0.54 Follow up 5.39 0.82 5.51 0.54 a p < .05 between groups
52 Figure 3 1. CONSORT flow d iagram
53 Figure 3 2. Mean SE change in w eight b y treatment group from baseline to post test and f ollow up 5 4 3 2 1 0 1 2 3 Baseline Post test Follow up Weight Change (kg) Treatment Control
54 Figure 3 3 Mean SE change in c aloric in take b y treatment group, from baseline to post test and f ollow up 200 150 100 50 0 50 100 Baseline Post test Follow up Change in Caloric Intake (kcal) Treatment Control
55 Figure 3 4 Mean SE change in physical a ctivity (square root t ransformed) by treatment group, from baseline to post test and f ollow up 25 20 15 10 5 0 5 Baseline Post test Follow up Change in Square Root of Weekly MET minutes Treatment Control
56 Figure 3 5. Within pa ean SE change in weight by treatment group from baseline to post test and f ollow up. 8 6 4 2 0 2 Baseline Post test Follow up Chaneg in Weight (kg) Treatment Control
57 CHAPTER 4 DISCUSSION The current study was a randomized trial of an innovative, brief lifestyle intervention for prevention of weight gain during the first semester of freshman year of college. Ninety five freshman college students were successfully recruited and randomized to either treatment or wait list control. The recruited sample of participants was racially and ethnically diverse, and both normal weight and overweight participants (categorized by BMI) were well represented. Participants in the treatment group had relatively good attendan ce and adherence to the program (measured by number of food records completed weekly). Primary Aim No significant difference was found between the treatment and wait list control group at post test or follow up. Nonetheless, the trends represented in Fig ure 3 2 demonstrate that while the control group appeared to maintain baseline weight over time, participants in the intervention group lost some weight from baseline to post test but regained most of that weight from post test to follow up. This pattern o f findings suggests two things. First, the current study may have been underpowered, as the power analysis conducted prior to recruitment was based on the assumption that participants in the control group would gain weight over the course of their first s emester of college. Previous research consistently documents a trend for college students to gain weight during their year of college (Anderson et al., 2003; Butler et al., 2004; Hajhosseini et al., 2006; Hovell et al., 1985; Levitsky et al., 2004; Lloyd R ichardson et al., 2009; Racette et al., 2005) which was not observed in the enrolled control participants.
58 The lack of weight gain in the control group may have been associated with the short follow up time (perhaps weight gain would have been seen if ra ndomization continued until the end of the freshman year) or recruitment techniques. Specifically, recruitment for the current study centered around advertisements that focused on the prevention of weight gain during the freshman year (e.g., the posters di splayed around potentially avoid the Freshman 15? We are recruiting research participants for a study aiming to improve the eating and exercise habits of freshman college studen manner of recruitment may have led to a sample of participants for both the treatment and control group who were more interested in maintaining body weight or improving eating and exercise habits compared to the average freshman college student Thus, while the average UF college student may have experienced weight gain during this first semester at college, the students recruited for this study may have represented a group that would have been successful at weight management without intervention This recruitment method also dictated a January start date for the wait list control list treatment groups into the sophomore year of college may have led to diminished i nitial recruitment, larger drop out in the control group, and feelings of frustration or knowledge the fact that all participants would be able to receive the inter vention during their freshman year, but half would attend groups in the fall and half in the spring). Future studies should focus on broader recruitment, for example focusing on recruiting gai n prevention, or
59 using other bro ad terms to describe the study. Further, these studies should investigate weight gain over the course of the entire first year of college, rather than just the first semester. The second key finding was that the intervention taking place over only four weeks, may have been too short to observe significant chan ges in behavior and body weight. I ndeed, participants in the treatment group lost on average, 0 .52 kg per week, w hich is a reasonable weight loss goal given the startin g weight of participants. Participants who were overweight or obese in the treatment group experienced an even larger weekly change of 1.05 kg per week. If this program were to be extended, perhaps over two months or over a full college semester (August D ecember), the treatment effect observed may have been much larger, and a significant treatment by group effect might then be observed. This increase would be particularly likely to improve outcomes given previous research demonstrating that increasing the length of lifestyle weight loss programs significantly improves efficacy and total weight loss (Perri, Nezu, Patti, & McCann, 1989). Secondary Aims Caloric Intake Similar to the primary results for body weight, there was not a significant difference between the treatment and control group in terms of caloric intake at post test and follow up. The patterns of change demonstrated in Figure 3 3, however, demonstrate d that while the control participants experienced a slight decrease in caloric intake from baseline to post test and again to follow up, participants in the treatment group experienced a larger (albeit non significant) decrease in caloric intake from baseline to post test, but then experienced an increase in caloric intake from post test to fol low up
60 such that changes from baseline to post test were washed out. Some success in clinically significant caloric change by group was observed as significantly more participants in the treatment group demonstrated success at decreasing caloric intake by 200 kcal/day from baseline to post test compared to the control group. This suggests that more participants in the treatment group were successful at decreasing overall caloric intake compared to participants in the control group. Further, these results s uggest that participants were responding well to treatment, and given a longer intervention (e.g., weekly groups over the course of the semester rather than over four weeks), this change may have led to a greater difference in overall calories and weight f or the treatment group compared to the control group. Participants in the treatment group were given caloric intake goals of 1,200 kcal/day during the intervention. Despite this recommendation representing a decrease in caloric intake of approximately 350 kcal/day from baseline on average, participants in the treatment decreased caloric intake from baseline to post test by about 85 kcal/day (representing a reduction from a baseline mean of 1 559 kcal/day to a post test mean of 1 474 kcal/day ). This smaller decrease may be a result of lack of sensitivity of the dietary recalls to change, or a result of participants failing to meet their caloric intake goals. assessment time point, thes e recalls may not accurately represent overall intake. For example, the final assessment time point corresponded with final exams and the holiday season; thus, participants may have consumed more during the week of assessment than typically consumed due to environmental influences and psychological stressors. Further, an important factor for caloric intake in college students that may not have been
61 captured using the recalls was alcohol intake. Research has demonstrated that freshman typically report an inc rease in alcohol intake, and that this intake is associated with weight gain (Anding et al., 2001; Butler et al., 2004). In the current study, however, very few participants reported alcohol intake. Considering all 3 time points (baseline, post test, and f ollow up), only 3.98% of records included an alcoholic drink. This demonstrates that participants were likely not reporting or underreporting alcoholic intake due. This may be due to fears of legal repercussion, as students in the current study were not of legal drinking age. Future work, however, should focus on accurately consumed. Physical A ctivity There were no significant differences between treatment groups in physic al activity (total METs/week) at post test or follow up. Further, there was no significant difference in the proportion of participants maintaining or increasing physical activity between the treatment or control groups from baseline to post test. W hile p articipants in the treatment group did not experience significant changes in physical activity from post test to follow up participants in the control group experienced significant decreases in total weekly METs and weekly METs from walking from baseline to follow up. This demonstrates that the intervention may have been successful in helping treatment participants maintain their baseline physical activity. Participants in the control group, however, appear ed to have decreased their participatio n in physic al activity, consistent with previous research that demonstrated that freshman students typically decrease their participation in physical activity during the transition to college (Butler et al., 2004). After the end of intervention, however,
62 participants in the treatment group demonstrate d a trend toward decreasing activity, suggesting that maintenance of treatment effects may be an issue longer term. Tertiary Aim Using only participants who were overweight or obese at baseline, a similar pattern of resul ts to those found in the primary aim was observed. Specifically, participants in the intervention group experienced non significant weight change from baseline to post test, a small non significant increase from post test to follow up, for a net non signif icant weight loss from baseline to post test, while participants in the control group did not experience changes from baseline to post test and follow up. One important finding, however, is that the weight change experienced by overweight and obese partici pants in the treatment group was much larger than the magnitude of all participants ( 4.22 kg for overweight and obese participants compared to 2.07 kg for all participants in the treatment group ). Unfortunately, this sub group analysis was underpowered and despite large clinical changes i n weight loss there were no sta ti sti cally significant changes in weight by group or between groups at any time point. These findings indicate, however, that this intervention may be particularly effecti ve in overweight and obese participants compared to normal weight participants. As discussed earlier, this effect has potential clinical significance, and would likely have an even bigger impact if the current intervention was lengthened past four weeks. E xploratory Aims Disordered Eating Behaviors There were no significant changes in disordered eating behaviors (operationalized as total EAT scores) amongst either intervention group from baseline to follow up. This finding supports previous literature sugg esting that weight management interventions do
63 not lead to disordered eating ( Jeffery & French, 1999; Klem et al., 1997; National Task Force on the Prevention and Treatment of Obesity, 2000 ). Eating H abits Scores on the Three Factor Eating Questionnaire de monstrated that, even though there was not a significant group by time interaction, participants in the treatment group had significantly lower scores on the disinhibition subscale at post test compared to control, likely following from non significant tre nds for participants in the treatment group to experience decreases in disinhibition from baseline to post test. These results, along with trends for participants in the treatment group to experience non significant weight losses from baseline to post test follow previous research suggesting that for obese individuals, decreased disinhibition was associated with improved weight loss outcomes (Bryant et al., 2012). Problem S olving Skills There was a significant group effect such that participants in the con trol group demonstrated lower problem solving skills than participants in the treatment group, but the hypothesized group by time interaction was not significant. While low scores for the control group may have been an artifact of non stratified randomizat ion, t his finding suggests that despite prog ram focus on increasing problem solving skills, participants did not report significantly improved problem solving skills. Due to the program length, the focus on problem solving skills may not have been sufficie nt to significantly improve skills such that changes were observed on the SPSI; thus, if this study were to be repeated, and conducted on a longer term b asis (i.e., groups longer than four weeks), increased time should be spent on structured problem solvin g skills training.
64 Weight Loss Self Efficacy Although there were no significant differences between the treatment and control groups in terms of weight loss efficacy at post test or follow up, there was a trend for weight loss efficacy to improve over time This effect of time may be partly due to the finding that control participants did not gain weight over time; thus, the expected improvement in weight self efficacy in the treatment group was also observed in the control group, who were also overall succ essful with weight maintenance. Nutrition Knowledge No significant differences were found by group for change in nutritional knowledge, as measured by the Nutritional Knowledge Questionnaire. This may suggest that individuals did not improve their nutrition knowledge during the four week intervention. It may also indicate that the NKQ was not an appropriate measu re of nutrition knowledge in this population. Upon further review of individual items on the NKQ, many did not assess information that was presented during groups. Although the NKQ was the only validated measure of nutritional knowledge that was available at the time of study start, it may have been beneficial to additionally include a measure of knowledge addressed during the intervention, even if unvalidated, to function as a manipulation check. Further, future research should focus on developing a US bas ed measure of nutritional knowledge that reflects current guidelines and recommendations. Limitations Limitations to the current study include sample size, potential recruitment bias, and generalizability. First, as discussed previously, the study may ha ve been underpowered due to the lack of weight gain in participants randomized to the control group. As power analyses were based on the expectation of weight gain in the control group, the study
65 was likely inadequately powered without this weight gain. Se cond, participants recruited for this study may have represented individuals who were more likely to be interested in healthy eating and exercise behaviors than the general freshman college student population. Finally, b ecause only female freshman college students were recruited for the current study, the results may not be generalizable to other populations (e.g., males or students past their first year of college). Strengths Despite the above limitations, the current study has several strengths. First, t his study represents the first randomized trial of lifestyle intervention for weight gain prevention in freshman college students. Previous studies have focused on education only (Matvienko et al., 2001) interventions, using only self monitoring (Levitsky et al., 2006) or alternate populations, including first and second year medical students ( Hivert et al., 2007) and young adults aged 18 35 (Gokee LaRose et al., 2010). This study demonstrated the feasibility of recruitment and intervention in female fresh man college students, which has not yet been documented within the literature. At study start, a primary concern was that students would not be interested in the intervention, or that the burden of attending sessions and completing food and activity record s may be too high to be acceptable. Instead, participants qualitatively reported being excited during recruitment and later reported enjoying the group format and material. Future Directions Despite initial concerns that students would not attend a longer term program, s everal students commented at the end of groups that they wished the groups had lasted longer than four weeks Specifically, they reported that despite initial difficulty fitting the groups into t heir schedule with first year classes and act ivities, once the time
66 was set aside it would have been easier to continue throughout the semester, akin to an additional college course. Future interventions should consider working within the college course framework to offer lifestyle interventions; e.g ., offering a semester or quarter long program that could potentially count for elective credit for participating students. The longer term impact of the current intervention is also unknown. As participants were taught weight management skills and made pl ans for weight gains that term participants may have more time to implement these skills to prevent later weight gain. Further, control participants may have experienced weight gain in comparison to the treatmen t group if longer term follow up were included. Thus, future studies should focus on the long term impact of intervention in addition to measuring short term, semester long changes. Conclusion The current study demonstrated feasibility of recruitment and l ifestyle intervention in female freshman college students. No significant differences were found between treatment and control participants at post test or follow up; however, trends suggest that the intervention may have led to some success in weight loss decreases in caloric intake, and maintenance of physical activity for par ticipants in the control group, but these changes were not maintained from post test to follow up, suggesting limited maintenance. The long term impact of these behavioral changes, however, is unknown. Given additional time in groups (i.e., if the i ntervention was lengthened from four weeks), additional time should be spent on assisting participants to develop plans for maintaining their beneficial behavior changes following the end of groups. Further, a tapered plan for group sessions (e.g., going from weekly groups to biweekly and then monthly) may help give individuals the chance to practice maintenance behaviors
67 indep endently while still meeting occasionally to problem solve any barriers met for maintaining these behaviors. Overall, targeting freshman college students for weight gain prevention demonstrates promise, however future interventions should focus on lengthen ing the initial intervention, broadening participant recruitment, and including longer term follow up assessment visits.
68 APPENDIX A A: PLAN OF SESSIONS Session 1: Introduction to the Intervention & Self Monitoring Impact of environment on weight monitoring Session 2: Problem Solving & Taking Control of Eating Patterns goals Session 3: Becoming Active & Understanding Fad Diets Session 4: Improving Nutrition & Body Image Session 5: Eating Out, Planning for Holidays & Special Events, & Long Term Success! ut eating too much term success
69 APPENDIX B B: MEASURES INTERNATIONAL PHYSICAL ACTIVITY QUESTIONNAIRE We are interested in finding out about the kinds of physical activities that people do as part of their everyday lives. The questions will ask you about the time you spent being physically active in the last 7 days Please answer each question even if you do not consider yourself to be an active person. Please think about the activities you do at work, as part of your house and yard work, to get from place to place, and in your spare time for recreation, exercise or sport. Think about all the vigorous and moderate activities that you did in the last 7 days Vigorous physical activities refer to activities that take hard physical effort and make you breathe much harder than normal. Moderate activities refer to activities that take moderate physical effort and make you breathe somewhat harder than normal. PART 1: JOB RELATED PHYSICAL ACTIVITY The first section is about your work. This includes paid jobs, farming, volunteer work, course work and any other unpaid work that you did outside your home. Do not include unpaid work you might do around your home, like housework, yard work, general maintenance, and caring for your family. These are asked in Part 3. 1. Do you currently have a job or do any unpaid work outside your home? Yes No Skip to PART 2: TRANSPORTATION The next questions are about all the physical activity you did in the last 7 days as part of your paid or unpaid work. This does not include traveling to and from work. 2. During the last 7 days on how many days did you do vigorous physical activities like heavy lifting, digging, heavy construction, or climbing up stairs as part of your work ? Think about only those physical activities that you did for at least 10 minute s at a time. _____ days per week No vigorous job related physical activity Skip to question 4 3. How much time did you usually spend on one of those days doing vigorous physical activities as part of your work? _____ hours per day _____ minutes per day 4. Again, think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days on how many days did you do moderate physical activities like carrying light loads as part of your work ? Please do not include walking.
70 _____ days per week No moderate job related physical activity Skip to question 6 5. How much time did you usually spend on one of those days doing moderate physical activities as part of your work? _____ hours per day _____ minutes per day 6. During the last 7 days on how many days did you walk for at least 10 minutes at a time as part of your work ? Please do not count any walking you did to travel to or from work. _____ days per week No job related walking Skip to PART 2: TRANSPORTATION 7. How much time did you usually spend on one of those days walking as part of your work? _____ hours per day _____ minutes per day PART 2: TRANSPORTATION PHYSICAL ACTIVITY These questions are about how you traveled from place to place, including to places like work, stores, movies, and so on. 8. During the last 7 days on how many days did you travel in a motor vehicle like a train, bus, car, or tram? _____ days per week No traveling in a motor vehicle Skip to question 10 9. How much time did you usually spend on one of those days traveling in a train, bus, car, tram, or other kind of motor vehicle? _____ hours per day _____ minutes per day Now think only about the bicycling and walking you might have done to travel to an d from work, to do errands, or to go from place to place.
71 10. During the last 7 days on how many days did you bicycle for at least 10 minutes at a time to go from place to place ? _____ days per week No bicycling from place to place Skip to question 12 11. How much time did you usually spend on one of those days to bicycle from place to place? _____ hours per day _____ minutes per day 12. During the last 7 days on how many days did you walk for at least 10 minutes at a time to go from place to place ? _____ days per week No walking from place to place Skip to PART 3 : HOUSEWORK, HOUSE MAINTENANCE, AND CARING FOR FAMILY 13. How much time did you usually spend on one of those days walking from place to place? _____ hours per day _____ minutes per day PART 3: HOUSEWORK, HOUSE MAINTENANCE, AND CARING FOR FAMILY This section is about some of the physical activities you might have done in the last 7 days in and around your home, like housework, gardening, yard work, general maintenance work, an d caring for your family. 14. Think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days on how many days did you do vigorous physical activities like heavy lifting, chopping wood, shoveling snow, or digging in the garden or yard ? _____ days per week No vigorous activity in garden or yard Skip to question 16 15. How much time did you usually spend on one of those days doi ng vigorous physical activities in the garden or yard? _____ hours per day _____ minutes per day
72 16. Again, think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days on how many days did you do moderate activities like carrying light loads, sweeping, washing windows, and raking in the garden or yard ? _____ days per week No moderate activity in garden or yard Skip to question 18 17. How much time did you usually spend on one of those days do ing moderate physical activities in the garden or yard? _____ hours per day _____ minutes per day 18. Once again, think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days on how many days did you do moderate activities like carrying light loads, washing windows, scrubbing floors and sweeping inside your home ? _____ days per week No moderate activity inside home Skip to PART 4: RECREATION, SPORT AND LEISURE TIME PHYSICAL ACTIVITY 19. How much time did you usually spend on one of those days doing moderate physical activities inside your home? _____ hours per day _____ minutes per day PART 4: RECREATION, SPORT, AND LEISURE TIME PHYSICAL ACTIVITY This section is about all the physical activities that you did in the last 7 days solely for recreation, sport, exercise or leisure. Please do not include any activities you have already mentioned. 20. Not counting any walking you have already mentioned, during the last 7 days on how many days did you walk for at least 10 minutes at a time in your leisure time ? _____ days per week No walking in leisure time Skip to question 22 21. How much time did you usually spend on one of those days walking in your leisure time?
73 _____ hours per day _____ minutes per day 22. Think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days on how many days did you do vigorous physical activities like aerobics running, fast bicycling, or fast swimming in your leisure time ? _____ days per week No vigorous activity in leisure time Skip to question 24 23. How much time did you usually spend on one of those days doing vigorous physical activities in your lei sure time? _____ hours per day _____ minutes per day 24. Again, think about only those physical activities that you did for at least 10 minutes at a time. During the last 7 days on how many days did you do moderate physical activities like bicycling at a regular pace, swimming at a regular pace, and doubles tennis in your leisure time ? _____ days per week No moderate activity in leisure time Skip to PART 5: TIME SPENT SITTING 25. How much time did you usually spend on one of those days doing moderate physical activities in your leisure time? _____ hours per day _____ minutes per day PART 5: TIME SPENT SITTING The last questions are about the time you spend sitting while at work, at home, while doing course work and during leisure time. This may include time spent sitting at a desk, visiting friends, reading or sitting or lying down to watch television. Do not include any time spent sitting in a motor vehicle that you have already told me about. 26. During the last 7 days how much time did you usually spend sitting on a weekday ? _____ hours per day _____ minutes per day 27. During the last 7 days how much time did you usually spend sitting on a weekend day ? _____ hours per day
74 _____ minutes per day This is the end of the questionnaire, thank you for participating.
76 EATING INVENTORY (THREE FACTOR EATING QUESTIONNAIRE) For questions 1 statement, or feel that it is false as applied to you. TRUE FALSE 1. When I smell a sizzling steak or see a juicy piece of meat, I find it very difficult to keep from eating, even if I have just finished a meal. 2. I usually eat too much at social occasions, like parties and picnics. 3. I am usually so hungry that I eat more than three times a day. 4. When I have eaten my quota of calories, I am usually good about not eating anymore. 5. Dieting is so hard for me because I just get too hungry. 6. I deliberately take small helpings as a means of controlling my weight. 7. Sometimes things just taste so good that I keep on eating even when I am no longer hungry. 8. Since I am often hungry, I sometimes wish that while I am eating, an expert would tell me that I have had enough or that I can have something more to eat. 9. When I feel anxious, I find myself eating. 10. Life is too short to worry about dieting. 11. Since my weight goes up and down, I have gone on reducing diets more than once. 12. I often feel so hungry that I just have to eat something 13. When I am with someone who is overeating, I usually overeat too. 14. I have a pretty good idea of the number of calories in common foods.
77 TRUE FALSE 15. Sometimes when I start 16. It is not difficult for me to leave something on my plate. 17. At certain times of the day, I get hungry because I have gotten used to eating then. 18. While on a diet, if I eat food that is not allowed, I consciously eat less for a period of time to make up for it. 19. Being with someone who is eating often makes me hungry enough to eat also. 20. When I feel blue, I often overeat. 21. I enjoy eating too much to spoil it by counting calories or watching my weight. 22. When I see a real delicacy, I often get so hungry that I have to eat right away. 23. I often stop eating when I am not really full as a conscious means of limiting the amount that I eat. 24. I get so hungry that my stomach often seems like a bottomless pit. 25. My weight has hardly changed at all in the last ten years. 26. I am always hungry so it is hard for me to stop eating before I finish the food on my plate. 27. When I feel lonely, I console myself by eating. 28. I consciously hold back at meals in order not to gain weight. 29. I sometimes get very hungry late in the evening or at night. 30. I eat anything I want, any time I want. 31. Without even thinking about it, I take a long time to eat.
78 32. I count calories as a conscious means of controlling my weight. TRUE FALSE 33. I do not eat some foods because they make me fat. 34. I am always hungry enough to eat at any time. 35. I pay a great deal of attention to changes in my figure. 36. While on a diet, if I eat a food that is not allowed, I often then splurge and eat other high calorie foods. Please answer the following questions by filling in the circle corresponding to the letter of the response that is appropriate to you. 37. How often are you dieting in a conscious effort to control your weight? rarely sometimes usually always 38. Would a weight fluctuation of 5 lbs affect the way you live your life? not at all slightly moderately very much 39. How often do you feel hungry? only at meal times sometimes between meals often between meals almost always 40. Do your feelings of guilt about overeating help you to control your food intake? never rarely often always 41. How difficult would it be for you to stop eating halfway through dinner and not eat for the next four hours?
79 easy slightly difficult moderately difficult very difficult 42. How conscious are you of what you are eating? not at all slightly moderately Extremely 43. How frequently do you avoid almost never seldom usually almost always 44. How likely are you to shop for low calorie foods? unlikely slightly likely moderately likely very likely 45. Do you eat sensibly in front of others and splurge alone? never rarely often Always 46. How likely are you to consciously eat slowly in order to cut down on how much you eat? unlikely slightly likely moderately likely very likely 47. How frequently do you skip dessert because you are no longer hungry? almost never seldom at least once a week almost every day 48. How likely are you to consciously eat less than you want? unlikely slightly likely moderately likely very likely 49. Do you go on eating binges even though you are not hungry? never rarely sometimes at least once a week
80 50. To what extent does this statement describe your eating behavior? during the day, by evening I have given up and eat what I want, promising myself to not like me little like me pretty good description of me describes me perfectly 51. On a scale of 1 to 6, where 1 means no restraint in eating (eat whatever you want, whenever you want it) and 6 means total restraint (constantly limiting food 1 2 3 4 5 6
91 REFERENCE LIST Adams, T., & Rini A. (2007). Predicting 1 year change in body mass index among college students. Journal of American College Health, 55, 361 366. doi:10.3200/JACH.55.6.361 366 Anderson, D. A., Shapiro, J. R., & Lundgren, J. D. (2003). The freshman year of college as a cri tical period for weight gain: An initial evaluation. Eating Behaviors, 4, 363 367. doi:10.1016/S1471 0153(03)00030 8 Anding, J., Suminski, R., & Boss, L. (2001). Dietary intake, body mass index, exercise, and alcohol: are college women following the dietar y guidelines for Americans? Journal of American College Health, 49, 167 171. doi:10.1080/07448480109596299 Bandura, A. (1985). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Booth, M. (2000). Asses sment of physical activity: an international perspective. Research Quarterly for Exercise and Sport, 71, S114. Bouchard, C., & P erusse, L. (1993). Genetics of o besity. Annual Review of Nutrition, 13, 337 354. doi:10.1146/annurev.nu.13.070193.002005 Boutell e, K., Neumark Sztainer, D., Story, M. & Resnick, M. (2002). Weight control behaviors among obese, overweight, and nonoverweight a dolescents. Journal of Pediatric Psychology, 27, 531 540. doi:10.1093/jpepsy/27.6.531 Bowman, S. A., & Vinyard, B. T. (2004). Fast food c onsump tion of U.S. Adults: Impact on energy and nutrient intakes and overweight s tatus. Journal of American College Nutrition, 23, 163 168. Bryant, E. J., Cauldwell, P., Hopkins, M.E., King, N. A., & Blundell, J. E. (2012). Psycho markers of we ight loss. The roles of TFEQ Disinhibition and Restraint in exercise induced weight management. Appetite, 58, 234 241. doi:10.1016/j.appet.2011.09.006 Butler, S., Black, D., Blue, C., & Gretebeck, R. (2004). Change in diet, physical activity, and body weig ht in female college freshman. American Journal of Health Behaviour, 28, 24 32. doi:10.5993/AJHB.28.1.3 Calfas, K. J., Sallis, J. F., Nichols, J. F., Sarkin, J. A., Johnson, M. F., Caparosa, S., et al. (2000). Project GRAD: Two year outcomes of a randomize d controlled physical activity intervention among young adults. American Journal of Preventive Medicine, 18, 28 37. doi:10.1016/S0749 3797(99)00117 8
92 Clark, M. M. Abrams, D. B., Niaura, R. S., Eaton, C. A., & Rossi, J. S. (1991). Self efficacy in weight ma nagement. Journal of Consulting and Clinical Psychology, 59, 739 744. doi:10.1037/0022 006X.59.5.739 Contento, I., Balch, G., Bronner, Y., Lytle, L., Maloney, S., Olson, C., et al. (1995). The effectiveness of nutrition education and implications for nutri tion education policy, programs, and research: a review of research. Journal of Nutrition Education, 27, 277 418. Craig, C., Marshall, A. Sjstrm, M., Bauman, A., Booth, M., Ainsworth, B., et al. (2003). International physical activity questionnaire: 12 c ountry reliability and validity. Medicine and Science in Sports and Exercise, 35, 1381 1395. doi:10.1249/01.MSS.0000078924.61453.FB Olivares, A. (2002). Manual for the Social Problem Solving Inventory Revised (SPSI R ). North Tonawanda, NY: Multi Health Systems, Inc. Donnelly, J. E., Blair, S. N., Jakicic, J. M., Manore, M. M., Rankin, J. W., & Smith, B. K. (2009). Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults Medicine & Science in Sports & Exercise, 41, 459 471. doi:10.1249/MSS.0b013e3181949333 Donnelly, J. E., Hill, J. O., Jacobsen, D. J., Potteiger, J., Sullivan, D. K., Johnson, S. L., et al. (2003). Effects of a 16 month randomized controlled exerci se trial on body weight and composition in young, overweight men and women: The Midwest exercise trial. Archives of Internal Medicine, 163, 1343 1350. doi:10.1001/archinte.163.11.1343 Elfhag, K., R ssner, S., Barkeling, B., Rooth, P. (2005). S ibutramine tr eatment in obesity: Initial eating behaviour in relation to weight loss results and changes in mood. Pharmacological Research, 51, 159 163. doi: 10.1016/j.phrs.2004.07.005 Epstein, L. H., & Squires, S. (1988). Stoplight diet for children: An eight week pro gram for parents and children. Boston, MA: Little Brown and Company. Flegal, K. M., Carroll, M. D., Kit, B. K., Ogden, C. L (2012). Prevalence of obesity and trends in the distribution of body mass index Among US adults, 1999 2010. Journal of the American Medical Association, 307, 491 497. doi:10.1001/jama.2012.39 Fontaine, K. R., & Cheskin, L. J. (1997). Self efficacy, attendance, and weight loss in obesity treatme nt. Addictive Behaviors, 22, 567 570. doi:10.1016/S0306 4603(96)00068 8 Forster, J., Jeffery, R., Schmid, T., & Kramer, F. (1988). Preventing weight gain in adults: A pound of prevention. Health Psychology, 7, 515 525. doi:10.1037/0278 6220.127.116.115
93 Garn, S., Clark, D., Lowe, C., Forbes, G., Garn, S., Owen, G., et al. (1976). Trends in fatness and the origins of obesity. Pediatrics, 57, 443 456. Garner, D. M., & Garfinkel, P. E. (1979). The Eating Attitudes Test: An index of the symptoms of anorexia n ervosa. Psychological Medicine, 9, 273 279. doi:10.1017/S0033291700030762 Garner, D. M., Olmsted, M. P., Bohr, Y., & Garfinkel, P. E. (1982). The Eating Attitudes Test: Psychometric features and clinical correlates. Psychological Medicine, 12, 871 878. doi :10.1017/S0033291700049163 Gokee LaRose, J., Tate, D. F., Gorin, A. A., & Wing, R. A. (2010). Preventing weight gain in young adults: A randomized controlled pilot study. American Journal of Preventive Medicine, 39, 63 68. doi:10.1016/S0306 4603(96)00068 8 Gordon Larsen, P., Adair, L. S., Nelson, M. C., & Popkin, B. M. (2004). Five year obesity incidence in the transition period between adolescence and adulthood: the National Longitudinal Study of Adolescent Hea lth. American Journal of Clini cal Nutrition, 8 0, 569 575. Gordon Larsen, P., Nelson, M. C., & Popkin, B. M. (2004). Longitudinal physical activity and sedentary behavior trends: Adolescence to adulthood. American Journal of Preventive Medicine, 27, 277 283. doi:10.1016/S0749 3797(04)00183 7 Gorman, B. S., & Allison, D.B. (1995). Measures of restrained eating. In D.B. Allison (Ed.), Handbook of assessment for eating behaviors and weight related problems. (pp. 149 184). Thousand Oaks, CA: Sage Publishing, Inc. Graham, M., & Jones, A. (2002). Fre shman 15: V alid theory or harmful myth? Journal of American College Health, 50, 171 173. doi:10.1080/07448480209596023 Hagstrmer, M., Oja, P., & Sjstrm, M. (2007). The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct vali dity. Public Health Nutrition, 9, 755 762. doi:10.1079/PHN2005898 Hajhosseini, L., Holmes, T., Mohamadi, P., Goudarzi, V., McProud, L., & Hollenbeck, C. B. (2006). Changes in body weight, body composition and resting metabolic rate (RMR) in first year univ ersity freshmen students. Journal of American College Nutrition, 25, 123 127. Haslam, D. W., & James, W. P. T. (2005). Obesity. The Lancet, 366, 1197 1209. doi:10.1016/S0140 6736(05)67483 1 Hill, J. O., & Peters, J. C. (1998). Environmental contributions t o the obesity epidemic. Science, 280, 1371 1374. doi:10.1126/science.280.5368.1371
94 Hill, J. O., Wyatt, H. R., Reed, G. W., & Peters, J. C. (2003). Obesity and the environment: Where do we go from here? Science, 299 853 855. doi:10.1126/science.1079857 Hiv ert, M., Langlois, M., Berard, P., Cuerrier, J., & Carpentier, A. (2007). Prevention of weight gain in young adults through a seminar based intervention program. International Journal of Obesity, 31, 1262 1269. doi:10.1038/sj.ijo.0803572 Hoffman, D., Polic astro, P., Quick, V., & Lee, S. (2006). Changes in body weight and fat mass of men and women in the first year of College: A study of the "freshman 15". Journal of American College Health, 55, 41 46. doi:10.3200/JACH.55.1.41 46 Hovell, M. F., Mewborn, C. R ., Randle, Y., & Fowler Johnson, S. (1985). Risk of excess weight gain in university women: A three year community controlled analysis. Addictive Behaviors, 10, 15 28. doi:10.1016/0306 4603(85)90049 8 Huang, T., Harris, K., Lee, R., Nazir, N., Born, W., & Kaur, H. (2003). Assessing overweight, obesity, diet, and physical activity in college students. Journal of American College Health, 52, 83 86. doi:10.1080/07448480309595728 Institute of Medicine. (1995). Weighing the options: criteria for evaluating weig ht management programs. Washington, DC: National Academies Press. Jakicic, J. M., & Otto, A. D. (2005). Physical activity considerations for the treatment and prevention of obesity. American Journal of Clinical Nutrition, 82, 226S 229S. Jeffery, R., & Fren ch, S. (1997). Preventing weight gain in adults: design, methods and one year results from the Pound of Prevention study. International Journal of Obesity and Related Metabolic Disorders, 21, 457. doi:10.1038/sj.ijo.0800431 Jeffery, R. W., Epstein, L. H., Wilson, G. T., Drewnowski, A., Stunkard, A. J., & Wing, R. R. (2000). Long term maintenance of weight loss: Current status. Health Psychology, 19, 5 16. doi:10.1037/0278 6133.19.Suppl1.5 Jeffery, R. W., & French, S. A. (1998). Epidemic obesity in the Unite d States: A re fast foods and television viewing contributing? American Journal of Public Health, 88, 277 280. doi:10.2105/AJPH.88.2.277 Jeffery, R. W., & French, S. A. (1999). Preventing weight gain in adults: The Pound of Prevention study. American Journa l of Public Health, 89, 747 751. doi:10.2105/AJPH.89.5.747 Jia, H., & Lubetkin, E. I. (2010). Trends in quality adjusted life years lost contributed by smoking and obesity. American Journal of Preventive Medicine, 38, 138 144. doi:10.1016/j.amepre.2009.09. 043
95 Klem, M., Wing, R., McGuire, M., Seagle, H., & Hill, J. (1997). A descriptive study of individuals successful at long term maintenance of substantial weight loss. American Journal of Clinical Nutrition, 66, 239 246. Klem, M. L., Wing, R. R., Simkin Si lverman, L., & Kuller, L. H. (1997). The psychological consequences of weight gain prevention in healthy, premenopausal women. International Journal of Eating Disorders, 21, 167 174. doi:10.1002/(SICI)1098 108X(199703)21:2<167::AID EAT7>3.0.CO;2 J Kral, T. V., Roe, L. S., & Rolls, B. J. (2004). Combined effects of energy density and portion size on energy intake in women. American Journal of Clinical Nutrition, 79, 962 968. Kral, T. V. E., & Rolls, B. J. (2004). Energy density and portion size: their independent and combined effects on energy intake. Physiology & Behavior, 82, 131 138. doi:10.1016/j.physbeh.2004.04.063 Levitsky, D. A., Garay, J., Nausbaum, M., Neighbors, L., & DellaValle, D. M. (2006). Monitoring weight daily blocks the freshman weight gain: a model for combating the epidemic of obesity. International Journal of Obesity, 30, 1003 1010. doi:10.1038/sj.ijo.0803221 Levitsky, D. A., Halbmaier, C. A., & Mrdjenovic G. (2004). The freshman weight gain: a model for the study of the epidemic of obesity. International Journal of Obesity and Related Metabolic Disorders, 28, 1435 1442. doi:10.1038/sj.ijo.0802776 Lewis, C. E., Jacobs, D. R., Jr., McCreath, H., Kiefe, C. I ., Schreiner, P. J., Smith, D. E., et al. (2000). Weight gain continues in the 1990s: 10 year trends in weight and overweight from the CARDIA study. American Journal of Epidemiology, 151, 1172 1181. Lloyd Richardson, E., Bailey, S., Fava, J., & Wing, R. (2 009). A prospective study of weight gain during the college freshman and sophomore years. Preventive Medicine, 48, 256 261. doi:10.1016/j.ypmed.2008.12.009 Lowry, R., Galuska, D. A., Fulton, J. E., Wechsler, H., Kann, L., & Collins, J. L. (2000). Physical activity, food choice, and weight management goals and practices among U.S. college students. American Journal of Preventive Medicine, 18, 18 27. doi:10.1016/S0749 3797(99)00107 5 Lutes, L. D., Winett, R. A., Barger, S. D., Wojcik, J. R., Herbert, W. G., N ickols Richardson, S. M., & Anderson, E. S. (2008). Small changes in nutrition and physical activity promote weight loss and maintenance: 3 month evidence from the ASPIRE randomized trial. Annals of Behavioral Medicine, 35, 351 357. doi:10.1016/j.appet.200 6.03.160
96 Matvienko, O., Lewis, D. S., & Schafer, E. (2001). A college nutrition science course as an intervention to prevent weight gain in female college freshmen. Journal of Nutrition Education, 33, 95 101. doi:10.1016/S1499 4046(06)60172 3 Megel, M. (19 94). Health promotion, self esteem, and weight among female college freshmen. Health Values: The Journal of Health Behavior, Education, & Promotion, 18, 10 19. Molenberghs, G., & Kenward, M. (2007). Missing Data in Clinical S tudies. Indianapolis, IN: Wiley Must, A., Gortmaker, S. L., & Dietz, W. H. (1994). Risk factors for obesity in young adults: Hispanics, African Americans and Whites in the transition years, age 16 28 years. Biomedicine & Pharmacotherapy, 48 143 156. doi:10.1016/0753 3322(94)90103 1 Na tional Heart, Lung, and Blood Institute (1998). Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults (No. 98 4083). Washington, D.C.: National Institutes of Health. National Task Force on the Prevention a nd Treatment of Obesity. (2000). Dieting and the Development of Eating Disorders in Overweight and Obese Adults. Archives of Internal Medicine, 160, 2581 2589. doi:10.1001/archinte.160.17.2581 Nezu, A. M., Nezu, C. M., Friedman, S. H., Faddis, S., & Houts, P. S. (1998). Coping with cancer: A problem solving approach. Washington, DC: American Psychological Association. doi:10.1037/10283 000 Nielsen, S. J., & Popkin, B. M. (2003). Patterns and Trends in Food Portion Sizes, 1977 1998. Journal of the American M edical Association, 289, 450 453. doi:10.1001/jama.289.4.450 Niemeier, H. M., Raynor, H. A., Lloyd Richardson, E. E., Rogers, M. L., & Wing, R. R. (2006). Fast food consumption and breakfast skipping: Predictors of weight gain from adolescence to adulthood in a nationally representative sample Journal of Adolescent Health, 39, 842 849. doi:10.1016/j.jadohealth.2006.07.001 Parmenter, K., & Wardle, J. (1999). Development of a general nutrition knowledge questionnaire for adults. European Journal of Clinical Nutrition, 53, 298 308. doi:10.1038/sj.ejcn.1600726 Perri, M. G., & Corsica, J. (2002). Improving the maintenance of weight lost in behavioral treatment of obesity. In T. Wadden & A. Stunkard (Eds.), Handbook of Obesity Treatment (pp. 357 379). New York: G uilford Press. Perri, M. G., & Nezu, A. (1993). Preventing relapse following treatment for obesity. In A. Stunkard & T. Wadden (Eds.), Obesity: Theory and T herapy (pp. 287 299). New York: Raven Press.
97 Perri, M.G., Nezu, A. M., Patti, E.T., & McCann, K. L. (1989). Effect of length of treatment on weight loss. Journal of Consulting and Clinical Psychology, 57, 450 452. doi:10.1037//0022 006X.57.3.450 Perri, M. G., Nezu, A. M., & Viegener, B. J. (1992). Improving the long term management of obesity: Theory, re search, and clinical guidelines. New York: John Wiley & Sons. Perri, M. G. (1998). The Maintenance of treatment effects in the long term management of obesity. Clinical Psychology: Science and Practice, 5, 526 543. doi:10.1111/j.1468 2850.1998.tb00172.x Pr eacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717 731. Racette, S., Deusinger, S., Strube, M., Highstein, G., & Deusinger, R. (2005). Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. Journal of American College Health, 53, 245 251. doi:10.3200/JACH.53.6.245 251 Rolls, B. J., Morris, E. L., & Roe, L. S. (2002). Portion size of food affects energy intake in normal weight and overweight men and women. American Journal of Clinical Nutrition, 76, 1207 1213. Rubin, D. (1976). Inference and missing data. Biometrika, 63, 581 592. doi:10.1093/biomet/63.3.581 SAS Institute, Inc. (2008). SAS Version 9.1.3. Cary, NC: SAS Institu t e, Inc. Schafer, J., & Graham, J. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147 177. doi:10.1037/1082 989X.7.2.147 Schmidt, M., Affenito, S. G., Striegel Moore, R., Khoury, P. R., Barton, B., Crawford, P., et al. (2005). Fast food intake and diet quality in Black and White g irls: The National Heart, Lung, and Blood Institute Growth and Health Study. Archives of Pediatric Adolescent Medicine, 159, 626 631. doi:10.1001/archpedi.159.7 .626 Schwartz, N. (1975). Nutritional knowledge, attitudes, and practices of high school graduates. Journal of the American Dietetic Association, 66, 28 31. Searles, R., Terry, R., & Amos, R. (1986). Nutrition knowledge and body image satisfaction of female adolescents. Journal of Nutrition Education 18, 123 127. Serdula, M. K., Collins, M. E., Williamson, D. F., Anda, R. F., Pamuk, E., & Byers, T. E. (1993). Weight control practices of U.S. adolescents and adults. Annals of Internal Medicine, 119, 667 671.
98 Sheehan, T. J., DuBrava, S., DeChello, L. M., & Fang, Z. (2003). Rates of weight change for black and white Americans over a twenty year period. International Journal of Obesity an d Related Metabolic Disorders, 27, 498 504. doi:10.1038/sj.ijo.0802263 Simkin Silverman, L., Wing, R., Boraz, M., & Kuller, L. (2003). Lifestyle intervention can prevent weight gain during menopause: results from a 5 year randomized clinical trial. Annals of Behavioral Medicine, 26, 212 220. doi:10.1207/S15324796ABM2603_06 Simkin Silverman, L., Wing, R. R., Hansen, D. H., Klem, M. L., Pasagian Macaulay, A., Meilahn, E. N., et al. (1995). Prevention of cardiovascular risk factor elevations in healthy premeno pausal women Preventive Medicine, 24, 509 517. doi:10.1006/pmed.1995.1081 Stunkard, A. J., & Messick, S. (1985). The three factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29, 71 83. do i:10.1016/0022 3999(85)90010 8 Subar, A. F., Thompson, F. E., Potischman, N., Forsyth, B. H., Buday, R., Richards, D., et al. (2007). Formative research of a quick list for an automated self administered 24 hour dietary recall. Journal of the American Diet etic Association, 107, 1002 1007. doi:10.1016/j.jada.2007.03.007 Tang, L., Song, J., Belin, T. R., & Untzer, J. (2005). A comparison of imputation methods in a longitudinal randomized clinical trial. Statistics in Medicine, 24, 2111 2128. doi:10.1002/sim. 2099 U.S. Department of Labor. (2001). Average Annual Expenditures and Characteristics of All Consumer Units, Consumer Expenditure Survey. Washington, D.C.: U.S. Department of Labor. U.S. Department of Labor. (2008). Average Annual Expenditures and Charact eristics of All Consumer Units, Consumer Expenditure Survey. Washington, D.C.: U.S. Department of Labor. World Health Organization (1995). Physical status: The use and interpretation of anthropometry. Geneva: World Health Organization. Wing, R., Matthews, K., Kuller, L., Meilahn, E., & Plantinga, P. (1991). Weight gain at the time of menopause. Archives of Internal Medicine, 151, 97 102. doi:10.1001/archinte.151.1.97 Zimmerman, T. P., Hull, S. G., McNutt, S., Mittl, B., Islam, N., Guenther, P. M., et al. (2 009). Challenges in converting an interviewer administered food probe database to self administration in the National Cancer Institute automated self administered 24 hour recall (ASA24). Journal of Food Composition and Analysis, 22, S48 S51. doi:10.1016/j. jfca.2009.02.003
99 BIOGRAPHICAL SKETCH Kathryn Ross Middleton graduated from Virginia Commonwealth University in 2006 with a B.S. in psychology. At VCU, she worked as a research assistant in the lab of Suzanne E. Mazzeo, Ph.D., working on studies inve stigating eating disorders and ambiguity for judgments made regarding obese individuals. Kathryn worked as a statistical analyst for the Virginia Department of Health, Division of WIC and Family Services, from spring 2005 until summer 2007, when she moved to Gainesville, Florida for graduate school. In graduate school, Kathryn worked with Michael G. Perri, Ph.D., focusing on behavioral obesity intervention and weight management programs. Kathryn graduated with her M.S. in psychology in spring 2009, with her thesis focusing on the impact of weight loss and physical activity on health related quality of life. In April 2012, Kathryn received her M.P.H. with a concentration in biosta tistics from the University of Florida To fulfill the final requirements of her Ph.D. program, Kathryn completed her internship in clinical psychology at the Alpert Medical School of Brown University in sts focus on the treatment and prevention obesity, particularly in the translation of effective behavioral weight management treatment into public health level programs.