Ecological Momentary Assessment of Obesogenic ... - Squarespace [PDF]

completed the Three Factor Eating Questionnaire and the Power of Food Scale (PFS), and carried a palmtop computer for 7â

0 downloads 7 Views 140KB Size

Recommend Stories


Reactivity to smartphone-based ecological momentary assessment of depressive symptoms
Learn to light a candle in the darkest moments of someone’s life. Be the light that helps others see; i

Ecological Momentary Assessment in Behavioral Research: Addressing Technological and Human
This being human is a guest house. Every morning is a new arrival. A joy, a depression, a meanness,

NSTA Assessment slides 2015.pptx - Squarespace [PDF]
May 9, 2016 - Thank you for taking the time to review and evaluate the enclosed material. In my time at Columbia College Chicago, I have worked to continually improve the effectiveness of my teaching, my scholarship in science and in education, and t

ecological assessment
If your life's work can be accomplished in your lifetime, you're not thinking big enough. Wes Jacks

Obesogenic Environments
The best time to plant a tree was 20 years ago. The second best time is now. Chinese Proverb

Untitled - Squarespace
Be like the sun for grace and mercy. Be like the night to cover others' faults. Be like running water

Obesogenic environments
Never let your sense of morals prevent you from doing what is right. Isaac Asimov

glenfield ecological assessment
You have to expect things of yourself before you can do them. Michael Jordan

Ecological Assessment Report
Be who you needed when you were younger. Anonymous

suzuki property ecological assessment
If your life's work can be accomplished in your lifetime, you're not thinking big enough. Wes Jacks

Idea Transcript


articles

nature publishing group

Behavior and Psychology

Ecological Momentary Assessment of Obesogenic Eating Behavior: Combining Person-Specific and Environmental Predictors J. Graham Thomas1, Sapna Doshi1, Ross D. Crosby2,3 and Michael R. Lowe1 Obesity has been promoted by a food environment that encourages excessive caloric intake. An understanding of how the food environment contributes to obesogenic eating behavior in different types of individuals may facilitate healthy weight control efforts. In this study, Ecological Momentary Assessment (EMA) via palmtop computers was used to collect real-time information about participants’ environment and eating patterns to predict overeating (i.e., greater than usual intake during routine meals/snacks, and eating outside of a participant’s normal routine) that could lead to weight gain. Thirty-nine women (BMI = 21.6 ± 1.8; age = 20.1 ± 2.0 years; 61% white) of normal weight (BMI 18.5–25) completed the Three Factor Eating Questionnaire and the Power of Food Scale (PFS), and carried a palmtop computer for 7–10 days, which prompted them to answer questions about eating events, including a count of the types of good tasting high-calorie foods that were available. None of the self-report measures predicted overeating, but BMI interacted with the number of palatable foods available to predict overeating (P = 0.035). Compared to leaner individuals who reported a relatively low frequency of overeating regardless of the availability of palatable food, the probability of overeating among heavier individuals was very low in the absence of palatable food, but quickly increased in proportion to the number of palatable foods available. Our findings suggest that the eating behavior of those with higher relative weights is susceptible to the presence of palatable foods in the environment. Individuals practicing weight control may benefit from limiting their exposure to good tasting high-calorie food in their immediate environment. Obesity (2011) 19, 1574–1579. doi:10.1038/oby.2010.335

Introduction

Excessive caloric intake, which is one of the most proximal causes of the current obesity epidemic (1), appears to have been promoted by changes in the food environment (2). One such change is a greater availability and variety of highly palatable high-calorie food. There is evidence to suggest that consumptive behavior increases when the variety of highly palatable high-calorie foods is increased (3,4). In the current food environment, the ready availability of a large variety of inexpensive, highly palatable, high-calorie foods may result in frequent episodes of overeating (i.e., consumption beyond current energy needs), that could eventually lead to the development of overweight and obesity. A recent report suggests that overeating by an average of 370 kcal/day is sufficient to produce overweight, while overeating by 680 kcal/day will produce obesity (1). Despite evidence that changes in the food environment have contributed to obesity, there is a relatively poor understanding of how individual differences in physiology and behavior

toward food and eating may affect energy intake in response to the food environment. This information is critical for determining who is at greatest risk for overeating in response to the food environment, and to shape interventions aimed at helping individuals resist the obesogenic effects of the food environment. Previous studies examining predictors of obesity and weight gain have often relied on questionnaire measures of eating behavior. Restrained eating and disinhibited eating are two of the most commonly evaluated constructs, which are typically measured via the Three Factor Eating Questionnaire (TFEQ; also known as the Eating Inventory) (5). Restraint is conceptualized as a conscious effort to restrict food intake for the purpose of weight control (5). While restraint usually increases with successful weight loss, it is also paradoxically associated with a higher weight and weight gain in crossectional and prospective studies (5), likely because restraint reflects a relative reduction in caloric intake (i.e., eating less than one wants) rather than an absolute reduction (i.e., eating less than needed

1 Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA; 2Department of Clinical Neuroscience, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota, USA; 3Neuropsychiatric Research Institute, Fargo, North Dakota, USA. Correspondence: J. Graham Thomas ([email protected])

Received 7 July 2010; accepted 7 December 2010; published online 27 January 2011. doi:10.1038/oby.2010.335 1574

VOLUME 19 NUMBER 8 | august 2011 | www.obesityjournal.org

articles Behavior and Psychology to maintain homeostatic energy balance) (6). Disinhibition is conceptualized as a tendency toward overeating in response to cues that may be internal (e.g., negative affect) or external (e.g., other people eating, the availability of palatable food) (7). Disinhibition is positively associated with food consumption, and like restraint, it is associated with a higher weight and weight gain (7). Hedonic hunger (e.g., a desire to eat that is cued by the availability of palatable food, in the absence of a current energy deficit) is a newer construct that has only recently begun to be measured (8). The Power of Food Scale (PFS) is a questionnaire that was developed to measure the motivation to consume highly palatable foods but not the actual intake of such foods (9). The PFS is associated with the potential for overeating (9–12), and may be related to weight and body size (9). The TFEQ and the PFS seem like ideal candidates for individual difference factors that could help us understand variability in susceptibility to the obesogenic effects of the food environment. Ideally, these relationships should be assessed in individuals’ natural environments. Previous studies of the food environment and individual eating behavior have relied primarily on retrospective self-report (e.g., questionnaires and clinical interviews) (9,13), written food diaries (14), and laboratory experimentation and/or observation (15,16). All are affected by biases inherent to retrospective self-report and do not necessarily generalize to the natural environment (17,18). The current study used Ecological Momentary Assessment (EMA) via palmtop computer to collect data in real-time in participants’ natural environment to investigate the interaction between person-specific factors and environmental factors in the prediction of obesogenic eating behavior (i.e., episodes of overeating). EMA is thought to be less affected by retrospective self-report biases because participants answer questions about what is happening in the current moment (19). Also, EMA findings are thought to be more generalizable than those obtained from lab studies because data are collected in participants’ natural environments (19). We hypothesized that a greater variety of palatable foods in the immediate environment would contribute to a greater likelihood of overeating. We also hypothesized that greater disinhibition and greater sensitivity to the rewarding properties of palatable food would increase, and higher levels of dietary restraint would decrease, the strength of the relationship between the variety of foods available and the probability of overeating. Individuals who are more sensitive to the rewarding properties of food were presumed to be highly motivated to consume highly palatable food when it is available. Alternatively, individuals who exhibit dietary restraint are assumed to closely monitor and attempt to control their food intake (5), and were therefore presumed to be less at risk for overeating in response to environmental cues. Methods and Procedures Participants Forty-three undergraduate women with a BMI within the normal weight range (i.e., 18–25) who were at least 18-years-old, and denied obesity | VOLUME 19 NUMBER 8 | August 2011

any history of an eating disorder, were enrolled in this study. Overweight participants were excluded from the current study, as the goal was to investigate risk factors for the development of overweight and not its maintenance. Four participants either dropped out before completing the EMA protocol (n = 1) or experienced technical failures resulting in a loss of data. The remaining 39 participants contributed data to the analysis of outcomes. Procedures The procedures were approved by the Drexel University institutional review board for research involving human subjects. The investigators recruited participants by posting flyers, and by in-person visits to undergraduate classes from several academic departments, on the Drexel University campus in Philadelphia, Pennsylvania. The study was described to participants as an investigation of health behaviors in young adults. Persons who expressed interest in the study met individually with an experimenter to receive detailed information about the study, complete the consent process, and have their height and weight measured. Participants were also led through a 24-h food recall in order to determine their usual pattern of food intake. This was done explicitly to prepare participants to identify eating episodes that occurred outside of their usual pattern of eating, and eating episodes in which they consumed “more than usual.” During this visit, participants were trained in the EMA procedures, which took ~45 min. During the training each participant used a palmtop computer to answer questions based on their usual pattern of eating as reported during the 24-h food recall. Participants were given a list of examples of good tasting highcalorie foods, which were defined as “foods high in fat and/or sugar, such as snack foods, junk foods, fatty meats, cheese, baked goods, and fried foods.” The training ended when participants were able to make ratings via the device without error. Lastly, the participants completed questionnaires. The EMA began for each participant immediately after meeting with the experimenter. Participants completed a minimum of 7 days of EMA, but were given the option to complete up to 10 days of EMA, depending on when they could be scheduled to return the device. The protocol implemented signal-contingent recordings, in which prompts from the palmtop computer signal participants to answer questions via the device (20). As in a previous study (21), participants received six semi-random prompts daily in order to sample evenly across the waking hours. Signal times were randomly distributed around six anchors (set at 8:30 am, 11:10 am, 1:50 pm, 4:30 pm, 7:10 pm, and 9:50 pm) in a normal distribution with a mean of 0 (i.e., the anchor point) and a standard deviation of 30 min (21). If a participant did not respond immediately to a prompt, they were signaled every 5 min until they either responded or canceled the alarm. At each prompt, participants answered questions about their most recent eating episode or eating opportunity. Participants also answered questions about their mood and their current level of dietary restraint (not described in this report). Responses made within 45 min of a prompt were considered eligible for analysis, and earned the participant $1 in compensation (maximum compensation was set at $60). Questionnaire measures PFS. This 15-item questionnaire was developed by Lowe et al. (9) to assess individual differences in the strength of the hedonic appetitive system, which motivates eating in response to the availability and palatability of food in the environment (as opposed to physiological need). The PFS measures the motivation to consume highly palatable foods but not the actual intake of such foods. The PFS supplies a total score and three subscale scores pertaining to situations in which food is (i) available, (ii) present, (iii) and tasted. This measure is relatively new, but initial reports indicate that its psychometric properties are good (9,12).

TFEQ. The cognitive restraint subscale of the TFEQ (TFEQ-R) (22) was used to measure dietary restraint, which has been defined as conscious efforts to restrict food intake for the purpose of weight control (5). The disinhibition subscale of the TFEQ (TFEQ-D) measures eating in response to emotional, cognitive, or social cues (as opposed 1575

articles Behavior and Psychology to ­hunger resulting from an energy deficit) (22). The reliability and validity of the TFEQ have been studied extensively, and while the questionnaire is not a perfect tool it is generally recognized as one of the best available measures of dietary restraint and disinhibited eating in noneating disordered individuals (5,7,18,23). The full TFEQ also includes a hunger subscale, but this was not administered as part of this study. EMA measures Eating events. Participants were asked to indicate whether they had eaten since last using the palmtop computer. In the event of an affirmative response (i.e., an eating episode), participants indicated whether they usually eat at that time, whether they were eating to make up for a missed meal or snack that they usually eat, and approximately how many types of good tasting high-calorie foods were available during the eating episode. The number of good tasting high-calorie foods available was rated on a likert scale ranging from 0 to 5 with the following categories: 0, 1–2, 3–5, 6–10, 11 or more. A food was considered “available” if it was visible to the participant and could be acquired during the eating event (e.g., in the refrigerator at home or on a buffet line in a cafeteria). Foods on a restaurant menu were not considered “available” unless they could be seen. Participants also indicated whether they ate more than usual, the same as usual, or less than usual during the eating episode. Participants who responded that they had not eaten since their last rating indicated whether they had had the opportunity to eat (i.e., food was available in their immediate environment), and if so, the approximate number of good tasting high-calorie foods available. Each eating episode and eating opportunity was coded as “overeating” or “not overeating” based on the following criteria: Overeating was defined as any episode in which a participant reported eating “more than usual,” or consumed food when they do not “usually eat at this time” and they were not “making up for a missed meal or snack.” All other eating episodes and opportunities were defined as “not overeating.” Statistical analysis Analyses incorporated all EMA ratings made within 45 min of a prompt. Generalized mixed models were used to calculate the likelihood that an eating event would be characterized by overeating or normal intake (coded as 1 and 0, respectively). A Bernoulli distribution was used to model the probability of the dichotomous outcome. Predictors included person-specific variables (BMI, PFS, TFEQ-R represented at level 2) and the number of good tasting high-calorie foods available (represented at level 1). The role of the person-specific factors (including the PFS total score and subscales) were investigated first in separate analyses and then in multivariate models allowing for interactions between the predictors. Person-specific predictors were centered using the grand mean prior to analysis. All analyses were conducted via Hierarchical Linear and Nonlinear Modeling version 6.06 (24). Missing data (i.e., prompts with no participant response) were not imputed. The continuous predictors (i.e., BMI, PFS, TFEQ, number of good tasting high-calorie foods available) appeared normally distributed. Results Description of sample

The 39 participants who provided sufficient EMA data for analysis were predominately white (62%), with an average age of 20.1 (s.d. = 2.0) years old. Additional demographic information and scores on the questionnaire measures are presented in Table 1. Participants’ scores on the TFEQ and PFS were similar to those obtained from other studies of comparable populations (5,22). BMI was associated with TFEQ-D (r = 0.33, P = 0.041), but not the TFEQ-R (r = 0.18, P = 0.268) or the PFS total score (r = 0.18, P = 0.281). 1576

Table 1 Characteristics of participants Age (mean ± s.d. years)

20.1 ± 2.0

Race (%)   Non-Hispanic white

24 (61.5%)

  Asian

8 (20.5%)

  African American

6 (15.4%)

  Hispanic

1 (2.6%)

Weight (mean ± s.d. kg)

57.4 ± 6.4

BMI (mean ± s.d. kg/m2)

21.6 ± 1.8

PFS (mean ± s.d.)

2.4 ± 0.8

TFEQ-R (mean ± s.d.)

8.5 ± 5.8

TFEQ-D (mean ± s.d.)

6.1 ± 3.1

PFS, Power of Food Scale; TFEQ-D, disinhibition subscale of the Three Factor Eating Questionnaire; TFEQ-R, cognitive restraint subscale of the Three Factor Eating Questionnaire.

Compliance with study procedures

Participants responded to 71% of EMA prompts. Compliance was not associated with time of day or the number of days with the EMA device. Eating events

A total of 1,221 ratings were made by participants, 708 (58.0%) of which contained information on eating events, while 222 (8.2%) depicted eating opportunities, and 291 (23.8%) indicated no eating event or opportunity. Nearly one third of eating events (N = 211; 29.8%) were characterized by overeating, 87 (41.2%) of which resulted from eating “more than usual,” and 124 (58.8%) of which resulted from consumed food when the participant does not “usually eat at this time.” The mean score for the item assessing number of different good tasting highcalorie foods available was 1.79 (s.d. = 1.37), which represents an average of about three types of good tasting high-calorie foods available during eating events. Prediction of overeating

When analyzed separately (i.e., in analyses containing no other predictors), none of the person-specific variables, nor the number of good tasting high-calorie foods available during eating events, predicted the likelihood that an eating event would be characterized by overeating. Furthermore, the only combination of variables that predicted the likelihood of overeating was a single statistically significant interaction between BMI and the number of good tasting high-calorie foods available (see Table 2 and Figure 1). For participants at the lower end of the BMI range (i.e., the lower 25th percentile), the probability of overeating was only minimally affected by the number of good tasting high-calorie foods available. For individuals at the upper end of the BMI range (i.e., the 75th percentile and above), the probability of overeating was quite low in the absence of good tasting high-calorie foods, but increased rapidly as the number of good tasting high-calorie foods increased. Secondary analyses (not reported in detail here) suggest that the two sources of overeating (i.e., eating VOLUME 19 NUMBER 8 | august 2011 | www.obesityjournal.org

articles Behavior and Psychology Table 2  Probability of overeating predicted by BMI and the number of good tasting high-calorie foods available 95% confidence interval of the odds ratio Estimate

s.e.

df

t-ratio

OR

Significance

Lower bound

Upper bound

  Intercept

−1.439511

0.178464

37

8.066

0.237044

0.000

0.165

0.340

  BMI

−0.104994

0.101724

37

1.032

0.900330

0.309

0.733

1.106

0.110419

0.064351

904

1.716

1.116745

0.086

0.984

1.267

0.075804

0.036032

904

2.104

1.078751

0.035

1.005

1.158

s.d.

df

χ2

Significance

Parameter Estimates of fixed effects

  Number of palatable foods   BMI × palatable foods

Estimate Estimates of variance components   Intercept (subject variance)

0.42676

0.65327

37

101.89

0.000

  Palatable foods slope

0.03791

0.19470

37

53.87

0.036

df, degrees of freedom; OR, odds ratio. 0.35

Low BMI Moderate BMI High BMI

Probability of overeating

0.30

0.25

0.20

0.15 0

1–2

3–5

6–10

11+

Number of good tasting high-calorie foods available

Figure 1  Illustration of modeled data depicting the interaction between the number of good tasting high-calorie foods available and BMI in the prediction of the probability of overeating. BMI tertials were used to depict the interaction graphically. Low BMI = lower 25th percentile of the sample BMI distribution, moderate BMI = 25th–75th percentile of the sample BMI distribution, high BMI = upper 25th percentile of the sample BMI distribution.

“more than usual” and eating when the participant does not “usually eat at this time”) contributed roughly equally to this finding. Notably, the correlation between BMI and the average number of good tasting high-calorie foods available during eating events was nonsignificant (r = −0.04, P = 0.818), suggesting that larger individuals do not tend to encounter a more varied food environment, which could have accounted for the moderating effect of BMI on the number of good tasting highcalorie foods available. Discussion

To our knowledge, this is the first study to investigate obesogenic eating behavior in the natural environment via EMA. We hypothesized that (i) a greater variety of palatable food in the environment would contribute to overeating, (ii) participants who scored highly measures of sensitivity to the rewarding properties of obesity | VOLUME 19 NUMBER 8 | August 2011

palatable food and disinhibited eating would experience more ­frequent overeating, and (iii) dietary restraint would protect against episodes of overeating. None of our a priori hypotheses were supported by the data. Neither the number of palatable foods available, scores on the PFS (measuring a sensitivity to the rewarding properties of food), nor scores on the TFEQ-R (measuring dietary restraint) or TFEQ-D (measuring disinhibited eating) predicted overeating, alone or in combination with each other. This was surprising given that certain individuals (e.g., those who are especially sensitive to the rewarding properties of food, prone to disinhibited eating, and/or are not restraining their eating) were expected to be especially susceptible to overeating. Though speculative, the absence of prediction by the PFS could be explained if the PFS only reflects hedonically-driven consumption that occurs after energy needs have been met. As for dietary restraint, there is evidence that restrained eaters may not actually eat less than unrestrained eaters in the natural environment (18). It is also possible that there are situational factors (e.g., variability in hunger, mood, stress, activity level, social context, and location of eating) and/or person-specific factors (e.g., taste preference, weight concerns, personality characteristics) that were unaccounted for in this study that are superior predictors of overeating behavior. This seems especially likely given the small number of predictors assessed in this study, and the findings obtained in our secondary analysis. A secondary exploratory analysis that combined BMI with the number of palatable foods available suggests that, in comparison to leaner individuals, heavier individuals are more likely to overeat when there is a large variety of palatable foods available, but less likely to overeat when there are very few or no such foods available. Leaner individuals reported a relatively low rate of overeating that was fairly constant regardless of the availability of palatable foods. These findings should be interpreted with caution because they were not predicted a priori. Nevertheless, they are reminiscent of Stanley Schachter’s early work, which suggest that overweight individuals are 1577

articles Behavior and Psychology more likely to rely on environmental cues to decide when to start and stop eating, whereas normal-weight individuals rely more on internal cues such as feelings of hunger and satiation (25). Given that the sample used in the current study was restricted to ­normal-weight women, our findings suggest that a pattern of eating that is motivated by external cues may be a factor leading to the development of overweight, rather than a consequence of the overweight or obese condition. However, the psychological mechanisms responsible for this pattern of externally motivated eating were not accounted for by the person-specific factors measured in this study. Additional research is needed to identify other constructs with greater predictive power. It is also important to remark on the overall frequency of overeating (29.8% of all eating events), which seems particularly high, especially given that participants would be expected to underreport overeating because of social desirability bias (26). It is possible that this bias was reduced by the EMA protocol, which was carried out in real-time in the absence of the researchers and involved many more responses than more traditional one-time assessments. However, it is also possible that the high overall frequency of overeating reflects a perception of overeating more so than a reality of excessive caloric intake. Without detailed dietary recalls, it is difficult to evaluate this possibility. However, we ultimately decided against including such dietary recalls in the current study because, (i) it was believed that it would increase participant burden to unreasonable levels, (ii) dietary recalls are often highly inaccurate (27), and (iii) even with accurate dietary recalls it is difficult to determine whether an individual is in a state of energy balance or energy excess without additional physiological measurements. Also, our approach allowed the maximum number of overeating episodes to be recorded, as any incident of eating could “count” as overeating as long as the participant considered it to be one. This subjective approach to defining behavior has been used in other EMA studies of eating (28). It is also worth mentioning that participants may have compensated for an episode of overeating by consuming less later in the day. There is mixed evidence for this; only 3.3% of overeating events were followed by at least one report of eating “less than usual” at a meal or snack later in the same day, but 23.3% of overeating events were followed by at least one report of eating “less than usual” at meal or snack on the following day. While this study represents an important first step in evaluating obesogenic eating behavior in real-time in the natural environment, future studies are encouraged to measure dietary intake in more detail, perhaps with the aid of new technologies (29). Despite the observational nature of the current study, and presuming that subsequent research replicates the relationship we found, practical implications may be inferred. Specifically, programs for weight loss and weight gain prevention may wish to emphasize the importance of limiting exposure to good tasting high-calorie food in the immediate environment (it may be too late once eating commences). This could be done in a variety of ways including avoiding settings in which good tasting 1578

high-calorie foods are immediately available (e.g., buffet restaurants, convenience stores), and refraining from purchasing these foods so that they are not available for consumption in the home environment. Preliminary efforts to control the food environment have yielded positive results (30). Additionally, our results suggest that individuals whose BMI puts them at the upper end of the range of desirable body weights may benefit from efforts to prevent further weight gain that could eventually produce medical comorbidites. Strengths of this study include the use of EMA to study realtime behaviors of participants in their natural environments and an analytic method that accurately models the many repeated observations made on each individual. The relatively homo­genous sample of self-selected, young, normal-weight women is a possible limitation of this study, as the results may not generalize to other populations. While normalweight women were chosen specifically to study the possible development of overweight (rather than the maintenance of overweight), this study should be replicated with overweight individuals—a group that is known to be at risk for weight gain. Other shortcomings include the subjective definition of “overeating,” the limited assessment of the food environment, and the use of only two questionnaires to assess person­specific factors related to eating behavior. It is also important to acknowledge that replication of the interaction between BMI and the number of palatable foods available in the prediction of overeating is especially important because this relationship was not initially predicted. Disclosure The authors declared no conflict of interest. © 2011 The Obesity Society

REFERENCES 1. Swinburn BA, Sacks G, Lo SK et al. Estimating the changes in energy flux that characterize the rise in obesity prevalence. Am J Clin Nutr 2009;89:1723–1728. 2. French SA, Story M, Jeffery RW. Environmental influences on eating and physical activity. Annu Rev Public Health 2001;22:309–335. 3. Remick AK, Polivy J, Pliner P. Internal and external moderators of the effect of variety on food intake. Psychol Bull 2009;135:434–451. 4. Wansink B. Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annu Rev Nutr 2004;24:455–479. 5. Lowe MR, Thomas JG. Measures of restrained eating: conceptual evolution and psychometric update. In: Allison D, Baskin ML (eds). Handbook of Assessment Methods for Obesity and Eating Behaviors. Sage: New York, 2009, pp 137–185. 6. Lowe MR, Levine AS. Eating motives and the controversy over dieting: eating less than needed versus less than wanted. Obes Res 2005;13: 797–806. 7. Bryant EJ, King NA, Blundell JE. Disinhibition: its effects on appetite and weight regulation. Obes Rev 2008;9:409–419. 8. Lowe MR, Butryn ML. Hedonic hunger: a new dimension of appetite? Physiol Behav 2007;91:432–439. 9. Lowe MR, Butryn ML, Didie ER et al. The Power of Food Scale. A new measure of the psychological influence of the food environment. Appetite 2009;53:114–118. 10. Forman EM, Hoffman KL, McGrath KB et al. A comparison of acceptanceand control-based strategies for coping with food cravings: an analog study. Behav Res Ther 2007;45:2372–2386. 11. Ochner CN, Green D, van Steenburgh JJ, Kounios J, Lowe MR. Asymmetric prefrontal cortex activation in relation to markers of overeating in obese humans. Appetite 2009;53:44–49. VOLUME 19 NUMBER 8 | august 2011 | www.obesityjournal.org

articles Behavior and Psychology 12. Cappelleri JC, Bushmakin AG, Gerber RA et al. Evaluating the Power of Food Scale in obese subjects and a general sample of individuals: development and measurement properties. Int J Obes (Lond) 2009;33: 913–922. 13. Campbell KJ, Crawford DA, Salmon J et al. Associations between the home food environment and obesity-promoting eating behaviors in adolescence. Obesity (Silver Spring) 2007;15:719–730. 14. Tuomisto T, Tuomisto MT, Hetherington M, Lappalainen R. Reasons for initiation and cessation of eating in obese men and women and the affective consequences of eating in everyday situations. Appetite 1998;30:211–222. 15. Kahn BE, Wansink B. The influence of assortment structure on perceived variety and consumption quantities. J Consum Res 2004;30:519–533. 16. Rolls BJ, Rowe EA, Rolls ET et al. Variety in a meal enhances food intake in man. Physiol Behav 1981;26:215–221. 17. Schwartz, N. Retrospective and concurrent self-reports: the rationale for real-time data capture. In: Stone A, Shiffman S, Atienza A, Nebeling L (eds). The Science of Real-Time Data Capture. Oxford University Press: New York, 2007, pp 11–26. 18. Stice E, Cooper JA, Schoeller DA, Tappe K, Lowe MR. Are dietary restraint scales valid measures of moderate- to long-term dietary restriction? Objective biological and behavioral data suggest not. Psychol Assess 2007;19:449–458. 19. Stone AA, Shiffman S, Atienza AA, Nebeling L. Historical roots and rationale of Ecological Momentary Assessment (EMA). In: Stone AA, Shiffman S, Atienza AA, Nebeling L (eds). The Science of Real-Time Data Capture: Self-Reports in Health Research. Oxford University Press, New York, 2007, pp 3–10.

obesity | VOLUME 19 NUMBER 8 | August 2011

20. Wheeler L, Reiss HT. Self-recording of everyday life events: origins, types, and uses. J Pers 1991;59:339–354. 21. Smyth JM, Wonderlich SA, Heron KE et al. Daily and momentary mood and stress are associated with binge eating and vomiting in bulimia nervosa patients in the natural environment. J Consult Clin Psychol 2007;75: 629–638. 22. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res 1985;29:71–83. 23. Williamson DA, Martin CK, York-Crowe E et al. Measurement of dietary restraint: validity tests of four questionnaires. Appetite 2007;48:183–192. 24. Raudenbush S, Bryk A, Cheong Y, Congdon R. HLM 6. Scientific Software International: Lincolnwood, IL, 2004. 25. Schachter S, Goldman R, Gordon A. Effects of fear, food deprivation, and obesity on eating. J Pers Soc Psychol 1968;10:91–97. 26. Maccoby EE, Maccoby N. The interview: a tool of social science. In: Gardiner L (ed). Handbook of Social Psychology, vol. 1. Addison-Wesley: Cambridge, MA, 1954, pp 449–487. 27. Trabulsi J, Schoeller DA. Evaluation of dietary assessment instruments against doubly labeled water, a biomarker of habitual energy intake. Am J Physiol Endocrinol Metab 2001;281:E891–E899. 28. Carels RA, Hoffman J, Collins A et al. Ecological momentary assessment of temptation and lapse in dieting. Eat Behav 2001;2:307–321. 29. McCabe-Sellers B. Advancing the art and science of dietary assessment through technology. J Am Diet Assoc 2010;110:52–54. 30. Gorin AA, Raynor HA, Niemeier HM, Wing RR. Home grocery delivery improves the household food environments of behavioral weight loss participants: results of an 8-week pilot study. Int J Behav Nutr Phys Act 2007;4:58.

1579

Smile Life

When life gives you a hundred reasons to cry, show life that you have a thousand reasons to smile

Get in touch

© Copyright 2015 - 2024 PDFFOX.COM - All rights reserved.