Free Health Essays - Weight Gain Obesity
1. Abstract
2. Introduction
Weight gain in humans is usually a very slow process, barely discernible by ordinary self observation, and caused by such small changes in energy balance that it is practically undetectable by current technology (Levitsky, Halbmaier & Mrdjenovic, 2004). However, these small undetectable imbalances in energy are responsible for a significant rise in the incidence of obesity. In 1994, 25% of U.S women and 20% of men were obese, defined as having a BMI of 30 and over.
An additional 25% and 39%, respectively, were considered overweight, defined as a BMI of 25.0 to 29.9 (Flegal, Carroll, Kuczmarski, & Johnson,1998). Thus, 55% of adult Americans were either overweight or obese. Alarmingly, data obtained in 1999 showed this figure to have increased, with 61% of adult Americans being classed as overweight or obese (National Center for Health Statistics, 1999).
Similar trends have been observed in nations other than the United States, to such an extent that the World Health Organisation has declared obesity a global epidemic (WHO, 1998). Physical inactivity and being overweight are closely linked to heart disease, and its associated risk factors such as hypertension and diabetes (Eckel & Krauss, 1998; Donahue, Fuster & Califf, R.M., 2001).
In fact, some have suggested that the numbers of people affected by obesity and the severity of its consequences are becoming so great that it threatens to overwhelm medical resources (Hall & Jones, 2002).
Consumers are spending upwards of $33 billion a year in the United States to achieve weight loss (Federal Trade Commission Bureau of Consumer Protection, 1997), yet effort by the public, and major health organisations, seems not to have the intended effect and prevalence of obesity continues to increase.
Research suggests that most people who successfully lose weight regain it within several years (Sarwer & Wadden, 1999). This poor long term outcome, coupled with numbers of people affected by obesity, indicate that prevention may be the most effective approach to combating the obesity epidemic. It is therefore important to empirically establish predictors of weight gain.
One strategy for identifying predictors of weight gain is to examine periods of life that have been associated with a high risk of weight gain. Data shows that the greatest increase in obesity from 1991 to 1998 in the United States, from 7.1% to 12.1%, occurred in the 18 to 29 year old age group. Moreover, an even greater increase, from 10.6% to 17.8%, was found in those in this sample having some college education (Mokdad et al. 199).
Thus, one possible model of the persistent increase in positive energy balance is the increase in body weight that evidence suggests occurs during university, particularly the freshman year. The term “freshman 15” has been coined to describe the 15 pounds that students presumably gain over their freshmen year. This claim has been somewhat substantiated.
Prospective Studies have shown that freshman show significant increases in weight, BMI, fat mass and rates of obesity (Anderson, Shapiro & Lundgren, 2003; Butler, Black, Blue & Gretebeck, 2004; Hodge, Jackson & Sullivan, 1993).
The average female student gains between 1.7 and 3.1kg during their first year at University (Anderson, Shapiro & Lundgren, 2003). The rate of weight gain in these students is considerably greater than that observed in the general population. One study found the gain in weight by students during the first 12 weeks of semester to be 1.9kg or 158.3g/week, far greater that the average 8g/week observed in the general population, yet such a rate of increase is considered an ‘epidemic’ (Levitsky, Halbmaier and Mrdjenovic, 2004).
However, some studies have found that freshman show no significant weight increase, and others that they do show a small increase in weight, but much less than the much reported freshman 15 (Graham & Jones, 2002; Hodge, Jackson & Sullivan, 1993).
The transition from high school to University may be associated with an increased risk of weight gain because it is a time when many factors that contribute to weight gain in the general population are prevalent. By examining this period of life that has been associated with a high risk of weight gain, it may be possible to empirically establish predictors of weight gain. Identifying the predictors of weight gain and obesity could have very practical implications, allowing for effective treatment and possible prevention.
Several predictors of weight gain have been identified, and the impact of these predictors studied in a student population. One robust predictor of weight gain is measures of restrained eating (Klesges, Isbell & Klesges, 1992; Stice, Cameron, Killen, Hayward & Taylor 1999; Stice, Presnell, Shaw & Rohde, 2005). In modern societies, characterised by abundant and easily accessible foods, restrained eating is often used as a method to limit weight gain.
Although it seems counterintuitive that restrained eating is associated with weight gain, some authors believe that varied diet patterns and disordered eating shown by retrained eaters contribute to appetite disregulation and eventual weight gain (Tuschl, Platte, Laessle, Stichler & Pirke, 1990; Garner & Wooley, 1991). Disinhibited eating, often characterised by the breakdown of restraint, is also related to weight gain.
Lahteenmaki and Tuorila (1995) found that disinhibition scores were related to frequency of use and liking of foods with high energy content, such as sweets, pastries, butter and margarine. Furthermore, disinhibition scores have been shown to be the strongest factor in differentiating between obese and nonobese subjects (Lindroos et al., 1997).
Lowe et al. (2006) examined the influence of restrained and disinhibited eating as predictors of weight gain in students during the freshman year, and found that neither of traditional self-report measures of restraint and disinhibited eating was predictive of weight gain.
Emotional eating has also been shown to be a predictor of weight gain, and can result in eating beyond ones energy requirements and weight gain over time. Studies concerning emotional eating indicate relative overeating in obese individuals during negative emotional states (Lowe & Fisher, 1983; Baucom & Aiken, 1981). However, Lowe et al. (2006) failed to find a link between emotional eating and weight gain in a sample of freshman.
The emotions associated with stress have also been found to effect food intake and body weight (Macht & Simons, 2000). Laitinen, Ek and Sovio (2002) found that eating linked to even a slight extent with stress was associated with an increased BMI, especially in women.
However; there is evidence that stress is associated with both increased and decreased intake of food (Oliver & Wardle, 1999; Kandiah, Yake, Jones & Meyer, 2006). Serlachius, Hamer & Wardle (2007) investigated the impact of stress on weight change in university students in the United Kingdom, and found that stress was related both to an increase and decrease in weight, and that a stronger link between stress and weight was found in women.
Physical activity is known to play a key role in the prevention of weight gain. Di Pietro (1999) reported that in cross-sectional data, lower levels of physical activity were usually related to higher body weight. In addition, a review of epidemiological studies found that a large volume of physical activity was usually associated with attenuated weight gain (Fogelholm & Kukkonen-Harjula, 2000).
In a longitudinal study of 3000 women over three years, Sternfeld and colleagues (2004) reported that women gained an average of 2.1 kg over the course of the study; however, women who increased their levels of physical activity gained less weight. Donnelly and colleagues (2003) conducted one of the few randomised, controlled experiments into the effects of exercise on body weight.
The participants were randomised to two groups, an exercise group that progressed to 45 minutes of physical activity, 5 days per week, or a no exercise control group. Female participants in the control group gained 3kg on average, whereas those in the exercise group prevented weight gain. Male participants in the control group maintained their weight, whereas those in the exercise group lost 5kg on average. Morrow et al. (2006) examined physical activity and weight gain in freshman.
They found that subjects who gained weight over their freshman year tended to be less physically active than those who lost weight during the year. In addition, Levitsky, Halbmaier and Mrdjenovic (2004) found that, in freshman during their first 12 weeks of university, decreased physical activity was significantly and positively correlated with observed weight gain.
There are many other well established predictors of weight gain, which have yet to be tested specifically in a student population. Binge eating is a prevalent problem among obese adults seeking help for their obesity (Spitzer et al., 1993; Yanovski, Nelson, Dubbert, & Spitzer, 1993).
In addition, compared with obese adults who do not binge, adults who binge are more likely to weigh more (French, Jeffery, Sherwood, & Neumark Sztainer, 1999), and there seems to be a positive correlation between binge eating severity and the degree of obesity (Bruce & Agras, 1992).
It is well established in the literature that women with eating disorders have more drug and alcohol problems compared with non-clinical populations (Russell, 1979). More recently, associations have been discovered between drinking and eating habits in non-clinical populations. For example, measures of dietary restraint are associated with higher levels of alcohol consumption in young women (Stewart, Angelopoulos, Baker & Boland, 2000).
In addition, the frequency and severity of dieting have been shown to be positively associated with alcohol use in female students (Krahn, Kurth, Gomberg & Drewnowski, 2005), and it has been further suggested that dieting may be a risk factor in the development of alcohol abuse (French, Story, Downes, Resnick & Blum, 1995; Striegel Moore & Huydic, 1993).
As well as dieting patterns and emotions having an impact on BMI, more general psychological characteristics are known to be associated with weight gain. For example, research has shown that reward sensitivity is associated with BMI, and may predispose individuals to eat beyond their nutritional requirements (Davis, Stachan & Berkson, 2004; Loxton & Dawe, 2001).
A strong sensitivity to reward is indicative of a strong appetitive drive for naturally reinforcing stimuli, so it is perhaps not surprising that individuals with this personality trait eat more that the required amount of palatable foods.
Hedonic hunger may be a predictor of weight gain. Hedonic hunger differs from homeostatic hunger, which is based on prolonged absence of energy intake. Rather it refers to the extent to which individuals are affected by food, or think about food when eating is not imminent. Although awareness of palatable food creates motivation to eat in people in general, there are large individual differences in the psychological influence of the food environment (Lowe & Butryn, 2007).
It is often assumed that the freshman year of university is the first time many young adults are independently in control of their lives. According to Levitsky, Halbmaier and Mrdjenovic (2004), for most freshmen, the transition from home to college is the mist dramatic change of environment in their lives.
Similarly, Lowe et al. (2006) believe that this is a critical development transition because, for many adolescents, this is the first time that they are solely responsible for self-regulation regarding caloric intake and physical activity. This may be an assumption, and to my knowledge, freshman weight gain studies have failed to investigate pre-university dietary behaviour.
Furthermore, a high level in experience in being solely responsible for one’s caloric intake may explain those studies that have failed to find a significant weight gain in freshman.
In summary, the transition from high school to University may be associated with an increased risk of weight gain because it is a time when many factors that contribute to weight gain in the general population are prevalent. Many of these factors, such as restraint, disinhibition, emotional eating, stress and physical activity have been investigated in terms of weight gain in freshman, but have found mixed results.
Other possible predictors, such as binge eating, power of food and sensitivity to reward, have strong associations with weight gain, but these associations have not been investigated in the specific population of first year university students. Other factors, such as hedonic hunger and pre-univeristy dietary behaviours, have not yet been investigated to discover associations with weight gain in the student population.
The current study will investigate student weight gain in terms of these predictors. The phenomenon of freshman weight gain may be an amplification of the same process that is occurring among the public, thus characterising the resistance and susceptibility to weight gain in these individuals might allow us to reduce or even reverse the secular trend towards increasing body weight and obesity in the general population.
3. Methods
3.1. Overview.
The study began in October 2007, when participants were recruited, and completed a packet of online questionnaires to assess various aspects of their dietary behaviour. They then attended the laboratory for the time part 1 measurements between November 5th and November 16th 2007, where their height, weight and waist circumference was recorded.
In addition, they completed a further questionnaire to asses their pre-university dietary behaviour. Participants were invited back to the laboratory 12 weeks later, between January 28th and February 8th 2008, for the time part 2 measurements.
Their weight and waist circumference was recorded, and used along with the time part 1 measurements to calculate the dependent variables of change in BMI and change in waist circumference.
3.2. Participants
Forty six students participated in this study, of which 9 were male and 37 were female. Participants had either signed up to join a psychology participant database at the University of Bristol Freshers fair, or were first-year Experimental Psychology students. The participants had a mean age of 20.4, SD 4.06. Participants’ mean body mass index (BMI) was 22.46 (SD = 2.58).
3.3. Online Questionnaires
The online questionnaires included a variety of questionnaires assessing demographics, stress, diet and eating attitudes.
Demographics
Demographic measures taken included DOB, gender, accommodation status, smoking status and general diet.
Exercise
Participants provided details of their most common physical activities, and each activities related frequency, intensity and duration.
The Undergraduate Stress Questionnaire. (USQ)(Serlachius et al. 2007).
Stress was assessed with the 10-item version of the Undergraduate Stress Questionnaire. Participant’s reported each stressor’s occurrence in the last 2 weeks (yes or no) and rated the severity of the stressor.
Three factor eating questionnaire (TFEQ) with full disinhibition scale (Karlsson 2000).
This questionnaire is a reduced version of the Stunkard and Messick’s (1985) three factor eating questionnaire. It refers to current dietary practice and measures three aspects of eating behaviour, restraint eating emotional eating and uncontrolled eating
Power of food scale (PFS) (Lowe 2007)
This 21-item scale is designed to assess individual differences in appetite responsiveness (hedonic hunger), including the impact of food availability, presence and taste on behaviour, thinking and feelings.
Sensitivity to reward questionnaire (SRQ) (Torrubia, 2001)
The sensitivity to reward questionnaire is a 24-item, yes-no response questionnaire. This questionnaire assesses an individuals’ tendency to approach and take pleasure from a variety of rewarding stimuli in their environment. Only one item relates directly to food and eating (“Is it easy for you to associate tastes and smells to very pleasant events?”).
Binge eating scale (BES) (Gormally, 1982)
The binge eating scale was originally developed to identify binge eaters within an obese population. It is a 16-item assessment of binge-eating behaviour and feelings surrounding binging episodes.
3-factor diet model (TFDM) (Current/history/suppression; Lowe 1993).
This questionnaire assesses diet behaviour that may influence weight status.
A six item version of this questionnaire was used, yielding data relating to current diet, diet history and weight suppression.
Alcohol Questionnaire (Townshend & Duka 2002).
This questionnaire assessed alcohol consumption, including units per week, drink rate and binge drinking behaviours.
3.4. Additional Measures
Pre-University dietary Behaviour Questionnaire (PUDB).
This questionnaire was devised to assess the participants’ confidence and control over their dietary intake before they came to university. It consisted of 5-items. Item 1 (How confident would you be preparing a main meal from scratch?) and item 2 (How confident would you be shopping for the ingredients to prepare a main meal?) assessed the participants’ general confidence in meal preparation. These items had 3 possible responses, not at all confident, fairly confident and very confident.
Items 3 to 5 assessed the control the participants’ had over their diet. There were 3 possible responses to Item 3 (How much control did you have over your diet?), not much control, some control and a lot of control. There were 5 possible responses to item 4 (How often did you choose what to have for your evening meal?) and item 5 (How often did you prepare a main meal for yourself?), never, rarely, sometimes, often and always.
3.5. Procedure
Students who had signed up to join a psychology participant database at the University of Bristol Freshers fair, and first-year Experimental Psychology students, were sent an e-mail inviting them to take part in a study looking at the factors which influence resistance and susceptibility to weight gain in Freshers. Compensation for taking part was in the form of personal feedback, provided via e-mail following the analysis.
Participants who responded positively to the recruitment e-mail were sent a registration e-mail, including a link to the online questionnaires, and their unique IDCode and password. The online questionnaires were hosted by SurveyMonkey (http://www.surveymonkey.com/).
The date that each participant was sent the registration e-mail was noted. Responses to the online questionnaires were checked, and categorised as complete, incomplete or partial. Participants categorised as incomplete or partial two weeks after they had received their registration e-mail, were sent a reminder e-mail.
3.5.1. Time Part 1 measurments
Once the participants had completed the online questionnaires, they were individually e-mailed to arrange laboratory attendance, which was subsequently timetabled and confirmed. Participants were advised to wear light clothes, and a top that was separate from the trousers or skirt to help with the laboratory measures.
On arrival, the participants signed a consent form confirming they agreed to provide the measurements. The participants’ height, weight and waist measurements were then taken. To take their height measurements, participants first removed their shoes. They then stood on the stadiometer, and their height was recorded to the nearest 0.5cm.
Before taking the participants’ weight, they removed any excess clothing and emptied their pockets. Each item of clothing that remained was judged as being light, medium or heavy. The participants then stepped on the scales, and their weight was recorded to the nearest 0.1kg. The participants’ waist circumference was measured with an anthropometric tape measure.
This measurement was taken either over or under clothing depending on the participants’ wishes, and the method was noted. The participant passed the tape around their waist and the ends to the experimenter. Once it was ensured that the tape was horizontal, and touching the skin without causing compression, the waist circumference was recorded to the nearest 1cm. Participants also completed the additional pre-university dietary behaviour questionnaire.
3.5.2. Time part 2 measurements
The laboratory sessions for the follow up measures took place 12 weeks after the participants’ time part 1 measurements. Participants were e-mailed, and laboratory attendance was again timetabled as close to 12 weeks after the time 1 measures appointment as possible, and confirmed.
The time of day of the participants’ appointments depended on the time at which they had attended the laboratory for the initial measurements.
For example, a participant who had previously attended during a morning session (before 12pm), also had their second measurements taken during a morning session. The participants’ weight and waist were recorded using the procedure outlined previously. Their clothes were once again categorised as light, medium or heavy.
3.6. Data Analysis
It is possible that participants may have been wearing heavier clothing during the second measurements, and thus before analysis, participants’ weight measurements were adjusted to take into account the weight of their clothes.
Each piece of clothing was categorised as light, moderate or heavy and given a weight. The sum weights of all the clothing was subtracted from the overall body weight measurement, and all statistical analysis was performed on the adjusted body weights.
Statistical analysis began with assessing the bivariate correlations between the dependent variable BMI change and the variables taken from the various questionnaire scores, using Pearson’s correlation coefficient. The same bivariate correlations were then performed between the dependent variable waist circumference change and the questionnaire variables.
Partial correlation was then used for some exploratory analysis, to discover the correlations between the dependent variables and the questionnaire variables, whilst holding the effects of the significant variables constant.
It was also reasoned that the variance in initial BMI may have had an impact on the simple correlations between the dependent variables and the questionnaire variables, and thus partial correlation was used to determine these correlations whilst holding the effects of initial BMI constant.
Finally, bivariate correlations using Pearson’s correlation coefficient were performed between the dependent variables, and each item of the pre-university dietary behaviour questionnaire, to assess the impact this may have on change in BMI and change in waist circumference.
4. Results
4.1. Participant characteristics
Over the 12 weeks between the initial and follow up sessions, 36 participants had gained weight, 9 had lost weight, and 1 had remained the same.
The participants mean body mass index went from 22.46 (SD 2.58) to 22.81 (SD 2.70). The average change in BMI across the full sample was 0.35.
In addition, 21 participants had an increased waist circumference, 19 had remained the same, and 6 had a reduced waist circumference. The average change in waist circumference across the full sample was 0.60cm.
4.2. Descriptive Statistics
Table 1 gives the means and standard deviations for the variables used in the analysis.
Variable Mean S.D.
Initial BMI 22.46 2.58
Accommodation status4.4.86 Smoking Status3.6.93
General diet- Vegetarian1.9.32
General Diet- Regular Meals1.2.43
General Diet- Diet to Gain Weight 1.9.29
Exercise- Frequency9.45.5
Exercise- Intensity4.53.0
Exercise- Duration11.78.4
USQ- Frequency 2.61.4
USQ-Intensity 2.61.9
TFEQ-Restraint1.91.7
TFEQ-Uncontrolled eating2.32.2
TFEQ-Emotional eating1.11.1
TFEQ-Disinhibition5.43.4
PFS49.517.1
SRQ10.24.1
BES10.97.9
TFDM – Current Dieting1.9.32
TFDM – Diet History1.6.50
TFDM – Weight Suppression4.65.8
TFDM – Reason for Weight Suppression2.21.3
Alcohol Units per week21.520.8
Alcohol Drink Rate1.77.93
Alcohol Binge Score20.915.2
Alcohol Total Score42.431.2
PUDB – item 12.41.58
PUDB – item 22.59.54
PUDB – item 32.46.59
PUDB – item 43.80.96
PUDB – item 53.33.92
4.3. Correlations – Change in BMI
Table 2 shows the bivariate correlations between change in BMI and the predictors, using Pearson’s correlation coefficient.
Variable r
Initial BMI .122 Accommodation status-.007 Smoking Status .004 General diet- Vegetarian .082 General Diet- Regular Meals .079 General Diet- Diet to Gain Weight .026 Exercise- Frequency .070 Exercise- Intensity .041 Exercise- Duration-.068 USQ- Frequency .004 USQ-Intensity .071 TFEQ-Restraint .304* TFEQ-Uncontrolled eating .009 TFEQ-Emotional eating .001 TFEQ-Disinhibition-.092 PFS-.133 SRQ .163 BES-.109 TFDM – Current Dieting .201 TFDM – Diet History .192 TFDM – Weight Suppression-.141 TFDM – Reason for Weight Suppression-.122 Alcohol Units per week .073 Alcohol Drink Rate-.160 Alcohol Binge Score .087 Alcohol Total Score .091
* p<0.05.
There was a positive relationship between a persons three factor eating questionnaire-restraint score, and their change in BMI, r=.304, p<0.05.
4.4. Correlations – Change in waist circumference.
Table 3 shows the bivariate correlations between change in waist circumference and the predictors, using Pearson’s correlation coefficient.
Variable r
Initial BMI -.084 Accommodation status-.134 Smoking Status-.154 General diet- Vegetarian .045 General Diet- Regular Meals .041 General Diet- Diet to Gain Weight .121 Exercise- Frequency .256 Exercise- Intensity .121 Exercise- Duration .059 USQ- Frequency -.054 USQ-Intensity -.145 TFEQ-Restraint .478** TFEQ-Uncontrolled eating .122 TFEQ-Emotional eating-.193 TFEQ-Disinhibition-.109 PFS .124 SRQ .218 BES - .406 TFDM – Current Dieting .301* TFDM – Diet History .246 TFDM – Weight Suppression-.146 TFDM – Reason for Weight Suppression-.255 Alcohol Units per week .125 Alcohol Drink Rate-.190 Alcohol Binge Score .025 Alcohol Total Score .155
**p<0.01 * p<0.05.
Change in waist circumference was significantly correlated with three factor eating questionnaire-restraint, r=.478, p<0.01, and current dieting, r=.301, p<0.05.
4.5. Partial correlations
Exploratory analysis was performed to assess the importance of the other factors, when keeping significant predictors constant.
A first order partial correlation was performed, controlling for the significant variable three factor eating questionnaire-restraint.
The partial correlation between change in waist circumference and exercise frequency, after controlling for TFEQ-R, was significant, r=.313, p<0.05.
No correlations were found using a first-order partial correlation, controlling for the significant variable current dieting.
Similarly, a second-order partial correlation, controlling for TFEQ-R and current dieting, found no additional significant correlations.
It was reasoned that variance in initial BMI may have had an impact on the bivariate correlations, so a first-order partial correlation was performed, controlling for initial BMI.
The partial correlation between change in waist circumference and exercise frequency, after controlling for initial BMI, was close to significant, r=.298, p=0.052. .
4.6. Correlations - Pre-University Dietary Behaviour
Table 4 shows the bivariate correlations between change in BMI/waist circumference and items 1 to 5 of the pre-university dietary questionnaire, using Pearson’s correlation coefficient.
Variable BMI change (r)Waist Change (r)
Item 1 -.294* -.175
Item 2 -.094 .014
Item 3 .063 .143
Item 4 -.052 .035
Item 5 -.092 .027
* p<0.05.
There was a significant negative relationship between the participants’ score on item 1 (How confident would you be preparing a main meal from scratch?) and their change in BMI, r= -.294, p<0.05.
Thus, as confidence in preparing a meal increases, change in BMI decreases.
Discussion
The results from this study support the notion of freshman weight gain, demonstrating that students experienced weight gain over 12 weeks, with an average increase in BMI of 0.35, and an average increase in waist circumference of 0.6cm.
Significant correlations were found between the observed changes in BMI and waist circumference, and restraint, as measured by the three factor eating questionnaire (Karlsson, 2000).
In addition, a significant correlation was found between a change in waist circumference and current dieting, measured by Lowe’s (1993) 3-factor diet model. Partial correlation revealed that, when controlling for the effects of restraint, the correlation between change in waist circumference and exercise frequency was significant.
Exercise frequency and change in waist circumference were also close to being significantly correlated, when variance in initial BMI was controlled for. Finally, a significant negative correlation was found between change in BMI and item 1 of the pre-university dietary behaviour questionnaire, which was related to confidence in preparing a main meal.
Previous studies have also found an association between restraint and increased BMI, specifically that measures of restrained eating prospectively predict weight gain (Klesges, Isbell & Klesges, 1992; Stice, Cameron, Killen, Hayward & Taylor 1999; Stice, Presnell, Shaw & Rohde, 2005).
There are many possible interpretations of the finding that restraint is associated with weight gain. According to the energy balance equation (Jequier, 1986), only energy intake and energy expenditure are involved in weight regulation, and thus dietary restraint must be impacting on one aspect this equation which, in turn, would increase a persons risk of weight gain.
On the basis of research, which suggests that dietary restraint is negatively associated with total intake (Klesges, Klem, & Bene, 1989), it seems likely that energy expenditure may be the mechanism of action (Klesges, Isbell & Klesges, 1992). Thus, it is possible that restrained eating may be associated with weight gain because it may decrease metabolic rate, leading to lower energy expenditure.
A study by Tuschl, Platte, Laessle, Stichler, and Prike (1990) supports this notion, finding that metabolic rate among restrained eaters to be 620 kilocalories per day lower than that of nonrestrained eaters. One explanation of this is that dieting and radical weight control methods may lead to a decrease in metabolic rate among restrained eaters.
However, laboratory research suggests that restrained eaters may also exhibit increased energy intake, which may explain the association between restraint and weight gain. For example, when restrained eaters are made to violate their diet by eating high-caloric foods, their subsequent consumption of food in the laboratory is increased, relative to unrestrained eaters who regulate for the high-caloric food by reducing their consumption (Herman & Mack, 1975).
In Lowe’s (1993) original model, he described current dieting as a current effort to reduce caloric intake to lose weight. Current diet status is closely linked to measures of restraint. In fact, Lowe’s (1993) three-factor model of dieting was developed to clearly distinguish between chronic and acute dieting, as he viewed the fact that restraint studies assess chronic dieting but attribute the effects of this to current dieting as a fundamental disjunction is restraint theory.
Most individuals who are currently dieting will have dieted unsuccessfully in the past, and will thus score as restrained eaters (Lowe et al. 1991). The findings of the current study, that current dieting is correlated with an increase in waist circumference, further support findings from Lowe et al. (2006).
They found that, in a group of freshman, the mean weight gain for current dieters was two times greater than former dieters, and three times greater than those who had never dieted. It is possible that the mechanism responsible for this finding is that current dieting is predictive of weight gain because it is a proxy of obesity-proneness, i.e. those most prone to weight gain may go on weight loss diets more often (Lowe & Timko, 2004).
General stuff – One interpretation of these findings overall is that those who are more likely to engage in weight loss behaviours (restrained eaters, current dieters), are more prone to weight gain in general and thus gained more weight in this study.
Thus, these factors do not cause the weight gain, rather, despite initially being able to promote weight loss, restrained eating and dieting ultimately fail to prevent a pre-existing propensity towards weight gain from manifesting itself.
It would be desirable for further research to …
Past freshman weight gain studies fail to investigate the previous dietary behaviour of their subjects, and assume a lack of independence prior to university attendance. The current study assessed pre-university dietary behaviour.
Scores on the questionnaire were surprisingly high in terms of independence, with most participants reporting confidence in meal preparation, and a high degree of control over their pre-university dietary intake. This finding is contrary to many assumptions of the transition from high school to university, which previous researchers suggested is the first time that young adults are solely responsible for self-regulation regarding caloric intake (Lowe et al. 2006).
The high degree of independence prior to university attendance in the participants may explain the relatively low change in BMI observed in this study, compared to previous findings (Levitsky, Halbmaier and Mrdjenovic, 2004). Furthermore, high control of one’s dietary intake prior to university may explain why some studies fail to find significant freshman weight gain.
A significant negative relationship was observed between confidence in meal preparation, and participants change in BMI. Thus, as confidence in preparing a meal increased, change in BMI decreased.
This finding may have important implications in terms of prevention of weight gain, as it suggests that the ability to prepare a meal, and confidence in that ability, may attenuate weight gain or encourage weight loss. Although further research is needed to substantiate this finding, if it is upheld it would suggest that a basic education in meal preparation may reduce the trend towards obesity.
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