Race and gender differences in the association of dieting and gains in BMI among young adults

Objective: To assess the relationship between dieting and subsequent weight change and whether the association varies by gender or race/ethnicity. Research Methods and Procedures: Male (n = 4100) and female (n = 4302) participants in the National Longitudinal Study of Adolescent Health who provided information on weight and height at baseline and two follow‐up assessments and were not missing information on weight control strategies or race were studied. Generalized estimating equations were used to assess whether dieting to lose or maintain weight at Wave I or II predicted BMI (kg/m²) change between adolescence and young adulthood (Wave II to III). Analyses were stratified by gender and took sampling weights and clustering into account. Results: At Wave I, the mean age of the participants was 14.9 years. Approximately 29.3% of female participants and 9.8% of male participants reported dieting in Wave I or II. Fewer African Americans than whites (6.2% vs. 10.0% and 25.5% vs. 31.2%, p = 0.007 and p = 0.02, among males and females, respectively) reported dieting. Between Waves II and III, participants gained on average 3.3 kg/m². Independent of BMI gain during adolescence (Waves I to II), female participants who dieted to lose or maintain weight during adolescence made larger gains in BMI during the 5 years between Waves II and III (mean additional gain, 0.39 kg/m²; 95% confidence interval, 0.08 to 0.71) than their nondieting peers. The association was not significant among the male participants. The association was largest among African‐American female participants. Discussion: The results suggest that not only is dieting to lose weight ineffective, it is actually associated with greater weight gain, particularly among female adolescents. Female African‐American dieters made the largest BMI gains.     

Accuracy of self-reported weight among bariatric surgery candidates: the influence of race and weight cycling

Objective: Research on the accuracy of self‐reported weight has indicated that the degree of misreporting (underestimating) weight is associated with increasing weight but is variable across patient groups. We examined the degree of discrepancy between actual and self‐reported BMI in severely obese bariatric surgery candidates, and whether the degree of accuracy varied by race and by eating‐related and psychological factors. Research Methods and Procedures: Participants were 179 obese female gastric bypass surgery candidates (31 black, 22 Hispanic, 126 white) who were asked to self‐report height and weight as part of a larger assessment battery. Actual height and weight were then measured and a discrepancy score was generated (actual BMI ? reported BMI). Results: In this group of severely obese patients, degree of misreporting was unrelated to BMI. The race groups did not differ in actual or self‐reported BMI but differed significantly in the degree of misestimation between self‐reported and actual BMI. Post hoc tests indicated that black women underestimated their BMI significantly more than white women; Hispanic women did not differ from the other race groups. No eating‐related or psychological variables assessed predicted percentage discrepancy; however, the accuracy in self‐reported weight was related to history of weight cycling. Discussion: Overall, obese bariatric surgery candidates were accurate in self‐report of weight, although the degree of accuracy differed by race and weight cycling history.     

Effects of weight status and barriers on physical activity adoption among previously inactive women

This study examined self‐reported physical activity (PA) barriers, and their effects on PA behavior change at 3 and 12 months among 280 previously inactive women enrolled in a PA promotion trial. Effect modification of baseline barriers by baseline weight status on PA behavior change was also examined. At baseline and month 12, obese women reported significantly greater PA barriers compared with normal and overweight women (P < 0.05). Individual barriers that were more likely to be elevated for obese vs. normal and overweight participants at baseline were feeling too overweight, feeling self‐conscious, reporting minor aches and pains, and lack of self‐discipline. Also, weight status moderated the effect of PA barriers on PA behavior change from baseline to month 3 (P < 0.05), but not to month 12 (P = 0.637), with obese participants reporting high barriers achieving 70 min/week fewer than those with low barriers (P < 0.05). Finally, the interaction between barriers (high vs. low) and weight status (obese vs. normal), shows PA barriers had a detrimental PA effect among obese participants that was 122.5 min/week (95% confidence interval (CI) = 15.7, 229.4; P < 0.05) lower than their effect on normal‐weight participants. These results suggest that for obese women, PA barriers have quantifiable effects on PA behavior change. This study has implications for the design of future weight loss and PA interventions, suggesting that a comprehensive assessment of PA barriers is a prerequisite for appropriate tailoring of behavioral PA interventions.     

Accuracy of Self‐reported Weight and Height in Binge Eating Disorder: Misreport Is Not Related to Psychological Factors

This study examined the degree of misreport in weight, height, and BMI among overweight adults (n = 392) with binge eating disorder (BED) and tested whether the degree of misreport was associated with eating disorder psychopathology and psychological variables. Male (n = 97) and female (n = 295) participants self‐reported height and weight, and were subsequently measured by clinic staff. Participants also completed a series of diagnostic interviews and self‐report assessments. Discrepancies between self‐reported and measured values were modest. The degree of misreport for weight, height, and BMI was not related to eating disorder features, depression, and self‐esteem. Overall, the errors in self‐reported weight and height by overweight patients with BED were very slight. The degree of discrepancy between self‐reported and measured values was not related to eating disorder or psychological features, suggesting that such data are not biased or systematically related to individual differences in overweight patients with BED.     

Comparison of self-reported and measured BMI as correlates of disease markers in US adults

Objective: The purpose of this study is to evaluate the validity of BMI based on self‐reported data by comparison with technician‐measured BMI and biomarkers of adiposity. Research Methods and Procedures: We analyzed data from 10,639 National Health and Nutrition Education Study III participants ≥20 years of age to compare BMI calculated from self‐reported weight and height with BMI from technician‐measured values and body fatness estimated from bioelectrical impedance analysis in relation to systolic blood pressure, fasting blood levels of glucose, high‐density lipoprotein‐cholesterol, triglycerides, C‐reactive protein, and leptin. Results: BMI based on self‐reported data (25.07 kg/m²) was lower than BMI based on technician measurements (25.52 kg/m²) because of underreporting weight (?0.56 kg; 95% confidence interval, ?0.71, ?0.41) and overreporting height (0.76 cm; 95% confidence interval, 0.64, 0.88). However, the correlations between self‐reported and measured BMI values were very high (0.95 for whites, 0.93 for blacks, and 0.90 for Mexican Americans). In terms of biomarkers, self‐reported and measured BMI values were equally correlated with fasting blood glucose (r = 0.43), high‐density lipoprotein‐cholesterol (r = ?0.53), and systolic blood pressure (r = 0.54). Similar correlations were observed for both measures of BMI with plasma concentrations of triglycerides and leptin. These correlations did not differ appreciably by age, sex, ethnicity, or obesity status. Correlations for percentage body fat estimated through bioelectrical impedance analysis with these biomarkers were similar to those for BMI. Discussion: The accuracy of self‐reported BMI is sufficient for epidemiological studies using disease biomarkers, although inappropriate for precise measures of obesity prevalence.     

Ethnic Differences in Self‐reported and Measured Obesit

As use of self‐reported data to classify obesity continues, ethnic differences in reporting errors remain unclear. The objective of this study is to elucidate misreporting disparities between African Americans (AAs) and European Americans (EAs). The Pennington Center Longitudinal Study (PCLS) is an ongoing investigation of environmental, behavioral, and biological factors associated with obesity, diabetes, and other common diseases. Self‐reported and measured height and weight were collected during initial screening for eligibility in various studies by telephone and clinic visits. All ethnicity‐sex groups (15,656 adults aged 18–65 years, 53% obese, 34% AA, 37% men) misreported heights and weights increasingly as measured values increased (P < 0.0001). More AA vs. EA women (P < 0.001) misreported height and weight, but more EA vs. AA men misreported their weight (P < 0.02). Obesity was underestimated more in AA vs. EA women (self‐reported ? measured prevalence = ?4.0% (AA) vs. ?2.6% (EA), P < 0.0001), but less in AA vs. EA men (?3.2% (AA) vs. ?4.2% (EA), P < 0.0001)). With measured obesity prevalence equalized at 53% in all groups, the self‐reported obesity prevalence in women was 50.4% (AA) vs. 49.6% (EA), and in men 49.8% (AA) vs. 47.3 (EA). Underestimation in women was ?2.6% (AA) vs. ?3.4% (EA); in men it was ?3.2% (AA) vs. ?5.7% (EA), P < 0.003. Self‐reported height and weight portend underestimation of obesity prevalence and the effect varies by ethnicity and gender. However, comparisons depend on the true prevalence within ethnicity‐gender groups. After controlling for obesity prevalence, disparity in underestimation was greater in EA than in AA men (P < 0.003) but not women.     

The validity of obesity based on self-reported weight and height: Implications for population studies

Objective: To validate self‐reported information on weight and height in an adult population and to find a useful algorithm to assess the prevalence of obesity based on self‐reported information. Research Methods and Procedures: This was a cross‐sectional survey consisting of 1703 participants (860 men and 843 women, 30 to 75 years old) conducted in the community of Vara, Sweden, from 2001 to 2003. Self‐reported weight, height, and corresponding BMI were compared with measured data. Obesity was defined as measured BMI ≥ 30 kg/m². Information on education, self‐rated health, smoking habits, and physical activity during leisure time was collected by a self‐administered questionnaire. Results: Mean differences between measured and self‐reported weight were 1.6 kg (95% confidence interval, 1.4; 1.8) in men and 1.8 kg (1.6; 2.0) in women (measured higher), whereas corresponding differences in height were ?0.3 cm (?0.5; ?0.2) in men and ?0.4 cm (?0.5; ?0.2) in women (measured lower). Age and body size were important factors for misreporting height, weight, and BMI in both men and women. Obesity (measured) was found in 156 men (19%) and 184 women (25%) and with self‐reported data in 114 men (14%) and 153 women (20%). For self‐reported data, the sensitivity of obesity was 70% in men and 82% in women, and when adjusted for corrected self‐reported data and age, it increased to 81% and 90%, whereas the specificity decreased from 99% in both sexes to 97% in men and 98% in women. Discussion: The prevalence of obesity based on self‐reported BMI can be estimated more accurately when using an algorithm adjusted for variables that are predictive for misreporting.     

The geographic distribution of obesity in the US and the potential regional differences in misreporting of obesity

Objective: State‐level estimates of obesity based on self‐reported height and weight suggest a geographic pattern of greater obesity in the Southeastern US; however, the reliability of the ranking among these estimates assumes errors in self‐reporting of height and weight are unrelated to geographic region. Design and Methods: Regional and state‐level prevalence of obesity (body mass index ≥ 30 kg m^?2) for non‐Hispanic black and white participants aged 45 and over were estimated from multiple sources: ( 1 ) self‐reported from the behavioral risk factor surveillance system (BRFSS 2003‐2006) (n = 677,425), ( 2 ) self‐reported and direct measures from the National Health and Nutrition Examination Study (NHANES 2003‐2008) (n = 6,615 and 6,138, respectively), and ( 3 ) direct measures from the REasons for Geographic and Racial Differences in Stroke (REGARDS 2003‐2007) study (n = 30,239). Results: Data from BRFSS suggest that the highest prevalence of obesity is in the East South Central Census division; however, direct measures suggest higher prevalence in the West North Central and East North Central Census divisions. The regions relative ranking of obesity prevalence differs substantially between self‐reported and directly measured height and weight. Conclusions: Geographic patterns in the prevalence of obesity based on self‐reported height and weight may be misleading, and have implications for current policy proposals.     

Weight Goals in a College‐age Population

Objective: Although a growing body of literature has found unrealistic weight loss goals to be common among older, primarily female, subjects, little is known about weight loss goals of younger adults. Research Methods and Procedures: Three hundred seventy‐nine college students had their height and weight taken and reported their “goal,” “dream,” “happy,” “acceptable,” and “disappointed” weights. A series of 2 (gender) × 2 (nonoverweight vs. overweight) ANOVAs were conducted with both absolute weight goals and percentage of weight loss needed to obtain those goals as dependent variables. Results: When examined in terms of absolute weight goals, women generally had lower body mass index (BMI) goals than men, and nonoverweight participants had lower BMI goals than overweight participants. Surprisingly, most overweight participants would accept a weight loss that would still place them in the overweight BMI range. When examined in terms of percentage loss needed to reach those goals, only overweight women chose goal and dream weights that would require a loss greater than can be expected from nonsurgical weight‐loss treatments, and all overweight participants chose happy and acceptable weights within 15% of current weight. Discussion: Participants in this study had generally reasonable weight‐loss goals, and even the most extreme weight loss goals were much more moderate than those found in previous studies. These results are surprising given the extreme social pressures for thinness facing young adults. Future studies should examine the variables that influence selection of goal weights and how goal weights affect actual dieting behavior.     

Adolescents' perceived weight associated with depression in young adulthood: a longitudinal study

Objective: The objective of this study is to examine whether adolescents’ measured BMI and self‐ or mother's perception of weight status at age 14 are associated with depression at age 21. Research Methods and Procedures: The study participants were a subsample of 2017 participants of the Mater–University of Queensland Study of Pregnancy and Its Outcomes, a population‐based birth cohort study, which commenced in 1981 in Brisbane, Australia, for whom measured BMI at ages 14 and 21 and information on self‐reported mental health problems were available at the age 21 follow‐up. A total of 1802 individuals had measured BMI and reported weight perception in a supplementary questionnaire at 14 years, and their self‐reported mental health problems were reported at 21 years. Mental health was measured using Center for Epidemiology Studies Depression Scale and Young Adults Self‐Reported depression/anxiety at 21 years of age. Results: We found that both young adult males and females who perceived themselves as overweight at age 14 had more mental health problems compared with those who perceived themselves as the right weight. When we combined adolescents’ weight perception with their measured BMI categories, weight perception but not measured overweight was associated with mental health problems for males and females at age 21. This association remained after adjusting for potential confounders, including adolescents’ behavioral problems, family meals, diet, physical activity, and television watching. Conclusions: This study suggests that the perception of being overweight during adolescence is a significant risk factor for depression in young adult men and women. The perception of being overweight during adolescence should be considered a possible target for a prevention intervention.     

Consistent self-monitoring of weight: a key component of successful weight loss maintenance

Objective: The objectives were to investigate the characteristics associated with frequent self‐weighing and the relationship between self‐weighing and weight loss maintenance. Research Methods and Procedures: Participants (n = 3003) were members of the National Weight Control Registry (NWCR) who had lost ≥30 lbs, kept it off for ≥1 year, and had been administered the self‐weighing frequency assessment used for this study at baseline (i.e., entry to the NWCR). Of these, 82% also completed the one‐year follow‐up assessment. Results: At baseline, 36.2% of participants reported weighing themselves at least once per day, and more frequent weighing was associated with lower BMI and higher scores on disinhibition and cognitive restraint, although both scores remained within normal ranges. Weight gain at 1‐year follow‐up was significantly greater for participants whose self‐weighing frequency decreased between baseline and one year (4.0 ± 6.3 kg) compared with those whose frequency increased (1.1 ± 6.5 kg) or remained the same (1.8 ± 5.3 kg). Participants who decreased their frequency of self‐weighing were more likely to report increases in their percentage of caloric intake from fat and in disinhibition, and decreases in cognitive restraint. However, change in self‐weighing frequency was independently associated with weight change. Discussion: Consistent self‐weighing may help individuals maintain their successful weight loss by allowing them to catch weight gains before they escalate and make behavior changes to prevent additional weight gain. While change in self‐weighing frequency is a marker for changes in other parameters of weight control, decreasing self‐weighing frequency is also independently associated with greater weight gain.     

Internalization of weight bias: Implications for binge eating and emotional well-being

Objective: This study examined the relationship between internalization of negative weight‐based stereotypes and indices of eating behaviors and emotional well‐being in a sample of overweight and obese women. Research Method and Procedures: The sample was comprised of 1013 women who belonged to a national, non‐profit weight loss organization. Participants completed an on‐line battery of self‐report questionnaires measuring frequency of weight stigmatization and coping responses to deal with bias and symptoms of depression and self‐esteem, attitudes about weight and obesity, and binge eating behaviors. In addition, participants were asked to list the most common weight‐based stereotypes and whether they believed them to be true or false. Results: Participants who believed that weight‐based stereotypes were true reported more frequent binge eating and refusal to diet in response to stigma experiences compared with those who reported stereotypes to be false. The degree to which participants believed stereotypes to be true or false was not related to types or amount of stigma experiences reported, self‐esteem, depression, or attitudes toward obese persons. In addition, engaging in weight loss strategies as a response to bias was not predicted by stereotype beliefs or by actual stigma experiences, regardless of the amount or types of stigma reported. Discussion: These findings suggest that obese individuals who internalize negative weight‐based stereotypes may be particularly vulnerable to the negative impact of stigma on eating behaviors and also challenge the notion that stigma may motivate obese individuals to engage in efforts to lose weight. This study highlights a new area of research that warrants attention to better understand weight stigma and its potential consequences for health.     

How Accurate are Recalls of Self‐Weighing Frequency? Data from a 24‐Month Randomized Trial

Introduction

Self‐weighing is an important component of self‐monitoring during weight loss. However, methods of measuring self‐weighing frequency need to be validated. This analysis compared self‐reported and objective weighing frequency.

Methods

Data came from a 24‐month randomized controlled trial. Participants received 12 months of a behavioral weight‐loss program and were randomly assigned to (1) daily self‐weighing, (2) weekly weighing, or (3) no weighing (excluded from analysis). Objective weighing frequency was measured by Wi‐Fi enabled scales, and self‐reported weighing frequency was assessed every 6 months by questionnaire. Objective weights were categorized to match the scale of the self‐report measure.

Results

At 12 months, there was 80.8% agreement between self‐reported and objective weighing frequency (weighted kappa = 0.67; P < 0.001). At 24 months, agreement decreased to 48.5% (kappa = 0.27; P < 0.001). At both time points in which disagreements occurred, self‐reported frequencies were generally greater than objectively assessed weighing. Both self‐reported and objectively assessed weighing frequency was associated with weight loss at 12 and 24 months (P < 0.001).

Conclusions

Self‐reported weighing frequency is modestly correlated with objective weighing frequency; however, both are associated with weight change over time. Objective assessment of weighing frequency should be used to avoid overestimating actual frequency.

     

Association between Adolescents' Physical Activity and Sedentary Behaviors with Change in BMI and Risk of Type 2 Diabetes

This study aims at identifying the association between physical activity (PA) and sedentary behavior (SB) patterns during adolescents on the future increase in BMI and risk of diabetes during young adulthood. A total of 3,717 participants aged 11 to 21 at baseline who completed Waves I (1994–1995), II (1996), III (2001–2002), and IV (2008) surveys of the National Longitudinal Study of Adolescent Health (Add Health) were analyzed. Physical activity and sedentary behavior patterns were assessed using an interviewer-administered questionnaire at Waves I, II, and III. A participant was classified as having diabetes at Wave IV according to WHO guidelines. The k-means cluster analysis was used to identify the number of PA and SB patterns assessed using interviewer-administered questionnaire. The k-means cluster analysis identified three clusters; 575 (15.5%), 2,140 (57.6%), and 1,002 (27.0%) participants belonged to the low PA high SB (LPAHSB), the LPALSB, and the HPALSB cluster respectively. Relative to the LPALSB cluster, the HPALSB cluster had lower increase in BMI from Wave III to Wave IV (P = 0.03), whereas the difference between LPAHSB cluster and LPALSB cluster was not significant (P = 0.09). The odds of developing diabetes at Wave IV was significant for the LPAHSB cluster (OR = 1.69, 95% CI = 1.04, 2.75) but not significant for the HPALSB cluster (OR = 0.87, 95% CI = 0.52, 1.47) relative to the LPALSB cluster. To conclude, PA but not SB during adolescence predicted change in BMI during young adulthood. SB but not PA during adolescence predicted type 2 diabetes during young adulthood.     

Intensive Lifestyle Intervention Improves Physical Function Among Obese Adults With Knee Pain: Findings From the Look AHEAD Trial

Lifestyle interventions have resulted in weight loss or improved physical fitness among individuals with obesity, which may lead to improved physical function. This prospective investigation involved participants in the Action for Health in Diabetes (Look AHEAD) trial who reported knee pain at baseline (n = 2,203). The purposes of this investigation were to determine whether an Intensive Lifestyle Intervention (ILI) condition resulted in improvement in self‐reported physical function from baseline to 12 months vs. a Diabetes Support and Education (DSE) condition, and whether changes in weight or fitness mediated the effect of the ILI. Outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, stiffness, and physical function subscales, and WOMAC summary score. ILI participants exhibited greater adjusted mean weight loss (s.e.) vs. DSE participants (?9.02 kg (0.48) vs. ?0.78 kg (0.49); P < 0.001)). ILI participants also demonstrated more favorable change in WOMAC summary scores vs. DSE participants (β (s.e.) = ?1.81 (0.63); P = 0.004). Multiple regression mediation analyses revealed that weight loss was a mediator of the effect of the ILI intervention on change in WOMAC pain, function, and summary scores (P < 0.001). In separate analyses, increased fitness also mediated the effect of the ILI intervention upon WOMAC summary score (P < 0.001). The ILI condition resulted in significant improvement in physical function among overweight and obese adults with diabetes and knee pain. The ILI condition also resulted in significant weight loss and improved fitness, which are possible mechanisms through which the ILI condition improved physical function.     

Prediction of measured weight from self‐reported weight was not improved after stratification by body mass index

Objective:

Self‐reported weight may underestimate measured weight. Researchers have tried to reduce the error using statistical models to predict weight from self‐reported weight. We investigate whether deriving equations within separate BMI categories improves the prediction of weight compared with an equation derived regardless of an individual's BMI.

Design and Methods:

The analysis included self‐reported and measured data from 20,536 individuals participating in the EPIC‐Norfolk study. In a derivation set (n = 15,381) two approaches were used to predict weight from self‐reported weight: (1) using a linear regression model with measured weight as outcome and self‐reported weight and age as predictors, and (2) using the same model fit separately within 3 strata defined by BMI (< 25, 25‐30, ≥30 kg m^?2). The performance of these approaches was assessed in a validation set (n = 5,155). Measured weight was compared to self‐reported weight and predicted weight.

Results:

Self‐reported weight underestimated measured weight (P < 0.0001): mean difference ?1.2 ± 3.1 kg (men), ?1.3 ± 2.5 kg (women). Underestimation was greater in obese participants (P < 0.0001). Predicted weight using approach 1 was not significantly different from measured weight (P < 0.05). However, in individuals with BMI < 25 kg m^?2, weight was overestimated in men (0.90 ± 3.87 kg) and women (0.57 ± 2.06 kg), but underestimated in overweight (?0.29 ± 3.58, ?0.20 ± 2.62 kg) and obese (?1.46 ± 5.05 kg, ?0.73 ± 3.54 kg) men and women.

Conclusions:

Using separate prediction equations in strata of BMI did not further improve prediction of weight. In conclusion, predicted weight was closer to measured weight compared with self‐reported weight, but using equations derived in strata of BMI did not further improve the prediction and are not recommended for prediction of weight.

     

Changes in the Perceptions of Self‐weighing Across Time in a Behavioral Weight Loss Intervention

Objective

Changes in beliefs about self‐weighing were examined across time in a behavioral weight loss intervention.

Methods

Active duty military personnel (n = 248) enrolled in a 12‐month counselor‐initiated or self‐paced intervention based on the Look AHEAD (Action for Health in Diabetes) Intensive Lifestyle Intervention. Using an electronic scale, participants were asked to self‐weigh daily. Self‐weighing perceptions were compared from baseline to 4 months (weight loss phase), from 4 months to 12 months (weight maintenance phase), and from baseline to 12 months (full intervention), as well as across time by behavioral and demographic characteristics.

Results

Overall, participants perceived self‐weighing as more helpful and positive, less frustrating, and making them less self‐conscious after the weight loss phase. After weight maintenance, individuals believed self‐weighing was less helpful and positive, more frustrating and anxiety provoking, and making them more self‐conscious. However, after the intervention, participants still viewed self‐weighing as more helpful and positive and less frustrating than at baseline. Weight change, self‐weighing behavior prior to the intervention, and intervention condition were associated with perception change. Controlling for these influencing factors, differences in gender, BMI, age, ethnicity, and race were observed in how beliefs changed across time.

Conclusions

Results suggest engaging in a weight loss intervention promoting daily self‐weighing increases positive and decreases negative beliefs about self‐weighing.     

The Relationship Between Parent and Child Self‐Reported Adherence and Weight Loss

Objective: Better adherence to treatment strategies in family‐based behavioral weight control programs may lead to greater weight reduction and improved weight maintenance in youth. This study assessed the influence of child and parent self‐reported adherence to behavioral strategies on changes in 2‐year child and parent percentage overweight. Research Methods and Procedures: Participants included 8‐ to 12‐year‐old children in ≥ 85th BMI percentile and their parents from 110 families taking part in two family‐based randomized controlled weight control studies. This study examined whether self‐reported adherence to behavioral strategies measured at 24 months increased prediction of child and parent percentage overweight change through 24‐month follow‐up after accounting for other factors that may influence weight change. Results: Child adherence to weighing and to preplanning for celebrations where high‐fat foods are served and parent adherence to praising the child and modeling healthy eating habits predicted 24‐month child percentage overweight change (p < 0.001). Child adherence to recording food and calories and parent adherence to modeling healthy eating habits predicted 24‐month parent percentage overweight change (p < 0.001). In hierarchical regression models, child weighing and preplanning and parent modeling were significant (p < 0.01) incremental predictors (r² of 24.8%) of 24‐month child percentage overweight. Child recording and parent modeling were significant (p < 0.01) incremental predictors (r² of 14%) of parent 24‐month percentage overweight change. Discussion: Child and parent adherence to specific components of family‐based behavioral weight control treatment are independent predictors of long‐term child and parent percentage overweight change.     

Correlates of BMI misreporting among apparently healthy individuals: the ATTICA study

Objective: The aim of this study was to investigate correlates of misreporting in BMI, based on self‐reported weight and height, in a randomly selected population sample of Greek adults and to evaluate the effect of obesity status misclassification on the associations between obesity and disease. Research Methods and Procedures: During 2001 to 2002, we randomly enrolled 1514 men (18 to 87 years old) and 1528 women (18 to 89 years old) from the Attica area, Greece; the sampling was stratified by the age‐sex distribution of the region. Various sociodemographic, clinical, and psychological characteristics were self‐reported, and weight and height were measured and recorded in all participants. Results: The proportions of true positives and true negatives for correct obesity status identification were 62% and 97%, respectively. Women were 9 times more likely to be under‐reporters than men, whereas men were 7.5 times more likely to be over‐reporters. A 10‐year increase in age was associated with a 48% higher likelihood of being an under‐reporter and 26% lower likelihood of being an over‐reporter, irrespective of sex and other characteristics of the participants. Clinical status, such as the presence of hypertension and diabetes, was associated with under‐reporting of body weight. Furthermore, the use of self‐reported data may substantially exaggerate associations between obesity and obesity‐related diseases, such as diabetes, hypercholesterolemia, and hypertension. Discussion: The study indicates that, apart from age and sex, disease status may be another factor that influences misreporting of obesity status, with diabetic and hypertensive people to be more likely to under‐report their overweight. Use of self‐reported data may bias obesity—disease associations.     

The Bias in Self‐reported Obesity From 1976 to 2005: A Canada–US Comparison

The objective of this study was to determine whether the bias in self‐reported estimates of obesity has changed over time and followed different patterns in Canada and the United States. Using age‐standardized data from three waves of the National Health and Nutrition Examination Survey (NHANES) in the United States and the Canadian Community Health Survey (CCHS) and the Canadian Heart Health Survey (CHHS) in Canada, discrepancies were compared between reported and measured estimates of height, weight, and obesity (based on the BMI) from 1976 to 2005. Results indicated that obesity increased in both countries, but rates were higher in the United States. The discrepancy between self‐reported and measured obesity was small in the United States with reported data underestimating measured prevalence by about 3%; this stayed relatively constant over time. In Canada, the discrepancy was large and doubled in the past decade (from 4 to 8%). In the United States, self‐reported data may be more accurate in monitoring changes in obesity over time, as the estimates have consistently remained about 3% below the measured estimates, whereas in Canada, monitoring obesity based solely on self‐reported height and weight may produce inaccurate estimates because of the increasing discrepancy between self‐reported and measured data.