Testing for independence between pairs of autocorrelated binomial data sequences

A problem arises in analyzing the existence of interdependence between the behavioral sequences of two individuals: tests involving a statistic such as chi-square assume independent observations within each behavioral sequence, a condition which may not exist in actual practice. Using Monte Carlo simulations of binomial data sequences, we found that the use of a chi-square test frequently results in unacceptable Type I error rates when the data sequences are autocorrelated. We compared these results to those from two other methods designed specifically for testing for intersequence independence in the presence of intrasequence autocorrelation. The first method directly tests the intersequence correlation using an approximation of the variance of the intersequence correlation estimated from the sample autocorrelations. The second method uses tables of critical values of the intersequence correlation computed by Nakamuraet al. (J. Am. Stat. Assoc., 1976,71, 214–222). Although these methods were originally designed for normally distributed data, we found that both methods produced much better results than the uncorrected chi-square test when applied to binomial autocorrelated sequences. The superior method appears to be the variance approximation method, which resulted in Type I error rates that were generally less than or equal to 5% when the level of significance was set at .05.     

Selective information processing and schematic content related to eating behavior

An adaptation of the Stroop color-naming task was used to investigate selective information processing related to eating behavior in 90 undergraduate women. This study differed from previous studies by (a) treating eating behavior as a continuous variable and (b) looking at five separate categories of words including color, neutral, food, body shape, and other emotionally salient words. We did not find a strong pattern of relation between the Stroop task and eating and body image measures. Results suggest that in a nonclinical population, direct methods of accessing cognitions related to eating may be more fruitful than indirect measures.     

The influence of caloric information on cafeteria food choices

We evaluated the effect of posting caloric information on food purchases at a cafeteria. Purchases of more than 14,300 entrees, vegetables, and salads by 6,970 customers were unobtrusively monitored via the cash register inventory control system during 15 evening observations. A quasi-multiple-baseline design across food groups was used to test the additive effect of labeling the three lowest caloric choices for vegetables, salads, and entrees. A linear logit analysis confirmed that labeling increased the probability of low calorie selections for vegetables and salads, but not for entrees. Observations of meals purchased by a subsample of 413 customers indicated labeling did not change the total caloric content of meals. The number of customers and total sales per evening were unaffected by the labeling intervention. The results suggest that manipulating environmental cues may be an effective method for changing food purchases in a cafeteria, but labeling individual items may not be the best way to decrease total calories purchased.     

Behavioral assessment of eating patterns and blood glucose in diabetes using the Self-Monitoring Analysis System

The Self-Monitoring Analysis System is a set of microcomputer programs that were developed to remove clinical judgment from the task of behavioral assessment using self-monitoring diaries. The program is written in Turbo Pascal and runs on MS-DOS (16-bit) or CP/M (8-bit) microcomputer systems. The program allows the user to custom design data bases with hundreds of binary, categorical, integral, and real variables. Full-screen data-entry forms can be custom designed, and random access files allow for flexible data entry and editing. The program makes extensive utilization of Pascal’s very flexible data structure capabilities. Dynamic allocation of memory allows the program to maximize its use of available RAM. Data from a patient with diabetes is presented as a case study illustrating the system’s utility.     

Situation Taxonomy and Behavioral Diagnosis Using Prospective Self-Monitoring Data: Application to Dietary Adherence in Patients with Type 1 Diabetes

The goals of this study were to create a taxonomy of problem situations for diet adherence in persons with insulin-dependent diabetes mellitus (Type 1 diabetes) and a behavioral diagnosis by grouping patients together who have similar adherence problems. In study one, 29 patients with Type 1 diabetes kept food diaries for 4 weeks. Adherence was assessed using eight behavioral measures: (1) compliance with a meal plan, (2) energy intake, (3) protein, (4) carbohydrates, (5) fat, (6) self-reported overeating, (7) self-reported undereating, and (8) ratings of impulsive eating. The occurrence of each behavior was quantified in a wide range of situational contexts including meal, social context, physical location, and mood. Cluster analysis of behaviors, situations, and behaviors-in-situations was used to create a hierarchical classification model consisting of five major categories of adherence problems: (1) compliance with meal plan, (2) undereating, (3) overeating, (4) impulsive eating, and (5) lifestyle differences. Within each category, situations were associated with everyday eating habits and special circumstances such as social events, and meals in restaurants. Grouping patients based on the similarity of their adherence problems resulted in five diagnostic groups: (1) emotional binge eaters, (2) restrained eaters, (3) unrestrained eaters, (4) undereaters, and (5) cyclical diet bingers. In study two, 28 subjects kept diaries during an intervention program designed to enhance dietary adherence. Study two results were similar to study one. Situation taxonomy and behavioral diagnosis are potentially useful ways to understand the intervention needs of those attempting maintaining dietary behavior changes.     

Body Image Testing System: A microcomputer program for assessing body image

An interactive computer program, the Body Image Testing System (BITS), was developed to assess different components of the body image construct. The BITS program displays a frontal and side view of a human body. Subjects can change the size of each of nine body parts independently (face, neck, shoulders, arms, chest, breasts, stomach, hips, and thighs). Subjects interact with the program until satisfied that the image created matches the instructions. Subjects also provide satisfaction ratings for the nine body segments. To validate BITS, a variety of body image and eating disturbance measures was obtained from over 500 subjects. A factor analysis of actual-ideal differences and a perceptual distortion measure, derived using multiple regression, resulted in five orthogonal factors. Factor 1 measured actual-ideal discrepancy for weight-sensitive body parts. Factor 2 measured the perceptual distortion of weight-sensitive body parts. Additional factors measured perception of face and neck, shoulders, and breasts. The five factor scores plus the satisfaction ratings were strongly associated with actual body size, body fat percentage, and circumference of specific body parts. In addition, there were strong associations between the BITS and other measures of body image. Finally, BITS scores accounted for between 15 and 60% of the variance in measures of eating disturbance.     

Muscularity concerns among men: development of attitudinal and perceptual measures

The current study reports the development and psychometrics of a figure rating scale with leanness and muscularity dimensions and a 13-item questionnaire assessing symptoms associated with muscle dysmorphia (MD). Three separate samples of men and women completed the muscle dysmorphic disorder inventory (MDDI), the bodybuilder image grid (BIG) or both to measure 1-week test–retest reliability, internal consistency, convergent and divergent validity and valid placement of BIG figures along interval scales of body fat and lean muscle mass. The MDDI and MDDI subscales (drive for size, appearance intolerance, and functional impairment) had good reliability, internal consistency, convergent and divergent validity. The BIG had good to excellent test–retest reliability, good convergent and divergent validity and validity as an interval scale. Implications for characterizing body image disturbance in men and benefits and limitations of the measures are discussed.     

Validation of self-ideal body size discrepancy as a measure of body dissatisfaction

Recently, body dissatisfaction has been conceptualized as the discrepancy between self and ideal body size estimates. This study evaluated the validity of this conceptualization using three methods for estimating actual and ideal body size: (a) the Body Image Assessment, (b) the Body Image Testing System, and (c) the Body Image Detection Device. The three body image assessment procedures were concurrently administered to a sample of 110 women diagnosed: bulimia nervosa (n=18),obese (n=34),and non-eating disorder (n=58).The Eating Disorder Inventory Body Dissatisfaction scale was also used to measure body dissatisfaction. Measures of self-ideal body size discrepancy were found to correlate more highly with measures of body dissatisfaction than were measures of current body size perception, ideal body size, body size estimation accuracy, or indices based on actual body size. Estimation of both current and ideal body size were found to significantly predict overall body dissatisfaction; thus, both self and ideal body size measures were found to be significant components in determining body size dissatisfaction. These data were interpreted as supportive of the conceptualization of body dissatisfaction as the discrepancy between self and ideal body size estimates.