Geoffrey W. Greene

Eating Slowly Led to Decreases in Energy Intake within Meals in Healthy Women

Although reducing eating rate is frequently advocated for control of food intake and thus body weight, empirical evidence is extremely limited and inconsistent. We sought to compare the impact of slow and quick eating rates on development of satiation in healthy women. In a randomized design, 30 healthy women (22.9+/-7.1 years; body mass index [calculated as kg/m(2)] 22.1+/-2.9) were studied on two test visits to compare slow and quick eating rates. Satiation was examined as the main outcome, using the objective measure of energy intake during ad libitum meals. At designated times, subjects also rated perceived hunger, satiety, desire to eat, thirst and meal palatability on visual analogue scales. Slow rates of ingestion led to significant decreases in energy intake (quick: 645.7+/-155.9 kcal; slow: 579.0+/-154.7 kcal; P<0.05) and significant increases in water consumption (quick: 289.9+/-155.1 g; slow: 409.6+/-205.8 g; P<0.05). Despite higher energy intake upon meal completion under the quick condition, satiety was significantly lower than the slow condition (P<0.05). Accordingly, the quick condition showed a lower Satiating Efficiency Index (quick: 0.1; slow: 0.2; P<0.05). After meal completion, pleasantness ratings tended to be higher under the slow condition (P=0.04; but not significant after Bonferroni adjustment). Ad libitum energy intake was lower when the meal was eaten slowly, and satiety was higher at meal completion. Although more study is needed, these data suggest that eating slowly may help to maximize satiation and reduce energy intake within meals.

Stages of change for reducing dietary fat to 30% of energy or less

OBJECTIVE To develop an algorithm that defines a persons stage of change for fat intake < or = 30% of energy. The Stages of Change Model describes when and how people change problem behaviors; change is defined as a dynamic variable with five discrete stages. DESIGN A stage of change algorithm for determining dietary fat intake < or = 30% of energy was developed using one sample and was validated using a second sample. SUBJECTS Sample 1 was a random sample of 614 adults who responded to mailed questionnaires. Sample 2 was a convenience sample of 130 faculty, staff, and graduate students. STATISTICS Subjects in sample 1 were initially classified in a stage of change using an algorithm based on their behavior related to avoiding high-fat foods. Dietary markers were selected for a Behavioral algorithm using logistic regression analyses. Sensitivity, specificity, and predictive value of the Behavioral algorithm were determined, then compared between samples using the Z test. RESULTS The following dietary markers predicted intake < or = 30% of fat (chi 2 = 131; P < .0001): low-fat cheese, breads without added fat, chicken without skin, low-calorie salad dressing, and vegetables for snacks. The specificity of the Behavioral algorithm was validated; the algorithm classified subjects consuming > 30% of energy from fat with 93% specificity in sample 1 and 87% in sample 2 (Z = 1.36; P > .05). Predictive value was also validated; 64% and 58% of subjects meeting the behavioral criteria had fat intakes < or = 30% of energy (Z = 1.1; P > .05). The algorithm was not sensitive, however; most subjects with fat intakes < or = 30% of energy from fat failed to meet the behavioral criteria. The sensitivity differed between samples 1 and 2 (44% and 27%, respectively; Z = 3.84; P < .0001). APPLICATIONS The Behavioral algorithm determines stage of change for fat reduction to < or = 30% of energy in populations with high fat intakes. The algorithm could be used in dietary counseling to tailor interventions to a patients stage of change.

Diabetes Self-Management: Self-reported recommendations and patterns in a large population

OBJECTIVE Diabetes self-management is the cornerstone of overall diabetes management. Yet many questions concerning self-management remain unanswered. The current study was designed to examine several questions about diabetes self-management: 1) What do individuals report being told to do? 2) What are their self-reported levels and patterns of self-care? 3) Are there differences on self-reported self-management recommendations and levels across various subgroups? RESEARCH DESIGN AND METHODS Mailed surveys were returned by 2,056 individuals (73.4% response rate). Of the total, 13.8% had IDDM and the remainder had NIDDM, with 65% of the NIDDM group using insulin. RESULTS The levels and patterns of self-management were consistent with those found in previous studies, i.e., individuals most regularly followed their prescribed medication regimen and least regularly followed recommendations for lifestyle changes of diet and exercise. There were significant differences on reported self-management recommendations across different subgroups. Comparisons on level of self-management across diabetes type revealed significant differences for diet and glucose testing. Differences were also found on self-management levels for a number of individual characteristics, including age, working status, and type of insurance, along with knowledge of the Diabetes Control and Complications Trial findings. CONCLUSIONS These findings provide important information on perceived self-management recommendations and the specific self-management levels and patterns in individuals with diabetes. The current findings may help health professionals better understand the levels and correlates of diabetes self-management and direct future research.

Multiple risk expert systems interventions: impact of simultaneous stage-matched expert system interventions for smoking, high-fat diet, and sun exposure in a population of parents.

Three stage-based expert system interventions for smoking, high-fat diet, and unsafe sun exposure were evaluated in a sample of 2,460 parents of teenagers. Eighty-four percent of the eligible parents were enrolled in a 2-arm randomized control trial, with the treatment group receiving individualized feedback reports for each of their relevant behaviors at 0, 6, and 12 months as well as a multiple behavior manual. At 24 months, the expert system outperformed the comparison condition across all 3 risk behaviors, resulting in 22% of the participants in action or maintenance for smoking (vs. 16% for the comparison condition), 34% for diet (vs. 26%), and 30% for sun exposure (vs. 22%). Proactive, home-based, and stage-matched expert systems can produce significant multiple behavior changes in at-risk populations where the majority of participants are not prepared to change.

Psychosocial factors influencing low fruit and vegetable consumption

A major national health campaign has recently been initiated to promote consumption of 5 or more servings of fruits and vegetables each day. This paper investigates psychosocial factors related to fruit and vegetable consumption to understand better who might be receptive and who might resist the national 5-A-Day campaign. We studied 405 adult respondents to a random-digit dial telephone survey. Applying the Transtheoretical Model, respondents were classified by stage of readiness to adopt the practice of eating 5 or more fruits and vegetables each day. Logistic regression models were developed for persons consuming 2 or fewer servings daily and for persons in the Precontemplation stage. Education was directly related to fruit and vegetable intake and indirectly related to being in the Precontemplation stage. Males were twice as likely as females to be in the Precontemplation stage and eat fewer than 2 servings a day. Of special interest, respondents with children at home were at greater risk of eating 2 or fewer servings a day than those without children at home (OR=1.63; 95% CI, 1.06–2.52). These results imply that stage of readiness to change should be considered as well as other factors in planning interventions for increasing fruit and vegetable consumption.

College students' barriers and enablers for healthful weight management: a qualitative study.

OBJECTIVE To identify barriers and enablers for healthful weight management among college students. DESIGN Sixteen on-line focus groups, homogeneous by sex and university. SETTING Eight universities in 8 states. PARTICIPANTS College students (N = 115; 55% female; mean age 19.7 +/- 1.6). ANALYSIS Qualitative software, Nvivo version 2 (QSR International, Victoria, Australia, 2002), was used; similar codes were grouped together and categorized using an ecological model. RESULTS Males and females cited the same barriers to weight management: intrapersonal (eg, temptation and lack of discipline); interpersonal (social situations); and environmental (eg, time constraints, ready access to unhealthful food). Similar enablers were identified by sex: intrapersonal (eg, regulating food intake, being physically active); interpersonal (social support); and environmental (eg, universitys environment supports physical activity). More barriers than enablers were given, indicating that these college students were more sensitive to barriers than the enablers for weight management. Factors viewed by some students as barriers to weight management were viewed as enablers by others. CONCLUSIONS AND IMPLICATIONS When designing weight management interventions for college students, sex specificity may not be as important as considering that a barrier for one student may be an enabler for another. From an ecological perspective, individually focused interventions must be implemented in conjunction with environmental-level interventions to facilitate behavior change.

Health-related quality of life following a clinical weight loss intervention among overweight and obese adults: intervention and 24 month follow-up effects

BackgroundDespite a growing literature on the efficacy of behavioral weight loss interventions, we still know relatively little about the long terms effects they have on HRQL. Therefore, we conducted a study to investigate the immediate post-intervention (6 months) and long-term (12 and 24 months) effects of clinically based weight management programs on HRQL.MethodsWe conducted a randomized clinical trial in which all participants completed a 6 month clinical weight loss program and were randomized into two 6-month extended care groups. Participants then returned at 12 and 24 months for follow-up assessments. A total of 144 individuals (78% women, M age = 50.2 (9.2) yrs, M BMI = 32.5 (3.8) kg/m2) completed the 6 month intervention and 104 returned at 24 months. Primary outcomes of weight and HRQL using the SF-36 were analyzed using multivariate repeated measures analyses.ResultsThere was complete data on 91 participants through the 24 months of the study. At baseline the participants scored lower than U.S. age-specific population norms for bodily pain, vitality, and mental health. At the completion of the 6 month clinical intervention there were increases in the physical and mental composite measures as well as physical functioning, general health, vitality, and mental health subscales of the SF-36. Despite some weight regain, the improvements in the mental composite scale as well as the physical functioning, vitality, and mental health subscales were maintained at 24 months. There were no significant main effects or interactions by extended care treatment group or weight loss group (whether or not they maintained 5% loss at 24 months).ConclusionA clinical weight management program focused on behavior change was successful in improving several factors of HRQL at the completion of the program and many of those improvements were maintained at 24 months. Maintaining a significant weight loss (> 5%) was not necessary to have and maintain improvements in HRQL.

Stages of Change for Reducing Dietary Fat Intake over 18 Months

OBJECTIVE To describe the stages of change that take place over 18 months, using the criterion of fat intake < or = 30% of total energy to define effective action and to investigate the effect of a single dietary feedback report on dietary fat reduction. DESIGN Subjects were randomly assigned to experimental or control conditions and assessed at 0, 6, 12, and 18 months for fat intake and stage of change. Subjects in the experiment group received 1 feedback report at baseline; all subjects received a report at 12 months. SUBJECTS Potential subjects (n = 614) were recruited by mail from a random sample of nonsmoking adults (32% response rate). Subjects were excluded if consuming < or = 30% of energy from fat or if pregnant or lactating (n = 145). Although 83% of subjects (n = 389) completed the 18-month study, only 296 provided complete data for all time points. The study was restricted to these 296. INTERVENTION Dietary feedback reports plus brief educational materials were provided following the experiment design. ANALYSES Repeated measures analysis of variance with fat intake (percent of energy from fat) as the dependent variable and baseline stage and condition as independent variables. In addition, t tests were used to compare groups at specific time points. RESULTS There was a main effect for time (F3,286 = 39, P < .0001) and baseline stage (F3,286 = 24, P < .0001), but no effect of feedback. There was a time-by-feedback interaction (F4,286 = 4.7, P < .01). There was a short-term effect of feedback over 6 months (t = 3.8, P < .001), but this effect was not significant at other time points. About 9% to 12% of subjects in the precontemplation or contemplation stages, 24% of subjects in the preparation stage, and 40% of unclassified subjects at baseline progressed to the action stage by 18 months. Between 12 and 18 months, subjects progressing at least 1 stage reduced their fat intake to a greater extent than subjects who failed to progress (t = 5.1, P < .0001). IMPLICATIONS Interventions targeted to stage of change have the potential for accelerating the rate of change for dietary fat reduction, but reaching the goal of fat intake < or = 30% of total energy may require more intensive interventions than a single dietary feedback report.

Transtheoretical Individualized Multimedia Expert Systems Targeting Adolescents' Health Behaviors

The transtheoretical model has advanced research and practice for many health behavior changes among adults, but few applications have been developed and applied among adolescents. This paper will describe an innovative and promising computer-based technology for standardized assessment and individualized theory-based intervention delivery called expert systems. Two different studies utilizing multimedia expert systems technology for assessing and intervening with adolescents targeting several health behaviors will be described. One study includes high school students and targets smoking cessation or prevention, sun protection, and dietary fat reduction. The other study includes urban adolescent female clients recruited in family planning clinics and targets condom adoption and either smoking cessation or prevention. The advantages and disadvantages of expert systems technology are reviewed. Multimedia expert system technology has the potential to enhance health promotion and adherence by integrating the strongest components from both clinical and public health models of intervention.

Predictive Versus Measured Energy Expenditure Using Limits-of-Agreement Analysis in Hospitalized, Obese Patients

BACKGROUND Accuracy of predictive formulae is crucial for therapeutic planning because indirect calorimetry measurement is not always possible or cost effective. Energy requirements are more difficult to predict in the acutely ill obese patient compared with lean patients because of an increased resting energy expenditure per lean body mass and a variable stress response to illness. METHODS A retrospective review of 726 patients identified 57 patients (32 spontaneous breathing, S; 25 ventilator dependent, V) with body mass indexes of 30-50 kg/m2. Limits-of-agreement analysis determined bias (the mean difference between measured and predicted values) and precision (the standard deviation of the bias) to evaluate the accuracy of predictive formulae compared with measured resting energy expenditure (MREE) by a Deltatrac Metabolic Monitor. Predictive accuracy was determined within+/-10% MREE. The predictive formulae examined were: variations of the Harris-Benedict equations using ideal, adjusted weights of 25% and 50% and actual weights with stress factors ranging from 1.0 to 1.5; the Ireton-Jones equation for obesity; the Ireton-Jones equations for hospitalized patients (S and V); and the ratio of 21 kcalories per kilogram actual weight. RESULTS The mean MREE was 21 kcal/kg actual weight. The adjusted Harris-Benedict average weight equation was optimal for predicting MREE for the combined S and V sets (bias = 182+/-123; 67%+/-10% MREE), as well as the S subset (bias = 159+/-112; 69%+/-10% MREE). CONCLUSIONS The Harris-Benedict equations using the average of actual and ideal weight and a stress factor of 1.3 most accurately predicted MREE in acutely ill, obese patients with BMIs of 30-50 kg/m2. Predictive formulae were least accurate for obese, ventilator-dependent patients.