Internal disinhibition predicts weight regain following weight loss and weight loss maintenance

Objective: The disinhibition scale of the Eating Inventory predicts weight loss outcome; however, it may include multiple factors. The purpose of this study was to examine the factor structure of the disinhibition scale and determine how its factors independently relate to long‐term weight loss outcomes. Research Methods and Procedures: Exploratory factor analysis of the disinhibition scale was conducted on 286 participants in a behavioral weight loss trial (TRIM), and confirmatory factor analysis was conducted on 3345 members of the National Weight Control Registry (NWCR), a registry of successful weight loss maintainers. Multivariate regressions were used to examine the relationships between the disinhibition scale factors and weight over time in both samples. Results: Using baseline data from TRIM, two factors were extracted from the disinhibition scale: 1) an “internal” factor that described eating in response to internal cues, such as feelings and thoughts; and 2) an “external” factor that described eating in response to external cues, such as social events. This factor structure was confirmed using confirmatory factor analysis in the NWCR. In TRIM, internal disinhibition significantly predicted weight loss at 6 months (p = 0.03) and marginally significantly predicted weight loss at 18 months (p = 0.06), with higher levels of internal disinhibition at baseline predicting less weight loss; external disinhibition did not predict weight loss at any time‐point. In NWCR, internal disinhibition significantly predicted one‐year weight change (p = 0.001), while external disinhibition did not. Discussion: These results suggest that it is the disinhibition of eating in response to internal cues that is associated with poorer long‐term weight loss outcomes.     

Dietary adherence during weight loss predicts weight regain

This study examined the relationship between previous dietary adherence during a low-calorie diet weight loss intervention and subsequent weight change during a 2-year follow-up for weight maintenance. One hundred and sixteen healthy, recently weight reduced (lost ~12 kg, BMI 22-25 kg/m2) premenopausal women were studied. Dietary adherence was assessed by doubly labeled water (DLW) and body composition change. Comparisons were made between the upper and lower tertiles for previous dietary adherence and subsequent weight change at 1- and 2-year follow-up. Percent weight regained was significantly lower (30.9 ± 6.7% vs. 66.7 ± 9.4%; P < 0.05) in the upper compared to the lower adherence tertile for previous weight loss dietary adherence (49.9 ± 8.8% vs. 96.8 ± 12.8% P < 0.05) at 1- and 2-year follow-up, respectively. This difference was partly explained by increases in daily activity-related energy expenditure (AEE) (+95 ± 45 kcal/day vs. -44 ± 42 kcal/day, P < 0.05) and lower daily energy intake (2,066 ± 71 kcal/day vs. 2,289 ± 62 kcal/day, P < 0.05) in the higher tertile for previous dietary adherence, compared to the lower. These findings suggest that higher adherence (i.e., higher tertile) to the previous low-calorie diet predicts lower weight regain over 2-year follow-up for weight maintenance, which is explained by lower energy intake and higher physical activity. Finally, how well an individual adheres to a low-calorie diet intervention during weight loss may be a useful tool for identifying individuals who are particularly vulnerable to subsequent weight regain.     

Three-year weight change in successful weight losers who lost weight on a low-carbohydrate diet

Objective: The purpose of this study was to evaluate long‐term weight loss and eating and exercise behaviors of successful weight losers who lost weight using a low‐carbohydrate diet. Research Methods and Procedures: This study examined 3‐year changes in weight, diet, and physical activity in 891 subjects (96 low‐carbohydrate dieters and 795 others) who enrolled in the National Weight Control Registry between 1998 and 2001 and reported ≥30‐lb weight loss and ≥1 year weight loss maintenance. Results: Only 10.8% of participants reported losing weight after a low‐carbohydrate diet. At entry into the study, low‐carbohydrate diet users reported consuming more kcal/d (mean ± SD, 1895 ± 452 vs. 1398 ± 574); fewer calories in weekly physical activity (1595 ± 2499 vs. 2542 ± 2301); more calories from fat (64.0 ± 7.9% vs. 30.9 ± 13.1%), saturated fat (23.8 ± 4.1 vs. 10.5 ± 5.2), monounsaturated fat (24.4 ± 3.7 vs. 11.0 ± 5.1), and polyunsaturated fat (8.6 ± 2.7 vs. 5.5 ± 2.9); and less dietary restraint (10.8 ± 2.9 vs. 14.9 ± 3.9) compared with other Registry members. These differences persisted over time. No differences in 3‐year weight regain were observed between low‐carbohydrate dieters and other Registry members in intent‐to‐treat analyses (7.0 ± 7.1 vs. 5.7 ± 8.7 kg). Discussion: It is possible to achieve and maintain long‐term weight loss using a low‐carbohydrate diet. The long‐term health effects of weight loss associated with a high‐fat diet and low activity level merits further investigation.     

The relation of rat liver wet weight to dry weight

To assess a reliable relation between the dry and wet weight of rat livers, the water content of liver samples was determined by freeze drying. The ratio between wet and dry weight of the livers turned out as 3.33 +/- 0.3 for male and 3.28 +/- 0.24 for female rats. Thus for calculations a value of 3.3 can be used irrespective of the sex of rats.     

Placental growth patterns affect birth weight for given placental weight

BACKGROUND: An important contributor to fetal growth is growth of the placenta, the fetus' sole source of nutrients and oxygen. Here we use placental growth measures (larger and smaller disk diameters, reflecting the laterally expanding chorionic plate, and disk thickness) to test the hypothesis that placental growth patterns, while associated with placental weight and birth weight, measure placental functional efficiency, and will have independent effects on the feto-placental weight ratio (FPR). METHODS: Placental measures were available from 23,313 participants in the Collaborative Perinatal Project delivered between 34 and 43 completed weeks. Continuous variables were analyzed by regression for associations with placental weight, birth weight, and FPR, to further explore effects of placental growth patterns on the FPR (lateral chorionic plate growth and chorionic disk thickness were grouped as low, normal, and high values). The relationships of the nine resultant combinations of placental growth categories to the FPR using birth weight adjusted for gestational age, infant gender, parity, and African American race were analyzed (ANOVA). RESULTS: As chorionic disk area and thickness increased, birth weight and placental weight increased, and the FPR decreased (each p < .0001) after adjustment for gestational age, parity, race, and infant gender. Small, thin placental disks had an adjusted FPR of 8.46; the largest, thickest placentas had an adjusted FPR of 6.33. The nine categories of FPRs were significantly different, consistent with chorionic plate area and disk thickness combining to determine the FPR. CONCLUSIONS: Patterns of placental growth, relating to different functional dimensions of the placenta, deliver a different birth weight for a given placental weight.     

The relation of rat liver wet weight to dry weight

Summary To assess a reliable relation between the dry and wet weight of rat livers, the water content of liver samples was determined by freeze drying. The ratio between wet and dry weight of the livers turned out as 3.33±0.3 for male and 3.28±0.24 for female rats. Thus for calculations a value of 3.3 can be used irrespective of the sex of rats.Supported by grants from the Deutsche Forschungsgemeinschaft (Sa 127/8-4)     

Online weight training

The purpose of this study was to determine how a traditional weight training class compared to nontraditional classes that were heavily laden with technology. Could students learn resistance exercises by watching video demonstrations over the Internet? Three university weight training classes, each lasting 16 weeks, were compared. Each class had the same curriculum and workout requirements but different attendance requirements. The online group made extensive use of the Internet and was allowed to complete the workouts on their own at any gym that was convenient for them. Seventy-nine college-aged students were randomized into 3 groups: traditional (n = 27), hybrid (n = 25), and online (n = 27). They completed pretest and posttest measures on upper-body strength (i.e., bench press), lower-body strength (i.e., back squat), and knowledge (i.e., written exam). The results indicated that all 3 groups showed significant improvement in knowledge (p < 0.05). The online group did not require the students to attend class and may have resulted in significantly lower scores on the bench press (p < 0.05) and squats (p < 0.05). This study indicates that an online weight training course may improve knowledge but not strength. Possible reasons for a lack of improvement in the online group included lack of motivation, low accountability, and the possibility that the self-reported workouts were not accurate. These results suggest that there is a limit to how much technology can be used in a weight training class. If this limit is exceeded, some type of monitoring system appears necessary to ensure that students are actually completing their workouts.     

Direct and correlated responses to selection for weaning weight,post-weaning weight gain and six-week weight in mice

Summary Four lines of mice were formed from a common base population and selected for 37 generations for either increased 3-week weight (weaning weight), 6-week weight, 3–6 week gain, or maintained as a randomly bred control line. Realised heritability estimates for short-term (long-term) responses were 0.33±0.20 (0.07±0.10), 0.46±0.14 (0.26±0.09), 0.36±0.14 (0.24±0.11) for 3-week weight, 6-week weight and 3–6 week gain, respectively. Realised genetic correlations estimated from short-term (long-term) responses were 0.23±0.08 (0.35±0.10) between 3-week weight and 3–6 week gain; 0.82±0.04 (0.58±0.08) between 3-week weight and 6-week weight; and 0.81±0.04 (0.97±0.04) between 3–6 week gain and 6-week weight. The genetic correlation between 3-week weight and 6-week weight was asymmetric with a greater correlated response for 3-week weight when selecting for 6-week weight (1.06) than vice versa (0.63).