Water consumption acutely reduces meal energy intake (EI) among middle‐aged and older adults. Our objectives were to determine if premeal water consumption facilitates weight loss among overweight/obese middle‐aged and older adults, and to determine if the ability of premeal water consumption to reduce meal EI is sustained after a 12‐week period of increased water consumption. Adults (n = 48; 55–75 years, BMI 25–40 kg/m2) were assigned to one of two groups: (i) hypocaloric diet + 500 ml water prior to each daily meal (water group), or (ii) hypocaloric diet alone (nonwater group). At baseline and week 12, each participant underwent two ad libitum test meals: (i) no preload (NP), and (ii) 500 ml water preload (WP). Meal EI was assessed at each test meal and body weight was assessed weekly for 12 weeks. Weight loss was ∼2 kg greater in the water group than in the nonwater group, and the water group (β = −0.87, P < 0.001) showed a 44% greater decline in weight over the 12 weeks than the nonwater group (β = −0.60, P < 0.001). Test meal EI was lower in the WP than NP condition at baseline, but not at week 12 (baseline: WP 498 ± 25 kcal, NP 541 ± 27 kcal, P = 0.009; 12‐week: WP 480 ± 25 kcal, NP 506 ± 25 kcal, P = 0.069). Thus, when combined with a hypocaloric diet, consuming 500 ml water prior to each main meal leads to greater weight loss than a hypocaloric diet alone in middle‐aged and older adults. This may be due in part to an acute reduction in meal EI following water ingestion.

Introduction Energy-containing beverages, specifically sugar-sweetened beverages (SSB), may contribute to weight gain and obesity development. Yet, no rapid assessment tools are available which quantify habitual beverage intake (grams, energy) in adults. Objective Determine the factorial validity of a newly developed beverage intake questionnaire (BEVQ) and identify potential to reduce items. Methods Participants from varying economic and educational backgrounds (n=1,596; age 43±12 yrs; BMI 31.5±0.2 kg/m2) completed a 19-item BEVQ (BEVQ-19). Beverages that contributed <10% to total beverage, or SSB, energy and grams were identified for potential removal. Factor analyses identified beverage categories that could potentially be combined. Regression analyses compared BEVQ-19 outcomes with the reduced version’s (BEVQ-15) variables. Inter-item reliability was assessed using Cronbach’s Alpha. Following BEVQ-15 development, a subsequent study (n=70; age 37±2 yrs; BMI 24.5±0.4 kg/m2) evaluated the relative validity of the BEVQ-15 through comparison of three 24-hour dietary recalls’ (FIR) beverage intake. Results Three beverage items were identified for elimination (vegetable juice, meal replacement drinks, mixed alcoholic drinks); beer and light beer were combined into one category. Regression models using BEVQ-15 variables explained 91–99% of variance in the four major outcomes of the BEVQ-19 (all P<0.001). Cronbach’s Alpha ranged 0.97–0.99 for all outcomes. In the follow-up study, BEVQ-15 and FIR variables were significantly correlated with the exception of whole milk; BEVQ-15 SSB (R2=0.69) and total beverage energy (R2=0.59) were more highly correlated with FIR than previously reported for the BEVQ-19. The BEVQ-15 produced a lower readability score of 4.8, which is appropriate for individuals with a fourth grade education or greater. Conclusion The BEVQ-19 can be reduced to a 15-item questionnaire. This brief dietary assessment tool will enable researchers and practitioners to rapidly (administration time of ~2 min) assess habitual beverage intake, and to determine possible associations of beverage consumption with health-related outcomes, such as weight status.

Total energy consumption among United States adults has increased in recent decades, and energycontaining beverages are a significant contributor to this increase. Because beverages are less satiating than solid foods, consumption of energy-containing beverages may increase energy intake and lead to weight gain; trends in food and beverage consumption coinciding with increases in overweight and obesity support this possibility. The purpose of this review is to present what is known about the effect of beverage consumption on short-term (i.e., meal) energy intake, as well as longerterm effects on body weight. Specific beverages addressed include water, other energy-free beverages (diet soft drinks, coffee and tea), and energy-containing beverages (soft drinks, juices and juice drinks, milk and soy beverages, alcohol). Existing evidence, albeit limited, suggests that encouraging water consumption, and substituting water and other energy-free beverages (diet soft drinks, coffee and tea) for energy-containing beverages may facilitate weight management. Energy-containing beverages acutely increase energy intake, however long-term effects on body weight are uncertain. While there may be health benefits for some beverage categories, additional energy provided by beverages should be compensated for by reduced consumption of other foods in the diet.

With the aging of the baby-boom generation and increases in life expectancy, the American population is growing older. Aging is associated with adverse changes in glucose tolerance and increased risk of diabetes; the increasing prevalence of diabetes among older adults suggests a clear need for effective diabetes prevention approaches for this population. The purpose of paper is to review what is known about changes in glucose tolerance with advancing age and the potential utility of resistance training (RT) as an intervention to prevent diabetes among middle-aged and older adults. Age-related factors contributing to glucose intolerance, which may be improved with RT, include improvements in insulin signaling defects, reductions in tumor necrosis factor-α, increases in adiponectin and insulin-like growth factor-1 concentrations, and reductions in total and abdominal visceral fat. Current RT recommendations and future areas for investigation are presented.

At the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.