We found that multiple lifestyle changes were independently associated with long-term weight gain, including changes in the consumption of specific foods and beverages, physical activity, alcohol use, television watching, and smoking habits. Average long-term weight gain in nonobese populations is gradual — in the cohorts we studied, about 0.8 lb per year — but accumulated over time, even modest increases in weight have implications for long-term adiposity-related metabolic dysfunction, diabetes, cardiovascular disease, and cancer.21-24 Whereas weight changes associated with any single lifestyle factor were relatively modest in our three cohorts, in the aggregate, changes in diet and physical activity accounted for large differences in weight gain. The results were similar across the three separate cohorts, increasing our confidence in the validity and generalizability of the findings.

All these relationships must be mediated by changes in energy intake, energy expenditure, or both. Total energy intake is not well estimated from dietary questionnaires, nor does it reflect energy balance, which is necessarily codetermined by energy expenditure. Thus, weight change is the best population metric of energy imbalance and at least partly captures energy intake after adjustment for determinants of expenditure (e.g., age, body-mass index, and physical activity).

Eating more or less of any one food or beverage may change the total amount of energy consumed, but the magnitude of associated weight gain varied for specific foods and beverages. Differences in weight gain seen for specific foods and beverages could relate to varying portion sizes, patterns of eating, effects on satiety, or displacement of other foods or beverages. Strong positive associations with weight change were seen for starches, refined grains, and processed foods. These findings are consistent with those suggested by the results in limited short-term trials: consumption of starches and refined grains may be less satiating, increasing subsequent hunger signals and total caloric intake, as compared with equivalent numbers of calories obtained from less processed, higher-fiber foods that also contain healthy fats and protein.27 Consumption of processed foods that are higher in starches, refined grains, fats, and sugars can increase weight gain.28-30

Some foods — vegetables, nuts, fruits, and whole grains — were associated with less weight gain when consumption was actually increased. Obviously, such foods provide calories and cannot violate thermodynamic laws. Their inverse associations with weight gain suggest that the increase in their consumption reduced the intake of other foods to a greater (caloric) extent, decreasing the overall amount of energy consumed. Higher fiber content and slower digestion of these foods would augment satiety, and their increased consumption would also displace other, more highly processed foods in the diet, providing plausible biologic mechanisms whereby persons who eat more fruits, nuts, vegetables, and whole grains would gain less weight over time.

Yogurt consumption was also associated with less weight gain in all three cohorts. Potential mechanisms for these findings are unclear; intriguing evidence suggests that changes in colonic bacteria might influence weight gain.31 It is also possible that there is an unmeasured confounding factor that tracks with yogurt consumption (e.g., people who change their yogurt consumption may have other weight-influencing behaviors that were not measured by our instruments).

Our findings with regard to sugar-sweetened beverages are consistent with the results of prior observational studies and short-term interventions.7,32,33 Consumption of 100%-fruit juice was associated with weight gains of smaller magnitude, possibly because these beverages may be consumed in smaller servings than are sugar-sweetened beverages or in different patterns (i.e., single rather than multiple servings).33 Findings have been inconsistent in prior studies of alcohol use and weight gain.34-37 In a previous analysis of alcohol consumption in relation to weight change in the NHS II cohort over a period of 8 years, the smallest weight gain was seen among women who remained moderate drinkers.36 The present findings suggest that the relationship between alcohol use and weight change is complex, and further analyses are needed that address potential heterogeneity with respect to sex, beverage type, baseline intake, direction of change, and duration of follow-up. Short-term controlled trials suggest that liquids are less satiating than solid foods, increasing the total amount of energy consumed.38 Overall, our analysis showed that changes in the consumption of all liquids except milk were positively associated with weight gain; our findings for high-carbohydrate beverages were consistent with those for refined carbohydrates and starches consumed in foods. Temporal trends render our findings especially relevant: between 1965 and 2002, U.S. beverage consumption increased from 11.8 to 21.0% of all calories consumed — 222 more kilocalories per person per day — with sugar-sweetened beverages and alcohol accounting for 60% and 32% of the increase, respectively.39

Our analysis showed relatively neutral associations between change in the consumption of most dairy foods and weight gains. Few prior studies have evaluated these relationships. Prior analyses of HPFS data showed associations similar to ours for the overall categories of whole-fat and low-fat dairy products,40 but subtypes (e.g., milk, cheese, and butter) were not evaluated independently. Among Swedish women, higher intakes of whole milk and cheese were inversely associated with weight gain; as in our study, significant associations with weight gain were not seen for other dairy foods.41 In several long-term studies, inverse associations between dairy consumption and the risk of insulin resistance, the metabolic syndrome, or diabetes were observed,42,43 but potential mediating effects on weight change were not evaluated. Limited short-term studies of dairy foods and satiety or weight change have had inconsistent results.44,45

Overall, our analysis showed divergent relationships between specific foods or beverages and long-term weight gain, suggesting that dietary quality (the types of foods and beverages consumed) influences dietary quantity (total calories). Several dietary metrics that are currently emphasized, such as fat content, energy density, and added sugars, would not have reliably identified the dietary factors that we found to be associated with long-term weight gain. For example, most of the foods that were positively associated with weight gain were starches or refined carbohydrates; no significant differences were seen for low-fat and skim milk versus whole-fat milk, and the consumption of nuts was inversely associated with weight gain. Clear patterns were also not seen in the relationship between weight change and the energy density of dietary components (e.g., beverages of low energy density were strongly associated with weight gain). Foods that contained higher amounts of refined carbohydrates — whether these were added (e.g., in sweets and desserts) or were not added (e.g., in refined grains) — were associated with weight gain in similar ways, and potato products (which are low in sugars and high in starches) showed the strongest associations with weight gain. No single metric appears to capture these complexities. Our findings highlight gaps in our mechanistic understanding of how particular dietary characteristics alter energy balance, suggesting directions for future research regarding pathways involved in hunger, satiety, absorption, metabolism, and adipocyte growth or hyperplasia. In general, changes in the consumption of refined or processed foods and liquid carbohydrates or alcohol were positively associated with weight gain, whereas changes in the consumption of unprocessed foods such as whole grains, fruits, nuts, and vegetables were inversely associated with weight gain. These results suggest that future policies and research efforts to prevent obesity should consider food structure and processing as potentially relevant dietary metrics.

Changes in physical activity were independently related to long-term changes in weight, supporting the biologic plausibility of our overall findings. Prior, smaller studies have shown inverse associations between activity changes and weight change.11,13 Prevalent (current) levels of physical activity are inconsistently related to weight change, with associations observed only for subgroups of persons14 or subtypes of activities.12 As seen in prior analyses of sugar-sweetened beverages,33 changes in lifestyle may be most relevant for weight gain. Persons may achieve a new steady-state weight within months after a change in regular physical activity, diet, or other lifestyle habits, highlighting the importance of repeated assessments of over time to discern long-term effects.

Many prior studies of television watching and obesity have been cross-sectional, limiting the ability to make inferences about which came first.15 In controlled interventions, decreased television watching reduced weight gain in children,16,17 an effect that was mediated more by improvements in dietary habits than by a change in physical activity. Television watching appears to encourage snacking during viewing and also influences food choices both during viewing and at other times.46-52 Our long-term prospective evaluation provides evidence that both the duration of television viewing and changes in the duration of viewing influence weight gain in adults. Because these effects are probably mediated by changes in diet and physical activity, and may also be mediated by changes in sleep, the multivariable (mediator)–adjusted associations may underestimate the full effects of television watching.

Decreases in sleep duration are concordant with the U.S. obesity epidemic.18,19,53 Data from cross-sectional studies and some prospective studies, including a prior analysis of NHS data, support the relationship of shorter sleep duration with obesity.18 In short-term trials, reduced sleep alters leptin, ghrelin, subjective hunger, and preferences for calorie-dense, refined-carbohydrate foods.19 Our results suggest that the association between sleep duration and long-term weight gain is characterized by a U-shaped curve — that is, weight gain is lowest among persons who sleep 6 to 8 hours a night and is higher among those who sleep less than 6 hours or more than 8 hours. Future studies should evaluate how changes in sleep over time are related to weight gain.

Our long-term follow-up data confirm prior observations that smoking cessation results in weight gain initially but in little weight change thereafter. The health benefits of cessation exceed any potential adverse effects — that is, active smokers are at higher risk for cardiovascular diseases, cancer, and diabetes than are former smokers.54 Smoking may also adversely alter the distribution of body fat, promoting visceral rather than femoral or subcutaneous fat deposition; thus, even in the setting of lower total weight, active smoking has adverse metabolic consequences, as evidenced, for example, by its links to a higher risk of type 2 diabetes.55 Any relative weight loss seen with active smoking should not be considered beneficial, nor should the relative weight gain soon after smoking cessation be considered harmful.

Our study has some limitations. Although dietary questionnaires specified portion sizes, residual, unmeasured differences in portion sizes among participants might account for additional independent effects on energy balance. For example, an average, large baked potato contains 278 calories, as compared with 500 to 600 calories for a large serving of french fries.56 The typical portion size of a specific food or beverage may therefore partly mediate its effects on weight gain (i.e., both average portion sizes and biologic effects). As for lifestyle behaviors, each was measured with some degree of error, which, if random, would underestimate their true relationships with weight change. Lifestyle changes were self-selected, and residual confounding from other lifestyle behaviors is possible. However, in contrast to prevalent behaviors, changes in these behaviors were generally not strongly correlated (r<0.05), which suggests that different behaviors are often changed relatively independently, thus minimizing potential confounding. A person's weight change could lead to changes in lifestyle rather than vice versa. Such reverse causation would generally underestimate true effects. For example, persons who are gaining weight might plausibly either reduce their intake of sugar-sweetened beverages and sweets or increase their consumption of vegetables, leading to reverse bias with respect to the observed associations.

As is the case with any biologic finding or medical intervention, our results represent the average population effect, and intraindividual variations exist. The cohorts studied here largely comprised white, educated U.S. adults, which potentially limits the generalizability of the findings. Conversely, the ranges of dietary intakes were broad and overlapped with national estimates. In addition, our findings were broadly consistent with cross-sectional national trends with respect to diet and obesity: between 1971 and 2004, the average dietary intake of calories in the United States increased by 22% among women and by 10% among men, primarily owing to the increased consumption of refined carbohydrates, starches, and sugar-sweetened beverages.39 Our findings were also consistent among the three cohorts and in analyses stratified according to smoking status, age, and baseline body-mass index, and it seems plausible that the biologic effects of many lifestyle factors would be qualitatively similar in other populations.

A habitual energy imbalance of about 50 to 100 kcal per day may be sufficient to cause the gradual weight gain seen in most persons.57,58 This means that unintended weight gain occurs easily but also that modest, sustained changes in lifestyle could mitigate or reverse such an energy imbalance. Our findings suggest that both individual and population-based strategies to help people consume fewer calories may be most effective when particular foods and beverages are targeted for decreased (or increased) consumption. Aggregate dietary changes accounted for substantial differences in weight gain, with additional contributions from changes in physical activity and television watching, thus highlighting specific lifestyle changes that might be prioritized in obesity-prevention strategies.