Serials, numbers and keys for B Association Pro4. Make your Software full version with serials from SerialBay. To create more accurate search results for B Association Version Pro try to exclude using commonly used keywords such as: crack, download, serial, keygen, torrent, warez, etc. Simplifying your search should return more download results.
Conference Card Forum
May 21-23, 2019, New Orleans, LA Card Forum 2019 is designed to help you increase program performance to offset downward pressure on interchange, create loyalty campaigns that grow customer spend and engagement and stay well ahead of technological innovation. Between today’s increasing number of mobile payment platforms, evolving security threats and pressure to stay two steps ahead of changing customer behaviors, issuers are faced with both critical decisions and terrific opportunities for gaining wallet share. Card Forum will help you prepare for the changes ahead to ensure you make the most of these opportunities.Conference The Most Powerful Women in Banking: LEAD
May 22, 2019, New York City The Most Powerful Women in Banking: LEAD is a one-day program that brings together the industry’s rising stars and newer entrants to hear from the women atop The Most Powerful Women in Banking lists. Together the group shares content and connections on the pressing issues facing all banking executives today along with the topics unique to women in the industry. This full day professional and personal development program is jam-packed with a variety of speakers in addition to the women from the Most Powerful Women in Banking ranking.Conference Digital Banking
June 19-21, 2019, Austin, TX Digital Banking is the leading and largest digital banking event in the industry, covering innovation in financial services for consumer and commercial customers around mobile, digital, AI, payments, data, blockchain, CX, disruption, innovation, privacy, API, channel and technology strategies.Conference In Vest
July 16-17, 2019, New York City In Vest has grown into the industry's largest and most anticipated digital wealth management event of the year. With 1,500+ executives attending from across the entire industry, it’s a show you won’t want to miss.Conference PayThink
September 18-20, 2019, Los Angeles, CA PayThink is the industry’s premier bank-focused payments forum with content covering the rapidly changing, inter-connected markets of debit, credit, mobile, prepaid and digital payments. PayThink convenes a targeted network of industry executives from banks, credit unions, FinTech providers and other financial institutions. As the payments industry strives for faster innovation to launch new products ahead of competitors, PayThink provides insight from market participants and innovators leading the way. The progrma is designed for executives looking to stay relevant in the ever-changing payments ecosystem by finding and honing their competitive edge.Conference Digital Mortgage
September 23-24, 2019, Las Vegas, NV Digital Mortgage is a potent blend of people, tech, and ideas that sizzles with the potential of an end-to-end digital mortgage. But it doesn’t stop there. Bringing together the visionaries and innovators from across the home buying and selling ecosystem, this is your opportunity to meet in person and land the deal that will change the trajectory of your business.Conference BankAI
October 7-8, 2019, Chicago, IL BankAI will bring together visionaries, practitioners and business leaders who are working through these questions and finding the sweet spot for artificial intelligence, where it improves products and services and brings efficiencies without creating unintended consequences.Conference small biz: banking
November 19-21, 2019, Los Angeles, CA In order to originate small business loans profitably, banks need to increase cross sales, improve internal systems and/or partner with alternative lenders. Attend this year’s small biz: banking conference to access thought leadership, best practices and leading solution providers.
doi: 10.2105/AJPH.2005.083782
PMID: 17329656
Author informationArticle notesCopyright and License informationDisclaimer
This article has been cited by other articles in PMC.
Abstract
In a meta-analysis of 88 studies, we examined the association between soft drink consumption and nutrition and health outcomes. We found clear associations of soft drink intake with increased energy intake and body weight. Soft drink intake also was associated with lower intakes of milk, calcium, and other nutrients and with an increased risk of several medical problems (e.g., diabetes).
Study design significantly influenced results: larger effect sizes were observed in studies with stronger methods (longitudinal and experimental vs cross-sectional studies). Several other factors also moderated effect sizes (e.g., gender, age, beverage type). Finally, studies funded by the food industry reported significantly smaller effects than did non–industry-funded studies. Recommendations to reduce population soft drink consumption are strongly supported by the available science.
Soft drink consumption has become a highly visible and controversial public health and public policy issue. Soft drinks are viewed by many as a major contributor to obesity and related health problems and have consequently been targeted as a means to help curtail the rising prevalence of obesity, particularly among children. Soft drinks have been banned from schools in Britain and France, and in the United States, school systems as large as those in Los Angeles, Philadelphia, and Miami have banned or severely limited soft drink sales. Many US states have considered statewide bans or limits on soft drink sales in schools, with California passing such legislation in 2005. A key question is whether actions taken to decrease soft drink consumption are warranted given the available science and whether decreasing population consumption of soft drinks would benefit public health.
The issue is not new. In 1942 the American Medical Association mentioned soft drinks specifically in a strong recommendation to limit intake of added sugar.1 At that time, annual US production of carbonated soft drinks was 90 8-oz (240-mL) servings per person; by 2000 this number had risen to more than 600 servings.2 In the intervening years, controversy arose over several fundamental concerns: whether these beverages lead to energy overconsumption; whether they displace other foods and beverages and, hence, nutrients; whether they contribute to diseases such as obesity and diabetes; and whether soft drink marketing practices represent commercial exploitation of children.3–5
The industry trade association in the United States (the American Beverage Association, formerly the National Soft Drink Association) counters nutrition concerns with several key points: (1) the science linking soft drink consumption to negative health outcomes is flawed or insufficient, (2) soft drinks are a good source of hydration, (3) soft drink sales in schools help education by providing needed funding, (4) physical activity is more important than food intake, and (5) it is unfair to “pick on” soft drinks because there are many causes of obesity and there are no “good” or “bad” foods. Similar positions have been taken by other trade associations such as the British Soft Drinks Association and the Australian Beverages Council.
Legislative and legal discussions focusing on soft drink sales often take place on political and philosophical grounds with scant attention to existing science. Our objectives were to review the available science, examine studies that involved the use of a variety of methods, and address whether soft drink consumption is associated with increased energy intake, increased body weight, displacement of nutrients, and increased risk of chronic diseases.
METHODS
We focused on research investigating the effects of sugar-sweetened beverages; diet and artificially sweetened beverages are noted only in certain cases for comparison purposes. We conducted a computer search through MEDLINE and PsycINFO using the key terms “soft drink,” “soda,” and “sweetened beverage.” We identified articles that assessed the association of soft drink consumption with 4 primary outcomes (energy intake, body weight, milk intake, and calcium intake) and 2 secondary outcomes (nutrition and health). We identified additional articles by searching each article’s reference section and the Web of Science database. Finally, we contacted the authors of each included article with a request for unpublished or in-press work, and we asked each author to forward our request to other researchers who might have relevant work. Our searches yielded a total of 88 articles that were included in the present analysis.
There is a great deal of variability in research methods in this literature. Studies vary in their design (i.e., cross-sectional, longitudinal, or experimental studies), sample characteristics (e.g., male vs female, adults vs children), and operational definitions of independent and dependent variables. Because such heterogeneity of research methods is likely to produce heterogeneity of effect sizes across studies (an effect size represents the magnitude of the relationship between 2 variables), we took 2 steps to assess the impact of research method on outcome.
Initially, for each primary outcome (energy intake, body weight, milk intake, and calcium intake), we assessed the degree of heterogeneity of effect sizes by testing the significance of the Q statistic, which is the sum of the squared deviations of each effect size from the overall weighted mean effect size. We did not assess the degree of heterogeneity for secondary outcomes (nutrition and health) because there were relatively few studies in these domains. Our analysis of primary outcomes revealed a significant degree of heterogeneity of effect sizes in each case, and thus we separated the studies according to research design. This procedure reduces the likelihood of aggregating effect-size estimates across heterogeneous studies. Moreover, some research designs are viewed as more powerful than others. Cross-sectional studies represent the weakest design, because such studies cannot determine causality. Longitudinal designs are considered stronger, but experimental designs are the strongest test of causal relationships. Thus, separating studies according to type of design allowed us to examine effect magnitudes as a function of strength of research design.
We further explored variability in effect sizes by examining a number of potential moderator variables, including (1) population studied (children and adolescents vs adults), (2) gender of participants (only male, only female, or male and female combined), (3) type of beverage (sugar-sweetened carbonated soft drinks vs a mix of sugar-sweetened and diet beverages), (4) whether the reported results were adjusted for covariates (e.g., age, gender, ethnicity, activity level), (5) assessment method (self-reports vs observations or measurements), and (6) presence or absence of food industry funding. A study was coded as “industry funded” if the authors acknowledged support from food companies, beverage companies, or trade associations. Articles that did not report a funding source or cited support from other sources (e.g., pharmaceutical industry, university, foundation, or government grants) were coded as “non–industry funded.”
We calculated average effect sizes (r values) using Comprehensive Meta-Analysis version x2 (Biostat, Englewood, NJ). In most cases, we entered data in the form in which they appeared in each individual study, including group means and standard deviations, correlation coefficients, t values, P values, and odds ratios and confidence intervals. In certain cases, it was necessary to manually calculate effect sizes. For example, when means for more than 2 groups were presented (e.g., low, moderate, and high soft drink consumption), we used the formulas for 1-way contrasts described by Rosenthal et al.6 In other cases, odds ratios were reported with uneven confidence intervals (as a result of rounding), and effect sizes were calculated directly from the odds ratio according to the method described by Chinn.7
When data from different subgroups were presented separately (e.g., data for male and female participants were presented independently), we calculated effect sizes separately for each subgroup. In the case of studies that reported multiple measures of a particular construct (e.g., both body weight and body mass index [BMI]), we computed the average effect size of the reported measures. When there was extraordinary variability in sample sizes across studies, we employed the conservative approach of limiting the sample size of the largest study in a particular domain (e.g., cross-sectional studies of energy intake) to the maximum sample size of the other studies in that domain. This approach ensured that the calculated average effect size would not be dominated by a single study. We considered an effect size of 0.10 or less as small, an effect size of 0.25 as medium, and an effect size of 0.40 or above as large.8
To assess the presence of publication bias, we computed a “fail-safe N” for each of the main outcomes; this value is an estimate of the number of unretrieved or unpublished studies with null results that would be required to render the observed effect non-significant. Rosenthal9 suggested that a fail-safe N greater than 5k + 10 (with k being the number of studies included in the analysis) indicates a robust effect; in the present analyses, each fail-safe N far exceeded Rosenthal’s recommendation, suggesting a low probability of publication bias.
RESULTS
Soft Drink Consumption and Energy Intake
The overall effect size (r) across all studies for the relation between soft drink consumption and energy intake was 0.16 (P < .001, Q46 = 715.46, fail-safe N = 9726). Because there was a significant degree of heterogeneity among the effect sizes, we separated studies according to type of research design. Effect sizes for soft drink consumption and energy intake are shown in Table 1 ▶.
TABLE 1—
Average Energy Intake Effect Sizes, by Type of Research Design
| Cross-Sectional | Longitudinal | Experimental (Short) | Overall | |||||
| r (95% CI) | No.a | r (95% CI) | No.a | r (95% CI) | No.a | r (95% CI) | No.a | |
| Gender | ||||||||
| Male | 0.06* (0.04, 0.08) | 2 | 0.27 (0.11, 0.42) | 1 | 0.16 (0.04, 0.27) | 6 | 0.07* (0.05, 0.09) | 10 |
| Female | 0.16* (0.14, 0.17) | 3 | 0.25 (0.23, 0.27) | 3 | 0.32 (0.21, 0.41) | 4 | 0.19* (0.18, 0.20) | 10 |
| Mixed | 0.14* (0.12, 0.15) | 8 | 0.20 (0.14, 0.26) | 2 | 0.10 (−0.04, 0.24) | 4 | 0.14* (0.13, 0.16) | 17 |
| Age group | ||||||||
| Children | 0.08* (0.06, 0.09) | 10 | 0.09* (0.05, 0.13) | 2 | 0.00 (−0.31, 0.31) | 1 | 0.08* (0.07, 0.09) | 13 |
| Adults | 0.28* (0.26, 0.30) | 2 | 0.29* (0.27, 0.31) | 3 | 0.22 (0.15, 0.29) | 11 | 0.28* (0.27, 0.30) | 19 |
| Type of beverage | ||||||||
| Sugared soda | 0.23* (0.21, 0.24) | 7 | 0.24 (0.23, 0.26) | 4 | 0.33* (0.23, 0.42) | 4 | 0.24* (0.23, 0.25) | 16 |
| Mixed/other | 0.06* (0.05, 0.07) | 5 | 0.38 (0.21, 0.53) | 1 | 0.10* (0.00, 0.20) | 8 | 0.06* (0.05, 0.08) | 16 |
| Soda intake | ||||||||
| Self-reported | 0.13 (0.12, 0.14) | 12 | 0.24 (0.23, 0.26) | 4 | . . . | . . . | 0.16* (0.15, 0.16) | 15 |
| Measured | . . . | . . . | 0.38 (0.21, 0.53) | 1 | 0.21 (0.14, 0.28) | 12 | 0.25* (0.20, 0.30) | 17 |
| Energy intake | ||||||||
| Self-reported | 0.13 (0.12, 0.14) | 12 | 0.24 (0.23, 0.26) | 5 | 0.09 (−0.07, 0.24) | 3 | 0.16 (0.15, 0.16) | 22 |
| Measured | . . . | . . . | . . . | . . . | 0.24 (0.16, 0.31) | 9 | 0.24 (0.17, 0.31) | 10 |
| Adjusted values | ||||||||
| No | 0.15 (0.13, 0.18) | 7 | 0.19 (0.14, 0.25) | 3 | 0.21 (0.14, 0.28) | 12 | 0.17 (0.15, 0.20) | 26 |
| Yes | 0.12 (0.11, 0.14) | 5 | 0.25 (0.23, 0.27) | 2 | . . . | . . . | 0.15 (0.15, 0.16) | 6 |
| Industry funded | ||||||||
| No | 0.21* (0.20, 0.23) | 9 | 0.25 (0.23, 0.27) | 4 | 0.25 (0.16, 0.33) | 7 | 0.23* (0.22, 0.24) | 22 |
| Yes | 0.04* (0.03, 0.06) | 3 | 0.18 (0.11, 0.24) | 1 | 0.16 (0.06, 0.27) | 5 | 0.05* (0.04, 0.07) | 10 |
Note. CI = confidence interval. Because some studies reported both cross-sectional and longitudinal data, and because long-term experimental studies are not displayed, the numbers for the Overall column do not necessarily equal the sum of the numbers for the other columns.
aNumber of studies included in the analysis.
* P < .0056 (adjusted for multiple comparisons) for differences in effect sizes within each column and research design variable.
Of the 12 cross-sectional studies examining the relation between soft drink consumption and energy intake, 10 reported a significant positive association,10–19 1 reported mixed results,20 and 1 reported no statistically significant effect.21 Two studies showed that the increase in energy intake associated with soft drink consumption was greater than what could be explained by consumption of the beverages alone,11,17 suggesting that such beverages might stimulate appetite or suppress satiety, perhaps because of a high glycemic index (foods with a high glycemic index produce a rapid rise in blood sugar).22 The average effect size of the association between soft drink consumption and energy intake across all cross-sectional studies was 0.13 (P < .001; Q15 = 433.67, P < .001).
The 5 longitudinal studies that we identified all reported positive associations between soft drink consumption and overall energy intake.17,23–26 The average effect size for these studies was 0.24 (P < .001; Q6 = 109.11, P < .001).
Four long-term experimental studies in which participants consumed soft drinks for between 3 and 10 weeks showed that individuals failed to compensate for the extra energy consumed in the form of sugar-sweetened beverages in that they did not reduce the rest of their food energy intake, resulting in a greater total daily energy intake.27–30 One study revealed that participants consumed 17% more energy than in their typical diet even after the energy from the soft drinks they consumed had been taken into account,27 suggesting again that soft drinks may influence other aspects of dietary intake. The average effect size was 0.30 (P < .001; Q4 = 2.37, P = .667). Because the Q statistic was not statistically significant, we did not investigate moderators for long-term experimental studies.
Findings from short-term experimental studies (i.e., those examining energy intake over the course of a subsequent meal or a single day) were mixed. Of 12 studies, 5 reported that individuals who consumed soft drinks consequently took in a greater amount of total energy (food energy plus beverage energy) than did those who consumed water.31–35 One study also revealed higher-than-expected energy intakes among participants given the energy they consumed from soft drinks.34 By contrast, 5 other studies reported that participants compensated at a subsequent meal for energy consumed from beverages.36–40 Still others reported mixed results, depending, for example, on how long before lunch participants consumed soft drinks.41,42 The average effect size for short-term experimental studies was 0.21 (P < .001; Q18 = 37.92, P = .004).
These results, taken together, provide clear and consistent evidence that people do not compensate for the added energy they consume in soft drinks by reducing their intake of other foods, resulting in increased total energy intakes. Not only do people fail to compensate for the energy consumed in soft drinks, but there is also some evidence that the increase in energy intake associated with soft drink consumption is even greater than what can be accounted for by the beverages alone, suggesting that food energy intake is also higher. The largest effect sizes were observed in long-term experimental studies, followed by short-term experimental and longitudinal studies. The smallest effects were found in cross-sectional studies. Further testing of moderators revealed significantly larger effect sizes among (1) women, (2) adults, (3) studies focusing on sugar-sweetened soft drinks, and (4) studies not funded by the food industry (Table 1 ▶).
Soft Drink Consumption and Body Weight
Research evaluating the relationship between soft drink consumption and body weight is complicated by the fact that researchers operationalize body weight in a number of different ways, even within the same study. When multiple measures of weight were provided in a single study, we calculated the average effect size across those measures. The overall effect size for studies examining the link between soft drink consumption and body weight was 0.08 (P < .001; Q47 = 337.73, P < .001, fail-safe N = 3173). Because there was a significant degree of effect size heterogeneity, we examined effect sizes separately for each research design. Effect sizes for soft drink consumption and body weight are shown in Table 2 ▶.
TABLE 2—
Effect Sizes for Average Body Weight, by Type of Research Design
| Cross-Sectional | Longitudinal | Experimental (Long) | Overall | |||||
| r (95% CI) | No.a | r (95% CI) | No.a | r (95% CI) No.a | r (95% CI) | No.a | ||
| Gender | ||||||||
| Male | 0.02* (−0.01, 0.05) | 5 | 0.03* (0.00, 0.05) | 2 | 0.17 (0.01, 0.32) | 2 | 0.03* (0.01, 0.05) | 9 |
| Female | 0.02* (−0.00, 0.04) | 8 | 0.11* (0.10, 0.13) | 5 | 0.49 (0.17, 0.72) | 1 | 0.09* (0.08, 0.10) | 13 |
| Mixed | 0.07* (0.06, 0.09) | 9 | 0.04* (0.01, 0.07) | 5 | 0.24 (0.18, 0.30) | 5 | 0.08* (0.06, 0.09) | 18 |
| Age group | ||||||||
| Children | 0.03* (0.01, 0.04) | 13 | 0.03* (0.01, 0.04) | 7 | 0.29 (0.22, 0.35) | 2 | 0.03* (0.02, 0.04) | 22 |
| Adults | 0.06* (0.05, 0.08) | 5 | 0.14* (0.13, 0.16) | 3 | 0.15 (0.05, 0.24) | 5 | 0.11* (0.10, 0.12) | 11 |
| Type of beverage | ||||||||
| Sugared soda | 0.04 (0.03, 0.05) | 10 | 0.13* (0.11, 0.14) | 7 | 0.15 (0.04, 0.25) | 4 | 0.09* (0.08, 0.10) | 19 |
| Mixed/other | 0.07 (0.04, 0.09) | 8 | 0.03* (0.01, 0.04) | 3 | 0.27 (0.21, 0.34) | 3 | 0.05* (0.04, 0.06) | 14 |
| Soda intake | ||||||||
| Self-reported | 0.05 (0.03, 0.06) | 17 | 0.09 (0.08, 0.10) | 9 | 0.07 (−0.12, 0.26) | 1 | 0.07 (0.07, 0.08) | 25 |
| Measured | 0.20 (0.04, 0.35) | 1 | 0.13 (−0.08, 0.33) | 1 | 0.15 (0.05, 0.24) | 5 | 0.16 (0.08, 0.23) | 7 |
| Weight | ||||||||
| Self-reported | 0.05 (0.03, 0.06) | 5 | 0.10* (0.09, 0.11) | 4 | . . . | . . . | 0.08 (0.07, 0.09) | 7 |
| Measured | 0.06 (0.03, 0.08) | 12 | 0.03* (0.00, 0.06) | 6 | 0.24 (0.18, 0.29) | 7 | 0.06 (0.05, 0.08) | 25 |
| Adjusted values | ||||||||
| No | 0.06 (0.05, 0.08) | 10 | 0.06 (0.02, 0.09) | 4 | 0.25 (0.20, 0.31) | 6 | 0.08 (0.06, 0.09) | 19 |
| Yes | 0.03 (0.02, 0.05) | 9 | 0.10 (0.09, 0.11) | 6 | 0.07 (0.12, 0.26) | 1 | 0.08 (0.07, 0.09) | 15 |
| Industry funded | ||||||||
| No | 0.06* (0.05, 0.08) | 12 | 0.13* (0.11, 0.14) | 8 | 0.26 (0.20, 0.31) | 5 | 0.10* (0.09, 0.11) | 23 |
| Yes | 0.02* (0.00, 0.04) | 6 | 0.03* (0.01, 0.04) | 2 | 0.11 (0.05, 0.26) | 2 | 0.02* (0.01, 0.04) | 10 |
Note. CI = confidence interval. Because some studies reported both cross-sectional and longitudinal data, the numbers for the Overall column do not necessarily equal the sum of the numbers for the other columns.
aNumber of studies included in the analysis.
* P < .0056 (adjusted for multiple comparisons) for differences in effect sizes within each column and research design variable.
Ease audio converter 5.30 keygen. In cross-sectional studies, outcomes varied depending on how body weight was operationalized. When the focus was on the association between soft drink consumption and BMI, 2 studies reported a significant positive association,43,44 whereas 9 did not.10,16,17,45–50 Two studies revealed a positive association between soft drink consumption and body fat percentage,21,43 but 1 study did not.51 In addition, 4 studies showed that people’s risk of being overweight or obese was positively associated with their soft drink consumption.43,46,52,53 Other studies reported a positive association between soft drink consumption and body weight54,55 and ponderal index18 but not skinfold thickness.55 Averaged across the different methods of operationalization, the mean effect size was 0.05 (P <.001; Q23 =64.36, P <.001; 1 study50 was excluded from this analysis because an effect size could not be computed from the available data).
There was some evidence from the longitudinal studies examined that soft drink consumption is associated with weight gain.11,17,23,54,56 One study showed that soft drink intake was significantly related to 1-year change in body weight among boys but that the association was no longer statistically significant when the analyses controlled for total energy intake.11 This finding suggests that the association between soft drink consumption and weight change was because of the increase in daily energy intake caused by soft drink consumption. Two other studies reported mixed results depending on how body weight was operationalized,57,58 and 4 studies reported no association between soft drink intake and BMI or change in BMI.15,25,26,59 The overall effect size for longitudinal studies was 0.09 (P <.001; Q15 =177.76, P <.001; 1 study57 was excluded from this analysis because an effect size could not be computed from the available data).
We found 7 studies that examined the connection between soft drink intake and body weight in an experimental or intervention context. Five reported a positive association.27–29,60,61 In 3 of these studies, participants who were given soft drinks to consume gained weight over the course of the experiment. Two intervention studies aimed at decreasing soft drink consumption among high school students showed that students in the intervention groups essentially maintained their weight over the treatment period, whereas those in the control groups exhibited significant weight gain. Two studies reported no statistically significant effect of soft drink consumption on weight gain.30,62 The average effect size for experimental studies was 0.24 (P < .001; Q7 = 24.57, P = .001).
Larger effect sizes were observed in experimental studies than in cross-sectional or longitudinal studies. Also, further testing of moderators revealed that effect sizes were larger among (1) women, (2) adults, (3) studies focusing on sugar-sweetened soft drinks, and (4) studies not funded by the food industry (Table 2 ▶).
Soft Drink Consumption and Milk and Calcium Intake
The overall effect size for milk intake was −0.12 (P < .001, Q33 = 300.43, P < .001, fail-safe N = 4048). The overall effect size for calcium intake was −0.04 (P < .001; Q28 = 368.65, P < .001, fail-safe N = 418). Effect sizes for soft drink consumption and milk and calcium intake are shown in Tables 3 ▶ and 4 ▶, respectively.
TABLE 3—
Average Milk Intake Effect Sizes, by Type of Research Design
| Cross-Sectional | Longitudinal | Overall | ||||
| r (95% CI) | No.a | r (95% CI) | No.a | r (95% CI) | No.a | |
| Gender | ||||||
| Male | −0.20* (−0.24, −0.16) | 2 | −0.15 (−0.39, 0.11) | 1 | −0.20* (−0.24, −0.16) | 3 |
| Female | −0.19* (−0.22, −0.16) | 4 | −0.12 (−0.24, 0.01) | 2 | −0.19* (−0.22, −0.16) | 6 |
| Mixed | −0.10* (−0.11, −0.08) | 11 | −0.25 (−0.32, −0.17) | 3 | −0.10* (−0.11, −0.09) | 14 |
| Age group | ||||||
| Children | −0.12 (−0.13, −0.11) | 14 | −0.21 (−0.27, −0.15) | 5 | −0.12 (−0.13, −0.11) | 19 |
| Adults | −0.09 (−0.12, −0.07) | 2 | . . . | . . . | −0.09 (−0.12, −0.07) | 2 |
| Type of beverage | ||||||
| Sugared soda | −0.06* (−0.08, −0.05) | 6 | −0.19 (−0.26, −0.10) | 2 | −0.07* (−0.08, −0.05) | 8 |
| Mixed/other | −0.19* (−0.21, −0.17) | 9 | −0.25 (−0.34, −0.15) | 3 | −0.19* (−0.21, −0.18) | 12 |
| Soda intake | ||||||
| Self-reported | −0.11 (−0.12, −0.10) | 15 | −0.17* (−0.23, −0.10) | 4 | −0.12* (−0.13, −0.11) | 19 |
| Measured | . . . | . . . | −0.58* (−0.70, −0.43) | 1 | −0.58* (−0.70, −0.43) | 1 |
| Milk intake | ||||||
| Self-reported | −0.11 (−0.12, −0.10) | 15 | −0.21 (−0.27, −0.15) | 5 | −0.12 (−0.13, −0.11) | 20 |
| Measured | . . . | . . . | . . . | . . . | . . . | . . . |
| Adjusted values | ||||||
| No | −0.09* (−0.10, −0.08) | 9 | −0.23 (−0.29, −0.16) | 4 | −0.10* (−0.11, −0.08) | 13 |
| Yes | −0.19* (−0.21, −0.16) | 6 | −0.14 (−0.28, 0.00) | 1 | −0.18* (−0.21, −0.16) | 7 |
| Industry funded | ||||||
| No | −0.15* (−0.16, −0.13) | 9 | −0.31 (−0.40, −0.21) | 3 | −0.15* (−0.16, −0.14) | 12 |
| Yes | −0.06* (−0.08, −0.04) | 6 | −0.15 (−0.23, −0.07) | 2 | −0.06* (−0.08, −0.05) | 8 |
Note. CI = confidence interval.
aNumber of studies included in the analysis.
* P < .0056 (adjusted for multiple comparisons) for differences in effect sizes within each column and research design variable.
TABLE 4—
Average Calcium Intake Effect Sizes, by Type of Research Design
| Cross-Sectional | Longitudinal | Overall | ||||
| r (95% CI) | No.a | r (95% CI) | No.a | r (95% CI) | No.a | |
| Gender | ||||||
| Male | 0.06 (−0.02, 0.14) | 1 | −0.14 (−0.18, −0.10) | 1 | −0.10* (−0.14, −0.07) | 2 |
| Female | −0.04 (−0.07, −0.02) | 5 | −0.13 (−0.16, −0.10) | 3 | −0.09* (−0.11, −0.07) | 8 |
| Mixed | −0.01 (−0.03, 0.00) | 9 | −0.05 (−0.15, 0.05) | 1 | −0.01* (−0.03, 0.00) | 10 |
| Age group | ||||||
| Children | −0.01 (−0.02, 0.00) | 12 | −0.08* (−0.12, −0.04) | 3 | −0.02* (−0.03, −0.01) | 15 |
| Adults | −0.06 (−0.09, −0.03) | 3 | −0.16* (−0.19, −0.13) | 2 | −0.12* (−0.14, −0.10) | 5 |
| Type of beverage | ||||||
| Sugared soda | 0.05* (0.03, 0.07) | 3 | −0.13 (−0.16, −0.11) | 3 | −0.02* (−0.03, −0.00) | 6 |
| Mixed/other | −0.07* (−0.08, −0.05) | 11 | −0.08 (−0.16, −0.00) | 2 | −0.07* (−0.08, −0.05) | 13 |
| Soda intake | ||||||
| Self-reported | −0.02 (−0.03, −0.01) | 14 | −0.13 (−0.15, −0.11) | 5 | −0.04 (−0.05, −0.03) | 19 |
| Measured | . . . | . . . | . . . | . . . | . . . | . . . |
| Calcium intake | ||||||
| Self-reported | −0.02 (−0.03, −0.01) | 14 | −0.15* (−0.18, −0.13) | 4 | −0.04 (−0.05, −0.03) | 18 |
| Measured | . . . | . . . | −0.08* (−0.12, −0.04) | 1 | −0.08 (−0.12, −0.04) | 1 |
| Adjusted values | ||||||
| Yes | 0.07* (0.04, 0.11) | 4 | −0.16 (−0.19, −0.13) | 2 | −0.07 (−0.09, −0.05) | 6 |
| No | −0.03* (−0.04, −0.02) | 10 | −0.08 (−0.12, −0.04) | 3 | −0.03 (−0.05, −0.02) | 13 |
| Industry funded | ||||||
| Yes | −0.05* (−0.06, −0.03) | 9 | −0.13 (−0.16, −0.11) | 3 | −0.07* (−0.09, −0.06) | 12 |
| No | 0.03* (0.01, 0.05) | 5 | −0.08 (−0.16, −0.00) | 2 | 0.03* (0.01, 0.05) | 7 |
Note. CI = confidence interval.
aNumber of studies included in the analysis.
* P < .0056 (adjusted for multiple comparisons) for differences in effect sizes within each column and research design variable.
Fifteen cross-sectional studies examined the association between soft drink consumption and milk intake; 13 reported that soft drink consumption was associated with lower intakes of milk and dairy products,10,14–16,19,20,51,54,63–67 1 reported no statistically significant association,68 and 1 reported a small positive association between milk intake and soft drink consumption.69 One study showed that a 1-oz decrease in soft drink consumption was related to approximately a 0.25-oz increase in milk consumption.63 In other words, reducing soft drink consumption by one 16-oz serving per day would be associated with an increase of approximately 4 oz of milk per day. The average effect size for milk intake was −0.11 (P <.001; Q27 =268.33, P <.001).
Calcium intake was also negatively associated with soft drink consumption in several cross-sectional studies,14,16,19,44,64,65,67–71 but the effect sizes were generally small. In addition, 4 other studies reported positive associations between soft drink consumption and calcium intake,51,63,69,72 and 1 reported mixed results.20 Across all studies, the average effect of soft drink consumption was −0.02 (P = .006, Q23 = 275.51, P < .001).
Results from longitudinal studies were similar to those from cross-sectional studies, but the magnitude of the effects was larger. Five longitudinal studies reported a negative relationship between soft drink intake and intakes of milk and dairy products,26,59,73–75 and 5 reported a negative relation between soft drink consumption and calcium intake.23,73,74,76,77 The average effect sizes for milk and calcium intakes were −0.21 (P < .001; Q5 = 23.09, P < .001) and −0.13 (P < .001; Q4 = 15.22, P = .004), respectively.
For milk intake, significantly larger effect sizes were observed in longitudinal studies and in studies that included a variety of beverages, provided adjusted values, and were not funded by the food industry (Table 3 ▶). For calcium intake, larger effect sizes were observed among adults and among studies that included a variety of beverages. In addition, studies funded by the food industry exhibited slight positive effects, whereas studies not funded by the food industry exhibited small negative effects (Table 4 ▶).
Soft Drink Consumption and Nutrient Intake
Soft drink consumption also has been examined in relation to a variety of other foods, macronutrients, and micronutrients. In the case of many of these outcomes, there were only a small number of studies (and sometimes only a single study). We therefore aggregated effect sizes across all studies without examining the impact of research design or any other potential moderator variables. Thus, these aggregated effects should be interpreted with caution. A complete list of the nutritional variables investigated is available from the authors.
Several studies reported a positive association between soft drink consumption and carbohydrate intake,13,14,17,19,20,24,27,28,31,35,39 whereas 1 study reported a negative relation54 and 2 others reported no relation16,38 (average r = 0.13; 4 studies33,34,41,42 were excluded from the analysis because effect sizes could not be computed from the available data). A few studies highlighted the specific sources of carbohydrate related to soft drink consumption. One study reported that children and adolescents in the highest quartile of soft drink consumption consumed between 122 and 159 g of added sugar, approximately 4 to 5 times the US Department of Agriculture’s recommended maximum of added sugar (32 g).64 Overall associations (r values) of soft drink consumption with added sugar, fructose, and sucrose were 0.18, 0.36, and 0.23, respectively. Other studies revealed a negative association of soft drink consumption with intake of both dietary fiber (r = −0.31) and starch (r = −0.27). Thus, these findings indicate that the increased carbohydrate intake associated with soft drink consumption primarily reflects greater consumption of added sugars.
Soft drink consumption also was associated with decreased intakes of protein (r = −0.14), fruit juice (r = −0.17), fruit (r = −0.09), and riboflavin (r = −0.12), among others. Overall, there was no evidence of an association between soft drink consumption and fat intake, nor was there an association with intake of certain vitamins, including A and B12 (all rs < 0.01). One study16 reported a negative association between soft drink consumption and an overall “healthy eating index,” and another study74 reported an overall negative association between soft drink consumption and average adequacy of intake of a variety of vitamins and nutrients.
Soft Drink Consumption and Health Outcomes
A number of studies examined links between soft drink consumption and various health outcomes. We report average effect sizes only when there was more than a single study for a particular outcome.
Perhaps the most striking link between soft drink consumption and health outcomes was the prospective evidence obtained for type 2 diabetes. In a study of 91249 women followed for 8 years, those who consumed 1 or more servings of soft drink per day were twice as likely as those who consumed less than 1 serving per month to develop diabetes over the course of the study.17 These effects were only slightly attenuated when various potential confounds, including BMI and energy intake, were controlled. When diet soft drinks replaced sugar-sweetened soft drinks in the analysis, the increased risk was no longer present, suggesting that the risk was specific to sugar-sweetened soft drinks. Another study reported a positive association between soft drink consumption and number of risk factors for metabolic syndrome.78 These effects also remained when BMI and energy intake were controlled.
Smaller associations were found with a number of other health outcomes. For example, 2 studies linked soft drink consumption with hypocalcemia (average r =0.38),79,80 and a 30-day follow-up involving a group of patients with hypocalcemia who were asked to refrain from consuming soft drinks revealed a significant increase in serum calcium.80 In addition, 2 studies reported a small but statistically significant negative association between soft drink consumption and bone mineral density,51,75 whereas 2 others did not44,68 (average r =−0.03). An association was also reported between soft drink consumption and increased risk of bone fracture (average r =0.06).68,81–83 Some research has shown no effect of soft drink consumption on calcium metabolism and urinary excretion of calcium, and it has therefore been suggested that the role of soft drinks in calcium deficiency and reduced bone mineral density is that of displacing other sources of calcium, such as milk.84,85
A small positive association was found between soft drink consumption and dental caries86–89 (r = 0.03; 2 studies90,91 were excluded from the analysis because effect sizes could not be computed from the available data). The association between soft drinks and dental caries was not observed for diet soft drinks.86 Also, 5 studies reported that soft drink consumption was positively associated with urinary or kidney stones, but 2 studies reported no association (average r = 0.05).76,77,92–96 Two of the 5 studies that found positive associations76,77 revealed that the effect of soft drink intake was no longer significant after other risk factors such as calcium, potassium, and sucrose intake had been controlled, suggesting that the effect of soft drink consumption on urinary stones may be a consequence of its influence on these other risk factors.
A 10-week experimental study showed that individuals who consumed sucrose-sweetened beverages exhibited an increase in both systolic and diastolic blood pressure over the course of the study, whereas individuals who consumed artificially sweetened beverages exhibited a decrease in blood pressure.28 Also, a cross-sectional study reported an association between caffeinated soft drink consumption and systolic blood pressure among African Americans but not European Americans.97 By contrast, another cross-sectional study revealed small negative correlations between soft drink intake and both systolic and diastolic blood pressure.18 Finally, 1 study reported a positive association between soft drink consumption and risk of hypertension.98
DISCUSSION
Intake of soft drinks and added sugars, particularly high fructose corn syrup, has increased coincident with rising body weights and energy intakes in the population of the United States. Yearly US per capita consumption of nondiet soft drinks rose 86% between 1970 and 1997 alone (22 gal [83.6 L] vs 41 gal).99 The prevalence of obesity increased 112% during that approximate time.100 US per capita energy consumption from added sugar rose from 984 kJ (235 kcal) per day in 1977 through 1978 to 1331 kJ (318 kcal) in 1994 through 1996, with soft drinks contributing far more to the total (440 kJ [105 kcal]) than foods such as fruit drinks (130 kJ [31 kcal]) and desserts (251 kJ [60 kcal]).101
Although informative, the data just described represent only broad correlations. A true test of links between an environmental agent such as soft drinks and various health outcomes requires a robust literature with studies involving different methods, populations, and outcomes, but most important is a critical mass of studies with strong methods and sufficient sample sizes. These conditions now exist, and several clear conclusions are apparent.
One of the most consistent and powerful findings is the link between soft drink intake and increased energy consumption. Fully 10 of 12 cross-sectional studies, 5 of 5 longitudinal studies, and all 4 of the long-term experimental studies examined showed that energy intake rises when soft drink consumption increases. The effect sizes for these studies, respectively, were 0.13, 0.24, and 0.30.
The available literature also supports the observation that people do not adequately compensate for the added energy they consume in soft drinks with their intake of other foods and consequently increase their intake of sugar and total energy. Noteworthy are findings from several studies that soft drink intake is associated with a higher level of energy consumption than can be accounted for by the soft drinks themselves.11,17,27,34 These findings raise the possibility that soft drinks increase hunger, decrease satiety, or simply calibrate people to a high level of sweetness that generalizes to preferences in other foods.
Bray et al.102 noted that the average American older than 2 years consumes 553 kJ (132 kcal) per day from high fructose corn syrup (the sole sweetener in US soft drinks) and that intake of this sweetener rose 1000% between 1970 and 1990. These authors proposed that fructose is digested, absorbed, and metabolized differently than glucose in ways that favor de novo lipogenesis and do not stimulate insulin secretion or enhance the production of leptin, both afferent signals in the regulation of food intake and body weight.
One would expect a weaker relationship of soft drink consumption with body weight than with energy intake because soft drinks are not the only source of energy in the diet. In addition, higher intake of diet drinks among people with elevated BMIs could reduce or cancel out a relationship between intake of soft drinks overall and body weight. Indeed, cross-sectional and longitudinal studies showed only small positive associations between soft drink consumption and BMI (rs=0.05 and 0.09, respectively). More impressive, however, is the fact that a moderate effect size (r =0.24) was observed for experimental studies that controlled for many extraneous variables.
In addition to effects on energy intake and weight, it is important to know whether soft drinks displace essential nutrients and contribute to overall poorer diets. Our review showed that increased soft drink intake is related to lower consumption of milk and calcium, but average effect sizes were small. Soft drink consumption was also related to higher intake of carbohydrates, lower intakes of fruit and dietary fiber, and lower intakes of a variety of macronutrients in cross-sectional, longitudinal, and longer-term experimental studies.
Interpreting the association between soft drink consumption and nutrient intake is complex. Soft drink intake could be a marker for poor nutrition, with individuals who consume more sweetened beverages eating poorer diets in general. Soft drinks might also stimulate people’s appetite for other nonnutritious foods. One study showed that individuals who consumed more soft drinks consumed diets with higher overall glycemic indexes,17 supporting the prediction that consumption of foods with high glycemic indexes (such as soft drinks) might stimulate intake of other such foods.22 Other studies showed that soft drink consumption is positively related to the consumption of foods such as hamburgers and pizza54 and negatively related to an overall healthy eating index.16
A number of studies suggest links between soft drink intake and medical problems. The issues of greatest concern are elevations in blood pressure and increased risk of diabetes. The most striking finding, in a study of 91 249 women followed for 8 years, was that those who consumed 1 or more servings of soft drinks per day (less than the US national average) were at twice the risk of developing diabetes as those who consumed less than 1 serving per month.17 This result alone warrants serious concern about soft drink intake, particularly in light of the unprecedented rise in type 2 diabetes among children.
Methodological Considerations
There is a great deal of variability in the methods employed in research on the effects of soft drink consumption, and some of these methodological factors have considerable effects on study outcomes. First, we found that effect magnitudes were consistently larger when studies involved more powerful designs (i.e., findings from experimental studies were consistently stronger than those from cross-sectional studies). Second, effect sizes varied significantly depending on other methodological variables such as participant gender, participant age, and beverage type. This heterogeneity not only influences research outcomes but also influences the conclusions that can be drawn from a given study. Different research methods and different definitions of key variables such as body weight further complicate interpretation of findings across studies. Future research with more uniform methodology (ideally experimental designs) would help clarify the impact of soft drink consumption on nutrition and health outcomes.
Industry Funding
The issue of industry funding has been the focus of considerable scrutiny in several areas of medical research, particularly pharmaceutical studies.103 Our analyses revealed that the overall pattern of results differed significantly when studies funded and not funded by the food industry were compared. As illustrated in Tables 1 ▶ through 4 ▶ ▶ ▶, the average overall effect size for industry-funded studies was significantly smaller than the average effect size for nonfunded studies. This discrepancy was particularly striking in studies examining the effects of soft drink consumption on energy intake; effect sizes were moderate (r = 0.23) for nonfunded studies and essentially nil (r = 0.05) for funded studies.
Similar results have been reported in other food research. Among studies supportive of the fat substitute olestra, for instance, 80% have been funded by the food industry; by contrast, 21% of neutral studies and 11% of studies critical of olestra have been funded by the industry. Game uk truck simulator bus mod indonesia. In addition, all of the authors disclosing an affiliation with the maker of olestra have published studies supportive of the product.104
Conclusions
Available data indicate a clear and consistent association between soft drink consumption and increased energy intake. Given the multiple sources of energy in a typical diet, it is noteworthy that a single source of energy can have such a substantial impact on total energy intake. Best of the bestest bennie k zip. This finding alone suggests that it would be prudent to recommend population decreases in soft drink consumption. The fact that soft drinks offer energy with little accompanying nutrition, displace other nutrient sources, and are linked to several key health conditions such as diabetes is further impetus to recommend a reduction in soft drink consumption.
Acknowledgments
This work was supported in part by the Rudd Foundation.
We thank all of the authors who responded to our request for unpublished and in press research.
Note. Personnel from the Rudd Foundation were not involved in this work in any way. All of the authors of the present article had full access to the data.
Notes
Peer Reviewed
Contributors
L. R. Vartanian co-originated the project, retrieved and coded the relevant articles, conducted the analyses, and cowrote the article. M. B. Schwartz assisted in the coding and analyses and cowrote the article. K. D. Brownell co-originated the project and cowrote the article.
References
1. American Medical Association Council on Foods and Nutrition. Some nutritional aspects of sugar, candy, and sweetened carbonated beverages. JAMA. 1942; 120:763–765. [Google Scholar]
2. Jacobson MF. Liquid candy: how soft drinks are harming Americans’ health. Available at: http://www.cspinet.net/new/pdf/liquid_candy_final_w_new_supplement.pdf. Accessed August 10, 2005.
3. Brownell KD, Horgen KB. Food Fight: The Inside Story of the Food Industry, America’s Obesity Crisis, and What We Can Do About It. New York, NY: McGraw-Hill Contemporary Books; 2004.
4. Linn S. Consuming Kids: The Hostile Takeover of Childhood. New York, NY: New Press; 2004.
5. Schor JB. Born to Buy: The Commercialized Child and the New Consumer Culture. New York, NY: Scribner; 2004.
6. Rosenthal R, Rosnow RL, Rubin DB. Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge, England: Cambridge University Press; 2000.
7. Chinn S. A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med. 2000;19:3127–3131. [PubMed] [Google Scholar]
8. Lipsey MW, Wilson DB. Practical Meta-Analysis. Thousand Oaks, Calif: Sage Publications; 2001.
9. Rosenthal R. Meta-Analytic Procedures for Social Research. Rev. ed. Newbury Park, Calif: Sage Publications; 1991.
10. Rajeshwari R, Yang SJ, Nicklas TA, Berenson GS. Secular trends in children’s sweetened-beverage consumption (1973 to 1994): the Bogalusa Heart Study. J Am Diet Assoc. 2005;105:208–214. [PubMed] [Google Scholar]
11. Berkey CS, Rockett HR, Field AE, Gillman MW, Colditz GA. Sugar-added beverages and adolescent weight change. Obes Res. 2004;12:778–788. [PubMed] [Google Scholar]
12. Cullen KW, Ash DM, Warneke C, de Moor C. Intake of soft drinks, fruit-flavored beverages, and fruits and vegetables by children in grades 4 through 6. Am J Public Health. 2002;92:1475–1478. [PMC free article] [PubMed] [Google Scholar]
13. Davy BM, Harrell K, Stewart J, King DS. Body weight status, dietary habits, and physical activity levels of middle school-aged children in rural Mississippi. South Med J. 2004;97:571–577. [PubMed] [Google Scholar]
14. Harnack L, Stang J, Story M. Soft drink consumption among US children and adolescents: nutritional consequences. J Am Diet Assoc. 1999;99:436–441. [PubMed] [Google Scholar]
15. Newby PK, Peterson KE, Berkey CS, Leppert J, Willett WC, Colditz GA. Beverage consumption is not associated with changes in weight and body mass index among low-income preschool children in North Dakota. J Am Diet Assoc. 2004;104:1086–1094. [PubMed] [Google Scholar]
16. Rodriguez-Artalejo F, Garcia EL, Gorgojo L, et al. Consumption of bakery products, sweetened soft drinks and yogurt among children aged 6–7 years: association with nutrient intake and overall diet quality. Br J Nutr. 2003;89:419–429. [PubMed] [Google Scholar]
17. Schulze MB, Manson JE, Ludwig DS, et al. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA. 2004;292:927–934. [PubMed] [Google Scholar]
18. Stanton MF, Ahrens RA, Douglass LW. Coffee and cola beverage consumption as heart disease risk factors in men. Experientia. 1978;34:1182–1183. [PubMed] [Google Scholar]
19. Guenther PM. Beverages in the diets of American teenagers. J Am Diet Assoc. 1986;86:493–499. [PubMed] [Google Scholar]
20. Bowman SA. Beverage choices of young females: changes and impact on nutrient intakes. J Am Diet Assoc. 2002;102:1234–1239. [PubMed] [Google Scholar]
21. Gillis LJ, Bar-Or O. Food away from home, sugar-sweetened drink consumption and juvenile obesity. J Am Coll Nutr. 2003;22:539–545. [PubMed] [Google Scholar]
22. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA. 2002;287:2414–2423. [PubMed] [Google Scholar]
23. Striegel-Moore RH, Thompson D, Affenito SG, et al. Correlates of beverage intake in adolescent girls: the National Heart, Lung, and Blood Institute Growth and Health Study. J Pediatr. 2006;148:183–187. [PubMed] [Google Scholar]
24. De Castro JM. The effects of the spontaneous ingestion of particular foods or beverages on the meal pattern and overall nutrient intake of humans. Physiol Behav. 1993;53:1133–1144. [PubMed] [Google Scholar]
25. Kvaavik E, Andersen LF, Klepp KI. The stability of soft drinks intake from adolescence to adult age and the association between long-term consumption of soft drinks and lifestyle factors and body weight. Public Health Nutr. 2005;8:149–157. [PubMed] [Google Scholar]
26. Mrdjenovic G, Levitsky DA. Nutritional and energetic consequences of sweetened drink consumption in 6- to 13-year-old children. J Pediatr. 2003;142: 604–610. [PubMed] [Google Scholar]
Keygen
27. DiMeglio DP, Mattes RD. Liquid versus solid carbohydrate: effects on food intake and body weight. Int J Obes Relat Metab Disord. 2000;24:794–800. [PubMed] [Google Scholar]
28. Raben A, Vasilaras TH, Moller AC, Astrup A. Sucrose compared with artificial sweeteners: different effects on ad libitum food intake and body weight after 10 weeks of supplementation in overweight subjects. Am J Clin Nutr. 2002;76:721–729. [PubMed] [Google Scholar]
29. Tordoff MG, Alleva AM. Effect of drinking soda sweetened with aspartame or high-fructose corn syrup on food intake and body weight. Am J Clin Nutr. 1990; 51:963–969. [PubMed] [Google Scholar]
30. Van Wymelbeke V, Beridot-Therond ME, de La Gueronniere V, Fantino M. Influence of repeated consumption of beverages containing sucrose or intense sweeteners on food intake. Eur J Clin Nutr. 2004;58: 154–161. [PubMed] [Google Scholar]
31. Almiron-Roig E, Drewnowski A. Hunger, thirst, and energy intakes following consumption of caloric beverages. Physiol Behav. 2003;79:767–773. [PubMed] [Google Scholar]
32. DellaValle DM, Roe LS, Rolls BJ. Does the consumption of caloric and non-caloric beverages with a meal affect energy intake? Appetite. 2005;44:187–193. [PubMed] [Google Scholar]
33. King NA, Appleton K, Rogers PJ, Blundell JE. Effects of sweetness and energy in drinks on food intake following exercise. Physiol Behav. 1999;66:375–379. [PubMed] [Google Scholar]
34. Mattes RD. Dietary compensation by humans for supplemental energy provided as ethanol or carbohydrate in fluids. Physiol Behav. 1996;59:179–187. [PubMed] [Google Scholar]
35. Melanson KJ, Westerterp-Plantenga MS, Campfield LA, Saris WH. Blood glucose and meal patterns in time-blinded males, after aspartame, carbohydrate, and fat consumption, in relation to sweetness perception. Br J Nutr. 1999;82:437–446. [PubMed] [Google Scholar]
36. Birch LL, McPhee L, Sullivan S. Children’s food intake following drinks sweetened with sucrose or aspartame: time course effects. Physiol Behav. 1989;45: 387–395. [PubMed] [Google Scholar]
37. Canty DJ, Chan MM. Effects of consumption of caloric vs noncaloric sweet drinks on indices of hunger and food consumption in normal adults. Am J Clin Nutr. 1991;53:1159–1164. [PubMed] [Google Scholar]
38. Holt SH, Sandona N, Brand-Miller JC. The effects of sugar-free vs sugar-rich beverages on feelings of fullness and subsequent food intake. Int J Food Sci Nutr. 2000;51:59–71. [PubMed] [Google Scholar]
39. Lavin JH, French SJ, Read NW. The effect of sucrose- and aspartame-sweetened drinks on energy intake, hunger and food choice of female, moderately restrained eaters. Int J Obes Relat Metab Disord. 1997; 21:37–42. [PubMed] [Google Scholar]
40. Rogers PJ, Carlyle JA, Hill AJ, Blundell JE. Uncoupling sweet taste and calories: comparison of the effects of glucose and three intense sweeteners on hunger and food intake. Physiol Behav. 1988;43:547–552. [PubMed] [Google Scholar]
41. Rodin J. Comparative effects of fructose, aspartame, glucose, and water preloads on calorie and macronutrient intake. Am J Clin Nutr. 1990;51:428–435. [PubMed] [Google Scholar]
42. Rolls BJ, Kim S, Fedoroff IC. Effects of drinks sweetened with sucrose or aspartame on hunger, thirst and food intake in men. Physiol Behav. 1990;48:19–26. [PubMed] [Google Scholar]
43. Giammattei J, Blix G, Marshak HH, Wollitzer AO, Pettitt DJ. Television watching and soft drink consumption: associations with obesity in 11- to 13-year-old schoolchildren. Arch Pediatr Adolesc Med. 2003;157: 882–886. [PubMed] [Google Scholar]
44. Kim SH, Morton DJ, Barrett-Connor EL. Carbonated beverage consumption and bone mineral density among older women: the Rancho Bernardo Study. Am J Public Health. 1997;87:276–279. [PMC free article] [PubMed] [Google Scholar]
45. Forshee RA, Storey ML, Ginevan ME. A risk analysis model of the relationship between beverage consumption from school vending machines and risk of adolescent overweight. Risk Anal. 2005;25:1121–1135. [PubMed] [Google Scholar]
46. Ariza AJ, Chen EH, Binns HJ, Christoffel KK. Risk factors for overweight in five- to six-year-old Hispanic-American children: a pilot study. J Urban Health. 2004; 81:150–161. [PMC free article] [PubMed] [Google Scholar]
47. Cullen KW, Baranowski T, Klesges LM, et al. Anthropometric, parental, and psychosocial correlates of dietary intake of African-American girls. Obes Res. 2004;12(suppl):20S–31S. [PubMed] [Google Scholar]
48. Forshee RA, Anderson PA, Storey ML. The role of beverage consumption, physical activity, sedentary behavior, and demographics on body mass index of adolescents. Int J Food Sci Nutr. 2004;55:463–478. [PubMed] [Google Scholar]
49. Forshee RA, Storey ML. Total beverage consumption and beverage choices among children and adolescents. Int J Food Sci Nutr. 2003;54:297–307. [PubMed] [Google Scholar]
50. Gilbert TJ, Percy CA, Sugarman JR, Benson L, Percy C. Obesity among Navajo adolescents: relationship to dietary intake and blood pressure. Am J Dis Child. 1992;146:289–295. [PubMed] [Google Scholar]
51. McGartland C, Robson PJ, Murray L, et al. Carbonated soft drink consumption and bone mineral density in adolescence: the Northern Ireland Young Hearts project. J Bone Miner Res. 2003;18:1563–1569. [PubMed] [Google Scholar]
52. Liebman M, Pelican S, Moore SA, et al. Dietary intake, eating behavior, and physical activity-related determinants of high body mass index in rural communities in Wyoming, Montana, and Idaho. Int J Obes Relat Metab Disord. 2003;27:684–692. [PubMed] [Google Scholar]
53. Nicklas TA, Yang S-J, Baranowski T, Zakeri I, Berenson G. Eating patterns and obesity in children: the Bogalusa Heart Study. Am J Prev Med. 2003;25:9–16. [PubMed] [Google Scholar]
54. Bes-Rastrollo M, Sanchez-Villegas A, Gomez-Gracia E, Martinez JA, Pajares RM, Martinez-Gonzalez MA. Predictors of weight gain in a Mediterranean cohort: the Seguimiento Universidad de Navarra Study 1. Am J Clin Nutr. 2006;83:362–370. [PubMed] [Google Scholar]
55. Novotny R, Daida YG, Acharya S, Grove JS, Vogt TM. Dairy intake is associated with lower body fat and soda intake with greater weight in adolescent girls. J Nutr. 2004;134:1905–1909. [PubMed] [Google Scholar]
56. Ludwig DS, Peterson KE, Gortmaker SL. Relation between consumption of sugar-sweetened drinks and childhood obesity: a prospective, observational analysis. Lancet. 2001;357:505–508. [PubMed] [Google Scholar]
57. Drapeau V, Despres JP, Bouchard C, et al. Modifications in food-group consumption are related to long-term body-weight changes. Am J Clin Nutr. 2004;80: 29–37. [PubMed] [Google Scholar]
58. Phillips SM, Bandini LG, Naumova EN, et al. Energy-dense snack food intake in adolescence: longitudinal relationship to weight and fatness. Obes Res. 2004;12:461–472. [PubMed] [Google Scholar]
59. Blum JW, Jacobsen DJ, Donnelly JE. Beverage consumption patterns in elementary school aged children across a two-year period. J Am Coll Nutr. 2005; 24:93–98. [PubMed] [Google Scholar]
60. Ebbeling CB, Feldman HA, Osganian SK, Chomitz VR, Ellenbogen SJ, Ludwig DS. Effects of decreasing sugar-sweetened beverage consumption on body weight in adolescents: a randomized, controlled pilot study. Pediatrics. 2006;117:673–680. [PubMed] [Google Scholar]
61. James J, Thomas P, Cavan D, Kerr D. Preventing childhood obesity by reducing consumption of carbonated drinks: cluster randomised controlled trial. BMJ. 2004;328:1237–1239. [PMC free article] [PubMed] [Google Scholar]
62. Grandjean AC, Reimers KJ, Bannick KE, Haven MC. The effect of caffeinated, non-caffeinated, caloric and non-caloric beverages on hydration. J Am Coll Nutr. 2000;19:591–600. [PubMed] [Google Scholar]
63. Storey ML, Forshee RA, Anderson PA. Associations of adequate intake of calcium with diet, beverage consumption, and demographic characteristics among children and adolescents. J Am Coll Nutr. 2004;23:18–33. [PubMed] [Google Scholar]
64. Frary CD, Johnson RK, Wang MQ. Children and adolescents’ choices of foods and beverages high in added sugars are associated with intakes of key nutrients and food groups. J Adolesc Health. 2004;34:56–63. [PubMed] [Google Scholar]
65. Ballew C, Kuester S, Gillespie C. Beverage choices affect adequacy of children’s nutrient intakes. Arch Pediatr Adolesc Med. 2000;154:1148–1152. [PubMed] [Google Scholar]
66. Skinner JD, Carruth BR, Moran J III, Houck K, Coletta F. Fruit juice intake is not related to children’s growth. Pediatrics. 1999;103:58–64. [PubMed] [Google Scholar]
67. Fisher JO, Mitchell DC, Smiciklas-Wright H, Birch LL. Maternal milk consumption predicts the tradeoff between milk and soft drinks in young girls’ diets. J Nutr. 2000;131:246–250. [PMC free article] [PubMed] [Google Scholar]
68. Ma D, Jones G. Soft drink and milk consumption, physical activity, bone mass, and upper limb fractures in children: a population-based case-control study. Calcif Tissue Int. 2004;75:286–291. [PubMed] [Google Scholar]
69. Barr SI. Associations of social and demographic variables with calcium intakes of high school students. J Am Diet Assoc. 1994;94:260–266, 269. [PubMed] [Google Scholar]
70. Guthrie JF. Dietary patterns and personal characteristics of women consuming recommended amounts of calcium. Fam Econ Nutr Rev. 1996;9:33–49. [Google Scholar]
71. Skinner JD, Bounds W, Carruth BR, Ziegler P. Longitudinal calcium intake is negatively related to children’s body fat indexes. J Am Diet Assoc. 2003; 103:1626–1631. [PubMed] [Google Scholar]
72. Salamoun MM, Kizirian AS, Tannous RI, et al. Low calcium and vitamin D intake in healthy children and adolescents and their correlates. Eur J Clin Nutr. 2005;59:177–184. [PubMed] [Google Scholar]
73. Fisher JO, Mitchell DC, Smiciklas-Wright H, Mannino ML, Birch LL. Meeting calcium recommendations during middle childhood reflects mother-daughter beverage choices and predicts bone mineral status. Am J Clin Nutr. 2004;79:698–706. [PMC free article] [PubMed] [Google Scholar]
74. Marshall TA, Eichenberger Gilmore JM, Broffitt B, Stumbo PJ, Levy SM. Diet quality in young children is influenced by beverage consumption. J Am Coll Nutr. 2005;24:65–75. [PubMed] [Google Scholar]
75. Whiting SJ, Healey A, Psiuk S, Mirwald R, Kowalski K, Bailey DA. Relationship between carbonated and other low nutrient dense beverages and bone mineral content of adolescents. Nutr Res. 2001;21:1107–1115. [Google Scholar]
76. Curhan GC, Willett WC, Rimm EB, Spiegelman D, Stampfer MJ. Prospective study of beverage use and the risk of kidney stones. Am J Epidemiol. 1996;143: 240–247. [PubMed] [Google Scholar]
77. Curhan GC, Willett WC, Speizer FE, Stampfer MJ. Beverage use and risk for kidney stones in women. Ann Intern Med. 1998;128:534–540. [PubMed] [Google Scholar]
78. Yoo S, Nicklas T, Baranowski T, et al. Comparison of dietary intakes associated with metabolic syndrome risk factors in young adults: the Bogalusa Heart Study. Am J Clin Nutr. 2004;80:841–848. [PubMed] [Google Scholar]
79. Guerrero-Romero F, Rodriguez-Moran M, Reyes E. Consumption of soft drinks with phosphoric acid as a risk factor for the development of hypocalcemia in postmenopausal women. J Clin Epidemiol. 1999;52: 1007–1010. [PubMed] [Google Scholar]
80. Mazariegos-Ramos E, Guerrero-Romero F, Rodriguez-Moran M, Lazcano-Burciaga G, Paniagua R, Amato D. Consumption of soft drinks with phosphoric acid as a risk factor for the development of hypocalcemia in children: a case-control study. J Pediatr. 1995; 126:940–942. [PubMed] [Google Scholar]
81. Wyshak G. Teenaged girls, carbonated beverage consumption, and bone fractures. Arch Pediatr Adolesc Med. 2000;154:610–613. [PubMed] [Google Scholar]
82. Wyshak G, Frisch RE. Carbonated beverages, dietary calcium, the dietary calcium/phosphorus ratio, and bone fractures in girls and boys. J Adolesc Health. 1994;15:210–215. [PubMed] [Google Scholar]
83. Wyshak G, Frisch RE, Albright TE, Albright NL, Schiff I, Witschi J. Nonalcoholic carbonated beverage consumption and bone fractures among women former college athletes. J Orthop Res. 1989;7:91–99. [PubMed] [Google Scholar]
84. Heaney RP, Rafferty K. Carbonated beverages and urinary calcium excretion. Am J Clin Nutr. 2001; 74:343–347. [PubMed] [Google Scholar]
85. Smith S, Swain J, Brown EM, et al. A preliminary report of the short-term effect of carbonated beverage consumption on calcium metabolism in normal women. Arch Intern Med. 1989;149:2517–2519. [PubMed] [Google Scholar]
86. Forshee RA, Storey ML. Evaluation of the association of demographics and beverage consumption with dental caries. Food Chem Toxicol. 2004;42:1805–1816. [PubMed] [Google Scholar]
87. Jensdottir T, Arnadottir IB, Thorsdottir I, et al. Relationship between dental erosion, soft drink consumption, and gastroesophageal reflux among Icelanders. Clin Oral Investig. 2004;8:91–96. [PubMed] [Google Scholar]
88. Marshall TA, Levy SM, Broffitt B, et al. Dental caries and beverage consumption in young children. Pediatrics. 2003;112:e184–e191. [PubMed] [Google Scholar]
89. Steinberg AD, Zimmerman SO, Bramer ML. The Lincoln Dental Caries Study: II. The effect of acidulated carbonated beverages on the incidence of dental caries. J Am Dent Assoc. 1972;85:81–89. [PubMed] [Google Scholar]
90. Heller KE, Burt BA, Eklund SA. Sugared soda consumption and dental caries in the United States. J Dent Res. 2001;80:1949–1953. [PubMed] [Google Scholar]
91. Ismail AI, Burt BA, Eklund SA. The cariogenicity of soft drinks in the United States. J Am Dent Assoc. 1984;109:241–245. [PubMed] [Google Scholar]
92. Hirvonen T, Pietinen P, Virtanen M, Albanes D, Virtamo J. Nutrient intake and use of beverages and the risk of kidney stones among male smokers. Am J Epidemiol. 1999;150:187–194. [PubMed] [Google Scholar]
93. Krieger JN, Kronmal RA, Coxon V, Wortley P, Thompson L, Sherrard DJ. Dietary and behavioral risk factors for urolithiasis: potential implications for prevention. Am J Kidney Dis. 1996;28:195–201. [PubMed] [Google Scholar]
94. Shuster J, Finlayson B, Scheaffer RL, Sierakowski R, Zoltek J, Dzegede S. Primary liquid intake and urinary stone disease. J Chronic Dis. 1985;38:907–914. [PubMed] [Google Scholar]
95. Shuster J, Jenkins A, Logan C, et al. Soft drink consumption and urinary stone recurrence: a randomized prevention trial. J Clin Epidemiol. 1992;45:911–916. [PubMed] [Google Scholar]
96. Soucie JM, Coates RJ, McClellan W, Austin H, Thun M. Relation between geographic variability in kidney stones prevalence and risk factors for stones. Am J Epidemiol. 1996;143:487–495. [PubMed] [Google Scholar]
97. Savoca MR, Evans CD, Wilson ME, Harshfield GA, Ludwig DA. The association of caffeinated beverages with blood pressure in adolescents. Arch Pediatr Adolesc Med. 2004;158:473–477. [PubMed] [Google Scholar]
98. Winkelmayer WC, Stampfer MJ, Willett WC, Curhan GC. Habitual caffeine intake and the risk of hypertension in women. JAMA. 2005;294:2330–2335. [PubMed] [Google Scholar]
99. Putnam JJ, Allshouse JE. Food Consumption, Prices and Expenditures, 1970–1997. Washington, DC: Economic Research Service, US Dept of Agriculture; 1999.
100. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288:1723–1727. [PubMed] [Google Scholar]
101. Popkin BM, Nielsen SJ. The sweetening of the world’s diet. Obes Res. 2003;11:1325–1332. [PubMed] [Google Scholar]
102. Bray GA, Nielsen SJ, Popkin BM. Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. Am J Clin Nutr. 2004;79: 537–543. [PubMed] [Google Scholar]
103. Bekelman JE, Li Y, Gross CP. Scope and impact of financial conflicts of interest in biomedical research: a systematic review. JAMA. 2003;289:454–465. [PubMed] [Google Scholar]
104. Levine J, Gussow JD, Hastings D, Eccher A. Authors’ financial relationships with the food and beverage industry and their published positions on the fat substitute olestra. Am J Public Health. 2003;93:664–669. [PMC free article] [PubMed] [Google Scholar]
Articles from American Journal of Public Health are provided here courtesy of American Public Health Association