Inequities in Activity Are a Better Predictor of Obesity Than Activity Alone

Adequate physical activity is the cornerstone of a healthy lifestyle. Being physically active can help to build muscle, strengthen bones, and manage weight. Despite the benefits, many worldwide fall short of physical activity recommendations. Researchers from Stanford University have embarked on a unique mission to compile physical activity data from around the world in order to create a picture of global activity.

A total of 717,527 individuals from 111 countries were included in their analyses. Step count information was obtained from the Argus smartphone application. By using the smartphone as a tool for data collection, the team was able to bypass the typical pitfalls of physical activity research. Most studies of this kind rely on self-reported activity which can often be biased or inaccurate. Others utilize technology like accelerometers that can quickly become expensive when scaling up the sample size. Smartphones are becoming more and more common, even in the developing world. Using these devices and the application to provide them with data allowed the researchers to gather information from across the globe. As with any method of data collection, this technique was not foolproof. Those who did not have access to a Smartphone could not be included. In addition, the application requires the user to have their phone with them in order to count their steps leaving out any activity during which it is unrealistic to carry a phone (i.e. playing sports, swimming, etc.).

As would be expected, investigators found that some countries are more active than others. For example, Japan was on the higher end with an average of 5,846 steps per day, and Saudi Arabia was on the lower end of the spectrum with an average of 3,103 steps per day. The United States ranked somewhere in the middle with an average step count of 4,774. One might assume that a country’s level of activity would directly relate to rates of obesity in that country. While that is true, it does not tell the whole story. Researchers noticed that not only did average step count vary between countries, but so did the variance of the responses. This means that in some countries, like Japan, the step counts from the sample fit within a narrow range. Others, like Saudi Arabia, had a wide range of responses that stretched out wide around the average. Countries like this have large activity inequity meaning that some people are highly active while others, women in particular, fall well below the average. After further examination of these characteristics, researchers determined that activity inequity was a better predictor of obesity than average step count across gender, age, and income.

The higher the activity inequity of a country, the higher the rates of obesity. This opens a few avenues for intervention. Researchers looked at several cities within the United States and rated them on “walkability.” They found that if a city was more walkable, it had lower activity inequity. They also found that the walkability of a city had the greatest impact on those who needed it most, like women. This indicates that making it easier to get around on foot helps to encourage activity in those who would otherwise be less active.

This information has major public health implications. This is a prime example of something that can be done at the local level and have widespread impacts. By making cities more walkable, citizens who are the most vulnerable to inequity may be encouraged to increase their activity and help to narrow the gap. It brings more nuance into the discussion of how physical activity impacts health. We know that regular activity is important for the health of the individual, but this study brings to the light the importance of staying active for our global community.

Source:

Althoff, T., Hicks, J. L., King, A. C., Delp, S. L., & Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature547(7663), nature23018.

Untangling the Connection between Binge Eating Disorder and Perfectionism

http://ppcorn.com/us/2016/02/27/binge-eating-disorder-15-things-you-didnt-know-part-2/

http://ppcorn.com/us/2016/02/27/binge-eating-disorder-15-things-you-didnt-know-part-2/

Binge eating refers to the consumption of large amounts of food in a shortened time frame. It has previously been associated with a host of negative outcomes, such as weight gain, smoking, and decreased well-being.  Binge eating is also commonly seen in those exhibiting perfectionist behaviors and mindset. What is less clear, however, is which comes first. Is it the perfectionism that is causing someone to binge eat, or is the binge eating inducing the perfectionism? Or, perhaps, the relationship cyclical. The January 2017 edition of Eating Behaviors included a recent study conducted by Smith et al. that examined this question.  As this is, to the best of the authors’ knowledge, the first study to explore the direction of this relationship, they were unsure of what they would find.

Researchers sampled 200 female undergraduate students, as binge eating is prominent (about 32%) in this population. Perfectionism was measured using multiple questionnaires. Items included, “My family expects me to be perfect,” and “The fewer mistakes I make, the more people will like me.” Participants indicated how strongly they agreed with the statements using a Likert-scale.  To assess binge eating behaviors, a compilation of previously validated eating disorder assessment scales, including Bulimia Test-Revised binge eating subscale, Eating Disorder Inventory Bulimia Scale, and Eating Disorder Diagnostic Scale, were used. Questionnaires were administered in four waves over four weeks. At the end of data collection, 95.5% of participants completed all four waves.

Results indicated that perfectionist qualities stayed stable across all time points. Similarly, binge eating remained moderately-to-strongly stable for the duration of the study. Furthermore, the data suggests that pre-existing perfectionist concerns are an antecedent to binge eating, meaning that perfectionism predicted binge eating behaviors.  Conversely, there was no evidence to support that binge eating behaviors would predict future perfectionism, nor did they find a cyclical relationship between the two constructs.

The authors note that there is room for further exploration and verification with these results. Four weeks is a relatively small snapshot of time, especially for such stable constructs. Examining a longer time period and a greater amount of time between waves of testing would help to create a more complete picture. It would also be interesting to see if these results hold across other demographics, including age and gender.

This knowledge can certainly be applied clinically to inform how healthcare professionals go about treating patients with binge eating disorders. As is the case with many eating disorders, the cause of the behavior may have little to nothing to do with food. Taking into consideration outside pressures and concerns may broaden the capacity of the counselor to meet the needs of their patients.

Source:

Smith, M. M., Sherry, S. B., Gautreau, C. M., Stewart, S. H., Saklofske, D. H., & Mushquash, A. R. (2017). Are perfectionistic concerns an antecedent of or a consequence of binge eating, or both? A short-term four-wave longitudinal study of undergraduate women. Eating Behaviors.

Should Playing Video Games be Considered a Risk Factor for Disease?

Note: Although this publication primarily focuses on high quality scientific findings, I believe it is also important to be able to identify shortcomings. The following entry serves as a critique and highlights the criteria needed to be considered “high quality.”

playing-video-games.jpg

Video games are no longer just for children or fringe cultures. They have made their way to mainstream, and more and more adults are jumping on the band wagon.  By their nature, video games often require players to be sedentary for hours at a time to play a game to completion. This has piqued the interest of a group of researchers out of New York University. In the present study, they explored how video game activity affected lifestyle behaviors in adults. They looked at both online and offline games and used “non-gamers” as a control group. They administered a survey to all participants and found that online gamers were more sedentary and consumed more calories from sugar sweetened beverages than non-gamers. The authors were on the right track in wanting to investigate this under-researched population, however, their methodology and design require closer review.

The authors appear to be well versed in the “gaming world” and clearly explained some of the nuance that exists between various gaming platforms.  While this is an interesting area of research, the authors greatly overstated the impacts gaming may have on lifestyle without sufficient supporting evidence.  They state “…online games…have resulted in death because of overexhaustion.” After review of the original news article, it described a man who died of cardiac arrest while playing computer games at an internet café but makes no mention of over exhaustion. The material has been misrepresented by the authors in an effort to make a more shocking case.  Moreover, a non-peer reviewed newspaper article is inappropriate reference material for a scholarly journal article. There is no theoretical framework defined for the formation of the hypotheses, nor are the hypotheses clearly defined. The term “lifestyle behaviors” needs to be more clearly defined in the present study in order to determine where this research will fit in the broader literature.

The sample was fully described with information about age, sex, education, marital status, race/ethnicity, and weight status. Most participants were white, male, and single. No comment was offered on whether or not this is reflective of the gaming population at large, but it may limit the generalizability of the results. The study was clearly defined as cross-sectional, as it was only assessing participants at one, discrete time point.

Three previously validated questionnaires were used to measure the construct of “lifestyle behaviors.” All of these questionnaires are multiple item giving them a higher probability of getting reliable information.  These methods do, however, rely solely on self-reported data making it difficult to be sure the information is accurate.  Based on the questionnaires used, it appears that food and beverage intake and level of activity are being used as proxies for “lifestyle.” Statistics and results were clearly presented. If eating behaviors, activity, and beverage consumption are in fact the constructs of interest, all were adequately addressed in the results section. More clear definitions of the hypotheses are needed in order to confirm them in this section.

The summary of the findings appears to be relatively accurate. The authors repeatedly compare gaming with watching television, however this presents some contradictory arguments. They stated that unlike television, video games do not have commercials and thus do not provide any break in the action. This seems to ignore the rise of Netflix and other streaming services that allow viewers to “binge watch” commercial free for unrestricted periods of time. In contrast, they argue that video game advertisements are more focused on fast food and sugar sweetened beverages than television. No evidence is provided to support this claim, and further investigation is needed to determine if there is a substantive difference between the two media outlets. The implications of this work for dietetic practice were discussed. It was argued that it is important for clinicians to be aware of the amount of time a patient is inactive, however, the evidence provided in the present study does not support the notion that being a gamer should be considered a risk factor for metabolic disease.

The present study confirmed previously held notions that time spent gaming contributes to overall inactivity, but did not provide further insight into the health of the gaming community. I would recommend that the authors review the current literature in the field to reevaluate the needs of the gaming community and make a stronger case for where this study fits in the larger picture.

Source:

Cemelli, C. M., Burris, J., & Woolf, K. (2016). Video games impact lifestyle behaviors in adults. Topics in Clinical Nutrition, 31(2), 96-110.

Revising SNAP to Encourage Healthier Eating Behaviors

In the United States, many low income individuals and families are eligible to participate in the Supplemental Nutrition Assistance Program (SNAP) to offset the cost of food.  In recent years, there has been a discussion about how to improve SNAP to address issues of obesity and chronic disease in recipients. Some suggest incentivizing participants to purchase healthier foods may improve their behavior. Others believe that limiting the foods that can be purchased using SNAP benefits, a protocol similar to that of Women Infants and Children (WIC), may be the answer. Currently, little research exists to support either of these claims.

The September issue of the Journal of the American Medical Association featured a study conducted by Harnack et al. that took a closer look at some of these proposed addendums to SNAP. Previously, it has been very difficult to experimentally test these propositions with current SNAP recipients, as it would drastically interfere with the efficiency of the delivery system.  For this study, the researchers recruited families who were near eligible for SNAP or those that qualify for SNAP, but were not enrolled.  “Near eligible” was defined as having a household income less than or equal to 200% of the poverty line. Participants were given a debit card to be used solely for the purchase of approved foods.  Money proportionate to family size was added to the card every four weeks for a 12-week period.  

Participants were split into one of four groups: incentive, restriction, incentive plus restriction, or control.  The incentive group was credited 30% of the purchase price on fruits and vegetables. This did not include items packaged in syrup or sauce, pickled vegetable, or white potatoes. The restriction group was not allowed to purchase sugar sweetened beverages, candies, or sweet baked goods using the debit card. The incentive plus restriction group received both the 30% incentive on fruit and vegetable purchases as well as the restriction on sweets. The control group followed the same guidelines as current SNAP recipients. Demographic information and three, unannounced 24-hour dietary recalls were taken from participants at baseline and follow up.

Several differences between the groups were observed at follow up.  Those in the restriction group had the greatest decrease in overall energy intake. Participants in the incentive plus restriction group had a greater reduction in overall discretionary calorie intake and the percentage of total energy coming from discretionary calories. “Discretionary calories” refers to calories coming from foods that are low in nutrient value.  They also showed the greatest increase in Healthy Eating Index (HEI) score. The incentive plus restriction group and the incentive group both showed improvements in the amount of solid fruit consumed and a reduction in sugar sweetened beverage consumption.  All study participants showed a significant increase in food security, regardless of group.

Based on the findings of this study, it is difficult to say which design would be the most effective for improving the diet quality of SNAP participants as each experimental group demonstrated different strengths.  In addition, data was only collected from the primary food purchaser in the household. These results, therefore, are not reflective of the effects the program may or may not have had on the family. They also failed to take into consideration the additional burden that food purchasing restrictions may place on already disadvantaged populations.  The primary intent of programs like SNAP is to provide low income populations with the means to feed their families and themselves.  The ethics of narrowing the types of foods they are able to purchase should be more closely examined in future research and policy.

Source:

Haranack, L., Oakes, J. M., Elbel, B., Beatty, T., Rydell, S., & French, S. (2016). Effects of subsidies and prohibitions on nutrition in a food benefit program: A randomized clinical trial. JAMA internal medicine.

How Much Should You Weigh?

Originally appeared in the Behavioral Health Nutrition Student Blog

Clinicians and the general public alike have been troubled by the question, “How much should I weigh?” The answer is not simple.  Numerous factors play into creating that number including height, age, gender, current health status, and health goals. All retain the purpose of reducing a person’s risk of disease throughout the lifespan.  Because this is a rather complex determination, several equations have been created over the years in an attempt to simplify the calculation.  These are known as Ideal Body Weight (IBW) equations. They are able to predict a recommended weight as a linear function of height. This means that as a person’s height increases, their recommended weight will also increase at an even rate. 

Linear relationship between weight and height

Linear relationship between weight and height

While these are useful tools, they do not always offer the most accurate information. IBW equations notoriously underestimate recommended body weight at shorter heights and overestimate at taller heights.  Even though many factors are built into an IBW equation, it is still impossible to account for all variances.  Risk of disease varies greatly among different demographics and ethnicities. One ideal weight could never serve in everyone’s best interest. In addition, most health care professionals agree that it is better to recommend a target range of body weights instead of a singular weight.  Many opt for the use of Body Mass Index (BMI) to determine a healthy body weight range for their patients. BMI calculations, however, are a bit complicated and require several steps. 

Peterson et al. aimed to rectify the concerns raised about IBW calculations in a recent article published in the American Journal of Clinical Nutrition. They aimed to combine the simplicity of the linear relationship of IBW equations with the clinical practicality of BMI estimations into one universal equation. Below is the result:

Wt(lb) = [5 X BMI +(BMI ÷ 5)] X (Ht – 60in)

Unlike previous equations, this calculation uses a target BMI to estimate IBW. In this way, an IBW can be estimated for any given BMI.  This can also be used to create a range of IBWs. For example, to estimate IBW for a normal BMI range, one would enter 18.5 for the low end and 24.9 for the high end. Since the relationship is linear, the same principle will hold for other BMI ranges as well. This could be useful in counseling individuals for whom a normal BMI may not be reasonable.

This universal equation also maintains a much higher accuracy than its predecessors.  Not only does it map onto BMI closely, but it also maintains accuracy at heights on the low and high ends of the spectrum. Likewise, it is not fazed by increasing BMI. This helps to reduce the problems of over- and underestimation encountered with previous methods.

Because the universal equation is so closely tied to BMI, it also falls victim to the same limitations. BMI often misidentifies those who are exceptionally muscular as being overweight. This means that this new equation may recommend an IBW that is too low for these individuals. The target BMI used in this scenario would have to be adjusted to account for the individuals increased muscle mass.

The universal equation proposed is an excellent tool that is easy to use and highly accurate for most body types. It is precise enough to be applied clinically to determine drug dosages while being simple to calculate making it ideal for anyone just looking to keep up on their health goals. 

Source:

Peterson, C. M., Thomas, D. M., Blackburn, G. L., & Heymsfield, S. B. (2016). Universal equation for estimating ideal body weight and body weight at any BMI. The American journal of clinical nutrition, 103(5), 1197-1203.

Commenting on Your Child’s Food May Do More Harm Than You Think

Originally appeared in the Behavioral Health Nutrition Student Blog

http://drjennifernewman.com/2013/12/family-can-hurt-your-career/

http://drjennifernewman.com/2013/12/family-can-hurt-your-career/

Raising healthy kids is no easy task.  Parents are constantly navigating the line between ensuring their children make healthy choices and allowing them to have the freedom to make their own decisions about food.  There are many unexpected pitfalls into which parents may inadvertently tumble. It is important for a child to have some say in what they choose to consume. Well meaning parents may become too involved at mealtime by predetermining every detail of what their child will eat. A parent’s concern with a child’s food choice may be born from his or her own preoccupation with weight and food.  These children are more likely to have difficulty managing their own intake later on in life, and these practices may lead to restrained or restrictive eating patterns in adulthood.

A recent study published in Eating and Weight Disorders aimed to determine exactly how much of an influence parents’ comments on weight and food had on their daughters’ eating behaviors and perceptions of self in adulthood.  Researchers acknowledged that women are often much more vulnerable to disordered eating patterns and frequently are the primary food purchasers in the household making them an important demographic to examine.  A group of 502 women were administered a questionnaire that inquired about how often they remember a parent making comments or expressing concern about their weight or food intake. It cannot be determined from the description provided in the paper, however, if the questionnaire had been properly validated.  Questions included items such as “Did your mother/father comment about your weight?” and “Did either parent often comment about you eating too much/too little?” Participants were also asked how satisfied they were with their current weight, how much weight they would like to lose, and demographic information.

The findings from the survey revealed a few interesting patterns. Women who recalled fewer comments from their parents about their weight had a lower body mass index (BMI) than those whose parents commented more frequently.  When only looking at women whose BMIs fall within the normal range (18.5 – 24.9), those with parents who made comments about their weight were significantly less satisfied with their own weight.  The more parents commented that their daughters were “eating too much,” the higher the woman’s BMI. In addition, parents’ concern with their own weight led to daughters who were also much more concerned with their weight.

This research brings to light several new questions. The present study lumped comments from mothers and fathers together. Is there a difference in the resulting effects of these comments based who said them?  What would be revealed if the same study was done with men? It is important to remember that the results presented here can only confirm a correlation between two events, not causation. This type of data is difficult to prove as an experimental design prescribing parents to comment on their daughter’s weight and food consumption would be largely unethical. A more detailed, longitudinal study would be the most useful way to gain more insight on this topic. These findings are certainly enough to make us pay more attention to how we talk about weight and food, especially to young girls.  Parental comments may have a much more lasting and detrimental effect than we realize.

Source:

Wansink, B., Latimer, L. A., & Pope, L. (2016). “Don’t eat so much:” How parent comments relate to female weight satisfaction. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 1-7.

Linking Gastric Bypass to Your Sweet Tooth

In recent years, gastric bypass surgery has increased in popularity and remains one of the few methods for the treatment of obesity. While there are several different procedures that can be done to resection the gut, all techniques are based on one of two basic premises: reducing the size of the stomach and/or skipping portions of the small intestine in order to reduce the ability of the gut to absorb nutrients. The success of these procedures is largely due to the resulting reduction in calorie intake.  This is, in part, related to the decrease in the physical size of the stomach, but when portions of the small intestine are bypassed, there is also a decrease in sweet appetite.  The mechanism behind this phenomenon, however, is largely unknown.

A recent study published in Cell Metabolism by Han et al. utilized a mouse model to illustrate some very interesting connections between the upper portion of the small intestine, the duodenum, and the animal’s drive to consume sweets.  While not always directly applicable to human physiology, mouse models are an essential step in bridging the gap between in vitro studies working at the microscopic, cellular level and clinical trials involving human subjects.  A hypothesis must show scientific potential and safety in a living, higher-level organism, commonly a mouse, before it can be tested in humans.

The researchers hypothesized that the duodenum would be able to sense sugar and would subsequently signal the brain to release dopamine. Increased dopamine is known to enhance our appetite for sweetness. Two types of mice were used. Some had complete, unaltered intestines, known as sham mice, and others had undergone a duodenal-jejunal bypass (DJB) intervention.  In these DJB mice, the stomach was rerouted to empty into a lower portion of the small intestine, the jejunum, instead of into the duodenum.

Left: Signaling pathway from gut to brain in sham mice Right: Signaling pathway from gut to brain in DJB mice

Left: Signaling pathway from gut to brain in sham mice 

Right: Signaling pathway from gut to brain in DJB mice

Researchers found that sham mice that regularly ate sugar, as apposed to a low-calorie sweetener, had a much greater appetite for sweetness. The DJB mice that ate sugar, however, did not have the same increased sweet tooth. This supports the idea that something different is happening when sugar passes through the duodenum specifically.  There is some signaling pattern to the brain that is missed when the duodenum is bypassed.

To get a clearer picture, researchers looked directly at the brain. They injected sugar straight into the duodenums of both sham and DJB mice. The sham mice experienced a significantly greater release of dopamine compared to the DJB mice. To be sure that it was specifically the duodenum that was causing this response, they also injected sugar into the jejunum and jugular vein.  Still, the duodenal infusions resulted in a much greater release of dopamine.

Looking forward, these results may be used to inform future interventions in humans.  As mentioned previously, mouse models are important for indicating what experiments should be pursued further. We cannot say for certain if the results seen so profoundly in these experiments will translate fully when applied to real, free living humans. Is the signaling between the gut and the brain as potent in a more complex organism? It is not likely that a person will consume a meal of sugar alone. What happens to this relationship when other components, like fat, protein, or fiber, are included in the meal? Future research should aim to answer these questions and better define how this new information can be used to support people who have undergone gastric bypass.

Source:

 Han, W., Tellez, L.A., Niu, J., Medina, S., Ferreira, T. L., Zhang, X., ... & De Araujo, I. E., (2016). Striatal dopamine links gastrointestinal rerouting to altered sweet appetite. Cell Metabolism, 23(1), 103-112.