Diet planning as conversation, not monologue

Implementing shared decision making in nutrition clinical practice: A theory-based approach and feasibility study
Original source: Implementation Science 2008, 3:48
Authors: Sophie Desroches (corresponding author), Sylvie Tapp, Marie-Pierre Gagnon, and France Legare
Publication date: 5 November 2008
DOI (digital document ID): 10.1186/1748-5908-3-48
Requires subscription? No
Publicly-available abstract: Yes

Synopsis:
Shared decision making is one of several methods that a health professional can use to help us change our eating habits to better control diabetes and other chronic illnesses. The investigators are looking primarily at dietary approaches to lower cholesterol levels.

The traditional low-fat/low-saturated fat strategy to lower cholesterol is perceived as restrictive, leading to a tendency for people to either "cheat" or to not follow the diet at all. Recently, a number of different dietary approaches have been shown effective in lowering cholesterol -- but the variety, added to the many diet systems discussed in the popular media, can leave the general public confused. Shared decision is a method in which the dietician works collaboratively with the patient, taking into account his cultural and financial perferences, to create a healthy diet that the patient is willing to follow. The process includes explaining the best evidence of the risks and benefits of different dietary models, and explicitly states that the method does include some uncertainties.

The authors of this investigation found no formal studies supporting the use and training of a shared decision making approach using evidence-based practice, and note that some dieticians have reported difficulty in suggesting and implementing this approach.

In this paper they propose a study to learn how shared decision-making is currently used in the field of nutritional consultation, to assess dieticians' intention to adopt two specific behaviors related to shared decision making using the Theory of Planned Behavior, and to learn what the obstacles are to dieticians adopting this approach. Their proposed protocol includes the education of dieticians, the introduction of the Theory of Planned Behavior, focus group discussions in which the dieticians' views and issues are discussed, and audiotaped observation of the dieticians' interactions with patients.

The proposed research project is expected to provide preliminary data about the adoption of shared decision making by dieticians and their patients, look at dieticians' beliefs regarding their intention to adopt shared decision making behaviors and comparing their actions to their intentions, and develop questionnaires to measure this information. The researchers hope that the results generated by the proposed research project will help improve the rate at which dieticians adopt a shared decision-making approach.

Reliability:
The initial study is intended to cover only 40 dieticians, but this is considered mostly as a focus group from which the parameters of a larger study can be planned.

Practical Applications:
The end goal of this research is to increase the adoption of shared decision making in the field of nutritional consulation. Having our dieticians engage us actively, and pay attention to our input, will help us find diets we can -- and will -- live with.

This synopsis was based on the entire article.

How does your body-fat distribution affect your risk of death?

General and Abdominal Adiposity and Risk of Death in Europe
Original source: New England Journal of Medicine Volume 359:2105-2120 November 13, 2008 Number 20
Authors: T. Pischon, H. Boeing, K. Hoffmann, M. Bergmann, M.B. Schulze, K. Overvad, Y.T. van der Schouw, E. Spencer, K.G.M. Moons, A. Tjønneland, J. Halkjaer, M.K. Jensen, J. Stegger, F. Clavel-Chapelon, M.-C. Boutron-Ruault, V. Chajes, J. Linseisen, R. Kaaks, A. Trichopoulou, D. Trichopoulos, C. Bamia, Ph.D., S. Sieri, D. Palli, R. Tumino, P. Vineis, S. Panico, P.H.M. Peeters, A.M. May, H.B. Bueno-de-Mesquita, F.J.B. van Duijnhoven, G. Hallmans, L. Weinehall, J. Manjer, B. Hedblad, E. Lund, A. Agudo, L. Arriola, A. Barricarte, C. Navarro, C. Martinez, J.R. Quirós, T. Key, D.Phil., S. Bingham, K.T. Khaw, P. Boffetta, M. Jenab, P. Ferrari, and E. Riboli
Publication date: November 13, 2008
DOI (digital document ID): (unavailable)
Requires subscription? No
Publicly-available abstract: Yes

Synopsis:
This article addresses the question of whether the risks of obesity are related to the distribution of body fat, rather than the existence of fat excess body weight alone.

The investigators examined Body Mass Index (BMI), waist circumference, and waist-to-hip ratio with the risk of death among 359,387 participants from nine countries over a period of almost ten years. The results were analyzed based on age at recruitment into the study, location at which they were entered into the study, height, educational level, whether or not the subject smoked, how much alcohol the person drank, and how physically active the person was.

The 1% of respondents with the highest and lowest energy intakes relative to estimated energy requirements were excluded from the study, as were respondents with a history of cancer, heart disease, or stroke.

"Normal" weight was considered to range from BMI 18.5 to BMI 25.0

The lowest risks of death based on BMI were at 25.3 for men and 24.3 for women. (This corresponds to the borderline between "normal weight" and "overweight".) After adjusting for this, absolute waist size and large waist-to-hip ratio were associated with greater risk of death. These last two correlations were stronger for people with lower BMI than for those with higher BMI, and stronger for men than for women.

One possible reason for the gender-related difference may be that overweight men tend to carry more fat in their midsections (visceral-fat) than equally-overweight women. This is important because visceral-fat tissue secretes a number of chemicals (lipokines) that can cause or exacerbate a number of chronic diseases.

Interestingly, it was found that the reasons for increased risk of death changed depending on waist measurement: those people who died and who were simply overweight or obese had more deaths from circulatory issues than from other causes, whereas those who had more abdominal obesity tended to die from respiratory issues rather than from other causes. (This may be a confounding issue, related to the observation that smokers tend to have more abdominal fat than non-smokers of similar BMI, and that smokers have higher risk of dying from respiratory system issues. The authors note that there is a known association between abdominal adiposity and chronic bronchitis, but do not state if this is a smoking-related association or not.)

The researchers conclude that these data suggest that both high body weight and abdominal fat are associated with higher risk of death, and support the use of waist circumference and or waist-to-hip ratio in addition to BMI in assessing the risk of death.

Reliability:
This study followed a large number of people for a relatively long period of time, making the study population relatively reliable; however, high BMI is not always related to adiposity. Body-builders, as a noted example, can have BMI in the obese range, but body fat measurement by either bioelectrical measurement or water immersion will indicate extremely low body fat percentages. On the other hand, these people generally have waist measurements and waist-to-hip ratios that are significantly smaller than people of the same body mass index, but with greater percentages of body fat. They also tend to have higher metabolic rates, making them more likely to place in the highest caloric intake per kilogram of body mass; some people who fit in this category were probably excluded from the study for that reason.

Another consideration, noted briefly by the investigators, is that there are a number of chronic diseases that were not excluded from the study. Some of these (such as Type 2 diabetes) affect, or are affected by, body weight and weight distribution. These could also affect the relative risks of death of the population observed.

Practical Applications:
The use of waist measurements and waist-to-hip ratios, in conjunction with BMI, can assist doctors and public health professionals in assessing individual and community health risks.

The authors' Figure 2, representing relative risk based on waist circumference and waist-to-hip ratio, shows a standardized risk of 1.0 for men at approximately 93 cm (about 36"), 0.94 waist-to-hip ratio (36" waist corresponding to about 39" hip), and for women at approximately 77 cm (about 30") waist size, 0.79 waist-to-hip ratio (30" waist corresponding to about 38" the hip). These measurements are lower than the standards currently used for determination of risk (102 cm/40" for men, 88cm/35" for women). Interestingly, these measurements correspond to the largest "standard" reference clothing sizes (men's pants to jacket size 42, women to U.S. Misses size 16/European size 42) as expressed in the home-sewing pattern industry, or "true-to-size" in the ready-to-wear industry.

This suggests that an average person should be able to determine his or her risk based on off-the-rack clothing size.

For further reading:
Home-sewing pattern measurements:

• Simplicity Patterns (United States):
  
  • US measurements
  • European measurements


• Burda Patterns (Germany):
  
  • US and European measurements

This synopsis was based on the complete article.

Food in the home and obesity

The validation of a home food inventory
Original source: International Journal of Behavioral Nutrition and Physical Activity 2008, 5:55
Authors: Jayne A Fulkerson (corresponding author), Melissa C Nelson, Leslie Lytle, Stacey Moe, Carrie Heitzler, and Keryn E Pasch
Publication date: 4 November 2008
DOI (digital document ID): 10.1186/1479-5868-5-55
Requires subscription? No
Publicly-available abstract: Yes

Synopsis:
A validated home food inventory can be a useful tool to assess what foods are readily available to a person or family, and by consequence, what foods are frequently eaten. This differs from a self-reported food inventory, as it allows a trained professional to independently inventory both the items in general and their relative quantities, and for the results to be compared with the self-reported inventory. The authors of this study are looking to see if there is a relationship between the types of food available in the home and the likelihood that members of that household are obese.

The authors find that in many previous studies on home food inventories, the list of foods leaves out a lot of categories and specific foods, and there have been few attempts to see if the person doing the reporting is accurately including all the food and food types in the household. They note four previous studies that have had validation, each of which has focused only on a small group of food items, with a particular health goal in mind.

Their goal is to create a comprehensive food inventory, which can be completed by an average adult, and which takes into account both healthy and unhealthy foods, as well as "reduced fat" versions of foods often associated with obesity.

Using both trained volunteers and volunteers from the community, they developed a home food inventory form. They validated the inventories for 51 families and analyzed the results for accuracy of self-reporting. They then distributed the inventory survey to 349 families with adolescent children, measured the adults and the children and recorded their demographic characteristics, and then asked them what foods they ate over the previous 24 hours. They were given information on how to estimate portion sizes, and told to expect three phone calls over the next month asking them to recall their food intake for the past 24 hours. In addition, dietary health questionnaires were distributed; these are shown to be relatively accurate in assessing the usual food and nutrients that the participant consumes.

The final home food inventory included 190 items which were community-specific. Participants were instructed on key package words that might indicate a reduced-fat version of a dessert or snack food, since these were categorized separately from the original (full-fat/full-sugar) versions.

Foods were ranked according to their association with obesity, or the likelihood that eating a lot of those foods would cause a person to become obese over time.

The home food inventories filled out by the initial 51 families in the pilot project were compared with the identical inventory filled out by the trained volunteers to determine whether or not the consumers understood brands as being part of larger categories, and how well they understood the questionnaire overall. The results showed a high degree of correlation, which the researchers take to mean that an unvalidated inventory should be reasonably accurate.

The researchers note several limitations of the study, including some issues with correctly categorizing a number of prepared foods and frozen desserts, the limitation of specific food items and types, and the limitation of food items and categories to "yes-no" answers rather than measuring quantity and variety. The initial set of families had a greater number of educated Caucasian adults than was representative of the community at large, and may not accurately represent the responses of less-educated and/or non-white populations.

Reliability:
The authors are affiliated with the University of Minnesota, in the professional realms of nursing and community health. The study was published in a peer-reviewed journal.
The researchers note that there are several food categories that were not as well-correlated as others -- primarily, prepared and frozen desserts, and breads. Some of this is due to the large variety of products available in these categories.
There is some limitation in correlating food inventories with dietary intake, as about half of the the foods listed in the 24-hour recalls were not on the home food inventories. (This would suggest a fair amount of eating outside the home.)

Practical Applications:
A useful, friendly home food inventory is a basic tool that anybody can use in menu planning, grocery shopping, and disaster planning. It is also a tool that can provide your nutritionist, social worker, or Certified Diabetes Educator a better idea of what foods you have, and what you regularly eat. This information is helpful in showing you ways to improve your diet to lose weight, maintain healthy blood glucose levels, and reduce your risks of cardiovascular disease.

A validated home food inventory is also a useful basis from which to gather information for community health and public health studies regarding nutrition and health.

This synopsis was based on the entire article.

Adapting "quality of life" measures to a local audience

Evaluating the reliability, validity and minimally important difference of the Taiwanese version of the diabetes quality of life (DQOL) measurement
Original source: Health and Quality of Life Outcomes 2008, 6:87
Authors: I-Chan Huang (corresponding author), Jung-Hua Liu, Albert W Wu, Ming-Yen Wu, Walter Leite, Chyng-Chuang Hwang
Publication date: 28 October 2008
DOI (digital document ID): 10.1186/1477-7525-6-87
Requires subscription? No
Publicly-available abstract: Yes

Synopsis:
The authors' goal was to develop ways to measure Health-Related Quality of Life (HRQoL) in the Chinese language, primarily for use in Taiwan. They translated a standard survey (called DQOL) to measure diabetes-related HRQoL, put it to a test, and tried to find out where it worked and where it needed tweaking.

There are a number of standards for measuring HRQoL in general, and diabetes-related quality-of-life in particular. One issue is that even among the diabetes-specific surveys, there are differences: some look at quality of life based on energy, mobility, ability to function, and social aspects along with the issues of testing and maintaining control; others look at the effect of medical treatment and satisfaction with that treatment, as well as concern about eventual development of complications. In order to be meaningful, the surveys have to pose questions that have meaning within the culture in which they are presented, and which put the patient at enough ease to answer truthfully. (For example, questions about sexual activity will not be answered in a society where that discussion is taboo.) One aspect of this is rewriting the survey in the local dialect.

A major issue with health-related quality of life surveys is determining what the patient means, and way he perceives the care he is receiving, and what changes to his responses mean over the course of time. There are several methods of statistically analyzing these responses and comparing them to an "impartial" standard -- physical measurements of the patient's health.

The clinical measurements used to validate the survey included HbA1c (a measurement of average long-term blood glucose levels), blood glucose levels two hours after meals ("two-hour postprandial"), fasting blood glucose levels, and the presence of some of the most common complications of diabetes (neuropathy - loss of sensation; foot ulcers, infections, and amputations; cardiovascular issues such as heart disease; and cerebrovascular (brain circulation) issues such as stroke).

In addition, two other diabetes-related quality of life surveys were translated and made more appropriate to Taiwanese culture, and used to verify the results of the Taiwan-adapted DQOL and to make those results meaningful to researchers and public health professionals.

The authors discuss their methods of translating and localizing the DQOL, their analysis of the data, and the limitations of their research.

Reliability:
The authors are all professionals in either public health or medicine, and interested in how patients perceive their quality of life.
The sample used was relatively small, but of a reasonable size to test the effectiveness of the surveys being developed; however, all of the respondents were from the same health center in Taiwan, and only four had Type 1 diabetes. The authors also noted that this analysis was done over a cross-section of patients at one time, and one of the real strengths of measuring quality of life is how it is perceived to change over time.

Practical Applications:
This is a first step in being able to understand, from the Taiwanese patient's viewpoint, the effects of diabetes on his quality of life. Applying this information to medical practice will help improve the level of care, and the effect of care, provided to these patients.

This synopsis was based on the entire article.