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Diet and Exercise

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

Introduction

In the last several decades there has been a transformation in the diet of Americans, and much debate about obesity, type 2 diabetes mellitus, hyperlipidemia, and the transformation of medical practice to a greater emphasis on preventive medicine. This occurs at a time that the Western countries are experiencing a large portion of the obesity epidemic, which actually diverts attention from a larger share of malnutrition in parts of Africa, Asia, and to a greater extent in India. This does not mean that obesity or malnutrition is exclusively in any parts of the world. But there is a factor at play that involves social factors, poverty, education, cognition, anxiety, and eating behaviors, food preferences and food balance, and activities of daily living. The epidemic of obesity also involves the development of serious long term health problems, such as, type 2 diabetes mellitus, sarcopenia, fracture risk, pulmonary disease, sleep apnea in particular, and cardiovascular and stroke risk. Nevertheless, this generation of Western society is also experiencing a longer life span than its predecessors. In this article I shall explore the published work on diet and exercise.

 

‘‘Go4Life’’ exercise counseling, accelerometer feedback, and activity levels in older people

Warren G. Thompson, CL Kuhle, GA Koepp, SK McCrady-Spitzer, JA Levine
Archives of Gerontology and Geriatrics 58 (2014) 314–319
http://dx.doi.org/10.1016/j.archger.2014.01.004

Older people are more sedentary than other age groups. We sought to determine if providing an accelerometer with feedback about activity and counseling older subjects using Go4Life educational material would increase activity levels. Participants were recruited from independent living areas within assisted living facilities and the general public in the Rochester, MN area. 49 persons aged 65–95(79.5 + 7.0 years) who were ambulatory but sedentary and overweight participated in this randomized controlled crossover trial for one year. After a baseline period of 2 weeks, group 1 received an accelerometer and counseling using Go4Life educational material (www.Go4Life.nia.nih.gov) for 24 weeks and accelerometer alone for the next 24 weeks. Group 2 had no intervention for the first 24 weeks and then received an accelerometer and Go4Life based counseling for 24 weeks. There were no significant baseline differences between the two groups. The intervention was not associated with a significant change inactivity, body weight, % body fat, or blood parameters (p > 0.05). Older (80–93) subjects were less active than younger (65–79) subjects (p = 0.003). Over the course of the 48 week study, an increase in activity level was associated with a decline in % body fat (p = 0.008). Increasing activity levels benefits older patients. However, providing an accelerometer and a Go4Life based exercise counseling program did not result in a 15% improvement in activity levels in this elderly population. Alternate approaches to exercise counseling may be needed in elderly people of this age range.

It is generally recommended that older adults be moderately or vigorously active for 150 min each week. A systematic review demonstrated that only 20–60% of older people are achieving this goal. These studies determined adherence to physical activity recommendations by questionnaire. Using NHANES data, it has been demonstrated that older people meet activity recommendations 62% of the time using a self-report questionnaire compared to 9.6% of the time when measured by accelerometry. Thus, objective measures suggest that older people are falling even more short of the goal than previously thought. Most studies have measured moderate and vigorous activity. However, light activity or NEAT (non-exercise activity thermogenesis) also has an important effect on health. For example, increased energy expenditure was associated with lower mortality in community-dwelling older adults. More than half of the extra energy expenditure in the high energy expenditure group came from non-exercise (light) activity. In addition to reduced total mortality, increased light and moderate activity has been associated with better cognitive function, reduced fracture rate (Gregg et al., 1998), less cardiovascular disease, and weight loss in older people. A meta-analysis of middle-aged and older adults has demonstrated greater all-cause mortality with increased sitting time. Thus, any strategy which can increase activity (whether light or more vigorous) has the potential to save lives and improve quality of life for older adults. A variety of devices have been used to measure physical activity.

A tri-axial accelerometer measures movement in three dimensions. Studies comparing tri-axial accelerometers with uniaxial accelerometers and pedometers demonstrate that only certain tri-axial accelerometers provide a reliable assessment of energy expenditure. This is usually due to failure to detect light activity. Since light activity accounts for a substantial portion of older people’s energy expenditure, measuring activity with a questionnaire or measuring steps with a pedometer do not provide an accurate reflection of activity in older people.

A recent review concluded that there is only weak evidence that physical activity can be improved. Since increasing both light and moderate activity benefit older people, studies demonstrating that physical activity can be improved are urgently needed. Since accelerometry is the best way to accurately assess light activity, we performed a study to determine if an activity counseling program and using an accelerometer which gives feedback on physical activity, can result in an increase in light and moderate activity in older people. We also sought to determine whether counseling and accelerometer feedback would result in weight loss, change in % body fat, glucose, hemoglobin A1c, insulin, and fasting lipid profile.

The main results of the study are both the experimental and control group lost weight (about 1 kg) at 6months (p = 0.04 and 0.02, respectively). The experimental group was less active at 6 months but not significantly while the control group was significantly less active at 6 months (p = 0.006) than at baseline. The experimental group had a modest decline in cholesterol (p = 0.03) and an improvement in Get Up & go time (p = 0.03) while the control group had a slight improvement in HgbA1c (p = 0.01). However, the main finding of the study was that there were no differences between the two groups on any of these variables. Thus, providing this group of older participants with an accelerometer and Go4Life based counseling resulted in no increase in physical activity, weight loss or change in glucose, lipids, blood pressure, or body fat. There were no differences within either group or between groups from 6 to 12 months on any of the variables (data not shown). While age was correlated with baseline activity, it did not affect activity change indicating that younger participants did not respond to the program better than older participants. Performance on the Get Up and Go test and season of the year did not influence the change in activity. There were no differences in physical activity levels at 3 or 9 months.

There was a significant correlation (r = -0.38, p = 0.006) between change in activity and change in body fat over the course of the study. Those subjects (whether in the experimental or control group) who increased their activity over the course of the year were likely to have a decline in % body fat over the year while those whose activity declined were likely to have increased %body fat. There was no correlation between change in activity and any of the other parameters including weight and waist circumference (data not shown).

Older adults are the fastest growing segment of the population in the US, but few meet the minimum recommended 30 min of moderate activity on 5 days or more per week (Centers for Disease Control and Prevention, 2002). Our study found that within the geriatric population, activity declines as people age. We saw a 2.4% decline per year cross-sectionally. This finding agrees with a recent cohort study (Bachman et al., 2014). In that study, the annual decline accelerated with increasing age. Thus, there is a need to increase activity particularly in the oldest age groups. The United States Preventive Services Task Force concluded that the evidence that counseling improves physical activity is weak (Moyer and US Preventive Services Task Force, 2012). The American Heart Association reached similar conclusions (Artinian et al., 2010). Thus, new ways of counseling older patients to counter the natural decline in activity with age are urgently needed.

Applying health behavior theory to multiple behavior change: Considerations and approaches

Seth M. Noar, Melissa Chabot, Rick S. Zimmerman
Preventive Medicine 46 (2008) 275–280
http://dx.doi.org:/10.1016/j.ypmed.2007.08.001

Background.There has been a dearth of theorizing in the area of multiple behavior change. The purpose of the current article was to examine how health behavior theory might be applied to the growing research terrain of multiple behavior change. Methods. Three approaches to applying health behavior theory to multiple behavior change are advanced, including searching the literature for potential examples of such applications. Results. These three approaches to multiple behavior change include

(1) a behavior change principles approach;

(2) a global health/behavioral category approach, and

(3) a multiple behavioral approach.

Each approach is discussed and explicated and examples from this emerging literature are provided. Conclusions. Further study in this area has the potential to broaden our understanding of multiple behaviors and multiple behavior change. Implications for additional theory-testing and application of theory to interventions are discussed.

Many of the leading causes of death in the United States are behavior-related and thus preventable. While a number of health behaviors are a concern individually, increasingly the impact of multiple behavioral risks is being appreciated. As newer initiatives funded by the National Institutes of Health and Robert Wood Johnson Foundation begin to stimulate research in this important area, a critical question emerges: How can we understand multiple health behavior change from a theoretical standpoint? While multiple behavior change interventions are beginning to be developed and evaluated, to date there have been few efforts to garner a theory-based understanding of the process of multiple health behavior change. Given that so little theoretical work currently exists in this area, our main purpose is to advance the conversation on how health behavior theory can help us to achieve a greater understanding of multiple behavior change. The approaches discussed have implications for both theory-testing as well as intervention design.

A critical question that must be asked, is whether there is a common set of principles of health behavior change that transcend individual health behaviors. This is an area where much data already exists, as health behavior theories have been tested across numerous health behaviors.The integration of findings from studies across diverse behavioral areas, is not what it could be. Godin and Kok (1996) reviewed studies of the TPB applied to numerous health-related behaviors. Across seven categories of health behaviors, they found TPB components to offer similar prediction of intention but inconsistent prediction of behavior.They concluded that the nature of differing health behaviors may require additional constructs to be added to the TPB, such as actual (versus perceived) behavioral control. Prochaska et al. (1994) examined decisional balance across stages of change for 12 health-related behaviors. Similar patterns were found across nearly all of these health behaviors, with the “pros” of changing generally increasing across the stages, the “cons” decreasing, and a pro/con crossover occurring in the contemplation or preparation stages of change. Prochaska et al. (1994) concluded that clear commonalties exist across these differing health behaviors which were examined in differing samples. Finally, Rosen (2000) examined change processes from the TTM across six behavioral categories, examining whether the trajectory of change processes is similar or different across stages of change in those health areas. He found that for smoking cessation, cognitive change processes were used more in earlier stages of change than behavioral processes, while for physical activity and dietary change, both categories of change processes increased together.

A second approach is the following: Rather than applying theoretical concepts to specific behaviors, such concepts might be applied at the general or global level. A general orientation toward health may not lead directly to specific health behaviors, but it may increase the chances of particular health-related attitudes, which may in turn lead to specific health behaviors. In fact, although Ajzen and Timko (1986) found general health attitudes to be poor predictors of behavior, such attitudes were significantly related to specific health attitudes and perceived behavioral control over specific behaviors. It is likely that when we consider multiple behaviors that we may discover an entire network of health attitudes and beliefs that are interrelated. In fact, studies of single behaviors essentially take those behaviors out of the multi-attitude and multi-behavioral context in which they are embedded. For instance, although attitudes toward walking may be a better predictor of walking behavior than attitudes toward physical activity, walking behavior is part of a larger “physical activity” behavioral category. While predicting that particular behavior may be best served by the specific measure, the larger category is both relevant and of interest. Thus, it may be that there are higher order constructs to be understood here.

A third approach is a multiple behavioral approach, or one which focuses on the linkages among health behaviors. It shares some similarities to the approach just described. Here the focus is more strictly on how particular  interventions were superior to comparison groups for 21 of 41 (51%) studies (3 physical activity, 7 diet, 11 weight loss/physical activity and diet). Twenty-four studies had indeterminate results, and in four studies the comparison conditions outperformed eHealth interventions. Conclusions: Published studies of eHealth interventions for physical activity and dietary behavior change are in their infancy. Results indicated mixed findings related to the effectiveness of eHealth interventions. Interventions that feature interactive technologies need to be refined and more rigorously evaluated to fully determine their potential as tools to facilitate health behavior change.

 

A prospective evaluation of the Transtheoretical Model of Change applied to exercise in young people 

Patrick Callaghan, Elizabeth Khalil, Ioannis Morres
Intl J Nursing Studies 47 (2010) 3–12
http://dx.doi.org:/10.1016/j.ijnurstu.2009.06.013

Objectives:To investigate the utility of the Transtheoretical Model of Change in predicting exercise in young people. Design: A prospective study: assessments were done at baseline and follow-up 6 months later. Method: Using stratified random sampling 1055 Chinese high school pupils living in Hong Kong, 533 of who were followed up at 6 months, completed measures of stage of change (SCQ), self-efficacy (SEQ), perceptions of the pros and cons of exercising (DBQ) and processes of change (PCQ). Data were analyzed using one-way ANOVA, repeated measures ANOVA and independent sample t tests.
Results:The utility of the TTM to predict exercise in this population is not strong; increases in self-efficacy and decisional balance discriminated between those remaining active at baseline and follow-up, but not in changing from an inactive (e.g.,Precontemplation or Contemplation) to an active state (e.g.,Maintenance) as one would anticipate given the staging algorithm of the TTM.
Conclusion:The TTM is a modest predictor of future stage of change for exercise in young Chinese people. Where there is evidence that TTM variables may shape movement over time, self-efficacy, pros and behavioral processes of change appear to be the strongest predictors

 

A retrospective study on changes in residents’ physical activities, social interactions, and neighborhood cohesion after moving to a walkable community

Xuemei Zhu,Chia-Yuan Yu, Chanam Lee, Zhipeng Lu, George Mann
Preventive Medicine 69 (2014) S93–S97
http://dx.doi.org/10.1016/j.ypmed.2014.08.013

Objective. This study is to examine changes in residents’ physical activities, social interactions, andneighbor-hood cohesion after they moved to a walkable community in Austin, Texas.
Methods. Retrospective surveys (N=449) were administered in 2013–2014 to collect pre-and post-move data about the outcome variables and relevant personal, social, and physical environmental factors. Walkability of each resident’s pre-move community was measured using the Walk Score. T tests were used to examine the pre–post move differences in the outcomes in the whole sample and across subgroups with different physical activity levels, neighborhood conditions, and neighborhood preferences before the move. Results. After the move, total physical activity increased significantly in the whole sample and all subgroups except those who were previously sufficiently active; lived in communities with high walkability, social interactions, or neighborhood cohesion; or had moderate preference for walkable neighborhoods. Walking in the community increased in the whole sample and all subgroups except those who were previously sufficiently active, moved from high-walkability communities, or had little to no preference for walkable neighborhoods. Social interactions and neighborhood cohesion increased significantly after the move in the whole sample and all subgroups.
Conclusion.This study explored potential health benefits of a walkable community in promoting physically and socially active lifestyles, especially for populations at higher risk of obesity. The initial result is promising, suggesting the need for more work to further examine the relationships between health and community design using pre–post assessments.

 

Application of the transtheoretical model to identify psychological constructs influencing exercise behavior: A questionnaire survey

Young-Ho Kim
Intl J Nursing Studies 44 (2007) 936–944
http://dx.doi.org:/10.1016/j.ijnurstu.2006.03.008

Background: Current research on exercise behavior has largely been attempted to identify the relationship between psychological attributes and the initiation or adherence of exercise behavior based on psychological theories. A limited data are available on the psychological predictors of exercise behavior in public health. Objectives: The present study examined the theorized association of TTM of behavior change constructs by stage of change for exercise behavior. Methods: A total of 228 college students selected from 2 universities in Seoul were surveyed. Four Korean-version questionnaires were used to identify the stage of exercise behavior and psychological attributes of adolescents. Data were analyzed by frequency analysis, MANOVA, correlation analysis, and discriminant function analysis.
Results: Multivariate F-test indicated that behavioral and cognitive processes of change, exercise efficacy, and pros differentiated participants across the stages of exercise behavior. Furthermore, exercise behavior was significantly correlated with the TTM constructs, and that overall classification accuracy across the stages of change was 61.0%. Conclusions:The present study supports the internal and external validity of the Transtheoretical Model for explaining exercise behavior. As this study highlights, dissemination must increase awareness but also influences perceptions regarding theoretically based and practically important exercise strategies for public health professionals.

 

 

Does more education lead to better health habits? Evidence from the school reforms in Australia?

Jinhu Li, Nattavudh Powdthavee
Social Science & Medicine 127 (2015) 83-91
http://dx.doi.org/10.1016/j.socscimed.2014.07.021

The current study provides new empirical evidence on the causal effect of education on health-related behaviors by exploiting historical changes in the compulsory schooling laws in Australia. Since World War II, Australian states increased the minimum school leaving age from 14 to 15 in different years. Using differences in the laws regarding minimum school leaving age across different cohorts and across different states as a source of exogenous variation in education, we show that more education improves people’s diets and their tendency to engage in more regular exercise and drinking moderately, but not necessarily their tendency to avoid smoking and to engage in more preventive health checks. The improvements in health behaviors are also reflected in the estimated positive effect of education on some health outcomes. Our results are robust to alternative measures of education and different estimation methods.

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