Obesity Management in Primary Care: Activity Monitor Study

Introduction

Innovations in healthcare technologies have nearly doubled the human lifespan over the past 170 years, and some authorities believe that American babies born today may live to be 150 years old or perhaps even older (Glor, 2012). On the other hand, the prevalence of obesity in the United States has become a serious public health threat with numerous life-shortening comorbidities — such as diabetes and heart disease — that threaten to limit lifespans among the young and old alike (Overweight and obesity statistics, 2017). On average, more than two-thirds of American adults are considered overweight or obese, another one-third are categorized as obese, and one-in-twenty is regarded as extremely obese (Overweight and obesity statistics, 2017).

Against this backdrop, identifying cost-effective, evidence-based primary care interventions for obesity represents a timely and valuable enterprise. This paper proposes an experimental study to evaluate the efficacy of an activity monitor intervention used over a 6-month period — in combination with other treatment protocols — in reducing body mass index (BMI) levels among obese adult patients in a primary care setting. The guiding research question is: "For adult obese patients in primary care who have a BMI greater than 30 kg/m² (P), does using activity monitors (I), over a period of 6 months (T), produce greater weight loss results (O) compared to obese patients that do not use activity monitors (C)?"

Problem, Purpose, and Research Question

Problem

The prevalence of obesity in the United States, like many other industrialized countries, has experienced a steady increase over the past decade, along with the numerous adverse healthcare consequences associated with overweight and obesity (Sheesley, 2016). For adults aged 20 years and over, obesity is defined as a body mass index (BMI) in excess of 30 (Calculating BMI, 2017). Despite the need for additional research concerning the condition's precise causes, obesity is widely regarded as a chronic, complex disorder that frequently requires multidisciplinary responses (Sheesley, 2016). There is also a general consensus that primary care is the optimal setting for obesity interventions (Gortmaker & Polacsek, 2015), and these issues form the basis for the purpose of the proposed study.

Purpose

The purpose of the project is to reduce BMI scores among obese adults (BMI >30 kg/m²) by using activity monitors such as a pedometer for a 6-month period, and to compare these results to a control group that does not use an activity monitor, in order to answer the guiding research question set forth below.

Research Question

The proposed study will be guided by the following research question: "For adult obese patients in primary care who have a BMI greater than 30 kg/m² (P), does using activity monitors (I), over a period of 6 months (T), produce greater weight loss results (O) compared to obese patients that do not use activity monitors (C)?"

Background, Significance, and Literature Review

Background and Significance

Obesity can cause substantially more complex medical problems for all patients and may include costly comorbid conditions that drive up the costs of healthcare services while diminishing quality of life for patients and their families (Gortmaker & Polacsek, 2015). At present, the economic costs of treating obesity in the United States are approximately $150 billion a year, and experts caution that even these enormous costs may increase dramatically in the foreseeable future. Zomosky (2013) emphasizes that "obesity-related healthcare costs could increase by more than 10% in 34 states and by more than 20% in nine states over the next 20 years" (p. 14). Furthermore, a member of the American College of Physicians' Board of Regents warns that "obesity is a much harder condition to treat than almost anything else. There isn't a simple solution as there is with other common conditions we address in the primary care setting" (as cited in Zomosky, 2013, p. 15).

The sustained epidemic levels of obesity demand efficacious interventions, most especially in primary care settings (Gortmaker & Polacsek, 2015). Although implementing interventions for obesity in the primary care setting may not be the complete solution the complexity of the disorder requires, primary care does represent the best place to initiate the process (Gortmaker & Polacsek, 2015). Notwithstanding the severity of the need, there remains a dearth of timely and relevant clinical studies concerning the respective efficacy of different primary care setting interventions for obesity (Gortmaker & Polacsek, 2015), a gap that underscores the significance of the study proposed herein.

Literature Review

BMI is calculated by dividing an individual's weight in kilograms by the square of their height in meters (Calculating BMI, 2017). The BMI is currently the most commonly used tool for estimating overweight and obesity in adults, as shown in the classification table below.

BMI Classifications for Adults Age 20 and Older

18.5–24.9: Normal weight | 25–29.9: Overweight | 30+: Obesity | 40+: Extreme obesity

Source: Overweight and obesity statistics, 2017

Although BMI is not a direct measure of body fat, the U.S. Centers for Disease Control and Prevention reports that "in general, BMI is an inexpensive and easy-to-perform method of screening for weight category, for example underweight, normal or healthy weight, overweight, and obesity" (Calculating BMI, 2017, para. 3).

While a significant percentage of the general adult population in the U.S. is considered overweight or obese, there are notable racial differences, with more Black and Hispanic people being considered overweight or obese compared to white people (76.7% vs. 66.7% and 78.8%, respectively) (Overweight and obesity statistics, 2017). According to analysts at the U.S. National Institute of Diabetes and Digestive and Kidney Diseases, "among adults in the United States in all racial categories, 68.8% were considered overweight or obese, 35.7% were considered obese, and 6.3% were considered to have extreme obesity" (Overweight and obesity statistics, 2017, para. 4). Moreover, although there have been some indications that obesity rates have stabilized somewhat since 2012, young adults in the U.S. still suffer from obesity at greater rates than their older counterparts (Daley & Brody, 2015).

Obesity is among the most difficult and complex conditions to treat, but there is general agreement among clinicians — supported by a growing body of evidence — that increasing physical activity represents an important component of any obesity intervention (Ducheckova & Forejt, 2014). Pann and Yehl (2013) emphasize that "poor diet and physical inactivity have been estimated to account for nearly 400,000 deaths a year and are contributing factors to obesity" (p. 63). Consequently, a number of different assessment methods for measuring physical activity levels have been developed in recent years as part of obesity-targeted interventions (Ducheckova & Forejt, 2014).

Although these methods vary in expense and accuracy, Ducheckova and Forejt (2014) report that "of these methods, the pedometer provides a low-cost and user-friendly assessment of physical activity in terms of the number of steps" (p. 1070). Recent innovations in smartphone technologies have reduced the costs of formerly expensive pedometers to nearly nothing or completely free, and these same innovations have increased the accuracy of these devices, making their use in obesity-targeted interventions in primary care settings highly cost-effective and valuable for recording physical activity levels (Ducheckova & Forejt, 2014). Adams, Sallis, and Norman (2013) point out that "pedometers are increasingly being linked to technologies (e.g., websites, mobile phones) and provide unique opportunities for the delivery of adaptive interventions" (p. 2). Moreover, pedometers are being incorporated into numerous studies concerning the efficacy of various obesity-related interventions (Adams et al., 2013).

Conclusion

The epidemic level of obesity in the United States is well documented, and most authorities agree that the problem is going to become far worse in coming years. A majority of American adults of all races is currently considered overweight and/or obese. Furthermore, the human and economic toll exacted by obesity is staggering, and obesity exacerbates the treatment of any comorbid condition. The research also demonstrates that obesity is an especially difficult condition to treat, and there remains a lack of timely and relevant research concerning the efficacy of different primary care setting interventions for the condition, making the need for studies of this type especially pronounced.

Finally, the intervention proposed herein represents a potentially valuable addition to the body of knowledge because it is highly cost-effective, convenient for patients, and data collection and analysis can be performed remotely without burdening patients with additional clinical visits. By leveraging widely available smartphone technology, the proposed study offers a practical and scalable model for future primary care obesity interventions.

References

Adams, M. A., Sallis, J. F., & Norman, G. J. (2013, December 9). An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS ONE, 8(12), 1–20.

Alfaris, N., Wadden, T., & Sarwer, D. B. (2015, January 22). Effects of a 2-year behavioral weight loss intervention on sleep and mood in obese individuals treated in primary care practice. Obesity: A Research Journal, 23(3), 558–564.

Blanck, H. M., & Collins, J. L. (2015, February 1). The Childhood Obesity Research Demonstration Project: Linking public health initiatives and primary care interventions community-wide to prevent and reduce childhood obesity. Childhood Obesity, 11(1), 1.

Calculating BMI. (2017). U.S. Centers for Disease Control. Retrieved from https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html.

Dalen, J., & Brody, J. L. (2015, October 1). A conceptual framework for the expansion of behavioral interventions for youth obesity: A family-based mindful eating approach. Childhood Obesity, 11(5), 577.

Ducheckova, P., & Forejt, M. (2014, August 1). Aerobic steps as measured by pedometry and their relation to central obesity. Iranian Journal of Public Health, 43(8), 1070–1077.

Glor, J. (2012, February 2). Could babies born today live to 150? CBS News. Retrieved from http://www.cbsnews.com/news/could-babies-born-today-live-to-150/.

Gortmaker, S. L., & Polacsek, M. (2015, April). Evaluation of a primary care intervention on body mass index: The Maine Youth Overweight Collaborative. Childhood Obesity, 11(2), 187.

Overweight and obesity statistics. (2017). National Institute of Diabetes and Digestive and Kidney Diseases. Retrieved from https://www.niddk.nih.gov/health-information/health-statistics/overweight-obesity.

Pann, J., & Yehl, A. (2013, October 1). Implementation and evaluation of a multidimensional nutrition and physical activity initiative funded by a community health foundation. The Foundation Review, 5(4), 62–66.

Sheesley, A. P. (2016, April). Counselors within the chronic care model: Supporting weight management. Journal of Counseling and Development, 94(2), 234–244.

Stantz, R. (2013, September 1). Behavioral counseling key to reimbursements for obesity. Medical Economics, 90(1), 58.

Zomosky, L. (2013, February 25). The obesity epidemic. Medical Economics, 90(4), 14–17.

Appendix A: Literature Summary

Adams, M. A., Sallis, J. F., & Norman, G. J. (2013) — Theory-based continuously adaptive behavioral intervention. Research question: Does an adaptive intervention result in more physical activity goals met and greater volume of physical activity (steps/day) compared to a static intervention? Sample: 20 adults. Design: Randomized controlled trial. Results: The multicomponent adaptive intervention was efficacious at increasing steps/day relative to a static physical activity intervention. A more intensive, adaptive goal-setting and reinforcement approach may be more efficacious than static interventions that focus only on achieving a threshold of 10,000 steps with minimal feedback.

Alfaris et al. (2015) — Health behavior change framework. Research question: What is the effect of weight loss on sleep duration, sleep quality, and mood in 390 obese men and women who received one of three behavioral weight loss interventions? Sample: 390 obese men and women recruited from six primary care practices. Design: Randomized controlled trial. Results: Three different behavioral weight loss interventions, delivered in primary care by practice staff, helped approximately one-fifth to one-third of participants in each group lose 5% or more of initial weight. Losses of this magnitude are considered clinically meaningful because of their reduction of traditional cardiovascular disease risk factors.

Blanck & Collins (2015) — Childhood Obesity Research Demonstration (CORD) project. Research question: How does CORD build on current obesity-related work in three U.S. communities? Sample: Imperial County, CA; Fitchburg and New Bedford, MA; and Austin and Houston, TX. Design: Meta-analysis. Results: All CORD sites leverage existing systems and provide quality training and resources for key individuals. Sites adapted evidence-based interventions in multiple settings using the strengths of existing community contexts and strong community input.

Dalen & Brody (2015) — Family systems theory. Research question: What is the influence of psychological factors on obesity levels in young people? Design: Qualitative study; 116 juried studies reviewed. Results: Mindful eating programs represent a promising approach to obesity given that they address key psychosocial variables associated with treatment outcomes and seek to provide a balanced, integrated approach to the prevention of weight-related problems. More research is needed in this area.

Ducheckova & Forejt (2014) — Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) framework. Research question: What are the relations between the number of daily steps and aerobic steps, and anthropometric variables using WHR and WHtR? Sample: 507 adults (380 females, 127 males) native to the Czech Republic, aged 16–73. Design: Randomized controlled trial. Results: Normal and aerobic steps are significantly associated with central obesity and other body composition variables. The number of steps and aerobic steps is significantly higher in individuals classified as having "normal" WHR and WHtR than in those classified as having "risky" WHR and WHtR.

Gortmaker & Polacsek (2015) — The Maine Youth Overweight Collaborative (MYOC), a primary-care-based intervention implemented from 2004 to 2009 over three phases. Research question: Does the MYOC intervention yield greater improvements in BMI z-score compared to the control condition? Sample: 265 young people in Maine. Design: Retrospective data collection spanning 1999–2008. Results: The results showed a decrease in the growth of BMI z-scores following the start of the intervention for subjects with obesity in both intervention and control sites. A statistically significant decline in the rate of increase of BMI z-score was also found for subjects with overweight and healthy weight.

Pann & Yehl (2013) — Social Ecological Model of obesity. Research question: Evaluation of four priority funding areas of the Health Foundation of South Florida to allow the foundation to better measure the impact of its funding. Sample: Three South Florida counties (Broward, Miami-Dade, and Monroe). Design: Systematic review / qualitative analysis. Results: Foundations can have a more powerful impact on factors related to obesity when they work with multiple constituencies on organizational, systemic, and policy changes.

Appendix B: Data Collection Forms

Table 2, Part A — Data Collection: Experimental Group

Columns: Subject #, Age, Baseline BMI, Average Monthly Steps (Months 1–6), Post-Intervention BMI, % Change +/–

Table 2, Part B — Data Collection: Control Group

Columns: Subject #, Age, Baseline BMI, Post-Intervention BMI, % Change +/–

Appendix C: Evidence Synthesis Tables

Evidence Level Classification (Articles 1–5):

Level II (Randomized controlled trial): Articles 1, 2, 5 | Level V (Systematic review of qualitative or descriptive studies): Article 3 | Level VI (Qualitative or descriptive study): Article 4

Synthesis Summary:

Article 1 — Adams et al. (2013): Randomized controlled trial. Sample: 20 adults. Intervention: Use of activity monitor for weight-loss regimen (adaptive goals and feedback algorithm). Outcome: The multicomponent adaptive intervention was efficacious at increasing steps/day relative to a static physical activity intervention. A more intensive, adaptive goal-setting and reinforcement approach may be more efficacious than static interventions focused only on achieving a threshold of 10,000 steps with minimal feedback.

Article 2 — Alfaris et al. (2015): Randomized controlled trial. Sample: 390 obese men and women from six primary care practices. Intervention: Behavioral weight loss interventions — individualized intervention or standard of care. Outcome: Three different behavioral weight loss interventions helped approximately one-fifth to one-third of participants in each group lose 5% or more of initial weight.

Article 3 — Blanck & Collins (2015): Meta-analysis. Sample: Collection of nine articles in a supplement to Childhood Obesity. Intervention: Childhood Obesity Research Demonstration Studies. Outcome: All CORD sites leverage existing systems and provide quality training and resources for key individuals.

Article 4 — Dalen & Brody (2015): Qualitative study. Sample: 116 juried studies. Intervention: Mindful eating programs / individualized or group approach. Outcome: Mindful eating programs represent a promising approach to obesity given that they address key psychosocial variables associated with treatment outcomes and seek to provide a balanced, integrated approach to preventing weight-related problems.

Article 5 — Ducheckova & Forejt (2014): Randomized controlled trial. Sample: 507 adults (380 females, 127 males), native to the Czech Republic. Intervention: Use of activity monitor for weight-loss regimen. Outcome: Normal and aerobic steps are significantly associated with central obesity and other body composition variables. The number of steps and aerobic steps is significantly higher in individuals classified as having normal WHR and WHtR than in those classified as having risky WHR and WHtR.