Use of Technology in the Management of Obesity: A Literature Review

By Shannon H. Houser, PhD; Reena Joseph, MHA; Neeraj Puro, PhD; and Darrell E. Burke, PhD

Abstract

Technology is intended to assist with diagnosing, treating, and monitoring patients remotely. Little is known of its impact on health outcomes or how it is used for obesity management. This study reviewed the literature to identify the different types of technologies used for obesity management and their outcomes. A literature search strategy using PubMed, CINAHL, Scopus, Embase, and ABI/Inform was developed and then was vetted by two pairs of researchers. Twenty-three studies from 2010 to 2017 were identified as relevant. Mobile health, eHealth, and telehealth/telemedicine are among the most popular technologies used. Study outcome measurements include association between technology use and weight loss, changes in body mass index, dietary habits, physical activities, self-efficacy, and engagement. All studies reported positive findings between technology use and weight loss; 60 percent of the studies found statistically significant relationships. Knowledge gaps persist regarding opportunities for technology use in obesity management. Future research needs to include patient-level outcomes, cost-effectiveness, and user engagement to fully evaluate the feasibility of continued and expanded use of technology in obesity management.

Keywords: health information technology, mobile health, eHealth, telehealth/telemedicine, health outcomes, obesity management

Introduction

According to the Centers for Disease Control and Prevention, more than 93 million individuals in the United States are obese, representing nearly 40 percent of the population.1 Obesity has been associated with multiple chronic conditions including hypertension, heart disease, type 2 diabetes, stroke, and some types of cancer, with an estimated annual cost of $147 billion in 2008 US dollars.2 National guidelines for the management of obesity recommend reducing excess body weight through a combination of decreased caloric intake and increased physical activity.3

Various therapy alternatives and interventions including a combination of diet modification, physical exercise, psychological counseling, and behavior therapy have been applied in the management of obesity. On the one hand, the success of these interventions depends on active patient and provider participation and engagement, which can be resource and time intensive and difficult because of geographic distances. On the other hand, over the past two decades, different forms of information technology have been used to assist with the diagnosis, treatment, and remote monitoring of patients with chronic diseases. Moreover, the use of digital technologies in the management of obesity has become popular because of advantages such as ease of use, access to service providers from a distance, and self-monitoring of behavioral changes.4 The technology medium used varies, including components of telehealth/telemedicine and eHealth such as mobile phones, smartphones, and wearable technology.

Despite the growing awareness and use of information technologies, little is known about the effect of these technologies on health outcomes, cost-effectiveness, and patient engagement. For instance, although the use of technology transfers some of the responsibility of care to patients, the lack of physical interaction between patients and providers can also serve to deter active patient engagement. Additionally, prior research has focused on the impact of a particular type of technology (e.g., eHealth or mobile health) on health outcomes or specific chronic diseases.5–8 No recent study has provided a comprehensive review of the different digital technologies used in the management of obesity in adults.

The primary purpose of this paper is to review the existing academic literature to identify the different types of technologies used in the management of obesity. The secondary objective is to examine the different health outcomes measured and their effectiveness based on the type of information technology used. The findings of this review will serve as a guide for healthcare providers by identifying technology positively associated with the management and reversal of obesity in adults.

Methods

Search Strategy

The literature search process was conducted based on the PRISMA model, a preferred guide for reporting items for systematic reviews and meta-analyses.9 A comprehensive search was conducted using five electronic databases (PubMed, Scopus, CINAHL, Embase, and ABI/Inform) to identify eligible studies related to the use of technology in the treatment and management of obesity published between 2010 to 2017. The search was conducted using medical subheading (MeSH) terms related to telemedicine and wearable technology, such as “telehealth,” “telemedicine,” “mHealth,” “eHealth,” and “mobile health,” in combination with terms such as “obesity” and “morbid obesity.”

Inclusion and Exclusion Criteria

The selected studies were included if they:

  1. were empirical studies published in English-language peer-reviewed journals from January 2010 to December 2017;
  2. included adult participants (aged 18 years or older); and
  3. used some form of digital technology, such as mobile phone, smartphone, or telehealth, in the management of obesity.

Studies were excluded if they:

  1. were conducted outside the United States;
  2. examined specific patient populations, such as children, or adults with cognitive impairments; or
  3. had a sample size smaller than 10.

Review Process

The initial searches yielded 732 articles across the selected databases. After duplicates and non-peer-reviewed articles were eliminated, 272 studies were reassessed to check for eligibility. Application of the inclusion and exclusion criteria to these 272 studies yielded 29 studies, which were independently reviewed by a pair of researchers to check for study design and content quality. Thus, studies were further eliminated from the review if they did not employ a rigorous study design, did not measure the impact of technology use in managing obesity, did not include patient-level outcomes, or contained more than one chronic condition. The review conducted by the original pair of researchers was reevaluated by a second pair of researchers to verify adherence to protocol and study selection. This process resulted in the 23 studies selected for inclusion in the review. A summary of the selection process is shown in Figure 1.

Results

All 23 studies were conducted in the United States. The study sample sizes ranged from 20 to nearly 2,500 participants. More than two-thirds of these studies (16 of 23 studies) used a randomized controlled design with a control group and one or more intervention groups, while the remaining 7 studies involved either observational, retrospective cohort, or pretest/posttest designs.

Technology Used

The most common type of technology used for the management of obesity was mobile devices, such as mobile phone applications and text messages (11 studies; 48 percent). Five studies (22 percent) used telehealth and telemedicine components, such as telephone counseling, videoconferencing, and interactive telemonitoring; six studies (26 percent) relied on eHealth in the form of websites and internet-based programs; and one study (4.3 percent) monitored weight loss using a wearable device. Table 1 outlines the different types of technologies used in the reviewed studies and our definitions of each type of technology.

Outcomes

Weight loss was the primary outcome evaluated in 20 of the 23 studies (87 percent). This was measured either in the form of absolute change in body weight in pounds (lbs.) or kilograms (kgs.) or in the form of percent change in weight. Besides weight loss, changes in body mass index (BMI), physical activity, dietary intake habits, and time spent in sedentary positions were also assessed as primary outcomes. Because the management of obesity involves a lifestyle change, the studies also assessed secondary outcomes such as changes in self-efficacy, blood pressure, step count, and adherence to the prescribed technology during the study (see Table 2).

Nearly half of the reviewed studies (14 of 23 studies) established a statistically significant association between weight loss and the use of technology. All studies reported improvements in dietary habits and physical activity, although the association was not always statistically significant.

When the studies were categorized by the type of technology used, 73 percent (8 of 11) of the studies using mobile health devices showed statistically significant associations between weight loss and technology used, whereas only 40 percent (2 of 5) of the studies of telehealth/telemedicine and 50 percent (3 of 6) of the eHealth studies reported statistically significant differences. The one study categorized as involving wearable technology was statistically significant.

Only one of the reviewed studies10 examined the cost-effectiveness of using technology in the management of obesity. In addition to conducting a cost-effectiveness analysis, the authors of that study also found that per-patient costs and the overall weight loss were higher in the groups using technology compared with the control group (see Table 2).

Discussion

This review summarized the wide range of types of technology adopted for obesity management in studies published between 2010 and 2017. We identified positive and statistically significant findings in each of the four technology categories measured: telehealth/telemedicine, wearable technology, eHealth, and mobile health, using weight loss as the outcome measure. We expect that advances in technology will create additional ways to interact with users and capture outcomes at a more granular level.

Mobile health, eHealth, and telehealth/telemedicine were the most commonly used technologies for obesity management among the reviewed studies. The most popular technology, mobile health, with its portable, easily accessible, and ubiquitous nature, was used to monitor and promote weight loss by changing behavioral factors that contribute to a healthy lifestyle. It was supported by smartphones, cellphones, and other handheld mobile devices and applications to track and monitor diet and physical activity, and text messages/short message services (SMS) were used to provide reminders or encourage certain behaviors. Given the popularity of smartphones in the United States, the use of mobile health as an intervention device for obesity management showed continuous wide adoption, and the observed study outcomes suggest a positive future for this technology. This review study focuses on the United States; however, many other countries share widespread use of these technologies. Thus, what works in the United States may also work in other countries.

The definition of eHealth varied widely from study to study.11 Our definition of eHealth focused on internet and website applications rather than other health information technology applications, such as electronic health records or mobile devices. In the reviewed studies, eHealth involved the use of internet- and web-based applications, such as a web-based interactive voice response system, a virtual world, and internet-based virtual coaching, to provide an interactive platform for communication and education on diet, physical activity, and exercise. With advances in health information technology occurring rapidly, the definition of eHealth may change to accommodate the advances. A challenge in the labeling or grouping of technology for a literature review is to maintain the distinctions between types of technology across the study time frame or context.

Weight loss is a major outcome measurement in the reviewed studies. Other measurements include behavior changes, such as changes in step counts, physical activities, dietary contents, and eating patterns. Although a majority of the studies measured weight loss as one of the outcome measurements, only half of these studies reported statistically significant differences between weight loss and technology use. The sample size of the studies and the types of technology used may explain this result. Among the mobile health studies, the studies reporting significant weight loss differences had sample sizes ranging from 28 to 384 participants, compared with 20 to 58 participants in the studies that found no significant differences. Some of the studies had sample sizes smaller than 10 in our initial review; these studies were excluded. Future studies should address these factors as specific objectives to document potential impacts of sample size and study design issues.

The types of technology used showed an association with weight loss. Studies using mobile health showed more instances of statistically significant association with weight loss, as compared with studies using telehealth/telemedicine and eHealth. User experience and technology usability are critical factors, especially for interventional studies. The technology adoption model theory suggests that usefulness and ease of use are factors associated with technology adoption and should be identified in interventional studies.12, 13 When intervention outcome effects are examined, technology and participant acceptance should be considered. If the technology involved is difficult to use, inefficient, or ineffective, all of these could cause user dissatisfaction and noncompliance, thereby contributing to nonachievement of the goal that the study was intended to measure. Only two of the reviewed studies measured technology use and user satisfaction as outcome measures. Further study exploring associations between the user experience of technology and outcome changes could fill the gap in this area.

This literature review limited the search to studies conducted in the United States and published in the English language. Therefore, we have no basis of comparison to similar studies conducted in other countries. In addition, our study was limited to the empirical studies that were published in the five identified databases. Some study outcomes in other sources, such as conference proceedings, nonempirical study reports, or unpublished studies, may have been overlooked in our study. The last limitation is related to the definition of the technology used. Because definitions for categorizing technology have not been fully standardized, our definitions, such as that of eHealth, may not be the same as those used in other studies.

Future research needs to include patient-level outcomes as well as cost-effectiveness and user engagement of technology to fully evaluate the feasibility of continued and expanded use of technology in obesity management. Future studies should also identify consumers’ individual and environmental factors, including consumers’ motivation to use a given technology, in order to evaluate whether these factors are correlated with the technology used and with weight loss. In addition, sustainability and weight loss maintenance with the use of different types of technology should also be explored. Likewise, because some studies may use more than one type of technology as an intervention approach, the use of multiple technologies and their outcomes should be explored.

Conclusion

Adoption of technology such as mobile health, eHealth, and telehealth/telemedicine in obesity management has shown positive outcomes and wide usage. Weight loss and behavior changes are among the measurements evaluated in studies of technology use for obesity intervention. In an environment of continual development and refinement of technology, and the advent of new software to apply those developments, this field of research has a positive future. Appropriate and effective technology use, in the hands of skilled researchers, promises more positive outcomes in obesity management and intervention. The greatest need ahead is for these positive research findings to be applied and developed in the field of practice.

Acknowledgments

This work was funded by the National Science Foundation’s Center for Healthcare Organization and Transformation (CHOT) Project 10-06181.

 

Shannon H. Houser, PhD, MPH, RHIA, FAHIMA, is a professor in the Department of Health Services Administration, Health Informatics Program at the University of Alabama in Birmingham, AL.

Reena Joseph, MHA, is a PhD student and graduate assistant in the Department of Health Services Administration at the University of Alabama in Birmingham, AL.

Neeraj Puro, PhD, MHA, is an assistant professor in the Health Administration Program at Florida Atlantic University in Boca Raton, FL.

Darrell E. Burke, PhD, is an associate professor (retired) in the Department of Health Services Administration at the University of Alabama in Birmingham, AL.

 

Notes

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