Chisholm, Anna MRes; Hart, Jo PhD; Mann, Karen V. PhD; Harkness, Elaine PhD; Peters, Sarah PhD
Obesity directly contributes to common long-term illnesses including type 2 diabetes, some cancers, and cardiovascular disease.1,2 In 2008, one in nine adults worldwide were considered obese (defined as having a body mass index [BMI] ≥ 30 kg/m2).3 Obesity, which has been labeled a global epidemic,4–6 also leads to escalating financial consequences and increasing workloads for health care systems. Each year, one-sixth of the U.S. health care budget (approximately $168 billion or £110 billion)7 and $6.6 billion or £4.2 billion in the United Kingdom8 is spent on illnesses caused by obesity. Because initiatives to combat obesity are urgently needed in most countries,3 it is crucial to identify the most effective methods of obesity management, particularly those to which health care systems can make contributions.
This article’s focus is on the prevention and control of obesity through behavioral management approaches (e.g., changes in diet and activity)1,9 rather than alternative methods (e.g., pharmacological or surgical interventions). Making sustained changes to obesity-related behavior is a notoriously difficult task, and success is influenced by various epidemiological and psychological factors.10–12 Additionally, obesity is a socially sensitive subject with the potential to damage the patient–practitioner relationship.13 Traditional knowledge-enhancing approaches within health care (in which patients are provided with only generic risk information) have been shown to be less successful in eliciting lifestyle changes than are methods that address specific determinants of patients’ behavior defined within theoretical frameworks.1,14–16 Because the latter approach allows tested, targetable factors to be identified,17 health care governing bodies are increasingly recognizing that health-related behavior change interventions should be based on relevant theories.18,19
Progress in the area of theory-based behavior change derives from a recently developed taxonomy that encompasses motivation, action, and organization theories20 and associated effective behavior change techniques (e.g., motivational interviewing [MI], goal setting, and stress management).17 Thus, there is opportunity to develop educational interventions targeting these techniques; some efforts to do so have been successful, particularly with regard to training medical professionals to use MI without increasing overall consultation times.21,22
Opportunities to discuss lifestyle changes are often missed, however.23,24 It is well documented that clinicians feel unconfident, unskilled, and uncertain about their specific roles and responsibilities in addressing behavior change with obese patients.1,13,25–27 Research nonetheless suggests that patients want their doctors to take a more active role in encouraging them to change their diet and physical activity patterns,28 and clinical guidelines recommend that physicians tackle obesity with patients through implementing tailored plans and exploring barriers to change.29 Moreover, even though lifestyle change discussions are most effective when they are theory based,30,31 this approach is not yet used regularly in medical practice.32 Because medical education ultimately affects the quality of patient care and patient outcomes,33 educators should consider how medical students are being prepared for the complex and increasingly common task of discussing behavior management strategies with obese patients.
Within the United Kingdom, Tomorrow’s Doctors (2009)34 stipulates that, as scholars and scientists, medical school graduates should be able to “discuss psychological aspects of behavioral change and treatment compliance” (section 9e) and, as practitioners, to “communicate appropriately in difficult circumstances, such as when breaking bad news, and when discussing sensitive issues such as alcohol consumption, smoking or obesity” (section 15d). Researchers also advocate training medical students in “patient activation” methods (such as MI), which stimulate patients to take responsibility for their own health, in order to create stronger pathways between medical education and meaningful patient outcomes.33
Yet, areas of medicine encompassing obesity management (e.g., health promotion/preventive care) are poorly established within medical school curricula.35–39 Although many medical students view obesity management as an important aspect of the physician’s role, their knowledge and satisfaction with associated education is significantly lower than that reported by student dieticians.40 In one study, 60% of responding primary care physicians reported that they had been provided with insufficient knowledge to be able to address unhealthy eating patterns with patients.41
Although these and other studies suggest a paucity of obesity-related education in medical schools,5 empirical investigations have not identified the extent to which current understanding of how to support patients’ health-related behavior changes42,43 is represented within medical education, or what the most effective educational methods may be. We aimed to fill this gap by conducting a systematic literature review to investigate the following research question: How effective are educational interventions in preparing medical students to facilitate lifestyle changes with obese patients?
In July 2010, one author (A.C.) conducted preliminary searches for relevant studies using key terms (e.g., obesity, medical students) within medical and social science databases. She refined these searches to identify studies that included literature reviews and searched the online Cochrane Library database, but she found no previously published systematic reviews on obesity management education for medical students.
Between July and November 2010, A.C. systematically searched the following electronic databases for relevant studies: MEDLINE, Excerpta Medica (EMBASE), PsycINFO, and Scopus. In line with Centre for Reviews and Dissemination (CRD) recommendations,44 we developed our search strategy using a PICOS (Population, Interventions, Comparators, Outcomes, Study design) format, which informed our search terms and inclusion criteria. The selected search terms were based on the target population, interventions (educational sessions for medical students addressing obesity-related behaviors/conditions), and outcomes (see List 1). We did not include terms related to comparators or study design because preliminary searches indicated that relevant studies would likely be omitted if interventions lacked control or comparison groups; thus, particular study designs would be excluded from the review. We combined search term sets (using AND) and exploded all search terms using the truncation ($) and key word advanced search (article title, abstract, full text, and caption text [.mp.]) functions. We also set time frame limitations (publication date of January 1990 through November 2010) in line with emerging calls in the 1990s45,46 by health care governing bodies for the inclusion of health promotion and related subjects in the undergraduate medical curriculum. (The full search strategy is available from the authors.)
Initially, A.C. selected studies for inclusion by screening the titles and abstracts of all studies. Those with features not relevant to the review, such as studies that did not include medical students (e.g., samples of nursing students) or that were not related to obesity (e.g., malnutrition studies), were excluded at this point. Studies with titles/abstracts that presented ambiguous information or did not indicate key details (e.g., identity of the study population) were included so that relevant studies would not be missed. This process was carried out by a single author because study selection was intended to be overinclusive at this stage. A.C. also checked reference lists manually for additional relevant articles, contacted study authors when full study details could not be accessed, and removed duplicates. She then assessed full texts of the remaining articles for eligibility against full PICOS inclusion/exclusion criteria (Table 1).
To ensure reliability, a second coder (M.N.), who was not involved with the study, independently repeated the selection process using PICOS eligibility criteria for all of the full-text articles identified by A.C. Results from both coders were compared; the calculated interrater reliability statistics (Cohen k = 0.89) demonstrated “almost perfect” agreement47 for selection of studies into the final dataset.
Data extraction tools
We developed two tools to enable data extraction: one to identify descriptive data, the other to identify methodological features to inform coder judgments about the quality of the included studies. All authors contributed to the development of these tools through meeting regularly to discuss design issues and subsequent revisions. The preliminary searches indicated that the tools should account for quantitative and qualitative designs as well as studies that did not have robust experimental designs (e.g., those that were not randomized controlled trials). Hence, we drew relevant components (such as study aim, design, and sample) from existing extraction and quality assessment tools used within health care and public health research,44,48–51 rather than using any one previously developed tool. We did not, therefore, incorporate redundant components that were not relevant to studies within this review (e.g., blinding to participant group, which is not applicable to studies with only one participant group). Consultations with experienced systematic review researchers (E.H. and another independent researcher, P.B.) further helped us determine which components should be selected so that the most salient information, relevant to the aims of this review, would be collected.
The final versions of our data extraction tools therefore incorporated the following components: Tool 1, for descriptive data, included study aim, sample, design, intervention content, intervention structure, outcome data, and conclusions. Tool 2, for methodological features, directed coders to evaluate the quality of studies according to three criteria:
* Intervention transparency: A study was deemed transparent if either the educational content of the intervention or the intervention evaluation procedures were described within the article. Where possible, coders noted whether descriptions were sufficient to allow for replication and compared notes to reach consensus.
* Control for risk of bias: A study was deemed to have attempted to control for bias if it explicitly described measures to overcome potential confounders (i.e., secular trends/selection bias).
* Conclusions supported by sufficient evidence: Conclusions were deemed reliable if they were clearly described and did not reach beyond data and if the study design was robust. Coders used descriptive data regarding study designs to support these appraisals.
To test the tools’ suitability, three authors (A.C., J.H., S.P.) piloted both tools on two studies and then discussed and dealt with arising queries as a group. For example, piloting indicated that the presence/absence of simple procedures to control for risk of bias (obtaining baseline measures, including control groups, randomization) should be explicitly coded for because some articles did not mention them.
One author (A.C.) completed data extraction using both tools for all articles included in the final dataset. To reduce risk of bias, her results were compared with independent ratings completed by three other research team members (J.H., K.M., S.P.), each of whom coded some articles. Thus, all included studies were second coded. Arising discrepancies were resolved through discussion, with a third team member acting as an arbitrator when necessary.
A total of 1,680 articles were initially identified by searches and screened for relevance to the research question. Of these, 36 (2%) were reviewed against all eligibility criteria, resulting in 12 articles (1%) being selected for inclusion in the final dataset (Figure 1).
We present a narrative synthesis of the data because of the diversity of methodologies and outcome measures used within the 12 studies; this is in line with CRD recommendations for systematic reviews that are based on heterogeneous groups of studies.44 Our findings lie within four categories: study characteristics, intervention structure and content, intervention outcomes and reported results, and risk of bias within studies.
The 12 educational interventions that met eligibility criteria37,38,52–61 were published in a range of academic journals between 1993 and 2010 (6 [50%] within the last five years of this range), and most (n = 9; 75%) were conducted within the United States. Demographic details of study samples were often unreported; data on participant gender were included in 6 (50%)52,53,55,57,58,61 (range = 29%–74% female), and ethnicity was included in 2 (17%).54,57 One study (8%) had a qualitative design,61 whereas the other 11 (92%) employed the following quantitative designs: controlled trial (nonrandomized),53,54,56,57 before-and-after study,38,58,59 or evaluation-only study (no preintervention data obtained).37,52,55,60 Of these quantitative-design studies, 7 (64%) did not, therefore, include control groups. Table 2 provides additional details about study characteristics.
Intervention structure and content
Educational interventions on obesity management varied widely in terms of their timing within medical school curricula (Table 2). Interventions occurred within ambulatory care blocks,37,38,53 preventive care modules,54,57 a first-year introductory course,59 a second-year clinical placement,55 a third-year family medicine clerkship,60 a fifth-year addiction medicine course,61 or across four separate areas throughout the medical school curriculum.56 Insufficient reporting prevented us from identifying this information in two studies (17%).52,58
Appendix 1 shows the variety of health professionals involved in intervention delivery (this was unclear in four studies [33%]), as well as intervention durations (estimated student contact time: median = 20.75 hours, range = 1–99.3 hours). In terms of delivery, the inclusion of didactic sessions was common to most studies (n = 9; 75%)37,38,52–57,59; other educational methods included classroom-based learning, opportunities to practice learned skills, assessment, self-monitoring, and experiential learning.
Intervention Content...Image Tools
Whereas five studies (42%) were exclusively related to educational interventions on obesity and the behaviors that govern it,55,56,58,60,61 others included education on obesity-related illnesses such as cardiovascular disease and diabetes.52,53,55,57 Seven studies (58%) addressed additional health topics, such as smoking and alcohol.37,38,52–54,57,59 Despite targeting obesity as a distinct topic within interventions, eight studies (67%) did not describe the strategies provided to students to help them tackle this issue with patients.52–58,61 Instead, these reports focused on how interventions were delivered to students. Interestingly, all four studies (33%) that we deemed transparent regarding educational content37,38,59,60 described MI techniques; three (25%) explicitly stated that educational content had been informed by theory37,38,59 (i.e., transtheoretical model of behavior change [TTM],62 health belief model [HBM],63 and social cognitive theory [SCT]64). All three of these studies included the TTM, whereas one study37 also used the HBM and SCT to inform the intervention content.
Intervention outcomes and reported results
The 12 included studies targeted a wide range of outcomes, which are detailed in Appendix 2. Below, we summarize the reported results of interventions by study design. (The results of the qualitative study61 are summarized in Appendix 2.)
Details of Reported ...Image Tools
Controlled trials (n = 4; 33%). Although the four controlled trials53,54,56,57 evaluated various changes in students’ knowledge, confidence, attitudes, and clinical skills as well as in their personal physiological and psychosocial measures, few statistically significant between-group differences were identified. Evidence was particularly limited for student knowledge, confidence, and attitudes (reported, respectively, in one,53 two,53,54 and no studies). Findings regarding performance measures were similarly limited: one study53 reported that, after the intervention, students who received the intervention’s education sessions were no more likely to engage in nutrition discussions with patients than were students in the control group (P = .067). Another study57 reported limited improvements to students’ diets as well as reduced physical activity levels after the intervention.
Before-and-after studies (n = 3; 25%). All three before-and-after studies38,58,59 reported significant postintervention knowledge improvements (of MI and/or the role of nutrition in cancer). Some also reported statistically significant improvements in confidence (counseling patients),38,59 attitudes (about physicians’ roles),58 and MI skills.38 Students reported intentions to change their approach with future patients38 and to spend more time counseling patients58 after interventions. Students’ perceptions of the educational interventions were generally positive, and two studies reported high levels of satisfaction.38,59
Evaluation-only studies (n = 4; 33%). In the four evaluation-only studies,37,52,55,60 self-report measures of knowledge suggested increases in students’ understanding about obesity and nutritional risk55 and the biopsychosocial approach to patient care.37 Students also reported improvements in their counseling skills,37 general research skills,60 and attitudes (being more open to the topic of behavior change).37 Finally, students reported improvements in diet and activity patterns (theirs52 or their patients’60). Despite these generally positive outcomes, students’ evaluations of the educational interventions were mixed.55,60
Risk of bias within studies
Coders’ appraisals of study quality were in line with our three main criteria: intervention transparency, control for risk of bias, and conclusions supported by sufficient evidence.
Intervention transparency. Four studies (33%)37,38,55,59 included descriptions of educational content that we deemed sufficient to allow for replication. In contrast, nine studies (75%) provided methodological descriptions that we judged would allow for replication.37,52–55,57,59–61
Control for risk of bias. Although seven studies (58%)38,53,54,56–59 made attempts to control for risk of bias, these efforts mainly involved baseline measures or control groups. (Only four studies [33%] included both baseline measures and control groups).53,54,56,57 None of the studies used randomization procedures or power calculations. Validation procedures for outcome measures were briefly described in five studies (42%),38,53,55–57 and participant response rates were below 50% for some outcome measures in four studies (33%).38,53,54,57
Conclusions supported by sufficient evidence. We judged six studies (50%)37,38,53,54,59,61 to have sufficiently qualified conclusions with evidence in the article. In evaluating the other six studies,52,55–58,60 the coders noted that conclusions were not clearly described, that assumptions reached beyond the results, and that the lack of control within studies prevented the reporting of convincing evidence to support conclusions. In addition, only one study38 calculated the effect sizes of significant differences reported between participant groups.
This systematic review of the literature identified just 12 studies published between 1990 and 2010 about educational interventions for medical students that addressed facilitating lifestyle changes with obese patients. Our analysis of these studies revealed numerous approaches to designing and implementing education in this area, with wide variations in terms of how curricula were structured and delivered (Appendix 1). None of the studies compared approaches, however, and thus the advantages or disadvantages of delivery within particular contexts or by certain individuals remain unclear. Our findings correspond to previous research indicating that education about specific health behaviors (e.g., smoking, physical activity) occurs infrequently within medical education65,66 and that general behavior change skills courses often lack consistency and structure in terms of their educational content.38,39 This, in addition to reports that frontline medical professionals feel unskilled in obesity management,13,40,41 suggests that calls from governing bodies encouraging integration of this topic into medical practice and education have not been met.29,34,39 Solving the problems related to integrating relevant content into medical curricula could be key to resolving this; such problems may include a lack of available subject experts, not assessing students’ knowledge or skills in this area, or existing perceptions of the topic (e.g., considering it a low-profile subject).5,67
One of our key findings from this review was the lack of theory reported to inform the content of studies’ educational interventions. We found that reported educational interventions were mostly atheoretical (75% of the 12 studies), contradicting evidence that theory-based behavior change interventions are the most effective.16,31 The few studies in which interventions were theoretically informed used the TTM, which is acknowledged to have a limited evidence base and has received criticism regarding its key assumptions.68,69 Further, it has been well documented that the content of behavior change interventions is often not described adequately, making studies difficult to replicate, evaluate, or compare.70 This issue clearly emerged within our review: Descriptions of the specific behavior management techniques taught were rare and, when present, were mostly limited to MI (Appendix 1) even though numerous techniques exist in the behavior change literature.71 Past interventions to prevent weight gain in community-based contexts can also be criticized for failing to incorporate existing knowledge of behavioral determinants included within theoretical frameworks.72 Thus, our findings build on evidence suggesting that practical application of techniques is not developing at the same pace as theoretical advances in behavior change research.32 This issue must be addressed if education provided to medical students in this increasingly important area of medicine is to be considered truly evidence based.
Our findings also highlight concerns regarding the methodological rigor of studies in this review. Although a range of positive intervention outcomes were reported, measures to control for the risk of bias were rare. Just four studies (33%) included both baseline measures and control groups,53,54,56,57 and none calculated power or used randomization procedures. Therefore, the evidence provided was insufficient to determine whether the variability reported in outcome measures derived from intervention effects or from other unknown factors. The lack of robust evaluations within the literature we reviewed means that we cannot confidently draw conclusions regarding the efficacy of interventions or the contribution of this literature in informing education within this area.
Our findings should, however, be considered within the strengths and weaknesses of this review. To ensure a rigorous, replicable review of the literature relevant to the study question, we designed a search strategy in line with standardized PICOS criteria.44 To reduce risk of bias, we assessed interrater reliability using an independent second coder at study selection. Search criteria were limited to English-language articles and published research, which may have led us to overlook other relevant research. Further, because we did not use existing data extraction tools, it is possible that we developed our tools in line with subjective judgments about what information would be most appropriate to extract. The methodological diversity of included studies made it more appropriate to select only the most relevant components from certain existing tools,44,48–50 thereby excluding many redundant items. Finally, because of our tailoring of data extraction tools to the needs of this review, potentially subjective coder judgments rather than objective criteria informed quality appraisals. Research team members conducted validity checks throughout data synthesis, and coders reached consensus at all stages, which suggests that possible coder bias was prevented.
Despite some limitations, our findings indicate that more work is needed to develop and identify evidence-based educational interventions about obesity-related lifestyle changes. Future research should ensure that intervention content is transparent and that methodological rigor is applied to study designs. Future interventions should specifically apply relevant theories and known behavior change techniques. Additionally, research outside the United States is needed because few medical schools from other countries were represented in this review. We conclude that current educational interventions for medical students that address obesity management are varied, and empirical tests of their efficacy are inadequate. It therefore remains unknown to what extent medical students are being prepared to facilitate important lifestyle changes with future obese patients or what the best methods to achieve this involve.
Acknowledgments: The authors would like to thank Dr. Peter Bower for his advice, particularly regarding systematic review design, and Melissa Noke for assisting with reliability procedures.
Other disclosures: None.
Ethical approval: Not applicable.
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