Globally, obesity rates have reached epidemic proportions that have led to a trend of obesity in older adults (Bales & Porter Starr, 2018). Approximately 40% of adults 60 and older are obese (Bhupathiraju & Hu, 2016). Aging and obesity compound health consequences, as increased metabolic and functional risks of aging add to the consequences of obesity, including hypertension, diabetes mellitus, coronary heart disease (Danaei et al., 2009; Neuhouser et al., 2012), and musculoskeletal problems (Drenowatz, Shook, Hand, Hébert, & Blair, 2014). Thus, obese older adults may experience considerable health burden that can result in reduced function and quality of life (Bales & Porter Starr, 2018). To add to the matter, by the year 2060, the number of adults 65 and older is expected to more than double in the United States (Colby & Ortman, 2015). With 40% of these individuals projected to be obese, the health care system will experience strain under this burden.
Obesity in Older Adults
Researchers suggest several explanations for obesity in older adults. During the normal aging process, fat mass increases as fat-free mass decreases (primarily skeletal muscle), causing weight gain because muscle burns more calories than fat. In addition, fat is redistributed from sub-cutaneous areas to intramuscular, intra-abdominal, and intrahepatic areas (Kalish, 2016; Villareal, Apovian, Kushner, & Klein, 2005). To add to the problem, older adults often have reduced physical activity and experience hormonal changes, including declines in testosterone and growth hormone production and reduced responsiveness to thyroid hormone and leptin. All of these circumstances can lead to an increase in total body fat (Kalish, 2016).
Another risk for obesity is poor dietary behavior, which is exceedingly prevalent in older adults. More than 80% of American adults 71 and older have been shown to overly consume foods high in fats (Krebs-Smith, Guenther, Subar, Kirkpatrick, & Dodd, 2010) and calories. High intake of solid fats (e.g., butter, shortening, beef fat) and added sugars (e.g., syrups added to processed foods and drinks) contribute to obesity, inflammation, and oxidative stress (Vergis et al., 2018). The aging process itself has also been associated with chronic, low-grade inflammation (Lopez-Moreno et al., 2017). Inflammatory cells that accumulate in the vascular walls of blood vessels increase atherosclerosis risk. This risk is further complicated by obesity, which is associated with metabolic syndrome (i.e., hypertension, hyperglycemia, dyslipidemia, reduced high-density lipoproteins cholesterol, and abdominal fat) and closely tied to chronic, low-grade inflammation. Thus, older adults with obesity have high proinflammatory cytokines and inflammation. Reducing dietary fat, as part of a healthy diet to prevent obesity and reduce the impact of chronic disease, is a critical goal for older adults.
Intervention Considerations for Older Adults
Clinicians should give special consideration to the aging experience to help older adults make health behavior changes, such as reducing dietary fat. Some older adults expect to have a hard time making a change or believe that it is too late to make a difference given their shortened future (Kuerbis & Sacco, 2013). Other older adults expect aging to be related to limitations and reduced health and therefore may be unmotivated to change (Bardach, Schoenberg, & Howell, 2015). Some older adults believe they are doing well compared to others their age, so do not see a need to make health behavior changes (Bardach et al., 2015). Finally, some older adults learn more slowly than their younger counterparts (Falk, Ekman, Anderson, Fu, & Granger, 2013), making redundancy and tailored approaches important intervention strategies. Motivational interviewing (MI) is a robust intervention that allows clinicians to incorporate these considerations to bring about health behavior change in this older population.
MI is defined as “a person-centered counseling style for addressing the common problem of ambivalence about change” (Miller & Rollnick, 2013, p. 29) and can be used to have productive behavior change conversations with patients (Rollnick, Miller, & Butler, 2008). During MI, the clinician facilitates a collaborative conversation to evoke older adults' beliefs about aging and behavior change (Miller & Rollnick, 2013). MI focuses on drawing out a person's intrinsic motivation for behavior change. The communication skills central to MI include: expressing empathy, supporting self-efficacy, avoiding argumentation, rolling with resistance, and developing discrepancy (Miller & Rollnick, 2013). MI can be conducted by telephone, eliminating travel for those with functional limitations or transportation issues (Cummings, Cooper, & Cassie, 2009). Nurses have used MI to improve health behaviors in many settings, including home visits (Kidd, Lawrence, Booth, Rowat, & Russell, 2015; Masterson Creber et al., 2016), phone-based counseling (Koelewijn-van Loon et al., 2008; Masterson Creber et al., 2016), and primary care clinics (Koelewijn-van Loon et al., 2008), making MI an important approach to improve dietary behaviors (Droppa & Lee, 2014; Masterson Creber et al., 2016).
Although there is considerable support for MI's effectiveness, there is little focus on the evidence for its use in older adults (Cummings et al., 2009), particularly the use of MI to address dietary changes in this population. As a result, the purpose of the current meta-analysis was to synthesize primary studies testing the effects of MI dietary interventions on fat consumption in older adults. Specifically, the overall effects or magnitude of MI dietary interventions on fat consumption were examined, and participants, methods, and intervention characteristics as moderators of MI's effect were explored. Lara et al. (2014) included MI in their synthesis of dietary interventions in older adults, but they did not separate the effects of MI from other interventions. When they analyzed MI studies separately, they combined older samples with younger samples. The current meta-analysis is unique as it focuses on the effect of MI on older adults, making these findings applicable to nursing practice.
Search Strategy and Selection Criteria
For the current meta-analysis, a thorough systematic search was performed. Ovid MEDLINE® 1946–2014 including In-Process & Other Non-Indexed Citations and Daily Update, CINAHL Plus with Full Text (EBSCOhost), PsycINFO (OvidSP), Scopus (Elsevier), ERIC (EBSCOhost), Web of Science (Thomson Reuters), Sociological Abstracts (ProQuest), Social Work Abstracts (EBSCOhost), EBM Reviews (OvidSP), and Global Health (EBSCOhost) were searched. Full text of the Journal of the American Dietetic Association, Health Psychology, Annals of Behavioral Medicine, American Journal of Health Promotion, Obesity Research & Clinical Practice, International Journal of Behavioral Medicine, and Obesity were searched electronically. The unpublished and fugitive literature in the ProQuest Dissertations & Theses database and on several professional organization websites, including the Society of Behavioral Medicine, Academy of Nutrition and Dietetics, Motivational Interviewing Network of Trainers, and MI Bibliography ( http://www.motivationalinterview.net/library/biblio.html), was also searched. Ancestry searches were conducted by reviewing reference lists of eligible studies and prior systematic reviews and meta-analyses. Lead researchers of qualifying studies were e-mailed requesting unpublished studies, including pilot studies and students' works. Two reviewers (D.T.S., J.K.S.) examined most titles and abstracts and, when in doubt, retrieved articles for review.
Search Strategies to Locate Primary Studies. The following keywords and controlled vocabulary search terms were used: (“motivation* interview*” OR “motivation* enhanc*”) AND (diet* OR nutrition* OR food OR eat* OR fruit* OR vegetable* OR fat). No date restrictions were used but primary study inclusion was limited to English language. The total number of potentially relevant records retrieved was 3,327 records. With duplicates removed, the title/abstracts of 1,688 records were screened and 1,173 were eliminated as clearly not meeting inclusion criteria. Finally, full text articles for 515 studies were reviewed for eligibility. Six primary studies met all criteria and were included in the meta-analysis (Figure 1) to synthesize the current state of the science. The effects of MI on fruit and vegetable consumption outcomes were previously published (Schneider, Wong-Anuchit, Stallings, & Krieger, 2017).
Flow diagram of studies screened.
Note. MI = motivational interviewing; FV = fruit/vegetable.
Inclusion/Exclusion Criteria. All studies with older adult samples (mean age >60 years) testing MI dietary interventions with fat consumption as an outcome were included. All studies had to have a control or usual care group for comparison. Education was considered usual care because it is the standard of care for many health professionals. Studies with samples of individuals with schizophrenia and eating disorders were excluded because these individuals have altered thought processes. Samples of individuals with physical conditions were included, as these might be common in older adults (e.g., cancer).
Data Extraction and Coding
Attributes of the studies that might influence study findings were coded: source (e.g., publication status, year, funding, country), methods (e.g., group assignment, masked, intention-to-treat, attrition), and participants (e.g., age, gender, ethnicity). Factors related to MI were also coded: interventionists' hours of training; number of days over which the intervention was delivered; minutes per session; and MI attributes such as expressed empathy and ambivalence. For fat consumption outcomes, baseline and follow-up means were identified and standard deviations were computed when researchers presented standard errors. When primary studies had more than two groups, the MI intervention group was compared with a matching comparison group without MI to provide separate effects of MI. These matched comparisons resulted in two comparisons (k) for one study (n) with four groups (Lin et al., 2013).
Data Management and Analysis
Using Comprehensive Meta-Analysis 2.0 (CMA), the magnitude or effect size (ES) of MI interventions across the two groups was computed, MI versus control. Hedges' g was used because it corrects for the small sample of studies. Means were standardized to compare different measurement scales across studies (Borenstein, Hedges, Higgins, & Rothstein, 2009). Because the studies differed for several reasons (e.g., mix of participants, implementation of interventions), a random-effects model was used, which takes into account two sources of variance: variability between study effect sizes and variability within each study (Borenstein et al., 2009). CMA computes effect sizes by weighting each study based on the variability within and between studies. To examine the discrepancy of effect sizes across studies (heterogeneity), forest plots and the Q statistic (total dispersion), I2 (percent variability reflecting real difference in MI effect), and r2 (variability of MI effect) were inspected. Next, the effect of MI between pre- and posttest fat consumptions were computed for the MI groups separately from control groups using single-group analyses to provide an idea of change across the intervention period. Because pre-post effect calculations require correlations, which are generally not reported in primary studies, this analysis was run assuming no relationship (r = 0) and a high relationship (r = 0.8). Moderator effects of the study, methods, participants, and intervention characteristics were explored using statistics similar to analysis of variance for categorical variables and multiple regression (meta-regression) for continuous moderators (Borenstein et al., 2009).
Publication bias was examined using a funnel plot, the Begg and Mazumdar rank correlation test, and Egger's test of the intercept. The funnel plot provides a visual idea of publication bias; symmetry suggests no bias. The Begg and Mazumdar test computes a correlation between the standardized treatment effect and the variances; a relationship reflecting asymmetry suggests publication bias (Borenstein, 2005). Egger's test is computed by a weighted regression (Borenstein, 2005). In the absence of bias, the regression line intercept approaches zero. Additional detail about the methods and analysis can be found elsewhere (Schneider et al., 2017).
Six primary studies (Anderson et al., 2014; Bowen et al., 2002; Jansink et al., 2013; Lin et al., 2013; Parsons et al., 2008; Parsons et al., 2013) provided seven comparisons. Sample sizes ranged from 40 to 302, for a total of 1,351 participants. Researchers of all six studies were funded and the studies were published. Four studies were conducted in the United States and two in Europe. Mean sample ages ranged from 60.6 to 66 years. Women accounted for a range of 0% to 93.7% across samples; African American individuals accounted for 6.3% to 17.1% of three samples. Table 1 provides a description of studies.
Description of Included Studies Using Motivational Interviewing (MI) Dietary Interventions (N = 6)
The number of MI dietary sessions ranged from 3 to 20 (n = 6), lasting 27 to 38 minutes (n = 2), across 21.8 to 61 weeks (n = 6). Fat consumption outcome data were collected immediately postintervention to 1-year postintervention. Attrition for the MI group was somewhat higher than the control group, ranging from 0% to 54.2% (n = 6); attrition for the control group ranged from 0% to 44.2% (n = 6). Most interventions were delivered by telephone (n = 5). Of the nine MI attributes coded, researchers reported three to eight total attributes (mean = 5, SD = 1.7; n = 6). Table 2 provides the descriptive characteristics across comparisons.
Characteristics of Comparisons Across the Primary Studies for Motivational Interviewing Dietary Interventions
Effect of MI Dietary Interventions
The overall effect size of MI comparing MI groups with control groups was 0.354 (p < 0.01; 95% confidence interval [CI] [0.215, 0.493]), which suggests a moderate effect. In addition, the studies had nonsignificant dispersion (heterogeneity; Q = 9.113, p = 0.167; I2 = 34.159), reflecting a consistent effect across studies. Figure 2 displays the forest plot of individual reports. Each square reflects the direction and magnitude of the effect; the size of the square reflects the weight assigned to the study and precision (Borenstein et al., 2009). The analysis was conducted so that a reduction in fat consumption reflects an improvement in fat consumption and, therefore, a higher effect size. Although the analysis across studies showed that MI tended to improve fat consumption compared to a control group, five comparisons (n = 4) showed a significant effect size (Anderson et al., 2014; Bowen et al., 2002; Lin et al., 2013; Parsons et al., 2008). MI group pre-post comparisons showed significant reductions in fat consumption for samples that were correlated (effect size = 0.334, p < 0.01) and not correlated (effect size = 0.333, p < 0.01). Control group pre-post comparisons showed no significant change in fat consumption (effect size = 0.007 [p = 0.934]) for correlated samples and 0.041 (p = 0.641) for uncorrelated samples (Table 3).
Effect sizes of motivational interviewing (MI) intervention on fat consumption.
Random Effects Model of Fat Consumption
The Begg and Mazumdar rank test was not significant (0.048, p = 0.881), nor was Egger's regression intercept (intercept = 0.94; 95% CI [–3.37, 5.25]; t(5) = 0.56; p = 0.598]) reflecting no publication bias. The funnel plot was visually asymmetrical, suggesting that small samples with non-significant findings were missing; this can reflect bias. Thus, publication bias showed mixed results.
Exploratory Moderator Effects of MI
Because of the small number of primary studies, moderator effects were examined only when data were available for all seven comparisons. As attrition in the MI group increased, the effect of MI on fat consumption was significantly reduced (Table 4). Studies with indicators that reflected higher quality interventions (e.g., data collectors masked to group) had higher MI effects, likely indicating better control. For example, when data collectors were blinded to group participation to eliminate bias, the effect of MI on fat consumption was significantly higher (Table 5). No other moderator variables tested had significant effects.
Continuous Moderator Results for Fat Consumption Comparing Motivational Interviewing (MI) to Control Groups
Dichotomous Moderator Results for Fat Consumption Comparing Motivational Interviewing to Control Groups
Overall, MI was a good intervention to reduce fat consumption in older adults, showing moderate effect (0.354). Because most (five of seven) comparisons showed significant effects of MI on fat consumption and all studies showed improved fat consumption, the evidence indicates that MI is effective in helping older adults reduce their dietary fat consumption and has the potential to impact obesity and health in this population.
Recommendations for weight loss have been controversial in older adult populations due to concerns of losing bone mineral density and muscle mass as lean body mass is lost (Bales & Porter Starr, 2018). However, there is strong evidence that the health complications and declines in physical functioning associated with obesity can lead to increased disability, hospitalizations, and institutionalization of older adults with long life spans (Bales & Porter Starr, 2018). Therefore, an overall approach to weight loss in older adults should comprise nutritional management that includes reducing foods high in fats and foods/drinks high in sugar to reduce weight gain (Drenowatz et al., 2014) and regular physical activity to maintain muscle strength and mass (Villareal et al., 2005).
Because some older adults learn more slowly and may have multiple chronic health conditions, they may find it challenging to change more than one health behavior at a time. Changing multiple health behaviors at once is often stressful and can hinder long-term beneficial health behaviors (Swoboda, Miller, & Wills, 2017). As one health behavior is changed (e.g., reduced fat intake) and incorporated into a individual's lifestyle, this can lead to beliefs that one can make other dietary (e.g., increasing fruit/vegetable intake) or health behavior (e.g., increased physical activity) changes (Swoboda et al., 2017). Although the current study examined the effect of MI on one health behavior, fat consumption, researchers have shown that improvement in diet is beneficial to older adults and can prevent many diseases associated with aging and obesity (Clegg & Williams, 2018; Drenowatz et al., 2014; Villareal et al., 2005).
Moderator analysis indicated that as the MI group's attrition rate increased, the effect of MI on dietary fat consumption decreased. These findings are surprising. Typically, attrition increases because those who lose interest drop out. The remaining participants are engaged in the study activities and it might be expected that the effect of the MI intervention on fat consumption would increase. Future researchers might explore reasons older adults drop out of MI interventions.
Primary studies in which researchers blinded data collectors showed higher effects of MI on fat consumption. This finding may reflect that researchers who imposed a stronger study design showed greater effects than those who imposed less control in their study design.
The main limitation of the current meta-analysis is the few studies available that focus on MI dietary interventions to reduce fat consumption in older adults. Yet, when clinicians need to choose interventions for practice, they likely rely on syntheses of the current evidence. The number of studies included in any synthesis depends on the available evidence. Valentine, Pigott, and Rothstein (2010) argue that two studies are enough for a meta-analysis. They state that alternative synthesis techniques (e.g., narrative reviews) are less transparent and/or of questionable validity. Borenstein et al. (2009) point out that in the Cochrane Database of Systematic Reviews (medical reviews of all areas of health care), the median number of trials included in a review is six. Thus, this meta-analysis reflects the current state of the science.
Implications for Practice
MI addresses the common problem of ambivalence about change (Miller & Rollnick, 2013). Older adults may think they are too old to make changes or that it is too late (Kuerbis & Sacco, 2013). These thoughts may leave older adults unmotivated to change behavior, providing an ideal opportunity for nurses to use MI to help patients understand thoughts that hinder health behaviors. In response to older adults who believe they are too old to change dietary behaviors, the nurse may state:
I understand you think you are too old to change your diet. Other patients your age have felt the same way. It is still important for us to come up with a dietary plan for you. What do you believe you will be able to do to improve your diet?
In this example, the nurse would be using many of the principles of MI in just a few minutes with the patient. The nurse has demonstrated reflective listening by restating what the patient said, expressed empathy by sharing similar thoughts of other older adults, and avoided argumentation about the patient's beliefs. In addition, the nurse is facilitating therapeutic communication with the goal of evoking feelings that lead patients to consider changing health behavior or begin making plans to change. As the conversation continues, the nurse might help the patient develop discrepancies about his/her diet by showing the inconsistency between goals or values and current behavior. The nurse may state:
You told me earlier that you wanted to lose weight so you would feel like leaving your home more often. Changing some of your dietary habits, like cutting down on high-fat foods, will help with weight loss and feeling better overall. Tell me more about why you have a hard time changing your diet.
The nurse in this scenario is forming a collaborative partnership that moves beyond just educating and giving advice to a more person-centered approach to health behavior change. Because MI is person-centered, older adults can set the pace, which allows sufficient time for learning across all levels of health literacy (Brodie, Inoue, & Shaw, 2008). MI can be used to start with older adults' perceptions and enhance their intrinsic motivation to change (Miller & Rollnick, 2013). Researchers showed that nurse-led MI interventions have resulted in improved health behaviors for patients with chronic diseases (Masterson Creber et al., 2016).
MI can be delivered by nurses and other clinicians, as well as trained non-clinicians. Increasing the number of clinicians who can deliver MI to community-dwelling older adults may be an ideal way to improve dietary health behaviors, including fat intake. At any age, individuals can make beneficial health behavior changes. MI is an effective intervention to resolve ambivalence, improve self-efficacy, confront barriers, and improve internal motivation toward health behavior changes. MI can be performed in-person or by telephone, making it a flexible option for older adults.
The current study examined the effects of MI dietary interventions on fat consumption in older adults. MI shows promise as an intervention to reduce fat intake in this population. Efforts to reduce high-fat diets among older adults can significantly reduce health risks associated with obesity, including hypertension, stroke, diabetes, and musculoskeletal problems, as well as reduce the economic burden on the health care system. Nurses trained in MI can use this therapeutic form of communication to provide more person-centered health education and counseling needed to improve dietary behaviors for the older adult population.
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Description of Included Studies Using Motivational Interviewing (MI) Dietary Interventions (N = 6)
|Authors (Year)||Theory||Biomarkers to Support Self-Report||Discipline Delivering MI||Exercise Encouraged||Weeks Across the Intervention (n)||MI Dietary Sessions (n)||N for Analysis||Mean BMI (kg/m2)|
|Anderson et al. (2014)||MI only||No||General health counselors||Yes||52.29||12||326||30.70|
|Bowen et al. (2002)||Transtheoretical model||No||Nutritionists||NR||21.79||3||164||29.55|
|Jansink et al. (2013)||MI only||No||Nurses||Yes||61.00||4||336||30.70|
|Lin et al. (2013)||Researcher developed||Yes||Nutritionists and general health counselors||Yes||21.79||20||246||32.60|
|Parsons et al. (2008)||Social cognitive theory||Yes||NR||No||26.14||13||69||NR|
|Parsons et al. (2013)||Social cognitive theory||Yes||NR||No||26.14||12||40||29.00|
Characteristics of Comparisons Across the Primary Studies for Motivational Interviewing Dietary Interventions
|Characteristic||Studies (n)||Comparisons (n)||Minimum||First Quartile||Median||Third Quartile||Maximum|
|Mean age (years)||6||7||60.6||60.6||64||65.3||66|
|Sample size analyzed||6||7||40||69||246||268||302|
|% of sample assigned to MI (baseline)||6||7||44||49.5||49.8||62.5||65.2|
|% African American individuals||3||4||6.3||6.7||11.3||16.5||17.1|
|MI sessions (n)||6||7||3||4||12||20||20|
|Average minutes per session||2||2||27||27||32.5||NA||38|
|Duration of MI (weeks)||6||7||21.8||21.8||26.1||52.3||61|
|Days after MI outcome was measured||6||7||0||0||0||366||366|
|% of attritiona|
| MI group||6||7||0||0||11.4||20||54.2|
| Control group||6||7||0||0||9.5||13.5||44.2|
|Interventionist training hours (n)||4||4||6||8.5||48||80||80|
|MI attributes reported (n)||5||6||3||3||6||7.5||9|
Random Effects Model of Fat Consumption
|Comparison||Comparisons (n)||ESa||p Value (ES)||95% CI||SE||Total Dispersion (Q)||p Value (Q)||Percent Variability (I2)|
|MI vs. control||7||0.354||<0.01||[0.215, 0.493]||0.071||9.113||0.167||34.159|
|MI group pre- vs. posttest (r = 0.8)||7||0.334||<0.01||[0.180, 0.489]||0.079||54.367||<0.01||88.964|
|MI group pre- vs. posttest (r = 0)||7||0.333||<0.01||[0.160, 0.505]||0.088||13.210||0.040||54.581|
|Control group pre- vs. posttest (r = 0.8)||7||0.007||0.934||[–0.161, 0.175]||0.086||68.745||<0.01||91.272|
|Control group pre- vs. posttest (r = 0)||7||0.041||0.641||[–0.131, 0.214]||0.088||13.986||0.030||57.101|
Continuous Moderator Results for Fat Consumption Comparing Motivational Interviewing (MI) to Control Groupsa
|Moderator||Slope||SE||MI Effect Variability (Tau2)||Total Dispersion (Qmodel)||p Value (Slope)|
| Publication year||−0.004||0.019||0.018||0.049||0.824|
| % women||−0.001||0.003||0.018||0.028||0.866|
| Number of MI sessions||0.013||0.009||0.006||2.002||0.157|
| Span of MI intervention (in weeks)||−0.006||0.004||0.006||2.107||0.147|
| Day from MI for outcome measure||0.000||0.000||0.016||0.253||0.615|
| Attrition of MI group||−0.008||0.003||0.000||6.568||0.010|
| Number of MI attributes reported||−0.048||0.043||0.009||1.2650||0.261|
Dichotomous Moderator Results for Fat Consumption Comparing Motivational Interviewing to Control Groups
|Moderator||Comparisons (n)||ESa||SE||Variance||95% CI||p Value||Total Dispersion (Qbet)||p Value (Qbet)|
| No||2||0.152||0.112||0.013|| [–0.069, 0.372]||0.177|
| Yes||5||0.411||0.064||0.004||[0.287, 0.536]||<0.01|
|Biomarkers to support self-report||1.095||0.295|
| No||3||0.283||0.098||0.010||[0.091, 0.475]||0.004|
| Yes||4||0.432||0.103||0.011||[0.230, 0.634]||<0.01|