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Correlates of nutritional behavior in individuals with multiple sclerosis
Disability and Health Journal 5 (2012) 284e291 www.disabilityandhealthjnl.com
Research Paper
Correlates of nutritional behavior in individuals with multiple sclerosis Matthew Plow, Ph.D.a,*, Marcia Finlayson, Ph.D., O.T. (C), O.T.R/Lb,c, and Chi Cho, M.S.b a
Department of Biomedical Engineering, Department of Physical Medicine and Rehabilitation, Cleveland Clinic Lerner Research Institute, 9500 Euclid Ave, ND-20, Cleveland, OH 44195, USA b Department of Occupational Therapy, University of Illinois at Chicago, 1919 W. Taylor Street (MC 811), Chicago, IL 60612, USA
Abstract Background: Adults with multiple sclerosis (MS) have many health problems that can interfere with healthy nutritional behaviors. Self-management activities (e.g., strategies used to manage emotions and functional limitations) may help facilitate engagement in healthy nutritional behaviors. However, few studies have documented such relationships. Objective: Identify predictors of nutritional behaviors from among a set of variables (i.e., personal characteristics, health status indicators, and self-management activities) linked to the International Classification of Function. Methods: Data were obtained from an online survey of 292 individuals with MS. Significant bivariate correlates were entered into a logistic regression analysis using backward and forward selection methods to identify predictors of healthy nutritional behaviors (i.e., endorsing 4 out of 5 questions about frequently making good food choices, limiting fat intake, consuming 5 servings of fruits and vegetables, reading food labels, and eating regularly). Results: Sex, nutritional self-efficacy, optimism/pessimism, body mass index, physical activity, emotional self-management, and communication with physician were used in the logistic analysis. Nutritional self-efficacy (b 5 0.69, p ! 0.001) was the strongest predictor of nutritional behaviors, followed by physician communication (b 5 0.08, p 5 0.029) and physical activity (b 5 0.01, p 5 0.035). Neither impairments nor activity limitations were significantly associated with nutritional behaviors. Conclusions: This study provides preliminary evidence that self-efficacy and self-management activities are correlates of nutritional behaviors in individuals with MS. Supporting the development of self-management skills and increasing self-efficacy might be methods for improving engagement in healthy nutritional behaviors among adults with MS. Ó 2012 Elsevier Inc. All rights reserved. Keywords: Obesity; Neurological Disorder; Nutrition; Disability; Symptoms
Introduction/background Multiple sclerosis (MS) is an immune-mediated disease that affects women almost twice as often as men.1 Although there is no cure, people with MS have a life expectancy that is similar to that of the general population.2 Thus, it is important to encourage engagement in healthy behaviors (e.g., physical activity and nutrition) to prevent co-morbid
Conflict of interest: The authors declare that they have no conflicts of interest. Funding: This work was supported through the National Multiple Sclerosis Society (NMSS) post-doctoral training grant. The information presented in this article does not necessarily reflect the position, ideas, or opinions of the NMSS. Abstract was presented at the American Neurological Association annual meeting. * Corresponding author. Tel.: þ1 216 445 3288; fax: þ1 216 444 9198. E-mail address: [email protected] (M. Plow). c Present address: School of Rehabilitation Therapy, Louise D. Acton Building, 31 George Street, Queen’s University, Kingston, Ontario K7L 3N6 Canada. 1936-6574/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.dhjo.2012.05.007
conditions from developing.3 There are a growing number of studies on MS and physical activity behaviors.4e6 Conversely, studies on MS and nutritional behaviors are sparse and have mainly focused on whether dietary intake is a risk factor for developing MS.7 Since malnutrition might be prevalent and under-diagnosed problem in the MS population,8e10 research is needed to identify possible strategies to promote healthy nutritional behaviors (i.e., dietary-related activities that help protect, promote, or maintain health). Most of what we know about the factors influencing nutritional behaviors e personal characteristics (e.g., age, sex, and education level),11 psychosocial factors (e.g., self-efficacy and outlook on life),12,13 and health problems (e.g., cognitive impairments, mobility impairments, and activity limitations)14e16 e is from studies on older adults and the general population. Health problems might negatively affect psychosocial factors that influence healthy nutritional behaviors,17e19 such as the perceived ability and confidence (i.e., self-efficacy) to engage in healthy nutritional behaviors. Documenting the relationship
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between personal characteristics, psychosocial factors, health problems, and healthy nutritional behaviors might provide guidance on developing nutritional interventions for individuals with MS. In contrast to health problems, self-management activities (i.e., tasks and skills used to manage one’s own health) could facilitate engagement in healthy nutritional behaviors.20 For example, MS symptoms (e.g., fatigue and spasticity) can serve as barriers to preparing a healthy meal. Such barriers might be overcome by supporting self-management activities.21,22 Physical activity, for instance, can reduce MS symptom severity23 and may improve psychosocial factors (e.g., confidence) that make it more likely to engage in healthy nutritional behaviors.24,25 Learning self-management skills (e.g., emotional regulation and physician communication) may help individuals reduce their risk for stress-related weight gain and asking their physician about how to engage in healthy nutritional behaviors while living with a disabling condition.26,27
Hypotheses The purpose of this study was to determine the importance of demographic/personal characteristics (i.e., age, sex, and education), health problems (i.e., impairments and activity limitations), psychosocial factors (i.e., selfefficacy and outlook on life), and self-management activities (i.e., physical activity, emotional self-management, and physician communication) in predicting nutritional behaviors in adults with MS. We undertook this study to determine whether predictors of nutritional behaviors in the general population also apply to those with MS and to advance the existing health behavior literature on disabling conditions by determining whether certain self-management activities are associated with nutritional behaviors. We used the International Classification of Function (ICF)28 and Lorig and Holman’s22 conceptualization of selfmanagement education to operationalize definitions and select variables for the analysis. The ICF provides a standard language to describe human functioning, and Lorig and Holman’s self-management framework provides guidance on defining self-management activities. We hypothesized that older age, less education, impairments (i.e., MS symptom severity and type, number of co-morbid conditions, and obesity), activity limitations (i.e., mobility problems, cognitive deficits, and inability to perform daily activities), lower self-efficacy scores, and a negative outlook on life are associated with unhealthy nutritional behaviors, whereas increased engagement in self-management activities (i.e., physical activity, emotional management, and physician communication) are associated with healthy nutritional behaviors.
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patient registry, which is a large database with over 35,000 participants. Researchers can pay a fee to recruit participants in the registry for other studies. NARCOMS staff sent out letters to a randomly selected sample of recent responders (n 5 1000) asking them to complete a 30e45 minute online survey using SurveyMonkey. Questions pertained to health status, MS-specific disease status, activity limitations, participation restrictions, and engagement in health-promoting behaviors. The response rate was 34% (335/1000). A proportion of the participants (n 5 165) completed the questionnaire twice (2.5 months apart) to obtain testeretest reliability data. An Institutional Review Board approved the study. Measures Dependent variable According to the ICF framework, healthy nutritional behaviors are in the participation domain (i.e., involvement in life situations). We measured such behaviors using questions from a previous survey on women with disabilities18 in which 386 women with disabling conditions completed the questionnaire. In that previous study, the scale demonstrated adequate unidimensionality and internal consistency; in a confirmatory factor analysis, the authors found a single factor that accounted for 50% of the variance, and reported a Cronbach’s alpha of 0.74. Thus, the questions were not modified for this study. Participants in this study were asked whether they often, sometimes, or rarely (five items): (1) make good food choices, (2) eat five servings of fruits and vegetables a day, (3) limit fat intake, (4) read labels, and (5) eat regularly. We operationalized healthy nutritional behaviors as whether participants answered 4 out of 5 questions with the response of ‘often’ (0 5 !4 questions with the response of ‘often’ and 1 5 >4 questions with the response of ‘often’). The variable was divided into two categories for the analysis because it was negatively skewed. Using this scoring approach allowed us to achieve a somewhat equal distribution between the two categories while maintaining strict criteria for what constitutes engagement in healthy nutritional behaviors. Testeretest reliability for this study was adequate (t (161) 5 0.98, p 5 0.33; r 5 0.77, p ! 0.001). Independent variables
Methods
Personal factors. According to the ICF, personal factors are an individual’s internal attributes that can influence participation in activities and life roles (e.g., demographic characteristics). We selected age, sex, and education (less than high school, high school graduate or some college, or college graduate) for the analysis because they predict obesity in the general population.11
Participants were recruited through the North American Research Committee on Multiple Sclerosis (NARCOMS)
Health problems (impairments). According to the ICF, impairments are problems in body functions and structures.
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We included four indicators of body function e MS symptom severity, number of co-morbid conditions, type of MS, and body mass index (BMI). Symptom severity was measured with the Symptoms of Multiple Sclerosis Scale (12 items),29 which measures the extent to which individuals experienced fatigue, pain, visual impairments, paralysis, bladder difficulties, lack of concentration, inability to communicate, bowel difficulties, numbness, tremors, loss of balance, and spasticity on a scale of 0 (never) through 4 (always). To generate a single score for the analysis, responses were summed (possible range 0e48). The questionnaire has been validated in the MS population.29 Testeretest reliability and internal consistency were adequate (t (146) 5 1.05, p 5 0.30; r 5 0.94, p ! 0.001; a 5 0.83). The number of co-morbid conditions was a count (possible range 0 to 15) in response to the following list: diabetes, arthritis, heart problems, high blood pressure, respiratory problems, previous stroke, thyroid condition, cancer, osteoporosis, stomach problems (e.g., ulcers), skin problems (e.g., pressure sores), vision problems (e.g., macular degeneration), hearing loss, depression, and anxiety. Testeretest reliability was good (t (158) 5 0.99, p 5 0.32; r 5 0.84, p ! 0.001). Type of MS was a three-level self-report variable in the analysis, including relapsing-remitting, progressive types of MS, and unknown. Self-reported height and weight were used to calculate BMI, which was used as a continuous variable in the model. Self-reported BMI had adequate testeretest reliability (t (159) 5 0.43, p 5 0.67; r 5 0.94, p ! 0.001). Health problems (activity limitations). Activity limitations are difficulties in executing a task or action. We measured them using the Self-Reported Functional Measure (SRFM),30 Perceived Deficits Questionnaire (PDQ),31,32 and the MS Walking Scale.33 All three scales have been validated in the MS population.30e33 The SRFM assesses one’s perceived ability to perform daily activities (13 items). Participants were asked to rate (on a scale ranging from no extra time or help to total help) how much help they need with tasks (e.g., eating, moving around the house, dressing). Responses were summed (possible range 0e52) for the analysis. Higher scores indicated greater difficulty in accomplishing such tasks. Testeretest reliability and internal consistency were good (t (147) 5 0.36, p 5 0.72; r 5 0.70, p ! 0.001; a 5 0.97). The PDQ assesses limitations in activities due to cognitive impairments. Participants rated (5 items) how often they had difficulty accomplishing tasks that involve memory, attention, and concentration (e.g., getting things organized and concentrating on things like watching a television program) on a scale of 1 (never) to 5 (almost always). Scores were summed (possible range 0e25 to 5e25) for the analysis. Higher scores indicated greater perceived cognitive deficits. Testeretest reliability and internal consistency
were good (t (163) 5 0.33, p 5 0.74; r 5 0.91, p ! 0.001; a 5 0.91). The MS Walking Scale assesses difficulty in various mobility tasks. Participants rated (12 items) how much MS limited or caused problems with walking, running, climbing stairs, and other activities over the previous two weeks on a scale of 1 (not at all) to 5 (extremely). Scores were summed and transformed to a scale with a possible range of 0e100. Higher scores indicated greater mobility problems. Testeretest reliability and internal consistency were good (t (164) 5 0.92, p 5 0.36; r 5 0.97, p ! 0.001; a 5 0.99). Psychosocial factors (temperament and personality functions). Temperament and personality functions are considered body functions in the ICF framework and refer to mental functions of disposition. We selected two variables: self-efficacy and optimism. Nutritional self-efficacy was measured using the survey from the women with disabilities study mentioned previously.18 Participants rated their level of confidence, on a scale of 1 (not confident at all) to 10 (completely confident), that they could: eat a wellbalanced diet, follow a diet recommended by a doctor, select foods that helped maintain weight, select appropriate vitamins and supplements, and identify nutrients by reading food labels. The five questions were averaged together for the analysis. Testeretest reliability and internal consistency were adequate (t (161) 5 1.28, p 5 0.20; r 5 0.71, p ! 0.001; a 5 0.85). This self-efficacy questionnaire differs from the dependent nutritional questionnaire in that it asks about participants’ confidence in engaging in nutritional behaviors rather than the frequency of such behaviors. Disposition toward optimism or pessimism was measured with the Life Orientation Test-Revised.34 Participants rated (10 items, 4 items are fillers) how much they agreed or disagreed with statements about their outlook on life. The 6 non-filler responses were summed (possible range 0e24). Higher scores indicated a disposition toward optimism. The questionnaire has previously been used with MS patients.35,36 Testeretest reliability and internal consistency were adequate for our study (t (154) 5 0.74, p 5 0.46; r 5 0.84, p ! 0.001; a 5 0.87). This measure was selected because of our prior qualitative research indicating that individuals with MS who were hopeless about their future tended to have a ‘‘what is the point’’ attitude toward engaging in physical activity; we hypothesized the same might also be true for nutritional behavior.37 Self-management activities. We selected three selfmanagement activities (physical activity, communication with physician, and cognitive/emotional management) for the analysis. Physical activity was measured with the Godin Leisure-Time Exercise Questionnaire (GLTEQ),38 which is considered a valid measure of physical activity levels in the MS population.39 The GLTEQ includes 3
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items that measure the frequency of strenuous, moderate, and mild exercise for periods of more than 15 minutes in a typical week. The weekly frequencies were multiplied by metabolic equivalents and summed; higher scores indicated greater amounts of activity. Testeretest reliability was adequate (t (161) 5 0.14, p 5 0.89; r 5 0.77, p ! 0.001). The Communication with Physician40 questionnaire consists of the following three questions on a scale that ranged from never (0) to always (5): Do you prepare a list of questions for your doctor; do you ask questions about the things you want to know about and don’t understand; do you discuss any personal problems that may be related to your illness? Scores were summed (possible range 0e15) for the analysis. The questionnaire has been previously validated in adults with chronic conditions as part of a randomized controlled trial examining the effects of a self-management course.40 Our testeretest reliability and internal consistency were adequate (t (163) 5 1.05, p 5 0.30; r 5 0.72, p ! 0.001; a 5 0.75). Cognitive/emotional management was measured with the Cognitive Symptom Management questionnaire,40 which included questions such as (6 items): ‘‘When you are feeling down in the dumps.(1) do you talk to yourself in a positive way, (2) play mental games, (3) practice progressive muscle relaxation?’’ Participants rated these questions on a scale that ranged from never (0) to always (5). Scores were summed (possible range 0e30) for the analysis. The questionnaire has been previously validated in adults with chronic conditions.40 Testeretest reliability and internal consistency were adequate (t [162] 5 0.68, p 5 0.51; r 5 0.71, p ! 0.001; a 5 0.77). Analysis First, we calculated the descriptive statistics and bivariate correlations. Significant bivariate correlations were identified with a Pearson Chi-Square test for categorical/ nominal independent variables and Spearman’s Rho for ordinal independent variables. A logistic regression model was used to determine the relative importance of independent variables in predicting the dependent variable. Variable selection for the model was performed using significant bivariate correlates in backward elimination and forward selection methods. A p-value criterion of 0.05 was used for inclusion and exclusion of independent variables in the model. All selection methods resulted in the same model being chosen. The HosmereLemeshow goodness-of-fit test (p 5 0.83) did not indicate any potential problems with the data fitting the model. The analytical sample for the logistic regression model consisted of 292 participants who had complete data on the dependent and independent variables entered into the logistic regression analysis. Thus, 43 participants were excluded from the logistic regression analysis because of missing data. However, based on the rule of 10 participants
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per independent variable in logistic regression analysis,41 we exceeded the minimum required number of participants. To help determine whether there were biases in the research sample, we examined differences in participants’ characteristics between the random sampling pool (N 5 1,000) and the full survey sample (n 5 335). Results Less than half of the research sample (47.5%) was classified as engaging in healthy nutritional behaviors. Over 50% of the sample was overweight or obese, and had at least one co-morbid condition, the most common being high blood pressure. Respondents were representative of the MS population as a whole: the majority was white (98.5%), upper middle-class (59.7% reporting an annual household income over $50,000), well-educated (61.2 % reporting more than 15 years of education), and female (79.7%). The average years since diagnosis was 15.0 years (SD 5 8.3). Most participants had relapsing-remitting MS (63.0%), followed by secondary progressive (20.0%), primary progressive (8.1%), and progressive-relapsing MS (2.4%); 6.6% were unsure. The majority of participants used a mobility aid (i.e., cane, walker, or wheelchair) sometimes or always (59.7%). There were no significant differences between the randomly selected individuals (n 5 1000) and the research sample (n 5 335) in terms of age or sex. However, the survey (n 5 335) sample had a significantly lower proportion of individuals from minority backgrounds (difference of 4.0%) and a significantly higher proportion of participants who reported moderate and severe disabilities (difference of 9.8%). Table 1 shows the descriptive statistics and bivariate correlations between the dependent and independent variables, and Table 2 shows the results of the logistic regression model. Education, age, MS symptoms, number of co-morbid conditions, type of MS, limitations in daily activities, mobility impairments, and cognitive deficits were not tested in the logistic analysis because they were nonsignificant bivariate correlates of nutritional behaviors. The final logistic model consisted of nutritional selfefficacy (b 5 0.69, p ! 0.001), physical activity (b 5 0.01, p 5 0.035), and physician communication (b 5 0.08, p 5 0.029). Discussion Our results are consistent with and advance the nutritional literature on adults with disabling conditions. This is one of the first studies to document an association between nutritional self-efficacy, self-management activities, and nutritional behaviors in individuals with MS. This is also one of the first studies to report preliminary data on the testeretest reliability of nutrition questionnaires in adults with MS. Given our regression model results, future research should explore whether efficacy-
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Table 1 Descriptive statistics (means and standard deviations (std) or frequency count and percent) by nutritional behavior category, and non-parametric bivariate correlations ( p-value) between independent variables and nutritional behavior Nutritional behaviors Nutritional behaviors !4 out 5 responses as often >4 out 5 responses as often Bivariate correlation Variables n 5 166 (52.5%) n 5 150 (47.5%) (p-value) Personal factors: Sex Females (count, %) Males (count, %) Education Less than high school (count, %) Some college (count, %) College graduate (count, %) Age (Mean, std) Body functions & structures: MS symptoms (Mean, std) # co-morbid conditions (Mean, std) Body mass index (Mean, std) Type of MS Relapsing-remitting Progressive types Unknown Temperament: Self-efficacy (Mean, std) Optimism/pessimism (Mean, std) Activity limitations: Daily activities (Mean, std) Mobility impairments (Mean, std) Cognitive deficits (Mean, std) Self-management tasks & skills: Physical activity (Mean, std) Emotional self-management (Mean, std) Communication with physician (Mean, std)
0.02 124, 74.7% 42, 25.3%
128, 85.3% 22, 14.7%
1, 0.6% 65, 39.2% 100, 60.2% 51.65, 10.41
0, 0% 55, 36.7% 95, 63.3% 54.00, 9.34
19.18, 8.19 1.04, 1.01 27.48, 6.91
19.16, 8.37 0.97, 1.01 24.91, 4.91
113, 68.1% 43, 25.9% 10, 6%
91, 60.7% 49, 32.7% 10, 6.7%
7.31, 1.86 7.98, 5.99
8.94, 1.15 5.61, 4.70
7.10, 12.24 42.95, 20.31 6.77, 5.07
5.27, 8.63 43.65, 21.37 5.85, 4.42
0.76 0.66 0.15
20.82, 21.03 6.18, 5.18 8.60, 3.80
28.72, 26.12 7.60, 5.75 9.86, 3.59
!0.01 0.03 !0.01
0.56
enhancement interventions that focus on improving self-management skills promotes healthy nutritional behaviors. Below we discuss our findings relative to other nutritional studies in people with MS and other chronic disabling conditions. Obesity prevalence The age-adjusted prevalence of overweight and obesity in U.S. adults is 68.0%,42 and adults with disabilities are 2 to 4 times more likely to be obese.16,43,44 Thus, our finding that less than 60% of the research sample was overweight or obese is somewhat lower than previous findings. In a sample of (mainly minority) women with disabilities, Nosek et al45 found that nearly two-thirds were overweight or obese, but that women with MS were more likely to be Table 2 Final logistic regression analysis results (forwards and backwards selection methods) Variables b Standard error p-value Nutritional self-efficacy Physical Activity Communication with physician
0.69 0.01 0.08
0.10 0.01 0.04
!0.001 0.04 0.03
Note: Sex, body mass index, optimism/pessimism, and emotional selfmanagement were excluded from the final model (p O 0.05).
0.06 0.91 0.53 !0.01 0.38
!0.01 !0.01
of normal weight than women with other disabling conditions. In comparison, Khurana et al46 found that more than 60% were overweight or obese in a large sample of veterans with MS (n 5 4703). Regardless of whether prevalence rates are higher or lower in the MS population compared to other populations of people with disabilities, we contend that promoting healthy nutritional behaviors is important because of the potential negative effects of obesity on the increased risk for developing comorbidities (e.g., diabetes and cardiovascular disease) that may accelerate MS-related functional declines.3,47 Demographic/personal characteristics Research in the general population indicates that demographic characteristics are associated with unhealthy nutritional behaviors and obesity.11 We did not find such an association. However, Timmerman and Stuifbergen48 and Khurana et al46 found that older age negatively influenced nutritional status in adults with MS (measured with food diaries or BMI, respectively). Khurana et al also found that sex, marital status, and employment status were associated with BMI. Furthermore, Nosek et al45 found that AfricanAmerican and Hispanic women with disabilities were more likely to be overweight or obese compared to non-Hispanic white women. It may be that the relative homogeneity of
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our sample precluded us from finding comparable associations. Health problems: Impairments and activity limitations Impairments and activity limitations are typically associated with nutritional behaviors in older adults, which conflicts with our results.14e16 Khurana et al46 found that pain was significantly associated with BMI, whereas general health status was not significantly associated with BMI in veterans with MS. Timmerman and Stuifbergen48 found that severity of illness was also not significantly related to nutritional status. Goodman et al49 found that fatigue was correlated with being too tired to cook, chew, and finish eating and that these correlations varied across MS disability status. Nosek et al18 found that mobility impairments and assistance with activities of daily living were associated with nutritional behaviors in a sample of women with disabilities. Compared to our study, Nosek et al used a more comprehensive mobility measure, which may have been more relevant to nutritional behaviors because the mobility measure included questions on transportation, days out of the house, and access issues in and outside the home. Furthermore, they included a measure of social function that was a small yet significant predictor of nutritional behavior. The variations in measurement of function and amount of social support across research samples may partially explain conflicting results. Activity limitations that directly pertain to nutritional behaviors (e.g., measuring the impact of fatigue on cooking and grocery shopping) might be more strongly related to nutritional behaviors than general health status and function indicators.49,50 Furthermore, it also may be appropriate to control for social support when exploring the relationship between activity limitations and nutritional behaviors.21 Individuals who have difficulty eating may have an informal caregiver who can help them overcome activity limitations that serve as barriers to healthy nutritional behaviors. Indeed, the relationship between activity limitations and nutritional status is complex. In fact, some studies have found that obesity has protective effects on health and function (i.e., obesity paradox).51,52 It may be that using traditional BMI categories is not valid among individuals with disabling conditions and has contributed to the variability in result across studies. Some researchers have argued that BMI categories are not valid in some disabling conditions because BMI categories do not account for differences between muscle mass and fat distributions.53 Thus, longitudinal studies that utilize both patient-reported and objective measures of disease progression, nutritional intake, health and function, and body composition are needed in the MS population. Psychosocial factors: Self-efficacy Our findings support previous research indicating that self-efficacy is a consistent predictor of nutritional
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behaviors in the general population. However, Timmerman and Stuifbergen48 found that nutritional self-efficacy was not significantly related to nutritional status in individuals with MS. Differences in nutritional behavior measurements (i.e., food diary versus body mass index versus frequency questionnaire) may help explain these discrepancies. For example, there may be a greater degree of correspondence between self-efficacy questionnaires and nutritional frequency questionnaires than between selfefficacy questionnaires and a food diary. Thus, the strong correlation between self-efficacy and nutritional behavior in our study is most likely due to the high degree of correspondence between the two questionnaires. Nosek et al18 also found that nutritional self-efficacy was the strongest correlate of nutritional behaviors among women with physical disabilities. They cautioned about concluding that low self-efficacy was indicative of low motivation, and that environmental factors, such as social isolation and poverty, should be considered. Self-management activities All self-management variables significantly correlated with nutritional behaviors. The association between physical activity and nutritional behaviors found in our study is consistent with general population studies.24,55 Gillman et al24 concluded that targeting diet and activity together may have synergetic effects and there is a need to identify modifiable factors that influence both behaviors simultaneously. The association between physician communication and nutritional behaviors is consistent with studies utilizing the Patient Activation Measure.56 The Patient Activation Measure is comprised of a series of questions, including questions about physician communication, which place patients into one of four stages of activation. Increased patient activation is associated with engagement in healthy behaviors, such as increased attention to fat intake.57 Our measure of physician communication may be an indicator of increased patient activation. Our results support Lorig’s conceptualization of selfmanagement education. Lorig and Holman22 suggest that there are six self-management skills (i.e., problem solving, decision-making, using resources, communicating with health care providers, taking action, and self-tailoring) that facilitate engagement in a variety of self-management tasks (i.e., medical, emotional, and role management). Thus, the skills required to routinely engage in physical activity might be similar to the skills involved in maintaining healthy nutritional habits. Likewise, when patients can communicate effectively with their physicians, they may be more likely to get their questions answered and receive advice about how to engage in healthy behaviors,27 while being able to effectively manage emotions might reduce stress that acts as a barrier to many different healthy behaviors.58 Future longitudinal studies should determine whether self-management skills are a common mediator
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across different healthy behaviors. Such research would help explain why increased patient activation is associated with self-management behaviors.56
among those with MS. Easy to implement self-management intervention strategies, such as developing an action plan with the patient, might be effective ways for health care providers to promote healthy nutritional behaviors.
Limitations Limitations of the study include the cross-sectional research design, unknown factors about the sample’s generalizability, and the use of self-reported questionnaires. The cross-sectional design precluded making inferences about the direction of causation. The extent of bias from selecting recent responders from the NARCOMS registry is unknown. Furthermore, there are inherent biases associated with online surveys, including issues with accessibility. Online survey methodology could disproportionally reduce survey accessibility among minorities and people with disabilities.59 On the other hand, some have argued that it may also increase the likelihood that individuals who feel socially stigmatized complete the survey.60 Thus, this might explain why our research sample had a disproportional number of minorities and individuals with more severe impairments compared to the pool of potential research participants. There is a need to recruit and study minorities with MS, as these individuals may have a much greater risk for developing unhealthy eating habits.43 All variables were self-report, which could have resulted in a misclassification of participants and an underestimation of regression coefficients. Furthermore, the high degree of correspondence between the self-efficacy questionnaire and the nutritional behavior questionnaire is another limitation. Finally, the healthy nutritional behavior questionnaire needs to be further validated, as its brevity could be useful in survey research. Although reliability was adequate in this study, future studies should explore the measure’s correlation with food diaries, food frequency questionnaires, and nutritional biomarkers in people with MS. We note that using different cut-off scores to classify participants resulted in similar regression model results, while using the nutritional behavior measure as a continuous variable resulted in the inclusion of optimism/pessimism and emotional self-management in the final model. However, using the nutritional behavior measure as a continuous variable violated the assumptions of normality needed for a linear regression model. Conclusions and implications for intervention development This study represents a first step in understanding the factors that influence healthy nutritional behaviors in adults with MS. Our findings provide preliminary evidence that self-efficacy and self-management activities are correlates of nutritional behaviors in individuals with MS, which is consistent with Lorig and Holman’s conceptualization of self-management education. Teaching patients selfmanagement skills and focusing on increasing self-efficacy might be methods to foster healthy nutritional behaviors
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43. Rimmer JH, Wang E. Obesity prevalence among a group of Chicago residents with disabilities. Arch Phys Med Rehabil. 2005;86(7): 1461e1464. 44. Weil E, Wachterman M, McCarthy EP, Davis RB, O’Day B, Iezzoni LI, et al. Obesity among adults with disabling conditions. J Am Med Assoc. 2002;288(10):1265e1268. 45. Nosek MA, Robinson-Whelen S, Hughes RB, Petersen NJ, Taylor HB, Byrne MM, et al. Overweight and obesity in women with physical disabilities: associations with demographic and disability characteristics and secondary conditions. Disabil Health J. 2008;1(2):89e98. 46. Khurana SR, Bamer AM, Turner AP, Wadhwani RV, Bowen JD, Leipertz SL, et al. The prevalence of overweight and obesity in veterans with multiple sclerosis. Am J Phys Med Rehabil. 2009;88(2):83e91. 47. Marrie RA, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity delays diagnosis and increases disability at diagnosis in MS. Neurology. 2009;72(2):117e124. 48. Timmerman GM, Stuifbergen AK. Eating patterns in women with multiple sclerosis. J Neurosci Nurs. 1999;31(3):152e158. 49. Goodman S, Gulick E. Dietary practices of people with multiple sclerosis. Int J MS Care. 2008;10:47e57. 50. Bartali B, Semba RD, Frongillo EA, Varadhan R, Ricks MO, Blaum CS, et al. Low micronutrient levels as a predictor of incident disability in older women. Arch Intern Med. 2006;166(21): 2335e2340. 51. Vemmos K, Ntaios G, Spengos K, Savvari P, Vemmou A, Pappa T, et al. Association between obesity and mortality after acute firstever stroke: the obesity-stroke paradox. The obesity-stroke paradox. Stroke. 2011;42(1):30e36. 52. Artham SM, Lavie CJ, Patel DA, Ventura HO. Obesity paradox in the elderly: is fatter really fitter? Aging Health. 2009;5(2):177e184. 53. Rimmer J, Wang E, Yamaki K, Davis B. Documenting disparities in obesity and disability. Focus: A Publication of the National Center for the Dissemination of Disability Research (NCDDR); 2010. p. 24. 54. AbuSabha R, Achterberg C. Review of self-efficacy and locus of control for nutrition- and health-related behavior. J Am Diet Assoc. 1997;97(10):1122e1132. 55. Jakicic JM, Wing RR, Winters-Hart C. Relationship of physical activity to eating behaviors and weight loss in women. Med Sci Sports Exerc. 2002;34(10):1653e1659. 56. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the patient activation measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4 Pt 1):1005e1026. 57. Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors? Health Serv Res. 2007;42(4):1443e1463. 58. Wenzel L, Glanz K, Lerman C. Stress, coping, and health behavior. In: Glanz K, Rimer BK, Lewis BA, eds. Health behavior and health education: theory, Research and Practice. San Francisco: Jossey-Bass, 2002; p. 210e239. 59. Wright KB. Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey Services. J Comput Mediat Commun. 2005;(3):10. 60. Evans JR, Mathur A. The value of online surveys. Internet Research. 2005;15:195e219.
Research Paper
Correlates of nutritional behavior in individuals with multiple sclerosis Matthew Plow, Ph.D.a,*, Marcia Finlayson, Ph.D., O.T. (C), O.T.R/Lb,c, and Chi Cho, M.S.b a
Department of Biomedical Engineering, Department of Physical Medicine and Rehabilitation, Cleveland Clinic Lerner Research Institute, 9500 Euclid Ave, ND-20, Cleveland, OH 44195, USA b Department of Occupational Therapy, University of Illinois at Chicago, 1919 W. Taylor Street (MC 811), Chicago, IL 60612, USA
Abstract Background: Adults with multiple sclerosis (MS) have many health problems that can interfere with healthy nutritional behaviors. Self-management activities (e.g., strategies used to manage emotions and functional limitations) may help facilitate engagement in healthy nutritional behaviors. However, few studies have documented such relationships. Objective: Identify predictors of nutritional behaviors from among a set of variables (i.e., personal characteristics, health status indicators, and self-management activities) linked to the International Classification of Function. Methods: Data were obtained from an online survey of 292 individuals with MS. Significant bivariate correlates were entered into a logistic regression analysis using backward and forward selection methods to identify predictors of healthy nutritional behaviors (i.e., endorsing 4 out of 5 questions about frequently making good food choices, limiting fat intake, consuming 5 servings of fruits and vegetables, reading food labels, and eating regularly). Results: Sex, nutritional self-efficacy, optimism/pessimism, body mass index, physical activity, emotional self-management, and communication with physician were used in the logistic analysis. Nutritional self-efficacy (b 5 0.69, p ! 0.001) was the strongest predictor of nutritional behaviors, followed by physician communication (b 5 0.08, p 5 0.029) and physical activity (b 5 0.01, p 5 0.035). Neither impairments nor activity limitations were significantly associated with nutritional behaviors. Conclusions: This study provides preliminary evidence that self-efficacy and self-management activities are correlates of nutritional behaviors in individuals with MS. Supporting the development of self-management skills and increasing self-efficacy might be methods for improving engagement in healthy nutritional behaviors among adults with MS. Ó 2012 Elsevier Inc. All rights reserved. Keywords: Obesity; Neurological Disorder; Nutrition; Disability; Symptoms
Introduction/background Multiple sclerosis (MS) is an immune-mediated disease that affects women almost twice as often as men.1 Although there is no cure, people with MS have a life expectancy that is similar to that of the general population.2 Thus, it is important to encourage engagement in healthy behaviors (e.g., physical activity and nutrition) to prevent co-morbid
Conflict of interest: The authors declare that they have no conflicts of interest. Funding: This work was supported through the National Multiple Sclerosis Society (NMSS) post-doctoral training grant. The information presented in this article does not necessarily reflect the position, ideas, or opinions of the NMSS. Abstract was presented at the American Neurological Association annual meeting. * Corresponding author. Tel.: þ1 216 445 3288; fax: þ1 216 444 9198. E-mail address: [email protected] (M. Plow). c Present address: School of Rehabilitation Therapy, Louise D. Acton Building, 31 George Street, Queen’s University, Kingston, Ontario K7L 3N6 Canada. 1936-6574/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.dhjo.2012.05.007
conditions from developing.3 There are a growing number of studies on MS and physical activity behaviors.4e6 Conversely, studies on MS and nutritional behaviors are sparse and have mainly focused on whether dietary intake is a risk factor for developing MS.7 Since malnutrition might be prevalent and under-diagnosed problem in the MS population,8e10 research is needed to identify possible strategies to promote healthy nutritional behaviors (i.e., dietary-related activities that help protect, promote, or maintain health). Most of what we know about the factors influencing nutritional behaviors e personal characteristics (e.g., age, sex, and education level),11 psychosocial factors (e.g., self-efficacy and outlook on life),12,13 and health problems (e.g., cognitive impairments, mobility impairments, and activity limitations)14e16 e is from studies on older adults and the general population. Health problems might negatively affect psychosocial factors that influence healthy nutritional behaviors,17e19 such as the perceived ability and confidence (i.e., self-efficacy) to engage in healthy nutritional behaviors. Documenting the relationship
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between personal characteristics, psychosocial factors, health problems, and healthy nutritional behaviors might provide guidance on developing nutritional interventions for individuals with MS. In contrast to health problems, self-management activities (i.e., tasks and skills used to manage one’s own health) could facilitate engagement in healthy nutritional behaviors.20 For example, MS symptoms (e.g., fatigue and spasticity) can serve as barriers to preparing a healthy meal. Such barriers might be overcome by supporting self-management activities.21,22 Physical activity, for instance, can reduce MS symptom severity23 and may improve psychosocial factors (e.g., confidence) that make it more likely to engage in healthy nutritional behaviors.24,25 Learning self-management skills (e.g., emotional regulation and physician communication) may help individuals reduce their risk for stress-related weight gain and asking their physician about how to engage in healthy nutritional behaviors while living with a disabling condition.26,27
Hypotheses The purpose of this study was to determine the importance of demographic/personal characteristics (i.e., age, sex, and education), health problems (i.e., impairments and activity limitations), psychosocial factors (i.e., selfefficacy and outlook on life), and self-management activities (i.e., physical activity, emotional self-management, and physician communication) in predicting nutritional behaviors in adults with MS. We undertook this study to determine whether predictors of nutritional behaviors in the general population also apply to those with MS and to advance the existing health behavior literature on disabling conditions by determining whether certain self-management activities are associated with nutritional behaviors. We used the International Classification of Function (ICF)28 and Lorig and Holman’s22 conceptualization of selfmanagement education to operationalize definitions and select variables for the analysis. The ICF provides a standard language to describe human functioning, and Lorig and Holman’s self-management framework provides guidance on defining self-management activities. We hypothesized that older age, less education, impairments (i.e., MS symptom severity and type, number of co-morbid conditions, and obesity), activity limitations (i.e., mobility problems, cognitive deficits, and inability to perform daily activities), lower self-efficacy scores, and a negative outlook on life are associated with unhealthy nutritional behaviors, whereas increased engagement in self-management activities (i.e., physical activity, emotional management, and physician communication) are associated with healthy nutritional behaviors.
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patient registry, which is a large database with over 35,000 participants. Researchers can pay a fee to recruit participants in the registry for other studies. NARCOMS staff sent out letters to a randomly selected sample of recent responders (n 5 1000) asking them to complete a 30e45 minute online survey using SurveyMonkey. Questions pertained to health status, MS-specific disease status, activity limitations, participation restrictions, and engagement in health-promoting behaviors. The response rate was 34% (335/1000). A proportion of the participants (n 5 165) completed the questionnaire twice (2.5 months apart) to obtain testeretest reliability data. An Institutional Review Board approved the study. Measures Dependent variable According to the ICF framework, healthy nutritional behaviors are in the participation domain (i.e., involvement in life situations). We measured such behaviors using questions from a previous survey on women with disabilities18 in which 386 women with disabling conditions completed the questionnaire. In that previous study, the scale demonstrated adequate unidimensionality and internal consistency; in a confirmatory factor analysis, the authors found a single factor that accounted for 50% of the variance, and reported a Cronbach’s alpha of 0.74. Thus, the questions were not modified for this study. Participants in this study were asked whether they often, sometimes, or rarely (five items): (1) make good food choices, (2) eat five servings of fruits and vegetables a day, (3) limit fat intake, (4) read labels, and (5) eat regularly. We operationalized healthy nutritional behaviors as whether participants answered 4 out of 5 questions with the response of ‘often’ (0 5 !4 questions with the response of ‘often’ and 1 5 >4 questions with the response of ‘often’). The variable was divided into two categories for the analysis because it was negatively skewed. Using this scoring approach allowed us to achieve a somewhat equal distribution between the two categories while maintaining strict criteria for what constitutes engagement in healthy nutritional behaviors. Testeretest reliability for this study was adequate (t (161) 5 0.98, p 5 0.33; r 5 0.77, p ! 0.001). Independent variables
Methods
Personal factors. According to the ICF, personal factors are an individual’s internal attributes that can influence participation in activities and life roles (e.g., demographic characteristics). We selected age, sex, and education (less than high school, high school graduate or some college, or college graduate) for the analysis because they predict obesity in the general population.11
Participants were recruited through the North American Research Committee on Multiple Sclerosis (NARCOMS)
Health problems (impairments). According to the ICF, impairments are problems in body functions and structures.
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We included four indicators of body function e MS symptom severity, number of co-morbid conditions, type of MS, and body mass index (BMI). Symptom severity was measured with the Symptoms of Multiple Sclerosis Scale (12 items),29 which measures the extent to which individuals experienced fatigue, pain, visual impairments, paralysis, bladder difficulties, lack of concentration, inability to communicate, bowel difficulties, numbness, tremors, loss of balance, and spasticity on a scale of 0 (never) through 4 (always). To generate a single score for the analysis, responses were summed (possible range 0e48). The questionnaire has been validated in the MS population.29 Testeretest reliability and internal consistency were adequate (t (146) 5 1.05, p 5 0.30; r 5 0.94, p ! 0.001; a 5 0.83). The number of co-morbid conditions was a count (possible range 0 to 15) in response to the following list: diabetes, arthritis, heart problems, high blood pressure, respiratory problems, previous stroke, thyroid condition, cancer, osteoporosis, stomach problems (e.g., ulcers), skin problems (e.g., pressure sores), vision problems (e.g., macular degeneration), hearing loss, depression, and anxiety. Testeretest reliability was good (t (158) 5 0.99, p 5 0.32; r 5 0.84, p ! 0.001). Type of MS was a three-level self-report variable in the analysis, including relapsing-remitting, progressive types of MS, and unknown. Self-reported height and weight were used to calculate BMI, which was used as a continuous variable in the model. Self-reported BMI had adequate testeretest reliability (t (159) 5 0.43, p 5 0.67; r 5 0.94, p ! 0.001). Health problems (activity limitations). Activity limitations are difficulties in executing a task or action. We measured them using the Self-Reported Functional Measure (SRFM),30 Perceived Deficits Questionnaire (PDQ),31,32 and the MS Walking Scale.33 All three scales have been validated in the MS population.30e33 The SRFM assesses one’s perceived ability to perform daily activities (13 items). Participants were asked to rate (on a scale ranging from no extra time or help to total help) how much help they need with tasks (e.g., eating, moving around the house, dressing). Responses were summed (possible range 0e52) for the analysis. Higher scores indicated greater difficulty in accomplishing such tasks. Testeretest reliability and internal consistency were good (t (147) 5 0.36, p 5 0.72; r 5 0.70, p ! 0.001; a 5 0.97). The PDQ assesses limitations in activities due to cognitive impairments. Participants rated (5 items) how often they had difficulty accomplishing tasks that involve memory, attention, and concentration (e.g., getting things organized and concentrating on things like watching a television program) on a scale of 1 (never) to 5 (almost always). Scores were summed (possible range 0e25 to 5e25) for the analysis. Higher scores indicated greater perceived cognitive deficits. Testeretest reliability and internal consistency
were good (t (163) 5 0.33, p 5 0.74; r 5 0.91, p ! 0.001; a 5 0.91). The MS Walking Scale assesses difficulty in various mobility tasks. Participants rated (12 items) how much MS limited or caused problems with walking, running, climbing stairs, and other activities over the previous two weeks on a scale of 1 (not at all) to 5 (extremely). Scores were summed and transformed to a scale with a possible range of 0e100. Higher scores indicated greater mobility problems. Testeretest reliability and internal consistency were good (t (164) 5 0.92, p 5 0.36; r 5 0.97, p ! 0.001; a 5 0.99). Psychosocial factors (temperament and personality functions). Temperament and personality functions are considered body functions in the ICF framework and refer to mental functions of disposition. We selected two variables: self-efficacy and optimism. Nutritional self-efficacy was measured using the survey from the women with disabilities study mentioned previously.18 Participants rated their level of confidence, on a scale of 1 (not confident at all) to 10 (completely confident), that they could: eat a wellbalanced diet, follow a diet recommended by a doctor, select foods that helped maintain weight, select appropriate vitamins and supplements, and identify nutrients by reading food labels. The five questions were averaged together for the analysis. Testeretest reliability and internal consistency were adequate (t (161) 5 1.28, p 5 0.20; r 5 0.71, p ! 0.001; a 5 0.85). This self-efficacy questionnaire differs from the dependent nutritional questionnaire in that it asks about participants’ confidence in engaging in nutritional behaviors rather than the frequency of such behaviors. Disposition toward optimism or pessimism was measured with the Life Orientation Test-Revised.34 Participants rated (10 items, 4 items are fillers) how much they agreed or disagreed with statements about their outlook on life. The 6 non-filler responses were summed (possible range 0e24). Higher scores indicated a disposition toward optimism. The questionnaire has previously been used with MS patients.35,36 Testeretest reliability and internal consistency were adequate for our study (t (154) 5 0.74, p 5 0.46; r 5 0.84, p ! 0.001; a 5 0.87). This measure was selected because of our prior qualitative research indicating that individuals with MS who were hopeless about their future tended to have a ‘‘what is the point’’ attitude toward engaging in physical activity; we hypothesized the same might also be true for nutritional behavior.37 Self-management activities. We selected three selfmanagement activities (physical activity, communication with physician, and cognitive/emotional management) for the analysis. Physical activity was measured with the Godin Leisure-Time Exercise Questionnaire (GLTEQ),38 which is considered a valid measure of physical activity levels in the MS population.39 The GLTEQ includes 3
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items that measure the frequency of strenuous, moderate, and mild exercise for periods of more than 15 minutes in a typical week. The weekly frequencies were multiplied by metabolic equivalents and summed; higher scores indicated greater amounts of activity. Testeretest reliability was adequate (t (161) 5 0.14, p 5 0.89; r 5 0.77, p ! 0.001). The Communication with Physician40 questionnaire consists of the following three questions on a scale that ranged from never (0) to always (5): Do you prepare a list of questions for your doctor; do you ask questions about the things you want to know about and don’t understand; do you discuss any personal problems that may be related to your illness? Scores were summed (possible range 0e15) for the analysis. The questionnaire has been previously validated in adults with chronic conditions as part of a randomized controlled trial examining the effects of a self-management course.40 Our testeretest reliability and internal consistency were adequate (t (163) 5 1.05, p 5 0.30; r 5 0.72, p ! 0.001; a 5 0.75). Cognitive/emotional management was measured with the Cognitive Symptom Management questionnaire,40 which included questions such as (6 items): ‘‘When you are feeling down in the dumps.(1) do you talk to yourself in a positive way, (2) play mental games, (3) practice progressive muscle relaxation?’’ Participants rated these questions on a scale that ranged from never (0) to always (5). Scores were summed (possible range 0e30) for the analysis. The questionnaire has been previously validated in adults with chronic conditions.40 Testeretest reliability and internal consistency were adequate (t [162] 5 0.68, p 5 0.51; r 5 0.71, p ! 0.001; a 5 0.77). Analysis First, we calculated the descriptive statistics and bivariate correlations. Significant bivariate correlations were identified with a Pearson Chi-Square test for categorical/ nominal independent variables and Spearman’s Rho for ordinal independent variables. A logistic regression model was used to determine the relative importance of independent variables in predicting the dependent variable. Variable selection for the model was performed using significant bivariate correlates in backward elimination and forward selection methods. A p-value criterion of 0.05 was used for inclusion and exclusion of independent variables in the model. All selection methods resulted in the same model being chosen. The HosmereLemeshow goodness-of-fit test (p 5 0.83) did not indicate any potential problems with the data fitting the model. The analytical sample for the logistic regression model consisted of 292 participants who had complete data on the dependent and independent variables entered into the logistic regression analysis. Thus, 43 participants were excluded from the logistic regression analysis because of missing data. However, based on the rule of 10 participants
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per independent variable in logistic regression analysis,41 we exceeded the minimum required number of participants. To help determine whether there were biases in the research sample, we examined differences in participants’ characteristics between the random sampling pool (N 5 1,000) and the full survey sample (n 5 335). Results Less than half of the research sample (47.5%) was classified as engaging in healthy nutritional behaviors. Over 50% of the sample was overweight or obese, and had at least one co-morbid condition, the most common being high blood pressure. Respondents were representative of the MS population as a whole: the majority was white (98.5%), upper middle-class (59.7% reporting an annual household income over $50,000), well-educated (61.2 % reporting more than 15 years of education), and female (79.7%). The average years since diagnosis was 15.0 years (SD 5 8.3). Most participants had relapsing-remitting MS (63.0%), followed by secondary progressive (20.0%), primary progressive (8.1%), and progressive-relapsing MS (2.4%); 6.6% were unsure. The majority of participants used a mobility aid (i.e., cane, walker, or wheelchair) sometimes or always (59.7%). There were no significant differences between the randomly selected individuals (n 5 1000) and the research sample (n 5 335) in terms of age or sex. However, the survey (n 5 335) sample had a significantly lower proportion of individuals from minority backgrounds (difference of 4.0%) and a significantly higher proportion of participants who reported moderate and severe disabilities (difference of 9.8%). Table 1 shows the descriptive statistics and bivariate correlations between the dependent and independent variables, and Table 2 shows the results of the logistic regression model. Education, age, MS symptoms, number of co-morbid conditions, type of MS, limitations in daily activities, mobility impairments, and cognitive deficits were not tested in the logistic analysis because they were nonsignificant bivariate correlates of nutritional behaviors. The final logistic model consisted of nutritional selfefficacy (b 5 0.69, p ! 0.001), physical activity (b 5 0.01, p 5 0.035), and physician communication (b 5 0.08, p 5 0.029). Discussion Our results are consistent with and advance the nutritional literature on adults with disabling conditions. This is one of the first studies to document an association between nutritional self-efficacy, self-management activities, and nutritional behaviors in individuals with MS. This is also one of the first studies to report preliminary data on the testeretest reliability of nutrition questionnaires in adults with MS. Given our regression model results, future research should explore whether efficacy-
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Table 1 Descriptive statistics (means and standard deviations (std) or frequency count and percent) by nutritional behavior category, and non-parametric bivariate correlations ( p-value) between independent variables and nutritional behavior Nutritional behaviors Nutritional behaviors !4 out 5 responses as often >4 out 5 responses as often Bivariate correlation Variables n 5 166 (52.5%) n 5 150 (47.5%) (p-value) Personal factors: Sex Females (count, %) Males (count, %) Education Less than high school (count, %) Some college (count, %) College graduate (count, %) Age (Mean, std) Body functions & structures: MS symptoms (Mean, std) # co-morbid conditions (Mean, std) Body mass index (Mean, std) Type of MS Relapsing-remitting Progressive types Unknown Temperament: Self-efficacy (Mean, std) Optimism/pessimism (Mean, std) Activity limitations: Daily activities (Mean, std) Mobility impairments (Mean, std) Cognitive deficits (Mean, std) Self-management tasks & skills: Physical activity (Mean, std) Emotional self-management (Mean, std) Communication with physician (Mean, std)
0.02 124, 74.7% 42, 25.3%
128, 85.3% 22, 14.7%
1, 0.6% 65, 39.2% 100, 60.2% 51.65, 10.41
0, 0% 55, 36.7% 95, 63.3% 54.00, 9.34
19.18, 8.19 1.04, 1.01 27.48, 6.91
19.16, 8.37 0.97, 1.01 24.91, 4.91
113, 68.1% 43, 25.9% 10, 6%
91, 60.7% 49, 32.7% 10, 6.7%
7.31, 1.86 7.98, 5.99
8.94, 1.15 5.61, 4.70
7.10, 12.24 42.95, 20.31 6.77, 5.07
5.27, 8.63 43.65, 21.37 5.85, 4.42
0.76 0.66 0.15
20.82, 21.03 6.18, 5.18 8.60, 3.80
28.72, 26.12 7.60, 5.75 9.86, 3.59
!0.01 0.03 !0.01
0.56
enhancement interventions that focus on improving self-management skills promotes healthy nutritional behaviors. Below we discuss our findings relative to other nutritional studies in people with MS and other chronic disabling conditions. Obesity prevalence The age-adjusted prevalence of overweight and obesity in U.S. adults is 68.0%,42 and adults with disabilities are 2 to 4 times more likely to be obese.16,43,44 Thus, our finding that less than 60% of the research sample was overweight or obese is somewhat lower than previous findings. In a sample of (mainly minority) women with disabilities, Nosek et al45 found that nearly two-thirds were overweight or obese, but that women with MS were more likely to be Table 2 Final logistic regression analysis results (forwards and backwards selection methods) Variables b Standard error p-value Nutritional self-efficacy Physical Activity Communication with physician
0.69 0.01 0.08
0.10 0.01 0.04
!0.001 0.04 0.03
Note: Sex, body mass index, optimism/pessimism, and emotional selfmanagement were excluded from the final model (p O 0.05).
0.06 0.91 0.53 !0.01 0.38
!0.01 !0.01
of normal weight than women with other disabling conditions. In comparison, Khurana et al46 found that more than 60% were overweight or obese in a large sample of veterans with MS (n 5 4703). Regardless of whether prevalence rates are higher or lower in the MS population compared to other populations of people with disabilities, we contend that promoting healthy nutritional behaviors is important because of the potential negative effects of obesity on the increased risk for developing comorbidities (e.g., diabetes and cardiovascular disease) that may accelerate MS-related functional declines.3,47 Demographic/personal characteristics Research in the general population indicates that demographic characteristics are associated with unhealthy nutritional behaviors and obesity.11 We did not find such an association. However, Timmerman and Stuifbergen48 and Khurana et al46 found that older age negatively influenced nutritional status in adults with MS (measured with food diaries or BMI, respectively). Khurana et al also found that sex, marital status, and employment status were associated with BMI. Furthermore, Nosek et al45 found that AfricanAmerican and Hispanic women with disabilities were more likely to be overweight or obese compared to non-Hispanic white women. It may be that the relative homogeneity of
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our sample precluded us from finding comparable associations. Health problems: Impairments and activity limitations Impairments and activity limitations are typically associated with nutritional behaviors in older adults, which conflicts with our results.14e16 Khurana et al46 found that pain was significantly associated with BMI, whereas general health status was not significantly associated with BMI in veterans with MS. Timmerman and Stuifbergen48 found that severity of illness was also not significantly related to nutritional status. Goodman et al49 found that fatigue was correlated with being too tired to cook, chew, and finish eating and that these correlations varied across MS disability status. Nosek et al18 found that mobility impairments and assistance with activities of daily living were associated with nutritional behaviors in a sample of women with disabilities. Compared to our study, Nosek et al used a more comprehensive mobility measure, which may have been more relevant to nutritional behaviors because the mobility measure included questions on transportation, days out of the house, and access issues in and outside the home. Furthermore, they included a measure of social function that was a small yet significant predictor of nutritional behavior. The variations in measurement of function and amount of social support across research samples may partially explain conflicting results. Activity limitations that directly pertain to nutritional behaviors (e.g., measuring the impact of fatigue on cooking and grocery shopping) might be more strongly related to nutritional behaviors than general health status and function indicators.49,50 Furthermore, it also may be appropriate to control for social support when exploring the relationship between activity limitations and nutritional behaviors.21 Individuals who have difficulty eating may have an informal caregiver who can help them overcome activity limitations that serve as barriers to healthy nutritional behaviors. Indeed, the relationship between activity limitations and nutritional status is complex. In fact, some studies have found that obesity has protective effects on health and function (i.e., obesity paradox).51,52 It may be that using traditional BMI categories is not valid among individuals with disabling conditions and has contributed to the variability in result across studies. Some researchers have argued that BMI categories are not valid in some disabling conditions because BMI categories do not account for differences between muscle mass and fat distributions.53 Thus, longitudinal studies that utilize both patient-reported and objective measures of disease progression, nutritional intake, health and function, and body composition are needed in the MS population. Psychosocial factors: Self-efficacy Our findings support previous research indicating that self-efficacy is a consistent predictor of nutritional
289 54
behaviors in the general population. However, Timmerman and Stuifbergen48 found that nutritional self-efficacy was not significantly related to nutritional status in individuals with MS. Differences in nutritional behavior measurements (i.e., food diary versus body mass index versus frequency questionnaire) may help explain these discrepancies. For example, there may be a greater degree of correspondence between self-efficacy questionnaires and nutritional frequency questionnaires than between selfefficacy questionnaires and a food diary. Thus, the strong correlation between self-efficacy and nutritional behavior in our study is most likely due to the high degree of correspondence between the two questionnaires. Nosek et al18 also found that nutritional self-efficacy was the strongest correlate of nutritional behaviors among women with physical disabilities. They cautioned about concluding that low self-efficacy was indicative of low motivation, and that environmental factors, such as social isolation and poverty, should be considered. Self-management activities All self-management variables significantly correlated with nutritional behaviors. The association between physical activity and nutritional behaviors found in our study is consistent with general population studies.24,55 Gillman et al24 concluded that targeting diet and activity together may have synergetic effects and there is a need to identify modifiable factors that influence both behaviors simultaneously. The association between physician communication and nutritional behaviors is consistent with studies utilizing the Patient Activation Measure.56 The Patient Activation Measure is comprised of a series of questions, including questions about physician communication, which place patients into one of four stages of activation. Increased patient activation is associated with engagement in healthy behaviors, such as increased attention to fat intake.57 Our measure of physician communication may be an indicator of increased patient activation. Our results support Lorig’s conceptualization of selfmanagement education. Lorig and Holman22 suggest that there are six self-management skills (i.e., problem solving, decision-making, using resources, communicating with health care providers, taking action, and self-tailoring) that facilitate engagement in a variety of self-management tasks (i.e., medical, emotional, and role management). Thus, the skills required to routinely engage in physical activity might be similar to the skills involved in maintaining healthy nutritional habits. Likewise, when patients can communicate effectively with their physicians, they may be more likely to get their questions answered and receive advice about how to engage in healthy behaviors,27 while being able to effectively manage emotions might reduce stress that acts as a barrier to many different healthy behaviors.58 Future longitudinal studies should determine whether self-management skills are a common mediator
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across different healthy behaviors. Such research would help explain why increased patient activation is associated with self-management behaviors.56
among those with MS. Easy to implement self-management intervention strategies, such as developing an action plan with the patient, might be effective ways for health care providers to promote healthy nutritional behaviors.
Limitations Limitations of the study include the cross-sectional research design, unknown factors about the sample’s generalizability, and the use of self-reported questionnaires. The cross-sectional design precluded making inferences about the direction of causation. The extent of bias from selecting recent responders from the NARCOMS registry is unknown. Furthermore, there are inherent biases associated with online surveys, including issues with accessibility. Online survey methodology could disproportionally reduce survey accessibility among minorities and people with disabilities.59 On the other hand, some have argued that it may also increase the likelihood that individuals who feel socially stigmatized complete the survey.60 Thus, this might explain why our research sample had a disproportional number of minorities and individuals with more severe impairments compared to the pool of potential research participants. There is a need to recruit and study minorities with MS, as these individuals may have a much greater risk for developing unhealthy eating habits.43 All variables were self-report, which could have resulted in a misclassification of participants and an underestimation of regression coefficients. Furthermore, the high degree of correspondence between the self-efficacy questionnaire and the nutritional behavior questionnaire is another limitation. Finally, the healthy nutritional behavior questionnaire needs to be further validated, as its brevity could be useful in survey research. Although reliability was adequate in this study, future studies should explore the measure’s correlation with food diaries, food frequency questionnaires, and nutritional biomarkers in people with MS. We note that using different cut-off scores to classify participants resulted in similar regression model results, while using the nutritional behavior measure as a continuous variable resulted in the inclusion of optimism/pessimism and emotional self-management in the final model. However, using the nutritional behavior measure as a continuous variable violated the assumptions of normality needed for a linear regression model. Conclusions and implications for intervention development This study represents a first step in understanding the factors that influence healthy nutritional behaviors in adults with MS. Our findings provide preliminary evidence that self-efficacy and self-management activities are correlates of nutritional behaviors in individuals with MS, which is consistent with Lorig and Holman’s conceptualization of self-management education. Teaching patients selfmanagement skills and focusing on increasing self-efficacy might be methods to foster healthy nutritional behaviors
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