The National Center for Health Statistics (2006)  predicted that by 2030, the United States will have an estimated population of 70 million older adults age ≥65 yr. This prediction indicates an immense need for increasing health-related services and education that would lead to health and quality of life among older adults. In fact, we are currently witnessing a paradigm shift in the nation’s health care focus from treatment of disease to prevention of disease, as well as promotion of healthy lifestyles. The refined Model of Successful Aging by Rowe and Kahn (1997, 1998) provided an optimistic perspective on how older adults actively seek to live a lifestyle that prolongs their years and allows them to enjoy their old age. As supported by the findings of a series of studies sponsored by the MacArthur Foundation Research Network on Successful Aging, lifestyle choices play an important role in determining health and vitality among community-dwelling older adults (Rowe & Kahn, 1998).

Factors such as exercise, nutrition, social and productive activities, leisure, self-care, spiritual and psychological well-being, and health promotion behaviors can be incorporated to construct a framework for services attempting to facilitate successful aging. Exercise and diet, when used as a preventative intervention in older adults, have a significant effect in reducing risk of cardiovascular disease, osteoporosis, arthritis, diabetes, some forms of cancer, obesity, cognitive decline, and depression (Galloway & Jokl, 2000; Woo et al., 2006). Studies also confirmed the positive correlations between the levels of social and productive activity participation and a range of health outcomes, including physical and cognitive functions, self-efficacy, and quality of life (Everard, Lach, Fisher, & Baum, 2000; Lawton, Winter, Kleban, & Ruckdeschel, 1999). Adequate habits, routines, and capability for activities of daily living (ADLs) and instrumental activities of daily living (IADLs) are linked to small risks of institutionalization, mortality, and depression (Sousa & Figueiredo, 2002; Steeman, Abraham, & Godderis, 1997). Religious involvement and spiritual activities of older adults have been found to be associated with a greater sense of well-being, life satisfaction, and purpose in life (Koenig, 2000; Thoresen, 1999); fewer physical symptoms (Koenig, George, Hays, et al., 1998; Matthews et al., 1998); and less anxiety and depression (Koenig, George, & Peterson, 1998; Matthews et al., 1998; Thoresen, 1999). Moreover, other lifestyle factors such as health literacy, self-management of health, cigarette smoking, and drug or alcohol use have been found to significantly affect various domains of health and function in older adults (Peel, McClure, & Bartlett, 2005; Pronk, Peek, & Goldstein, 2004; Wolf, Gazmararian, & Baker, 2007). All of these research findings support the protective effects of healthy lifestyle behaviors against aging.

Occupational therapy has been known to adopt a holistic approach in evaluating and managing different lifestyle factors and occupations that can determine health and wellness (Clark et al., 1997, 2001; Hay et al., 2002). The overarching statement of the Occupational Therapy Practice Framework: Domain and Process,2nd Edition, “supporting health and participation in life through engagement in occupation” (AOTA, 2008, p. 626), suggests the role of occupational therapy in helping people shape their lifestyle through health-promoting occupations. The range of the lifestyle factors that contribute to successful aging are, in fact, deeply embedded in the domain and process of the Framework such as ADLs, IADLs, rest and sleep, work, leisure, social participation, habits and routines, create or promote, and prevention. As professionals emphasizing the holistic care approach, occupational therapists must be aware of lifestyle choices in older adults and provide opportunities for enhanced levels of health and wellness.

By far, few, if any, psychometrically sound measurements encompass the diverse aspects of health-related lifestyle. A comprehensive, systematic measure of various lifestyle factors will reflect the nation’s current health care emphasis as well as the role of occupational therapy in services for older adults.

The Health Enhancement Lifestyle Profile (HELP) is a newly developed self-report questionnaire designed for screening and monitoring health-related lifestyle factors and examining the outcome of interventions aimed to promote healthy lifestyles with aging. A synthesis of the existing literature on successful aging and lifestyle factors, the domain and terminology included in the Framework, and input from community-dwelling older adults provided a strong framework for the conceptualization and formation of HELP. This article aims to describe the development and validation of HELP through the application of the Rasch measurement model.

The HELP consists of two major sections. The first section consists of personal background and health information such as age, gender, ethnicity, marital status, chronic disease or disabling conditions, and self-rated heath. The second section of HELP includes seven scales measuring different aspects of health-related lifestyle: (1) exercise, (2) diet, (3) social and productive activities, (4) leisure, (5) ADLs, (6) stress management and spiritual participation, and (7) other health promotion and risk behaviors (sample items are available online at www.ajot.ajotpress.net [navigate to this article, and click on “supplemental materials”]). Each scale consists of eight items (questions) that examine the frequency of the respondent's engagement in various health-related activities or events. People are asked to respond to each question according to their typical or routine performance during the past 3-mo period. Response categories generally include (1) never, (2) 1–2 days, (3) 3–4 days, (4) 5–6 days, and (5) 7 days (per wk). However, as suggested by participants in the field pretesting study, an additional category “(6) 1–2 days a month” was added. For scoring, each response was given a numerical value reflective of the relative frequency, which results in a 0- to 5-point rating scale. Scores from negatively worded items are to be reversed. A subtotal score can be computed for each HELP scale, ranging from 0 to 40, where a higher score indicates a more favorable level of lifestyle. The total score of HELP ranges from 0 to 280.

Piloting of HELP was completed through the methods of focus group and field pretesting. A focus group led by the author included a convenience sample of eight community-dwelling older adults solicited from three local senior citizen centers. Discussion involved confirming the definition and scope of various aspects of health-related lifestyle as well as reviewing the content of HELP on an item-by-item basis. Accordingly, revisions were made to enhance the relevance and clarity of this measurement tool so as to better reflect the perspectives of healthy lifestyle among older adults. Subsequently, field pretesting of the revised HELP was conducted with another convenience sample of 16 participants representative of the target population. On the completion of the measure, each participant was given a one-on-one debriefing interview in which the participant related how he or she experienced the HELP questions and identified any confusion or suggestions for improving the understandability of the questions. Each briefing interview was led by one of the six occupational therapist senior graduate students specially trained for this research project. As a result, further revisions and modifications were made to different technical aspects of HELP such as question formation (e.g., separation of double-barrel questions), item wording, and response categories. Positive comments and quotes on HELP were also documented throughout the interviews. Some sample quotes recorded are as follows:

Participants in this study consisted of community-dwelling (noninstitutionalized) older adults age ≥55 residing in southern California who were cognitively intact and able to communicate in English. No other specific exclusion criteria were considered. The study was approved by the institutional review board of the California State University, Dominguez Hills. Convenience sampling and snowball sampling methods were used to recruit participants from a diversity of community sites, including senior citizen centers, senior residential communities, independent living facilities, adult day health care centers, local senior social and activities groups, and religious groups or organizations. HELP was completed by each participant through one of the following methods: (1) a direct interview in which a graduate student or a staff member read through questions and recorded the responses for a participant, (2) on-site paper-and-pen administration to an individual or a group of participants, and (3) mail survey.

Rasch analysis of the Andrich’s Rating Scale Model (Linacre, 2009) was used to determine the internal validity and clinical applicability of the HELP scales. Analysis was conducted through the WINSTEPS Rasch model computer program, Version 3.68.0 (SWREG Inc., 2009). Psychometric characteristics, including unidimensionality (by means of analyses of principal components of residuals, fit statistics, local dependency, and differential item functioning), item hierarchy, person separation index and reliability, and rating scale validity, were examined for each scale of HELP. Details of these Rasch procedures are described along with their findings in the Results section.

A total of 257 older adults were recruited for this study; 4 were excluded from data analysis because of erroneous or incomplete responses. The remaining 253 participants consisted of 148 women (58.5%) and 105 men (41.5%). Participants’ ages ranged from 55 to 92 yr, with a mean of 71.4 and a standard deviation of 10.5. (A summary of participants’ demographics and health-related information is available online at www.ajot.ajotpress.net [navigate to this article, and click on “supplemental materials”]). Most participants were unemployed or retired. The most commonly reported health problems or impairments among the participants included hypertension, arthritis, visual impairment, back or neck pain, and diabetes. The number of health problems or impairments reported by each participant ranged from 0 to 10, with a mean of 2.77 and a standard deviation of 2.03. More than half of the participants considered their health status to be “good,” and only approximately 5% considered themselves to be in poor health.

Unidimensionality of the HELP scales was first confirmed by principal components analysis (PCA) of standardized residuals after an initial Rasch factor has been extracted. The following criteria were adopted to indicate the unidimensional feature of a scale: (1) a cutoff of 60% of the variance explained by the initial Rasch factor, (2) the variance explained by the first contrast in the residuals less than 10%, and (3) the eigenvalue of the first contrast smaller than 3.0 (Linacre, 2009; Smith, 2002). Table 1 depicts the results of PCA with each HELP scale. All but the Social and Productive Activities scale demonstrated >60% of variance explained by the primary Rasch factor, and all seven scales met the other criteria for the variance of residuals (<10%) and eigenvalues (<3.0). The proposed PCA criteria for unidimensionality of HELP were generally met.

Rasch goodness-of-fit statistics further determined how well the items of each HELP scale fit the one-dimensional model of linear measure. In Rasch analysis, Infit MnSq (i.e., information-weighted mean square residual goodness-of-fit statistic) has been considered a more critical and sensitive indicator of unidimensionality than Outfit MnSq (i.e., outlier-sensitive mean square residual goodness-of-fit statistic), because Outfit MnSq is less threatening to measurement and easier to manage (Bond & Fox, 2001; Linacre, 2002). Given the sizes of the study sample and the scales, a HELP item with an Infit MnSq >1.3 or <0.7 in combination with a ZStd (i.e., standardized mean square) >2.0 was used to indicate the lack of fit in unidimensionality (Bond & Fox, 2001; Linacre, 2002). In particular, an Infit MnSq >1.3 indicates 30% more variance than expected and thus suggests that the item measures a construct different from the overall scale. Table 2 demonstrates fit statistics for HELP, including measures (item calibration: logits), standard errors, Infit MnSqs, and ZStds. Of the 56 items from the seven HELP scales analyzed, 3 were identified as a misfit to the Rasch measurement model, based on the criterion used: “How often during a week do you go to work (paid work)?” in the Social and Productive Activities scale, “How often during a week do you talk with a special someone to share with your life, happiness, and sadness?” in the Stress Management and Spiritual Participation scale, and “How often during a month do you take pain medicine to control any form of body pain (such as migraine headache, arthritic pain, or back pain)?” in the Other Health Promotion and Risk Behaviors scale.

The correlation of standardized residuals was used to detect “local dependence” between pairs of items, namely, a subsidiary dimension in the measurement that is not accounted for by the initial Rasch factor (Linacre, 2009). In particular, negative local dependence is often an indicator of multidimensionality. The Social and Productive Activities scale demonstrated a low negative residual correlation (r = –.38) between the items “How often during a week do you go to paid work?” and “How often during a week do you go to volunteer work?” That is, the endorsement of either item may contradict the response to the other. Nevertheless, all other residual correlations within the HELP scales remained at the negligible level (r_s = –0.20 to 0.20).

The testing of differential item functioning (DIF) further identified potential misfit to the Rasch model through the inspection of the differences in item calibration within the data. DIF occurs when item difficulty estimates vary across demographic groups, thus indicating that the scale is not sufficiently unidimensional and that other exogenous factors may sway the responses (Linacre, 1998). For the DIF analysis, the participants were divided by (1) gender; (2) age, based on the median of 72 (“young-old,” 55–72 yr, vs. “old-old,” 73–92 yr); and (3) race (White vs. non-White), respectively. The ethnicity categories were dichotomized because of the relatively small and disproportionate samples among the non-White subgroups. The criteria used for the DIF analysis were DIF contrast >0.50 (a difference of 0.5 logits in item difficulty calibrations between two groups) and p < .006 (adjusted for multiple comparisons by a Bonferroni correction; Linacre, 2009). The HELP scales were mostly free of DIF. However, one item in the Leisure scale regarding “carpentering, auto/boat/house fixing, or any other mechanical work for your hobby” was easier to endorse for male participants (DIF = 0.58 logits, t[251] = 2.87, p = .003). The previously identified problematic item “How often during a week do you go to paid work?” in the Social and Productive Activities scale was also found to be easier to endorse for “young-old” participants (DIF = 0.79 logits, t[251] = 3.40, p = .001).

The Rasch model establishes the hierarchy of items and people along an equal-interval continuum. The item measures expressed in logits (see Table 2) were used to arrange the items within each HELP scale in their hierarchical order of difficulty from easiest at the top to most difficult at the bottom. For example, for the Diet scale, the first item, “healthy foods rich in protein” that was the easiest to endorse had a difficulty estimate of –1.19 logits. Consecutively, the second item, “healthy foods rich in calcium,” demonstrated a difficulty estimate of –0.72 logits, and, proceeding with the continuum, the last (most difficult to endorse) item, “two servings of sweets or desserts,” revealed the highest difficulty estimate (1.33 logits) of the scale.

The person separation index (PSI), strata, and reliability coefficient of each HELP scale are summarized in Table 3. The number of strata was calculated using the formula Strata = (4PSI + 1)/3 (Wright & Masters, 1982). The greater the strata, the more distinct levels of healthy lifestyle were revealed from the sample. Acceptable to good separation and reliability indexes were demonstrated, and the resultant distinct number of healthy lifestyle strata for each HELP scale approximated to three or more.

Table 4 reports the analysis of the functioning of rating scale structure (0- to 5-point scale) for each HELP scale. The results include the observed average measure, model expected measure, MnSq fit statistics, and step calibration of each rating category.

First, the observed average measure is an empirical indicator of the context in which the rating category is used (Linacre, 1999a, 2009). In general, observations in higher categories must be consistently produced by higher measures. That is, no category disordering should be found; otherwise, the meaning of the rating scale is indecisive for that data set, and consequently any derived measures can be of minimal utility. As seen in Table 4, the average measures by category advance monotonically up the rating scale. This pattern of increase is an empirical confirmation of scale functioning that higher rating categories indicate more of the latent variable measured (i.e., healthy lifestyle). Correspondingly, the expected measures are the values that the Rasch model predicts for the measures of various rating categories (Linacre, 1999a, 2009). The residual difference between the expected values and observed measures assists in determining the degree to which data obtained using the existing rating scale would fit the Rasch model. As shown in Table 4, most rating categories in the HELP scales appear to have their observed average measures reasonably close to the expected values.

MnSq fit statistics for each rating category are also reported in Table 4. For rating scales, a high MnSq fit suggests that there is more unexplained noise than explained noise, thus indicating the existence of misinformation in the observations on the basis of the use of the rating scale. In general, a rating category is thought to be used in an unexpected manner if its unweighted MnSq fit statistics (Outfit MnSq) is >2.0 (Linacre, 1999a, Wright & Masters, 1982). As can be seen in the table, all the rating categories in the HELP scales were found to have an Outfit MnSq smaller than the critical value of 2.0.

Step calibration (or Rasch–Andrich threshold) is a Rasch-model parameter that indicates a relative pairwise measure of the transition between the adjacent rating categories (Linacre, 1999b). A positive value of step calibration indicates that the lower of the pair of categories is more likely to be observed; conversely, a negative value indicates that the higher category of the pair is more likely to be observed. The step calibration is expected to increase with category value. As shown in Table 4, there was no step disordering; the criterion of monotonic advance was met for each set of step calibration in the HELP scales.

Occupational therapists working with older adults should help monitor and manage lifestyle factors as part of their holistic health promotion plan. Up to now, there has been a paucity of instruments that were developed to measure lifestyle factors in a holistic and systematic manner. Although abundant studies have been conducted to determine the effectiveness of programs aimed at promoting healthy lifestyle among elderly people, most of these studies focused on health outcome measures such as physical functioning, health status, quality of life, or life satisfaction, yet failed to include a direct measure of those lifestyle factors or behaviors as a dimension of outcome evaluation. HELP was therefore developed to bridge this gap. Evidence of the preliminary psychometric properties of HELP was provided in this study through the application of the Rasch measurement model with data derived from a sample of community-dwelling older adults.

Several procedures were implemented to ensure the existence of a single, unidimensional variable as well as the data–model fit for each of the seven HELP scales. Despite a small number of problematic items that called for further investigation, the overwhelming majority of the items met the Rasch requirements. First, PCA of the residuals confirmed that there was no factor structure to the residuals (i.e., <10% of variance in the residuals), and, thus, that each HELP scale measured a single distinct aspect of health-related lifestyle. Second, goodness-of-fit statistics further revealed that all but three items fit the Rasch one-dimensional model. Third, the correlation of standardized residuals between pairs of items largely supported the criterion of “local independence” of items within each HELP scale. Finally, the DIF analysis corroborated the consistence of item calibration (i.e., item invariance) across different groups of the study participants.

The different HELP scales were developed with an attempt to encompass the various physical, psychological, social, and spiritual components of lifestyle that have been holistically or separately emphasized in both research and practice. Evidence of the unidimensionality of the HELP scales increases therapists’ confidence in using the instrument for assessment, monitoring, and outcome measurement of the targeted lifestyle factors. Although it is important to recognize the framework of healthy lifestyle as a whole, the subtotal score generated from each HELP scale can represent a conceptually distinct contributor to the healthy lifestyle. Accordingly, service planning can emphasize strategies to systematically facilitate or modify behaviors relevant to those specific lifestyle factors concerning the individual. For example, with a goal to improve the subtotal scores of the Exercise, Diet, and ADL scales, intervention strategies can include establishing a routine in which the individual walks to a nearby grocery store and shops for ingredients to cook healthy homemade meals, instead of frequently driving to order meals at a fast-food restaurant.

The Rasch-derived item hierarchy formed using logits provides an expected pattern of healthy lifestyle behaviors, which holds promise for extending the clinical usefulness of HELP. For example, in the Diet scale, the items placed on the “difficult/frequent” extreme of the hierarchy were found to highly reflect the typical dietary pattern of Americans, characterized by high intakes of red meat, sugary desserts and drinks, and high-fat products (Centers for Disease Control and Prevention [CDC], 2007). The formation of dietary pattern can go beyond an individual’s habitual decisions into more stubborn, predetermined cultural and environmental factors (Shatenstein & Ghadirian, 1998; Shepherd, 1999), as is the case with the U.S. “meat–sweet diet” (U.S. Department of Health and Human Services, 2007). Accordingly, an individualized goal to improve on those extreme items pertaining to intakes of “desserts and sugary drinks” and “saturated or trans fats” (see Diet Items 7 and 8 in Table 2) is expected to be far more challenging than a goal aimed to emphasize healthy foods rich in protein and calcium (Diet Items 1 and 2 in Table 2). The hierarchical structure of HELP can, therefore, help define a continuum of healthy lifestyle activities or behaviors that can be used to establish successive, realistic goals in promoting health as well as to monitor any progressive change to a series of relevant lifestyle behaviors as a result of intervention.

Acceptable to good PSIs and reliability coefficients found in the HELP scales further support the clinical applicability and consistency of the HELP scores. In particular, the strata derived from the separation indexes suggest that each HELP scale can conceptually differentiate people into at least two to three distinct lifestyle groups. For example, 2.37 strata found in the Leisure scale indicate the capacity of the scale to distinguish people who are highly leisure oriented from those who are not frequently engaged in leisure activities. Similarly, the Exercise scale (3.16 strata) may stratify people into three exercise behavior levels, namely, exercise frequently, exercise sometimes, and barely or never exercise. Given the nondiagnostic purpose of HELP as well as the small set of items in each scale, the numbers of stratum generated, although relatively small, can be considered clinically discriminative and meaningful (Mallinson, Stelmack, & Velozo, 2004).

Finally, Rasch analysis of the rating scale structure confirmed the psychometric functioning of the 0- to 5-point rating scale of HELP. Consistency found between the observed average measures and expected measures along with the agreeable results of fit statistics indicate that the rating categories in the HELP scales correspond to the expected function specified in the Rasch model. The criterion of monotonic advance was met throughout the subsequent step calibrations in the rating scale. The 0- to 5-point rating scale used in HELP consists of response categories that reveal the actual frequency of lifestyle behaviors expressed in numeric form (e.g., 1–2 days/wk, 3–4 days/wk). In fact, numeric-formatted rating scales often provide a sound response categorization that is well defined and mutually exclusive and thus yield a more explicit and precise measure of the construct or behavioral variable than the typical Likert scale using response categories such as never, seldom, sometimes, often, and always (Wright & Stone, 1979). The 0- to 5-point frequency-based rating scale used in HELP has thus proven a suitable method for quantifying various aspects of healthy lifestyle behaviors among older adults.

Several limitations contribute to the need for caution in the use of HELP as well as for future studies. First, several problematic items identified by the Rasch measurement model call for further scrutiny and revision to strengthen their fit to the unidimensional scale. Second, given the dynamic, multifaceted nature of the lifestyle context, other critical factors, such as motivation, self-efficacy, functional status, and environmental supports or barriers, should be evaluated along with HELP through other methods or instruments for more adequate problem identification and goal setting. Moreover, to substantiate the practical use of HELP, clinical studies can be conducted to endorse various health- and lifestyle-promoting programs with HELP incorporated into the stages of goal establishment and outcome measure. Consequently, the degree of sensitivity of HELP on monitoring change to lifestyle behaviors can be determined. Finally, the current study was based on data derived from a relatively small sample consisting exclusively of community-dwelling older adults who demonstrated a full or partial range of independent living skills. Given the preliminary psychometric properties and promising features of HELP, future funding can be pursued to conduct a normative study through which the norms and cutoff scores can be established for subgroups of older adults with different characteristics (e.g., age, gender, health status, diagnosis, living arrangement).

Both the pilot testing and the thorough item analysis using the Rasch modeling techniques support the preliminary psychometric properties of HELP. The seven scales of HELP provide a holistic view of a wide range of health-promoting occupations and risk behaviors in older adults. Occupational therapists working with elderly populations can carefully review HELP completed by each client and incorporate the results into the client-centered evaluation and intervention planning processes. Personal goals for promoting different aspects of healthy lifestyle behaviors can be established according to the individual’s priorities, interests, routines, health and functional status, occupational history, and environmental resources. In turn, readministration of HELP at the conclusion of the service provides the context for a direct, explicit outcome measure of change to lifestyle behaviors.

I thank the participants who contributed their time and effort to complete HELP. I also thank the group of MSOT students for their enthusiasm and participation in the training and data collection of the study. This study was funded by Sally Casanova Memorial Research, Scholarship and Creative Activities Program (RSCAAP), California State University–Dominguez Hills.