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Research Article  |   November 2012
Development and Validation of a 15-Item Lifestyle Screening for Community-Dwelling Older Adults
Author Affiliations
  • Jengliang Eric Hwang, PhD, OTR/L, is Associate Professor, Department of Occupational Therapy, California State University, Dominguez Hills, 1000 East Victoria Street, Carson, CA 90747; ehwang@csudh.edu
Article Information
Assessment Development and Testing / Geriatrics/Productive Aging / Health and Wellness / Productive Aging
Research Article   |   November 2012
Development and Validation of a 15-Item Lifestyle Screening for Community-Dwelling Older Adults
American Journal of Occupational Therapy, November/December 2012, Vol. 66, e98-e106. doi:10.5014/ajot.2012.005181
American Journal of Occupational Therapy, November/December 2012, Vol. 66, e98-e106. doi:10.5014/ajot.2012.005181
Abstract

The Health Enhancement Lifestyle Profile–Screener (HELP–Screener) is a 15-item self-report questionnaire conceptually excerpted from the original 56-item HELP. This article describes the development and validation of the HELP–Screener with a sample of 483 community-dwelling older adults. Data derived from the sample were first analyzed using the Rasch dichotomous model. Unidimensionality and data–model fit of the HELP–Screener were largely supported by the analyses of principal components of residuals, fit statistics, local dependency, differential item functioning, and item hierarchy. To delineate the clinical significance of the test results, the cutoff score for the HELP–Screener was established through the mean and standard deviation generated from the study sample. The HELP–Screener can serve as a time-efficient screening for identifying older adults who may require a comprehensive evaluation through HELP and, as a result, who may benefit from a lifestyle intervention. Of note, more studies are planned to further corroborate psychometric properties of this new instrument.

As a result of medical advances taking place on an almost daily basis, people are living longer lives. According to the National Center for Health Statistics (2011), approximately 10,000 people in the United States reach age 65 every day. By 2030, roughly 1 in 5 Americans are predicted to be ages 65 or older, yielding an estimated population of 72 million older adults.
Healthy People 2020 is a nationwide health promotion and disease prevention plan developed by the U.S. Department of Health and Human Services (2010) . One of the overarching goals of Healthy People 2020 is to promote healthy behaviors that lead to enhanced quality of life across all ages. It entails an immense increase in health-related resources and education, especially to help the growing population of older adults live a healthy lifestyle as well as engage in activities that increase their quality of life.
Occupational therapy practitioners are in a unique position to assist older adults because they promote healthy lifestyles, emphasize occupation as an important element of health promotion strategies, and provide interventions enabling maximal participation in meaningful occupations (American Occupational Therapy Association [AOTA], 2008). Clark et al. (2001)  claimed that it is critical for society to identify reasonable interventions to prevent age-related declines in function and health. As stated in the landmark Well Elderly Study, “diet, lifestyle and daily routine, degree of social support, amount of exercise, and sense of autonomy and control” (Clark et al., 1997, p. 1321) are paramount in maintaining health and independence among older adults. A healthy lifestyle provides undeniable benefits to older adults because of its potential to improve physiological and psychological health (Everard, Lach, Fisher, & Baum, 2000; Fernandez, Scales, Pineiro, Schoenthaler, & Ogedegbe, 2008), prevent disease (Jackson, Carlson, Mandel, Zemke, & Clark, 1998; Li, Chen, & Kuo, 2005), improve quality of life (Correa et al., 2009; McReynolds & Rossen, 2004), and imbue individual lives with meaning (Everard et al., 2000; Jackson et al., 1998). As professionals emphasizing the holistic care approach, occupational therapy practitioners must assess older adults’ diverse lifestyle factors and choices so as to provide opportunities for enhanced levels of health and wellness.
A paucity of measurements assess the wide-ranging aspects of health-related lifestyles (Earvolino-Ramirez, 2006). Hwang (2010b)  developed the Health Enhancement Lifestyle Profile (HELP), a comprehensive, systematic measure of lifestyle factors that reflects the nation’s current health care emphasis as well as occupational therapy’s role in services for older adults. HELP is a self-report questionnaire consisting of seven subscales measuring different aspects of lifestyle behavior: Exercise, Diet, Productive and Social Activities, Leisure, Activities of Daily Living, Stress Management and Spiritual Participation, and Other Health Promotion and Risk Behaviors. Each subscale includes eight items that examine the frequency of the respondent’s engagement in a distinct area of health-promoting occupations. For example, the Exercise subscale covers such questions as the following:
  • How often during a week do you walk outside or on a treadmill for at least 20 min as a form of exercise?

  • How often during a week do you work out at the gym or at home (such as aerobic exercise or dance) for at least 20 min?

  • How often during a week do you go bicycling, jogging, or hiking?

The approximate time needed to complete HELP is 20–30 min (Hwang, 2010b).
HELP’s psychometric properties have been supported by multiple procedures and studies. Pilot testing of HELP was completed through focus group and field pretesting to ensure the measure’s relevance and clarity (see Hwang, 2010b). The Rasch measurement model was used to establish the unidimensionality and data–model fit of each HELP subscale as well as to confirm the functioning of the adopted rating scale structure (i.e., 5-point Likert scale; Hwang, 2010b). Additional evidence of HELP’s reliability and validity was provided through classical test theory. The α coefficients ranged from .75 to .92 among the HELP subscales, indicating acceptable to good internal reliability (Hwang, 2010c). Construct validity was supported by the hypothesis testing method, which examined (1) the patterns of interrelationship among the seven HELP subscales and (2) the correlations between the HELP results and the global health status (Hwang, 2010c). Finally, stepwise multiple regression models revealed that five of the seven HELP subscales served as significant predictors for older adults’ health-related quality of life (Hwang, 2010a).
Since its dissemination in professional conferences and journals, HELP has been made available to occupational therapists and other practitioners on request. I have constantly attended to users’ comments on their experience with the instrument. Many practitioners working with older adults in community settings were interested in screening large numbers of clients for lifestyle behaviors in a more time-efficient manner. A plan to develop a brief form of HELP was thus proposed.
The HELP–Screener is a 15-item questionnaire that requires yes-or-no responses. Conceptually excerpted from its original version, the HELP–Screener also encompasses diverse aspects of health-related lifestyle behavior, such as exercise, diet, socialization, leisure, and spirituality. Some sample items are as follows:
  • I exercise more than twice a week.

  • I consume a variety of healthy foods rich in protein, fiber, or calcium every day (e.g., white meat, fish, fruits, vegetables, milk, soy products).

  • I engage in activities in my community (e.g., attending senior center, volunteering) at least once a week.

  • I frequently monitor my health (e.g., blood pressure, blood sugar, body weight).

The expectation is that such a brief and easy-to-score questionnaire will be used by occupational therapy practitioners in their routine practice to screen for and identify older clients who may further benefit from an all-inclusive lifestyle evaluation or consultation.
The purpose of this study was to develop and validate the HELP–Screener with a sample of community-dwelling older adults. Clinical application and usefulness of this instrument are also discussed.
Method
Research Design
This methodological study involved the development and initial validation of the HELP–Screener. Two specific procedures included in the study were (1) the use of the Rasch dichotomous model (Linacre, 2011) to substantiate the psychometric properties of unidimensionality and data–model fit and (2) the determination of the HELP–Screener’s cutoff score through the study sample.
Participants and Sampling
The study sample consisted of older adults residing in southern California. Each participant met the following criteria: age 55 or older, community dwelling, and adequate cognitive and English- or Spanish-language capabilities for responding to a questionnaire.
This study was approved by the institutional review board of California State University, Dominguez Hills. Convenience and network–snowball sampling methods were used to recruit participants from diverse community sites, including senior citizen centers, senior residential communities, independent living facilities, regular residential houses or apartments, adult day health care centers, local senior social and activities groups, and religious groups and organizations. Letters of permission or support were received from the participating community sites. To avoid potential overrepresentation of any specific demographic characteristics or functional levels of participants, each recruitment site was restricted to no more than 10 or 20 participants, as dictated by the size of the facility. The network–snowball sampling method was adopted by asking participants to help recruit their acquaintances who currently lived in regular residential housing (e.g., single-family homes, condominiums, rental apartments), a process that helped ensure the inclusion of older adults who might not otherwise be seen at sites exclusive to older adults. In addition, a quota sampling technique was also used throughout participant recruitment to constantly monitor participants’ demographics (e.g., gender, age, race, residential type and location) so as to solicit additional participants from groups that were once underrepresented in study samples.
Instrument
The HELP–Screener is a self-report questionnaire consisting of 15 yes-or-no questions. Before data collection, the questionnaire draft was pilot tested with a convenience sample of 32 community-dwelling older adults. On completion of the questions, each participant was given a one-on-one debriefing interview in which the participant identified any confusion and provided suggestions for improving the questions’ relevance and understandability. Each briefing interview was led by 1 of the 12 occupational therapy graduate students specially trained for this research project. In general, the HELP–Screener was considered easy to understand and time efficient. As a result, only minor revisions were made to strengthen the questions’ semantic clarity. All the questions were positively worded. Responses were coded as 1 (yes) and 0 (no), yielding a score range of 0–15; higher scores are indicative of healthier lifestyles. The time needed to complete the HELP–Screener is <5 min. A Spanish version of the HELP–Screener was also developed by two bilingual occupational therapy graduate students using the translation and back-translation method (Sperber, DeVellis, & Boehlecke, 1994).
Data Collection
Participants completed the HELP–Screener by means of (1) on-site paper-and-pen administration to an individual or a small group of participants; (2) a direct interview in which a graduate student or a staff member at the site read the questions and recorded the participant’s responses (this method was commonly used for participants with visual or reading difficulties); or (3) hand-delivered, postal mail, or e-mail with simple instructions enclosed. All returned questionnaires were checked for completeness before data entry.
Data Analysis
Rasch analysis for the dichotomous response model (Linacre, 2011) was used to determine the HELP–Screener’s internal validity. Analysis was conducted using the Winsteps® Rasch model computer program, Version 3.70.0 (Linacre, 2010). Psychometric characteristics, including unidimensionality and data–model fit, were examined by means of principal components analyses (PCAs) of residuals, fit statistics, local dependency, differential item functioning (DIF), and item hierarchy. Details of these Rasch procedures are described along with the findings in the Results section.
To determine the cutoff score for the HELP–Screener, the standard deviation from the central tendency measure was used. First, the total scores on the HELP–Screener derived from the study sample were examined for normality using Fisher’s skewness coefficient (i.e., skewness divided by standard error skewness; Pett, 1997). Coefficients between 1.96 and −1.96 indicate that the distribution of scores is not significantly different from a normal distribution. On confirmation of the normal distribution assumption, the mean and standard deviation of the total scores from the study sample were used to delineate clinical significance. It is noteworthy that, although diagnostic screening tools (e.g., depression screening) frequently require the use of sensitivity and specificity statistics to determine their cutoff, researchers have validated the use of the population mean and standard deviation for establishing the cutoff for nondiagnostic behavioral scales (Kendall & Grove, 1988; Marwit & Meuser, 2002; Matson, Fodstad, & Mahan, 2009). Therefore, given the nondiagnostic nature of the HELP–Screener, 1 standard deviation below the sample mean was calculated as the cutoff score suggesting the need for further lifestyle scrutiny or consultation.
Results
A total of 494 older adults were recruited for this study; 11 were excluded from data analysis because of incomplete responses. Of the remaining 483 participants, 275 were women (57%) and 206 were men (43%); 2 participants did not respond to the gender item. Participants’ ages ranged from 55 to 97 yr (mean = 70.3, standard deviation = 9.9). Table 1 provides a summary of participant demographics, including ethnicity, marital status, education, and employment status. The majority of participants were White (46%), followed by Mexican-American (18%) and African-American (13%). Slightly more than half of participants were married (51%), and 65% were unemployed or retired.
Table 1.
Participants’ Demographic Characteristics (N = 483)
Participants’ Demographic Characteristics (N = 483)×
Characteristicn%
Gender
 Male20643
 Female27557
 No answer20
Ethnicity
 White22146
 African-American6313
 Mexican-American8518
 Asian-American/Pacific Islander5812
 Other Latino or Hispanic235
 Native American112
 Other184
 No answer41
Marital status
 Never married388
 Married24551
 Divorced6814
 Separated316
 Widowed8918
 Cohabitated92
 No answer31
Education
 Elementary school4710
 Middle school9119
 High school17035
 Community college (associate’s degree)6113
 Undergraduate7916
 Graduate and above306
 No answer51
Employment status
 Employed full time11023
 Employed part time5611
 Unemployed or retired31365
 No answer41
Table 1.
Participants’ Demographic Characteristics (N = 483)
Participants’ Demographic Characteristics (N = 483)×
Characteristicn%
Gender
 Male20643
 Female27557
 No answer20
Ethnicity
 White22146
 African-American6313
 Mexican-American8518
 Asian-American/Pacific Islander5812
 Other Latino or Hispanic235
 Native American112
 Other184
 No answer41
Marital status
 Never married388
 Married24551
 Divorced6814
 Separated316
 Widowed8918
 Cohabitated92
 No answer31
Education
 Elementary school4710
 Middle school9119
 High school17035
 Community college (associate’s degree)6113
 Undergraduate7916
 Graduate and above306
 No answer51
Employment status
 Employed full time11023
 Employed part time5611
 Unemployed or retired31365
 No answer41
×
Unidimensionality and Data–Model Fit
Unidimensionality of the HELP–Screener was first confirmed by PCA of standardized residuals after an initial Rasch factor had been extracted. The following criteria were adopted to indicate the unidimensional feature of the HELP–Screener: (1) ≥60% of the variance had to be explained by the initial Rasch factor, (2) variance explained by the first contrast in the residuals <10%, and (3) the eigenvalue of the unexplained variance by the first contrast <3 (Linacre, 2011; Smith, 2002). The PCA indicated that 67.5% of the variance in HELP–Screener scores was accounted for by the primary Rasch factor, with only 8.6% of the variance explained by the first contrast (eigenvalue = 2.3). The assumption of the HELP–Screener’s unidimensionality was met.
Goodness-of-fit statistics further determined how well the 15-item HELP–Screener fit the Rasch dichotomous model. Given the total number of test items and this study’s sample size, an item with an infit mean square (MnSq) and an outfit MnSq > 1.3 or < 0.7 combined with a standardized mean square (ZStd) > 2.0 was determined to indicate a misfit to the Rasch model (Bond & Fox, 2007; Linacre, 2002). Both infit MnSq and outfit MnSq have an expected value of 1. An item with a MnSq > 1.3 suggests a misfit with at least 30% more variance than the expected value; stated otherwise, the item may measure a construct different from the overall questionnaire. In contrast, an item with a MnSq < 0.7 denotes at least 30% less variance than the expected value; namely, the item may be too predictable or redundant. Table 2 provides fit statistics for the 15 HELP–Screener items, including measures (item calibration: logits), standard errors, infit and outfit MnSqs, and ZStds. The only marginal misfit was found for the item “I consume a variety of healthy foods rich in protein, fiber, or calcium everyday,” with outfit MnSq = 0.68 and ZStd = −2.8. All other items met both infit and outfit statistical criteria.
Table 2.
Fit Statistics of the HELP–Screener
Fit Statistics of the HELP–Screener×
ItemMeasure, LogitsStandard ErrorInfit
Outfit
MnSqZStdMnSqZStd
1. I spend sufficient time taking good care of myself (e.g., grooming, showering, cooking, housecleaning).−1.140.150.91−0.90.70−2.0
2. I avoid health-risk behaviors (e.g., excessive drinking, smoking, consuming over-the-counter drugs).−0.850.130.98−0.20.98−0.1
3. I consume a variety of healthy foods rich in protein, fiber, or calcium every day (e.g., white meat, fish, fruits, vegetables, milk, soy products).−0.710.130.87−1.80.68–2.8
4. I go out with my family or friends at least once a week.−0.520.121.020.31.050.5
5. I pursue my hobbies at least once a week.−0.420.121.060.91.080.8
6. I have skills for coping with stress.−0.310.121.020.41.121.1
7. I frequently monitor my health (e.g., blood pressure, blood sugar, body weight).−0.210.110.93−1.30.85−1.6
8. I frequently get quality sleep and rest.−0.060.111.000.11.030.4
9. I engage in my religious/spiritual activities at least once a week.0.190.111.081.81.121.7
10. I frequently avoid those foods high in fat, cholesterol, sodium, or sugar (e.g., red meat, butter, eggs, canned soup, desserts).0.360.110.95−1.20.90−1.6
11. I frequently read the nutrition facts labels of food products before buying/eating them.0.470.100.95−1.40.95−0.9
12. I exercise more than twice a week.0.580.100.95−1.20.92−1.4
13. I engage in activities in my community (e.g., attending senior center, volunteering) at least once a week.0.730.101.112.91.142.6
14. I frequently look for resources or information on health promotion through the mass media, health practitioners, or classes/clubs.0.890.100.97−0.70.94−1.2
15. I frequently avoid sedentary activities/behaviors (e.g., watching TV, sitting and reading).1.080.101.174.61.214.2
Table Footer NoteNote. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.
Note. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.×
Table 2.
Fit Statistics of the HELP–Screener
Fit Statistics of the HELP–Screener×
ItemMeasure, LogitsStandard ErrorInfit
Outfit
MnSqZStdMnSqZStd
1. I spend sufficient time taking good care of myself (e.g., grooming, showering, cooking, housecleaning).−1.140.150.91−0.90.70−2.0
2. I avoid health-risk behaviors (e.g., excessive drinking, smoking, consuming over-the-counter drugs).−0.850.130.98−0.20.98−0.1
3. I consume a variety of healthy foods rich in protein, fiber, or calcium every day (e.g., white meat, fish, fruits, vegetables, milk, soy products).−0.710.130.87−1.80.68–2.8
4. I go out with my family or friends at least once a week.−0.520.121.020.31.050.5
5. I pursue my hobbies at least once a week.−0.420.121.060.91.080.8
6. I have skills for coping with stress.−0.310.121.020.41.121.1
7. I frequently monitor my health (e.g., blood pressure, blood sugar, body weight).−0.210.110.93−1.30.85−1.6
8. I frequently get quality sleep and rest.−0.060.111.000.11.030.4
9. I engage in my religious/spiritual activities at least once a week.0.190.111.081.81.121.7
10. I frequently avoid those foods high in fat, cholesterol, sodium, or sugar (e.g., red meat, butter, eggs, canned soup, desserts).0.360.110.95−1.20.90−1.6
11. I frequently read the nutrition facts labels of food products before buying/eating them.0.470.100.95−1.40.95−0.9
12. I exercise more than twice a week.0.580.100.95−1.20.92−1.4
13. I engage in activities in my community (e.g., attending senior center, volunteering) at least once a week.0.730.101.112.91.142.6
14. I frequently look for resources or information on health promotion through the mass media, health practitioners, or classes/clubs.0.890.100.97−0.70.94−1.2
15. I frequently avoid sedentary activities/behaviors (e.g., watching TV, sitting and reading).1.080.101.174.61.214.2
Table Footer NoteNote. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.
Note. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.×
×
Local dependence of the HELP–Screener was examined through correlational analysis of standardized residuals between pairs of items, namely, a subsidiary dimension in the measurement that is not accounted for by the initial Rasch factor (Linacre, 2011). In particular, negative local dependence is often indicative of multidimensionality. Two items representing negative local dependence would result in the endorsement of either item’s contradicting the response to the other. The analysis demonstrated that the correlations of standardized residuals between all the item pairs of the HELP–Screener ranged from −.188 to .112, remaining within the negligible level (rs = −.20 to .20). Therefore, the measurement was considered to be free from the issue of local dependence.
The testing of DIF further identified potential misfit to the Rasch model through inspection of the differences in item calibration within the data. DIF occurs when item difficulty estimates vary across demographic groups, thus indicating that the assessment 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 age of 70 (young-old: 55–70 yr vs. old-old: 71–97 yr); (3) ethnicity (White vs. non-White); and (4) employment status (unemployed or retired vs. full- or part-time employed). The ethnicity categories were dichotomized because of the relatively small and disproportionate samples among the non-White subgroups. A DIF contrast <0.50 logits was considered no DIF; a contrast of 0.50–1.00 logits, a minimal DIF; and a contrast >1.00 logits, a notable DIF (Linacre, 1998; Wright & Douglas, 1975). The α was adjusted for multiple comparisons by a Bonferroni correction (i.e., α = .003). The HELP–Screener was found to be free of DIF by gender, age, and ethnicity. However, two items showed minimal DIF by employment status: “I go out with my family or friends at least once a week” and “I engage in activities in my community at least once a week” were likely to favor unemployed or retired participants (DIF = 0.56 logits, t[270] = 1.95, p < .0001, and DIF = 0.73 logits, t[259] = 2.33, p < .0001, respectively).
The item hierarchy of the HELP–Screener was established through logits that formed the order of difficulty estimates ranging from the easiest at the top to the most difficult at the bottom (see Table 2). For example, the first item, “I spend sufficient time taking good care of myself,” which was the easiest to endorse, had a difficulty estimate of −1.14 logits. Consecutively, the second item, “I avoid health-risk behaviors,” demonstrated a difficulty estimate of −0.85 logits, and, proceeding along the continuum, the last item, “I frequently avoid sedentary activities/behaviors,” had the highest difficulty estimate (1.08 logits). As seen in Table 2, the intervals between adjacent items were mostly ≥0.15 logits, suggesting sufficient differentiation of item indicators on the latent variable (i.e., lifestyle; Bond & Fox, 2007). In addition, on the basis of the distribution of logits, no noticeable item gap (i.e., ≥0.50 logits of gap between adjacent items) or item overlap (i.e., items with same logits) was found for the 15-item questionnaire.
Determination of the Cutoff
The mean of HELP–Screener total scores for the study sample (N = 483) was 10.86 (SD = 2.27). Figure 1 demonstrates the distribution of the scores. Participants’ total scores ranged from 6 to 15, with minor negative skewness (skewness = −0.191; standard error of skewness = 0.111). The resultant Fisher skewness coefficient of −1.72 suggested that the distribution of data derived from this study sample was not significantly different from a normal distribution. In addition, no extreme outliers (i.e., beyond mean ± 3.0 standard deviations) were identified in the study sample. Therefore, 1 standard deviation below the mean, 8.59, was calculated, and to minimize the possibility of false negatives, the rounded whole number, 9, was determined as the cutoff score for the HELP–Screener. As a result, 83% of the study sample (n = 402) was found to score higher than or equal to the cutoff.
Figure 1.
Distribution of the HELP–Screener scores.
Note. N = 483; mean = 10.86, standard deviation = 2.273. HELP = Health Enhancement Lifestyle Profile.
Figure 1.
Distribution of the HELP–Screener scores.
Note. N = 483; mean = 10.86, standard deviation = 2.273. HELP = Health Enhancement Lifestyle Profile.
×
Discussion
Particularly germane to the scope of occupational therapy practice is the promotion of health and wellness for all populations to optimize participation in daily occupations, namely, “Living Life To Its Fullest” (AOTA, 2010). As lifetimes grow longer and the U.S. baby boom generation ages, living life to its fullest will require prevention of health risks and maintenance of health-promoting behaviors to ensure a healthier lifestyle and greater quality of life in old age. As a result, occupational therapy practitioners working with older adults must bear the responsibility for appraising and cultivating clients’ healthy lifestyle behaviors conducive to enhanced quality of life.
The original 56-item HELP was developed as a clinical tool to bridge a gap in lifestyle assessments by providing a comprehensive measure covering multiple factors beyond the domains of exercise and diet often addressed in preventive medicine. The original HELP’s psychometric properties and clinical applicability have been well supported through a series of studies (Hwang, 2010a, 2010b, 2010c; Peralta-Catipon & Hwang, 2011). Echoing the manifold constructs embedded in the original HELP, the 15-item HELP–Screener was created to provide a time-efficient method for identifying older adults in need of a more comprehensive evaluation through HELP and, as a result, those who may benefit from a lifestyle intervention.
The preliminary psychometric properties of the HELP–Screener were substantiated through the Rasch dichotomous model (Linacre, 2011). First, PCA of the standardized residuals confirmed the unidimensional construct of the HELP–Screener, thus suggesting that the application of the 15-item questionnaire as a measure of the overall lifestyle behavior is warranted. Second, goodness-of-fit statistics further supported the fit of the items to the model, with the exception of one item (“I consume a variety of healthy foods rich in protein, fiber, or calcium everyday”) that exhibited a marginal misfit by outfit MnSq. Of note, outfit MnSq is normally considered an outlier-sensitive index that is less threatening to measurement and easier to manage (Bond & Fox, 2007; Linacre, 2002). I did not deem it urgent to amend the item. However, the item’s clinical suitability can be further verified through its prospective users. Third, the correlation of standardized residuals between pairs of items largely supported the criterion of local independence (free of residual covariance) for the 15 HELP–Screener items. Moreover, the DIF analysis corroborated the consistency of item calibration (i.e., item invariance) across the gender, age, and ethnicity subgroups. However, 2 items (“I go out with my family or friends at least once a week” and “I engage in activities in my community at least once a week”) posed a minimal DIF by employment status. This finding may be ascribed to the fact that older adults who are employed tend to spend less time in social and community activities than those who are unemployed or retired (Sayer & Gornick, 2009; Wilkie, Peat, Thomas, & Croft, 2007). Finally, the item hierarchy demonstrated through logits formed a sequential ordering of HELP–Screener items from least to most difficult with no indication of item overlap or gaps; this result further proved the questionnaire’s efficiency and precision (Bond & Fox, 2007).
The Rasch analysis results reported earlier are important in finalizing the HELP–Screener’s intended purpose. In keeping with the critical feature of being concise but all encompassing, the 15 yes–no questions were carefully excerpted from the original 56-item HELP. These questions were pilot tested with the target population to ensure their semantic clarity and relevance before data collection for this study. The subsequent analysis through Rasch modeling further revealed that these 15 items assessing various lifestyle behaviors as a whole were generally free from the detrimental issues of irrelevance, incoherence, and redundancy. Although the promotion of healthy lifestyles among older adults as a primary prevention strategy has been a recent initiative, as mentioned, much has been done in service and research focusing overwhelmingly or exclusively on diet, physical activity, and health risk behaviors (e.g., smoking, excessive drinking; Koertge et al., 2003). The HELP–Screener, however, defines a healthy lifestyle in broader terms that encompass the physical, dietary, psychological, social, spiritual, and occupational aspects of health-promoting behavior. The inclusion of these seemingly distinct but pertinent factors into a holistic screening of health-related lifestyle proved psychometrically legitimate.
Another purpose of this study was to establish a cutoff score for the HELP–Screener through the mean and standard deviation of data derived from the study participants. Although a large representative sample randomly drawn from the target population would have been preferable in such a procedure, it was not feasible in this study because of budgetary restraints. The incorporation of several nonprobabilistic sampling methods was thus adopted so as to maximize the representativeness of the study sample (see Participants and Sampling section). The skewness coefficient indicated that the distribution of the obtained data resembled a normal distribution. Accordingly, 1 standard deviation below the mean was determined as the cutoff point. Although this procedure has been considered standard in delineating clinical significance for nondiagnostic behavioral screenings in which the indices of sensitivity and specificity are usually not applicable (Kendall & Grove, 1988; Marwit & Meuser, 2002; Matson et al., 2009), I nevertheless suggest that the cutoff for the HELP–Screener be seen more as indicative rather than definitive. Practitioners can always exercise professional judgment in making clinical decisions after the screening.
Clinical Application
To expedite quality care with an increasing workload, occupational therapy practitioners working with older adults in community settings can benefit from using a short, wide-ranging lifestyle screening as part of their routine evaluation. Administering the HELP–Screener can help practitioners gain initial awareness of potential health-risk behaviors and determine the need for a more thorough lifestyle inquiry among their clients. More specifically, if a client’s score on the HELP–Screener is <9, the established cutoff, the practitioner can then conduct the original 56-item HELP (Hwang, 2010b), leading to a more in-depth understanding of particular areas that warrant lifestyle modifications or regimens. Throughout the process, the practitioner must also ensure the depth of the client’s motivation and self-efficacy.
Aside from using the cutoff score to provide a time-efficient way to guide clinical decisions, a thorough examination of the unendorsed items is also strongly recommended because it may provide additional insight into clients’ unique lifestyle problems. For example, a client who scores above the cutoff may still be found to exhibit a health-risk behavioral pattern pertaining to physical inactivity as evidenced by three unendorsed items (“I exercise more than twice a week,” “I engage in activities in my community at least once a week,” and “I frequently avoid sedentary activities/behaviors”). Subsequently, the therapist may decide to administer several pertinent HELP subscales (e.g., Exercise, Productive and Social Activities, Leisure) that offer an opportunity to uncover a rich array of health-promoting behaviors favoring physical activity, such as walking, dancing, hiking, jogging, bicycling, camping, swimming, diving, sailing, gardening, and volunteering (Hwang, 2010b). Along with the HELP subscales, personal choices and goals to increase the breadth and frequency of physical and social activities can accordingly be established, concurring with the client’s interests, routines, health and functional status, history, and personal and environmental resources.
Limitations and Future Directions
Several limitations warrant the need for caution in the use of the HELP–Screener as well as for future studies. First, the HELP–Screener is a self-report instrument that entails clients’ recognition of the evaluation’s purpose and their truthful response to maintain the usefulness and accuracy of the results. Second, because this instrument was developed and validated using data derived from a nonprobabilistic sample of southern Californians, generalizability of its psychometric utility and related findings is limited. The HELP–Screener’s current cutoff was determined through the mean and standard deviation obtained from this study sample. With the ongoing expansion of data collection, the cutoff may be adjusted according to new means and standard deviations or refined through more sophisticated methodology (e.g., linear regression between scores of HELP and HELP–Screener). Last, this new instrument should continually be explored for other aspects of its psychometric properties. In particular, studies to examine test–retest reliability of the HELP–Screener and concurrent validity between the HELP–Screener and HELP should be prioritized.
Implications for Occupational Therapy Practice
The results of this study have the following implications for occupational therapy practice:
  • The HELP–Screener is a 15-item screening tool derived from the original 56-item HELP. Occupational therapy practitioners working with older adults in community settings can use such a time-efficient lifestyle screening as part of their routine evaluation.

  • The established cutoff score of the HELP–Screener can be used by practitioners to determine the need for administering the HELP, which yields a more in-depth understanding of particular areas requiring lifestyle interventions.

  • When engaging a client in collaborative treatment planning based on the results of the HELP–Screener and the HELP, the practitioner should take into account the client’s interests, routines, health and functional status, history, and personal and environmental resources so as to yield a client-centered lifestyle intervention.

Conclusion
The HELP–Screener is a 15-item screening tool conceptually derived from the 56-item HELP. The study was conducted to validate the psychometric properties of this new tool. The results of pilot testing with the target population indicate that the HELP–Screener is an easy-to-understand and time-efficient questionnaire for use with older adults. The HELP–Screener’s internal validity was substantiated using the Rasch dichotomous model. Its psychometric properties, including unidimensionality, data–model fit, local independence, item invariance, and item hierarchy, were largely supported for the test items. The established cutoff score for the HELP–Screener delineates clinical significance that helps identify older adults who may require further evaluation with the 56-item HELP and, as a result, may benefit from lifestyle regimens. Other aspects of the HELP–Screener’s reliability and validity and its clinical suitability should continually be examined to strengthen the tool’s psychometric and clinical soundness.
Acknowledgments
I thank all the facilities and participants who contributed their time and effort to complete HELP–Screener and related materials. I also thank the group of occupational therapy graduate students for their enthusiasm and participation in training and the data collection for the study.
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Figure 1.
Distribution of the HELP–Screener scores.
Note. N = 483; mean = 10.86, standard deviation = 2.273. HELP = Health Enhancement Lifestyle Profile.
Figure 1.
Distribution of the HELP–Screener scores.
Note. N = 483; mean = 10.86, standard deviation = 2.273. HELP = Health Enhancement Lifestyle Profile.
×
Table 1.
Participants’ Demographic Characteristics (N = 483)
Participants’ Demographic Characteristics (N = 483)×
Characteristicn%
Gender
 Male20643
 Female27557
 No answer20
Ethnicity
 White22146
 African-American6313
 Mexican-American8518
 Asian-American/Pacific Islander5812
 Other Latino or Hispanic235
 Native American112
 Other184
 No answer41
Marital status
 Never married388
 Married24551
 Divorced6814
 Separated316
 Widowed8918
 Cohabitated92
 No answer31
Education
 Elementary school4710
 Middle school9119
 High school17035
 Community college (associate’s degree)6113
 Undergraduate7916
 Graduate and above306
 No answer51
Employment status
 Employed full time11023
 Employed part time5611
 Unemployed or retired31365
 No answer41
Table 1.
Participants’ Demographic Characteristics (N = 483)
Participants’ Demographic Characteristics (N = 483)×
Characteristicn%
Gender
 Male20643
 Female27557
 No answer20
Ethnicity
 White22146
 African-American6313
 Mexican-American8518
 Asian-American/Pacific Islander5812
 Other Latino or Hispanic235
 Native American112
 Other184
 No answer41
Marital status
 Never married388
 Married24551
 Divorced6814
 Separated316
 Widowed8918
 Cohabitated92
 No answer31
Education
 Elementary school4710
 Middle school9119
 High school17035
 Community college (associate’s degree)6113
 Undergraduate7916
 Graduate and above306
 No answer51
Employment status
 Employed full time11023
 Employed part time5611
 Unemployed or retired31365
 No answer41
×
Table 2.
Fit Statistics of the HELP–Screener
Fit Statistics of the HELP–Screener×
ItemMeasure, LogitsStandard ErrorInfit
Outfit
MnSqZStdMnSqZStd
1. I spend sufficient time taking good care of myself (e.g., grooming, showering, cooking, housecleaning).−1.140.150.91−0.90.70−2.0
2. I avoid health-risk behaviors (e.g., excessive drinking, smoking, consuming over-the-counter drugs).−0.850.130.98−0.20.98−0.1
3. I consume a variety of healthy foods rich in protein, fiber, or calcium every day (e.g., white meat, fish, fruits, vegetables, milk, soy products).−0.710.130.87−1.80.68–2.8
4. I go out with my family or friends at least once a week.−0.520.121.020.31.050.5
5. I pursue my hobbies at least once a week.−0.420.121.060.91.080.8
6. I have skills for coping with stress.−0.310.121.020.41.121.1
7. I frequently monitor my health (e.g., blood pressure, blood sugar, body weight).−0.210.110.93−1.30.85−1.6
8. I frequently get quality sleep and rest.−0.060.111.000.11.030.4
9. I engage in my religious/spiritual activities at least once a week.0.190.111.081.81.121.7
10. I frequently avoid those foods high in fat, cholesterol, sodium, or sugar (e.g., red meat, butter, eggs, canned soup, desserts).0.360.110.95−1.20.90−1.6
11. I frequently read the nutrition facts labels of food products before buying/eating them.0.470.100.95−1.40.95−0.9
12. I exercise more than twice a week.0.580.100.95−1.20.92−1.4
13. I engage in activities in my community (e.g., attending senior center, volunteering) at least once a week.0.730.101.112.91.142.6
14. I frequently look for resources or information on health promotion through the mass media, health practitioners, or classes/clubs.0.890.100.97−0.70.94−1.2
15. I frequently avoid sedentary activities/behaviors (e.g., watching TV, sitting and reading).1.080.101.174.61.214.2
Table Footer NoteNote. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.
Note. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.×
Table 2.
Fit Statistics of the HELP–Screener
Fit Statistics of the HELP–Screener×
ItemMeasure, LogitsStandard ErrorInfit
Outfit
MnSqZStdMnSqZStd
1. I spend sufficient time taking good care of myself (e.g., grooming, showering, cooking, housecleaning).−1.140.150.91−0.90.70−2.0
2. I avoid health-risk behaviors (e.g., excessive drinking, smoking, consuming over-the-counter drugs).−0.850.130.98−0.20.98−0.1
3. I consume a variety of healthy foods rich in protein, fiber, or calcium every day (e.g., white meat, fish, fruits, vegetables, milk, soy products).−0.710.130.87−1.80.68–2.8
4. I go out with my family or friends at least once a week.−0.520.121.020.31.050.5
5. I pursue my hobbies at least once a week.−0.420.121.060.91.080.8
6. I have skills for coping with stress.−0.310.121.020.41.121.1
7. I frequently monitor my health (e.g., blood pressure, blood sugar, body weight).−0.210.110.93−1.30.85−1.6
8. I frequently get quality sleep and rest.−0.060.111.000.11.030.4
9. I engage in my religious/spiritual activities at least once a week.0.190.111.081.81.121.7
10. I frequently avoid those foods high in fat, cholesterol, sodium, or sugar (e.g., red meat, butter, eggs, canned soup, desserts).0.360.110.95−1.20.90−1.6
11. I frequently read the nutrition facts labels of food products before buying/eating them.0.470.100.95−1.40.95−0.9
12. I exercise more than twice a week.0.580.100.95−1.20.92−1.4
13. I engage in activities in my community (e.g., attending senior center, volunteering) at least once a week.0.730.101.112.91.142.6
14. I frequently look for resources or information on health promotion through the mass media, health practitioners, or classes/clubs.0.890.100.97−0.70.94−1.2
15. I frequently avoid sedentary activities/behaviors (e.g., watching TV, sitting and reading).1.080.101.174.61.214.2
Table Footer NoteNote. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.
Note. Boldface indicates misfit statistics. HELP = Health Enhancement Lifestyle Profile; MnSq = mean square; ZStd = standardized mean square.×
×