THE DETERMINANTS OF A HEALTH-PROMOTING
LIFESTYLE IN OLDER ADULTS
Patricia A. Stockert, B.S.N., M.S.
A Dissertation Presented to the Faculty of the Graduate School of Saint Louis University in Partial
Fulfillment of the Requirements for the Degree of Doctor of Philosophy
2000
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c Copyright by Patricia A. Stocked
ALL RIGHTS RESERVED
2000
i
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THE DETERMINANTS OF A HEALTH-PROMOTING
LIFESTYLE IN OLDER ADULTS
Patricia A. Stockert, B.S.N., M.S.
A Digest Presented to the Faculty of the Graduate School of Saint Louis University in Partial
Fulfillment of the Requirements for the Degree of Doctor of Philosophy
2000
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Health promotion for the older adult is of critical concern for nursing.
Health promotion activities may help this group maintain their health, experience
optimal functional capacity, remain independent, and lower medical costs.
Limited and conflicting information on health promotion activities and factors
that influence these behaviors is available in this population. The purpose of this
study was to identify determinants of a health-promoting lifestyle in an older adult
population by examining components of the Health Promotion Model. The study
also used structural equation modeling to examine the psychometric properties of
the Health Promoting Lifestyle Profile II (HPLPII) in older adults.
A survey design was used to gather data. Convenience sampling
techniques were used to survey 900 adults over the age of 60. Subjects were
recruited via a network of persons known to the researcher and through senior
organizations and independent living facilities in Central Illinois. The subjects
completed four questionnaires: The Participant Profile, Laffrey Health
Conception Scale, Perceived Health Competence Scale, and the Lifestyle Profile
n. All questionnaires were returned anonymously by mail.
Analysis showed that older adults had scored higher in health promotion
activities related to spiritual growth, interpersonal relations, and stress
management. Path analysis and multiple regression showed that perceived health,
gender, education, race, definition of health, and self-efficacy were significantly
related to the older adults practice of health promotion behaviors.
Structural equation modeling was used to test the psychometric properties
of the HPLPII. Testing of the measurement models reduced the HPLPII from 52
1
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2
to 22 items. Examination of the R2 and T-values for the 22-item higher order
model indicated that the instrument was a reliable and valid measure of health-
promoting lifestyle in an older adult population. Cronbach’s alpha showed the 22-
item instrument had a high degree of internal consistency (r=0.89).
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COMMITTEE IN CHARGE OF CANDIDACY
Associate Professor Mary Ann Lavin, Chairperson and Advisor
Associate Professor Margie S. Edel
Assistant Professor Doris M. Rubio
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DEDICATION
This project is dedicated to my husband, Drake, and
my daughters, Sara and Kelsey -
I could not have completed school or this project
without their love and support; and to
my parents, James and Evelyn Clark,
who stressed to me the importance of education
iii
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ACKNOWLEDGMENTS
I would like to acknowledge and thank the following:
My dissertation committee for all their time, wonderful suggestions, encouragement, and much appreciated help;
Sister Mary Ludgera, Dean, of Saint Francis Medical Center College of Nursing, for her personal and financial support of my academic endeavors;
My colleagues at the College for their words of encouragement and support throughout the pursuit of my degree;
The Sharon Foss Education Fund of Saint Francis Medical Center College of Nursing for partial funding of the study;
The Illinois League for Nursing for its support in the form of funding for thestudy;
And to all my family and friends who offered encouragement, and provided help so I had the time to go to school, read, and write.
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TABLE OF CONTENTS
List of Tables . .vi
List of Figures. vii
Chapter I. Introduction . . . . . . 1Background and Problem . 1Purpose . . . . . . 7Research Questions . . . . . 8
Chapter H Literature Review . . . . . 9Theoretical Framework .9The Health Promotion Model. .12Determinants of Health-Promoting Behaviors .16Determinants of Health-Promoting Behaviorsin Older Adults . . . . .21
Chapter HI. Methodology . . . . . .24Subjects . . . . . .24Procedure . . . . . .24Instrumentation . . . . .26
Chapter IV. Data Analysis . . . . . .31Sample Characteristics .31Research Question 1 . .34Research Question 2,3, and 4 .63
Chapter V. Discussion . . . . . .70Summary of Research Findings .70Implications for Nursing Practice .75Limitations . . . . . .76Implications for Future Research .78Conclusions . . . . . .79
Appendices .81A. Informed Consent Letter for Subjects .81B. Participant Profile . . . . . . .84C. Laffrey Health Conception Scale . . . . .87D. Perceived Health Competence Scale .91E. The Lifestyle Profile II . . . . . .93F. Twenty-Two Item Lifestyle Profile II .98
References . 101
VitaAuctoris . 112
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LIST OF TABLES
Table
1. Mean Scores and Standard Deviations of theHPLPH Subscales . . . . . . .33
2. Results of Multivariate Normality Testing .36
3. Health Responsibility Measurement Models . .38
4. Spiritual Growth Measurement Models .41
5. Interpersonal Relations Measurement Models .44
6. Physical Activity Measurement Models .47
7. Stress Management Measurement Models .50
8. Nutrition Measurement Models .53
9. Six Latent Construct Measurement Models . .56
10. Twenty-two Item Measurement Model .59
11. Higher Order Measurement Model of HPLPII .61
12. Variance Explained and T-values in Health-Promoting Lifestyle . . . . . . . .62
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LIST OF FIGURES Figure
1. T-values for Hypothesized Health ResponsibilityMeasurement Model . . . . . . .39
2. T-values for Hypothesized Spiritual GrowthMeasurement Model . . . . . . .42
3. T-values for Hypothesized Interpersonal RelationsMeasurement Model . . . . . . .45
4. T-values for Hypothesized Physical ActivityMeasurement Model . . . . . . .48
5. T-values for Hypothesized Stress ManagementMeasurement Model . .51
6. T-values for Hypothesized NutritionMeasurement Model . . . . . . .54
7. T-Values of Direct Effects of Individual Characteristics onHealth Promoting Behaviors . . . . . .65
8. Hypothesized Model of Individual Characteristics Effects onHealth Promotion As Mediated Through Self-Efficacy .67
9. Health Promotion Model Testing . . . . .69
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CHAPTER I
INTRODUCTION
Background and Problem
Health promotion is an essential component of nursing practice (Morgan
& Marsh, 1998). The American Nurses’ Association in its Social Policy
Statement (199S) identified health promotion as a significant activity for nursing,
stating, “Nursing involves practices that are restorative, supportive, and promotive
in nature” (p. 11). The Healthy People 2000 initiative, developed by the United
States Public Health Service in the 1980’s, identified major health problems and
strategies for improvement in health. An overall aim of Healthy People 2000 was
to increase the healthy life span of individuals. This means that as people reach
the final quarter century of life, they are free of chronic disease, preventable
infections, and serious injury (Mason & McGillis, 1990; United States
Department of Health and Human Services, 1990). By 199S, progress toward the
set goals of improving overall health had not proceeded as planned. In the health
promotion areas, 10 of the 17 target behaviors were making progress in the right
direction, 4 were proceeding in the wrong direction, one had not changed, and two
did not have data available to assess progress (McGinnis & Lee, 199S). Clearly, a
challenge still exists and more work on improving health promoting target
behaviors is needed. As a result, Healthy People 2010 has been developed. Four
overall goals have been identified which focus on improving health promoting
behaviors, protecting health, achieving access to quality health care, and
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strengthening community prevention (United States Department of Health and
Human Services, 1999).
Health promotion for the older adult, in particular, is of critical concern for
nursing (Fowler, 1996; Pender, Barkauskas, Hayman, Pace & Anderson, 1992).
The focus of health promotion in this population needs to be on the older adults’
strengths and abilities not on their diseases (Stanley & Beare, 1999). Older
persons are able to maximize their health, experience full functional capacity, and
remain independent through the use of health promoting behaviors (Black &
Kapoor, 1990; Mason & McGillis, 1990; Ruffing-Rahal, 1991). Older adults see
health promoting behaviors as important actions to take to maintain healthy
lifestyles (Fowler, 1996). Older adults are willing to engage in behaviors to
improve their health. This group sees these behaviors as increasing the quality as
well as the quantity of their lives (Young, 1996).
A difficulty in studying the concept of health promotion is that it has
multiple definitions and meanings to individuals (King, 1994; Kulbok & Baldwin,
1992; Maben & Clark, 199S). Health promotion is not the same as disease
prevention, health protection or health maintenance (Brubaker, 1983; Kulbok &
Baldwin, 1992; Murray & Zentner, 1993; Parse, 1990; Pender, 1996; Smith,
1990). Health promotion has a positive focus. Brubaker (1983) states that health
promotion is focused on growth and improvement in well-being. Along with
increasing well-being, the idea of actualizing or maximizing the human health
potential to promote change and growth has also been incorporated into the
definition (Murray & Zentner, 1993; Pender, 1996; Stanley & Beare, 1999).
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Stanley and Beare (1999) state that health promotion also seeks to minimize the
effects of aging in older adults. Decision-making and health practices for health
promotion improve at an individual level. Parse (1990) defines health promotion
as an action taking process aimed at enhancing the quality of life in the human
interactive process. Health promotion in nursing focuses on the whole person
(Murray & Zentner, 1993; Smith, 1990). These definitions focus on improving
quality of life over increasing quantity only.
Health promotion behaviors or activities are defined as the use of health
care services, routine activities to maintain health, actions to prevent disease,
compliance with medical regimen, or actions to achieve a higher level of well
being, actualization or personal fulfillment (Laffrey, Loveland-Cherry, &
Winkler, 1986; Morgan & Marsh, 1998; Murray & Zentner, 1993; Palank, 1991).
If an individual’s attitude is toward valuing health promotion behaviors, then the
individual will be more likely to practice those behaviors (Yoder, Jones, & Jones,
1985). The likelihood of an individual to engage in health promoting behaviors is
affected by numerous factors. Some of these factors include importance of health
to the individual, desire for competence, self-esteem, definition of health,
perceived benefits and barriers, and perceived self-efficacy (Laffrey et al., 1986;
Murray & Zentner, 1993; Pender, 1996). Health behaviors taken by an individual
may move them toward a positive state or away from a negative state (Pender,
1996).
Health promotion activities that are appropriate for older adults include:
regular physical, mental, and social activity; nutrition and weight control, and
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stress management (Stanley & Beare, 1999). These health promotion activities
help individuals live longer and healthier lives, with less need for medical
services (Bandura, 1997; Fries et al., 1993). Fries et al. indicate that this decreased
need for medical services because of increased health promotion activities
actually decreases medical costs in older persons.
The adoption and maintenance of good health promoting behaviors require
change on the part of an individual (Morgan & Marsh, 1998). “Health promotion
should be expected to accomplish changes in health behavior” (Green, Wilson, &
Lovato, 1986, p. 509). Change has been defined as a process that leads to an
alteration in a person’s behavior or lifestyle (Nunnery, 1997). The adoption of
health promoting behaviors is a result of planned change that involves conscious
effort of the individual to move toward a desired outcome. Two difficulties for
persons changing to adopt preventive health care behaviors have been identified
(Bums, 1992). The first is the difficulty in convincing an individual they are at
risk for developing health problems. The second is that long-term behavior
change and compliance are difficult to achieve and maintain.
To understand, explain, or predict a change in behavior necessitates not
only considering the individual but also the environment where the change takes
place. Change of behavior does not occur in a vacuum. Therefore, it is essential
that the person’s interaction with the environment is a critical component of
understanding behavior change. Health policy, access to health services and
facilities, reasonable costs, and reduction of health hazards in the environment are
all factors that impact an individual’s change in health behavior (Green et al.,
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1986; Morgan & Marsh, 1998). The philosophical position of nursing regarding
change is consistent with the Empirical-Rational school. This is the belief that
assumes people are rational and are personally interested in change (Nunnery,
1997). The nurse must use good communication and interpersonal skills in
planning and implementing strategies to reduce resistance to change in
individuals.
To be able to study the concept of health promotion and the related
behaviors people undertake to improve their health, it is necessary to have a
reliable and valid instrument that measures health promotion behaviors in the
target population. A review of the literature found the Health-Promoting Lifestyle
Profile (HPLP) developed by Walker, Sechrist, & Pender (1987) to be a widely
used general measure of health promotion activities in nursing. The original
measure consisted of 48 items scored on a 4-point response scale. The instrument
was divided into six subscales that measured the following domains of health
promotion: physical activity, nutrition, spiritual growth, interpersonal relations,
health responsibility, and stress management. The HPLP has been used with older
adults but was not specifically developed for that age group (Duffy, 1993; Duffy
& MacDonald, 1990; Foster, 1992; Huck & Armer, 1996; Riffle, Yoho, & Sams,
1989; Walker, Volkan, Sechrist, & Pender, 1988). After extensive use of the
HPLP with a variety of age groups, the instrument was revised to the Health-
Promoting Lifestyle Profile II (HPLPII). The HPLPII is a 52-item measure scored
on a 4-point response scale that retained the same six subscale structure as the
HPLP. Cronbach’s alpha for the HPLPII was r=0.94. The Cronbach’s alpha for
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the subscales ranged from r=0.79 to 0.87.The theorized six subscale format was
supported by factor analysis (S. N. Walker, Ed.D., R.N., personal communication,
March 1998).
Limited information on the use of the HPLPII is found in the literature.
The revised instrument was used to measure health-promoting lifestyles in college
students (Larouche, 1998); persons with Parkinson’s Disease (Fowler, 1997);
persons with multiple sclerosis (Stuifbergen & Roberts, 1997; Stuifbergen, 1999;
Stuifbergen, Seraphine, & Roberts, 2000); black women with diabetes (Jefferson,
Melkus, & Spollett, 2000); and women who were homeless or experiencing other
crises (Alley, Macnee, Aurora, Alley, & Hollifield, 1998). A Spanish version of
the HPLPII is being tested (Carlson, 2000).
No reliability or validity estimates for the instrument with six of these
groups were reported (Fowler, 1997; Jefferson, et al., 2000; Larouche, 1998;
Stuifbergen, 1999; Stuifbergen & Roberts, 1997; Stuifbergen, et al., 2000). Alley
et al. (1998) reported a reliability of 0.95 for the total instrument. Reliabilities for
the subscales ranged from 0.75 to 0.87 (Alley et al.). Carlson (2000) reported a
Cronbach’s alpha of 0.80 for the Spanish version of the HPLPII. The alphas for
the subscales ranged from 0.86 (Physical Activity) to 0.54 (Interpersonal
Relations). No reports of testing the HPLPII in older adults were found in the
literature. Testing of the HPLPII for reliability and validity with older adults is
needed to determine if the HPLPII is an appropriate measure of health-promoting
behaviors in this population.
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Purpose
The purpose of this study is to identify determinants of a health-promoting
lifestyle in an older adult population by examining components of the revised
Health Promotion Model and their influence on health behaviors. The model
needs further testing since its revision (Pender, 1996). The individual
characteristics impact on health promotion behaviors in older adults will be
examined because there is limited or conflicting information found in the
literature on these factors. The factors from the model, health conception,
perception of health, and perceived self-efficacy, will be included in the testing of
the HPM. Reliable and valid instruments to measure these concepts are available.
The study will also examine the reliability and validity of the Health-
Promoting Lifestyle Profile II in older adults. Once the determinants of health-
promoting behaviors in this group are identified, interventions need to be
developed and tested to increase or improve health promotion in the older adults.
Improvement in health promotion activities in the older adult has the potential to
increase the length of life as well as improve the quality.
Research Questions
Flowing from the analysis of this problem and subsequent literature review,
this study examined the following research questions.
1. What are the psychometric properties of the HPLPII when used in a
population of older adults?
A. To what extent is the HPLPII internally consistent?
B. What is the factorial structure of the HPLPII?
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C. To what extent does the HPLPII demonstrate construct validity?
2. Which individual variables (age, gender, education, marital status, perception
of health, and conception of health) directly impact health promotion
behaviors in older adults?
3. Does self-efficacy directly impact health promotion behaviors in older adults?
4. Which individual variables impact health promotion behaviors in older adults
through the mediating variable of self-efficacy?
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CHAPTER n
REVIEW OF RELATED LITERATURE
The purpose of the review of the related literature section is two-fold.
First, the theoretical foundation of the study is presented. Second, an analysis is
presented of the current literature related to the Health Promotion Model; the
determinants of health-promoting behaviors in general; and the determinants of
health-promoting behaviors in older adults.
Theoretical Framework
The theoretical model chosen for this study is The Health Promotion
Model (HPM) developed by Pender. The HPM first appeared in the literature in
the early 1980’s (Pender, 1982; Pender, 1996). The HPM is a framework that
integrates components from nursing and behavioral sciences to represent the
complex nature of persons interacting with the environment during their pursuit of
health. The framework is used by nurses and other researchers to examine the
complex processes that lead a person to practice behaviors that foster health
promotion. Pender (1996) states health promotion is “directed toward increasing
the level of well-being and self-actualization of a given individual or group” (p.
34). The HPM examines health behaviors across the life span using a competence
or approach-oriented mode.
Health promoting behavior is the behavioral outcome depicted in the
HPM. Health promoting behavior is aimed at moving an individual toward a
positive state of health or a high-level of health and well-being (Walker, Sechrist,
& Pender, 1987; Pender, 1996). Attainment of positive health outcomes is the
9
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goal of the health-promoting behavior. An individual achieves a positive health
experience throughout their life span by integrating health-promoting behaviors
into their lifestyle (Pender, 1996).
Multiple studies using and testing the HPM have led to a revision of the
model (Pender, 1996). Pender reported that the factors: importance of health,
perceived control of health, and cues to action that appeared in the original model,
were found to explain little variance in health-promoting behaviors. They were
deleted in the revised model. The influencing factors on health behavior were
reduced from thirteen in the original HPM to ten repositioned in the current model
(Pender, 1996). According to the revised HPM, health behaviors are determined
by individual characteristics and experiences, behavior-specific cognitions and
affect, commitment to a plan of action, and immediate competing demands and
preferences.
Individual characteristics and experiences are unique to each person and
the importance of these factors is related to the target health behavior. These
factors include prior related behavior and personal factors. The personal factors
are divided into biological (gender, age, body mass), psychological (definition of
health, self-esteem, motivation, perceived health status, personal competence),
and sociocultural (race, ethnicity, education, socioeconomic status). Individual
characteristics may directly influence the health behavior or may be modifiers that
exert influence through an effect on the behavior-specific cognitions.
The behavior-specific cognitions and affect are major sources of
motivation and a prime target area for intervention. These factors include
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perceived benefits and barriers to action, perceived self-efficacy, activity-related
affect, interpersonal influences (family, peers, norms, social support) and
situational influences (available options, aesthetics of environment for behavior,
demand characteristics). The behavior-specific cognitions may directly influence
the likelihood of the health behavior occurring. They may also exert their
influence on the occurrence of health behaviors through the commitment to a plan
of action and the immediate competing demands and preferences.
Commitment to a plan of action and dealing with immediate competing
demands and preferences are two new components added to the revised model.
Commitment to a plan of action is the cognitive process used in carrying out a
specific behavior and the accompanying strategies. Immediate competing
demands and preferences are alternative behaviors that arise immediately prior to
carrying out a planned health behavior (Pender, 1996). These two components
directly influence the health behavior.
The HPM is based on assumptions emphasizing that individuals take an active
role in carrying out health promoting behaviors as they interact with the
environment. The assumptions are:
1. Persons seek to create conditions of living through which they can express their unique human health potential.
2. Persons have the capacity for reflective self-awareness, including assessment of their own competencies.
3. Persons value growth in directions viewed as positive and attempt to achieve a personally acceptable balance between change and stability.
4. Individuals seek to actively regulate their own behavior.5. Individuals in all their biopsychosocial complexity interact with the
environment, progressively transforming the environment and being transformed over time.
6. Health professionals constitute a part of the interpersonal environment, which exerts influence on persons throughout their life span.
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7. Self-initiated reconfiguration of person-environment interactive patterns in essential to behavior change. (Pender, 1996, p. 54-55)
The Health Promotion Model
The HPM was used to provide a framework for studies related to health
promotion. The model provided the framework to: identify factors in medication
compliance among patients with seizure disorders and develop a treatment plan
(Lannon, 1997); analyze existing knowledge related to bicycle helmet use and
plan strategies to increase use (Coppens & McCabe, 1995); examine nurses
understanding of health promotion and their role in a neuro-rehabilitation setting
(Davis, 1995); describe successful smoking cessation in women (Puskar, 1995);
describe variables related to health promotion behaviors in college students
(Martinelli, 1999); and review the current knowledge on risk factors for
cardiovascular disease in school-age/adolescent children (Pittman & Hayman,
1997). Harrison (1990) recommends maternal-child nurses use the HPM as a
framework for wellness education for patients. Simmons (1990) used the HPM as
a component in the development of a new model that integrated health promotion
and self-care. The HPM was also used as the conceptual framework in the
development of a questionnaire that measured health promotion activities in
midlife women (Flowers & McLean, 1996). These studies show the versatility of
the model for examining current knowledge and planning for health promotion
activities.
The HPM has been used extensively as a theoretical framework in
research to examine health promotion and behaviors across the lifespan. The
health-promoting lifestyle of college students has been examined (Larouche,
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1998; Martinelli, 1999). Several studies examined health-promoting behaviors in
young women. The HPM was used to examine factors influencing physical
activity in average and overweight women (Felton & Parsons, 1994);
contraceptive use at first and most recent coitus (Felton, 1996); and the
relationship between adolescent health-promoting lifestyle and parental health-
promoting lifestyle (Gillis, 1994). The health promoting behaviors of well adults
were frequently studied (Ahijevych & Bernhard, 1994; Duffy, 1989; Duffy,
Rossow, & Hernandez, 1996; Fleetwood & Packa, 1991; Frauman & Nettles-
Carlson, 1991; Goldberg, 1994; Kemp & Hatmaker, 1993; Kerr & Ritchey, 1990;
Rew, 1990; Volden, Langemo, Adamson, & Oechsle, 1990; Walker et al., 1988;
Weitzel, 1989). Multiple studies were conducted using the HPM as the framework
to study health-promoting behaviors in older adults (Duffy, 1993; Duffy & Mac
Donald, 1990; Foster, 1992; Huck & Armer, 1996; Jones & Nies, 1996; Riffle et
al., 1989; Speake, 1987; Speake, Cowart, & Pellet, 1989; Speake, Cowart, &
Stephens, 1991; Walker et al., 1988). The ability to successfully use the HPM in
examining health promotion across the lifespan is demonstrated by these studies.
The HPM was also used to examine health-promoting behaviors in
populations with chronic diseases. Studies using the model have been conducted
to: identify determinants of exercise in persons with arthritis (Neuberger, Kasai,
Smith, Hassanein, & DeVmey, 1994); study the relationship between hope and
health-promoting lifestyle in persons with Parkinson’s Disease (Fowler, 1997);
examine factors related to osteoporosis preventive behaviors (Ali & Bennett,
1992); predict health-promoting lifestyles in persons with disabilities (Stuifbergen
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& Becker, 1994) and ambulatory cancer patients (Frank-Stromborg, Pender,
Walker, & Sechrist, 1990); investigate the relationship between health-promoting
behaviors and quality of life in persons with chronic disabling conditions
(Stuifbergen, Seraphine, & Roberts, 2000); and examine health-promoting
behaviors of spousal caregivers of persons with multiple sclerosis (O’Brien, 1993)
and women with multiple sclerosis (Stuifbergen & Roberts, 1997). The HPM has
use in both a well population as well as persons with chronic illness or
disabilities.
The HPM has also been used as a framework and tested with different
racial and cultural groups. Four studies specifically examined health promotion in
African American women and older adults (Ahijevych & Bernhard, 1994; Foster,
1992; Jones & Nies, 1996; Nies, Buffington, Cowan, & Hepworth, 1998). Two
studies examined health-promoting behaviors in Mexican-Americans (Duffy et
al., 1996; Kerr & Ritchey, 1990). Other studies with adults that examined health-
promoting behaviors were conducted with a predominately white sample but did
include African Americans and Asians.
The HPM has been used to predict specific health promoting behaviors
related to occupation. Several studies used the HPM as the framework to study
use of hearing protection in workers (Lusk & Keleman, 1993; Lusk, Ronis, &
Hogan, 1997; Lusk, Ronis, Kerr, & Atwood, 1994). The HPM also provided the
framework to study health-promoting behaviors in blue and white-collar workers
(Lusk, Kerr, & Ronis, 1995; Pender, Walker, Sechrist, & Frank-Stromborg, 1990;
Weitzel, 1989).
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In analyzing the studies that have been based on the HPM, several
findings emerged. The ages of the participants covered the lifespan, ranging from
16 to 99 years of age. The subjects were predominantly white, although African
Americans, Mexican-American, and Asians were also studied. The studies
examined health-promoting behaviors in both men and women. Level of
education for the subjects ranged from grade school levels up to doctorally
educated subjects, with the majority of subjects being high school graduates or
having education beyond high school. Occupation also varied in subjects from
unemployed to white-collar workers although most subjects tended to be in the
middle and upper middle classes.
Several studies testing the HPM found the model did not hold up as
hypothesized. Johnson, Ratner, Bottorff, and Hayduk (1993) used LISREL to test
the HPM. The results of the study indicated that the modifying factors or
individual characteristics influenced health-promoting behaviors directly and not
through the cognitive perceptual factors as the model predicted. These modifying
factors explained little variance in health-promoting lifestyle. Ratner, Bottorf£
Johnson, and Hayduk (1994,1996) conducted two additional tests of the HPM
using LISREL. The findings of the 1994 study indicated that the individual
characteristics and the behavior-specific cognitions explained little variance in the
health-promoting lifestyle. The results of the 1996 study indicated a model that
failed to meet data constraints. One problem with the earlier studies may have
been that the testing of the model was done using a secondary national data set.
The researchers only selected one or two items from the data set rather than using
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well-established scales to measure several components of the model and proxy
measures were selected for several other model components (Pender, 1996).
Pender states, “These proxy items were not aggregated as a measure of overall
health-promoting lifestyle, nor did they capture the multiple dimensions of each
lifestyle domain” (p. 58).
Determinants of Health-Promoting Behaviors
The revised HPM indicates that the personal factors either directly
influence the health promoting behavior or exert influence through the behavior-
specific cognitions (Pender, 1996). Lusk, Ronis, and Hogan (1997) in a test of the
revised model, found age, race, gender, and marital status to have an indirect
relationship mediated by the behavior-specific cognitions to the use of hearing
protection.
The majority of the studies examined the influence of the individual
characteristics on health-promoting lifestyles using the original HPM. Findings
contributed to the revision of the model. Gender, education, and age were found
to be significant predictors of health-promoting lifestyle with an increase of
healthy behaviors seen in women, older persons, and those with higher levels of
education (Lusk, Kerr, & Ronis, 1995). Age, income, education, marital status,
and employment, mediated by the behavior-specific cognitions, made modest
contributions to the variance in health-promoting lifestyles in a variety of
individuals (Frank-Stromborg, Pender, Walker, & Sechrist, 1990; Frauman &
Nettles-Carlson, 1991; Neuberger, Kasai, Smith, Hassanein, & DeViney, 1994;
Pender, Walker, Sechrist, & Frank-Stromborg, 1990; Wehzel, 1989; Woods,
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17
Lentz, & Mitchell, 1993). Education was significantly related to health promotion
practices in women who were homeless or in crises (Alley et al., 1998).
Gender is significantly correlated to health-promoting behaviors. Females
were found to engage more frequently in health-promoting behaviors and
lifestyles than males (Larouche, 1998; Lonquist, Weiss, & Larsen, 1992; Lusk,
Kerr, & Ronis, 1995; Martinelli, 1999; Stuifbergen & Becker, 1994; Volden,
Langemo, Adamson, &Oechsle, 1990). Ratner, Bottorff Johnson, and Hayduk
(1994) found gender to be a significant factor in predicting health-promoting
lifestyle but not as a function of the behavior-specific cognitions. Conflicting
results were found in Laffrey (1990) that reported no significant difference
between genders in the practice of health behaviors in adults.
Another individual characteristic that influences health behavior is
perceived health status. Perceived health status is the person’s overall global
perception of their health. A person’s perceived health status is related to their
medical diagnosis or disease state but is not determined exclusively by this
(Levkoff Cleary, & Wetle, 1987). Perceived health status has been found to have
a significant direct relationship to use of hearing protection in construction
workers (Lusk et al, 1997). Pender, et al. (1990) found perceived health status to
be a significant predictor of health-promoting lifestyle. The study findings
indicated that persons with a positive health status had a more health-promoting
lifestyle. Perceived health status was one of the most powerful predictors of
health promotion behaviors in blue-collar workers (Weitzel, 1989). It was also
significantly predictive of healthy lifestyle behaviors in college students
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(Larouche, 1998; Martinelli, 1999) and women who were homeless or in other
crises (Alley et al., 1998).
Health is subjective and based on personal perspective with each person
defining health based on its meaning to them (Phillips, 1990). Health is typically
defined or conceptualized in one of four ways (Shaver, 1985; Simmons, 1989;
Smith, 1981). The eudaimonistic view defines health as general well-being, self-
realization or actualization and directed toward fulfillment and development of
one’s potential. The second definition is health as adaptation that is the person’s
ability to adapt or interact effectively with the physical and social environment.
Health is defined as role performance or the individual’s ability to function in
appropriate socially identified roles. Finally, health is also defined as absence of
disease.
The view of health a person holds has been found to be related to the
practice of health promotion behaviors (Laffrey, 1985; Palank, 1991). Laffrey
(1985) found that persons with a more complex eudaimonistic view of health
practiced more health-promoting behaviors than persons who defined health as
absence of disease, role performance, or adaptation. Employees who defined
health as well-being or as adaptation reported more health-promoting lifestyles
(Pender et al., 1990). A positive wellness health conception was a significant
contributor of variance in a health-promoting lifestyle in ambulatory cancer
patients (Frank-Stromborg, Pender, Walker, & Sechrist, 1990). Frauman and
Nettles-Carlson (1991) also reported that well adults in a primary care setting who
had a eudaimonistic view of health practiced more health-promoting behaviors
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19
than persons who defined health as absence of disease, adaptation, or role
performance. Stuifbergen and Becker (1994) identified a wellness-orientation to
health to be a predictor of likelihood to engage in a health-promoting lifestyle in
persons with disabilities. Definition of health was a significant contributor to the
variance in health-promoting lifestyles of community dwelling older adults
(Pender, 1996).
One of the major behavior-specific cognitions is perceived self-efficacy.
Bandura (1977, 1989,1997) developed the concept of self-efficacy using social
cognitive theory as the basis. Bandura defines self-efficacy as an individual’s
belief in his capabilities to carry out a behavior to achieve a specific outcome.
According to Bandura, both efficacy expectancy (the belief that a behavior can be
carried out) and outcome expectancy (the belief that a behavior will produce a
specific outcome) influence behavior. Self-efficacy is developed through four
sources: personal or mastery experiences, vicarious learning, verbal persuasion,
and physiological cues experienced in particular situations. An individual’s
perception of self-efficacy is dynamic; it varies; and influences an individual’s
decision on which health promotion behaviors to practice (Bandura, 1977,1989,
1997; Jenkins, 1988; Maibach & Murphy, 1995; Pender, 1996).
Self-efficacy has been shown to correlate with health-promoting behaviors
that lead to positive health changes and maintenance of these behaviors (Strecker,
DeVellis, Becker, & Rosenstock, 1986). Self-efficacy has been found to be a
significant predictor of exercise behavior (Hofstetter, Hovel, & Sallis, 1990;
McAuley, Coumeya, & Lettunich, 1991; McAuley, Coumeya, Rudolph, & Lox,
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20
1994; McAuley & Jacobson, 1991; Robertson & Keller, 1992). Dennis and
Goldberg (1996) found a correlation between self-efficacy and weight loss. Self-
efficacy was positively correlated with smoking cessation (Janz et al., 1987) and
testicular self-examination (Boehm, et al., 199S; Brubaker &Wickersham, 1990).
Healthy elderly who had good health practices were found to have higher levels
of self-efficacy (Waller & Bates, 1991). In testing of the HPM, self-efficacy has
been found to be a significant predictor of health-promoting lifestyle (Lusk et al.,
1994; Lusk et al., 1997; Martinelli, 1999; Stuifbergen & Becker, 1994; Weitzel,
1989).
Additional behavior-specific cognitions that have been studied include
perceived benefits, perceived barriers, and the interpersonal influence of social
support. Perceived benefits are defined as anticipated positive effects of a
behavior (Pender, 1996). These anticipated consequences or effects are based on
personal or vicarious experiences (Bandura, 1977,1997; Bandura, Adams, &
Beyer, 1977; Pender, 1996). When perceived benefits are high, a person is
motivated toward health behavior. Anticipated barriers may be real or imaginary
and affect health promotion behavior. When perceived barriers are high, health
promotion behavior is unlikely to occur (Pender, 1996).
Several studies have examined the influence the behavior-specific
cognitions have on health promotion. Perceived benefits were a significant
predictor of exercise participation (Neuberger, Kasai, Smith, Hassanein, &
DeViney, 1994) and in use of hearing protection for workers (Lusk et al., 1994;
Lusk, et al., 1997). Perceived barriers were another significant predictor with a
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21
direct positive path to use of hearing protection in workers (Lusk, et al., 1994;
Lusk et al., 1997). Stuifbergen (1999) found that barriers explained more variance
in health promotion activities for urban residents with multiple sclerosis that for
rural residents. Health promotion behaviors were found to be more frequent in
females who had a supportive family environment (Rakowski, Julius, Hickey, &
Halter, 1987).
Determinants of Health-Promoting Behaviors in Older Adults
The practice of health-promoting behaviors is associated with positive
health in the older adult or elderly person (Belloc & Breslow, 1972). Bausell
(1986) found that the elderly reported greater compliance with health promotion
behaviors and respondents indicated that these behaviors were of value in
maintaining health. The elderly reported higher frequency in the practice of 14 of
21 health activities than young and middle-age adults (Prohaska, Leventhal,
Leventhal, & Keller, 198S). Older adults also reported the rationale for doing
these health promotion activities was that these activities helped to prevent
specific illness. Walker, Volkan, Sechrist, and Pender (1988) found that older
adults had significantly higher scores in overall health-promoting lifestyles and
higher health responsibility, nutrition, and stress management subscales scores on
the Health-Promoting Lifestyle Profile than young and middle-aged adults. Young
(1996) found that 84% of the adults over 75 years of age practiced some health
promoting behaviors.
Several studies have examined the correlates or determinants of health-
promoting lifestyle and behaviors in the elderly. Perceived health status has been
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22
studied frequently as a determinant of health promotion in this group using the
original HPM. Perceived health status has been significantly correlated with and
was a significant predictor of health-promoting behaviors and lifestyle in this
population (Duffy, 1993; Duffy & MacDonald, 1990; Padula, 1997; Riffle, Yoho,
& Sams, 1989; Speake, 1987; Speake, Cowart, & Pellet, 1989; Speake, Cowart, &
Stephen, 1991). Brown and McCreedy (1986) found a weak correlation between
perceived health status and health behaviors in the hale elderly.
Conflicting findings have been reported when gender was studied as a
predictor of health behaviors in older adults. Rakowski et al. (1987) reported that
being female was a significant predictor of performing health promotion
practices. Whereas, Brown and McCreedy (1986) found that married men
participated in more health promotion behaviors. Padula (1997) found no effect
with gender on health promotion activities in elderly couples.
Self-efficacy and perceived barriers were found to exert the most influence
on exercise behaviors in older adults (Conn, 1998). Resnick (2000) and Resnick
and Spellbring (2000) found self-efficacy a significant influence on exercise in
both healthy older adults and older adults in long-term care settings. Resnick and
Spellbring (2000) also found expected benefits to be an influence on exercise
behaviors in older aduhs.
Additional factors that are positively correlated with health-promoting
lifestyles in older adults are education (Padula, 1997; Riffle et al., 1989; Speake,
1987; Speake et al., 1989), marital status (Brown & McCreedy, 1986; Speake,
1987), income (Duffy, 1993), relationship quality and social support (Padula,
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23
1997), and self-esteem (Duffy, 1993). The research on these factors in older
adults is limited. Conflicting results are also seen from study to study. These
factors as contributors to health-promoting lifestyle in older adults warrant further
investigation.
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CHAPTER ID
METHODOLOGY
Subjects
Nine hundred older adults were invited to participate in the study. The
participants had to be at least 60 years of age, able to read and speak English, and
living independently. Convenience sampling techniques were used to obtain
subjects. Subjects were obtained from senior citizen groups and senior citizen
living facilities.
Nine hundred subjects were invited to participate with the objective of
obtaining a minimum sample size o f400. The literature indicates that response
rates for mailed one-time surveys on average to be 48% (Fowler, 1993; Heberlein
& Baumgartner, 1978). Heberlein and Baumgartner reported that response rates
for salient questionnaires varied on average from 66% to 77%. A sample size of
400 subjects was the minimum needed to test the model and the psychometric
properties of the HPLPII using structural equation modeling, as five subjects per
parameter were needed for statistical analysis (Schumacker & Lomax, 1996).
Procedure
Subjects were obtained using convenience sampling techniques. When
900 subjects had been invited to participate, the distribution of research packets
was discontinued. The researcher had knowledge of approximately 100 potential
subjects through personal acquaintance. A mailing list composed of them and
those with whom they network was developed. Research packets were distributed
to them. The packet included the informed consent letter (Appendix A), the
24
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25
questionnaires, and a self-addressed, postage paid return envelope. All
questionnaires in the research packets were returned anonymously.
The directors or managers of two senior organizations agreed to distribute
the research packet at their meetings or include it in a mailing to their members.
The subjects who received the packet at a meeting were invited to take it home to
complete it. Packets distributed by mail had-return postage provided by the
researcher to cover the cost of the mailing.
Managers at the five senior residential centers in Central Illinois agreed to
distribute the questionnaires to their residents. The researcher provided return
postage to cover the cost of mailing.
For this study, informed consent from the participants was signified by
their return of the instruments. Anonymity for all subjects was assured. The
Institutional Review Board of Saint Louis University approved the study.
Approval was also obtained from the Community Institutional Review Board for
Peoria, Illinois, which is in the proposed study area. Because of the survey design,
risks to the participants were minimal.
Subjects who decided to participate completed the instruments and
returned them to the researcher in the envelope provided. Subjects were instructed
not to identify themselves neither on the instruments nor on the envelope. All
mailing lists to which the researcher had access because of personal acquaintance
or network sampling were destroyed upon completion of the study.
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26
Instrumentation
Age-related visual changes occur in older adults and must be considered
when administering self-report instruments. The instruments for this study used a
14-point font and had black lettering on non-gloss white paper to ease the visual
task (Frank-Stromborg & Olsen, 1997).
Individual Characteristics. Personal factors are a component of the
individual characteristics and experiences that affect health-promoting behaviors
(Pender, 1996). Biological factors of age and gender and their effect on health-
promoting lifestyle were examined. The sociocultural factors of race, education,
and marital status were collected and analyzed for their effect on health-
promoting lifestyle. A Participant Profile was developed by the researcher to
gather this information. It is found in Appendix B.
Health Conception. The subjects’ conception of health was measured
using the Laffrey Health Conception Scale (LHCS) (Laffrey, 1986). Permission to
use the instrument was obtained from Dr. S. Laffrey (S. C. Laffrey, Ph.D., R.N.,
personal communication, September, 1999). The LHCS measured the individual’s
perception of the meaning of health. It is based on Smith’s (1981) work that
defines health in one of four ways: clinical, role performance, adaptation, or
eudaimonistic. Clinical health is defined as absence of disease. Health as role
performance is defined as the individual’s ability to perform societal roles. Health
as adaptation is the ability to adjust to changes or to maintain stability in one’s
life. Eudaimonistic is defined as exuberant well-being or the ability to reach
one’s highest potential or self-actualization. The tool has 28 items that describe
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27
what health cr being healthy means. Responses are made on a 6-point Likert scale
from ‘strongly agree’ to ‘strongly disagree’. The scores for each dimension of the
LHCS range from 7.0 to 42.0. The instrument takes approximately 10 minutes to
complete. Dimension and total scores may be obtained. The LHCS is found in
Appendix C.
Initial testing of the LHCS by Lafifrey (1986) was completed on 141
students enrolled in a Master’s degree in nursing program in the Western United
States. The subjects were 24-61 years of age with a mean age of 32.4 years. The
LHCS has demonstrated a high degree of internal consistency with Cronbach’s
alpha ranging from 0.867 to 0.884. Test-retest reliability was 0.84 for the summed
dimension scores. A panel of seven nurse experts who were asked to place each
item into one of the four health conception categories determined content validity.
There was 100% interrater agreement on 25 of the 28 items. Of the remaining
items, three had 75% interrater agreement and one had 65% interrater agreement
(S. C. Lafifrey, Ph.D., R.N., personal communication, September, 1999).
Construct validity was supported by a principal components factor analysis that
identified four factors identical to the four health dimensions conceptualized
(Lafifrey, 1986). The LHCS has been used in numerous studies with adults whose
ages ranged from 21-85 years (Frank-Stromborg, et al., 1990; Frauman & Nettles-
Carlson, 1991; Pender etal., 1990; Stuifbergen & Becker, 1991).
Perceived Health Status. Perceived health status was measured using a
single item that asked, “How do you rate your overall health?” Subject responses
were on a five-point scale from ‘poor’ to ‘excellent’ (Frank-Stromborg et al,
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28
1990). Tissue (1972) reported that health ratings are a combination of subjective
and objective components of health as perceived by individuals. Single item
indicators that ask respondents to rate a specific concept allow subjects to
consider the phenomena and weigh aspects of it based on their values. This is
consistent with nursing’s holistic focus on the individual (Youngblut & Casper,
1993). Single-item indicators have been found to be reliable and valuable as
global ratings of specific concepts (Cunny & Perri III, 1991; Youngblut & Casper,
1993). LaRue, Bank, Jarvik, and Hetland (1979) found self-report of health status
to be a reliable and valid indicator of health in older person. Mossey and Shapiro
(1982) reported that self-rated health status was able to detect variance in
perceived health status in the elderly.
Perceived Self-efficacv. The Perceived Health Competence Scale (PHCS)
developed by Smith, Wallston, & Smith (1995) measured perceived self-efficacy.
The PHCS is a measure of perceived competence or self-efficacy at an
intermediate level of specificity that measured the degree to which the individual
felt able to effectively manage his or her health outcomes. The eight-item scale
includes both outcome and behavioral expectancies. The PHCS uses a five point
Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’ for each item.
Scores on the PHCS range from 8.0 to 40.0. The PHCS is found in
Appendix D.
The reliability of the PHCS was tested in five groups (Smith et al., 1995).
The groups were: 238 adults with rheumatoid arthritis whose mean age was 56
years; 100 well adults aged 26-65 years; 186 undergraduate college students aged
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29
17-23 years; S4 undergraduate college students aged 18-23 years; and S28 West
Point Cadets aged 17-21 years. Internal consistency ranged from 0.82 to 0.90 in
these groups. Stability was shown to be 0.82 with a test-retest interval of one
week in undergraduate students. Moderate stability (0.60) was demonstrated with
a test-retest interval of 2.5 years in patients with arthritis (Smith et al., 199S).
Construct validity was supported in several ways. Using a ‘known groups’
method, the PHCS showed lower levels of health competence in groups with
chronic conditions (Smith et al., 1995). The PHCS correlated with indicators of
health status (r=0.4 to 0.5). Theoretically predicted relationships with health locus
of control and susceptibility were supported. Finally, the PHCS was consistently
positively correlated with active coping style (r=0.18 to 0.31) and indicators of
psychological well being (r=0.42 to 0.52) (Smith et al., 1995).
Health-Promoting Lifestyle. The Health-Promoting Lifestyle Profile II
(HPLPII) measured health-promoting lifestyle. The instrument is a revision of the
Health-Promoting Lifestyle Profile developed by Walker, Sechrist, and Pender
(1987). Permission to use the HPLPII was received from Dr. S. N. Walker (S. N.
Walker, Ed.D., R.N., personal communication, March, 1998). The HPLPII
measured “health-promoting behavior, conceptualized as a multidimensional
pattern of self-initiated actions and perceptions that serve to maintain or enhance
the level of wellness, self-actualization and fulfillment of the individual” (S. N.
Walker, Ed.D., R.N., personal communication, March 1998). The HPLPII is
found in Appendix E.
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30
The self-report instrument is a 52 item summated behavior rating scale
that uses a 4 point choice response to measure the frequency of health-promoting
behaviors. The HPLPII is divided into six domains of health-promoting lifestyle
that provide subscale scores. The six domains are: physical activity, nutrition,
health responsibility, spiritual growth, interpersonal relations, and stress
management. Calculating a mean of the individual’s responses to all 52 items
scores the instrument. Subscale scores are calculated by obtaining a mean for all
subscale items. The mean scores allow for comparison across subscales (S. N.
Walker, Ed.D., R.N., personal communication, March 1998). The scores on the
HPLPII and each subscale range from 1.0 to 4.0.
The HPLPII has demonstrated reliability and validity (S. N. Walker,
Ed.D., R.N., personal communication, March 1998). Cronbach’s alpha for the
total HPLPH is reported as 0.943. Cronbach’s alpha for the six subscales ranged
from 0.793 to 0.872. A principal axis factor analysis was done to test construct
validity. The factor analysis supported the presence of the six factors as the
subscales (S. N. Walker, Ed.D., R.N., personal communication, March 1998). No
reports of testing the HPLPII in older adults were found in the literature.
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CHAPTER IV
DATA ANALYSIS
Sample Characteristics
Nine hundred questionnaires were distributed to a convenience sample of
adults 60 years of age or older. Four hundred ninety-eight were returned for a
55.3% response rate. Of the 498 surveys, 46 were unusable because they were
either not completed or the respondent circled more than one answer per item
yielding a sample size o f452.
The sample consisted o f295 (65.3%) females and 156 (34.5%) males. The
age of the subjects ranged from 60 to 98 years with a mean age of 74.1 years
(SD=7.5 years). The sample was predominantly White, Non-Hispanic (n=449,
99.3%) and married (n=275,60.8%). Thirty-one percent (n=140) were widowed.
Overall, the subjects were well educated: 106 (23.5%) had some college; 72
(15.9%) had an associate or baccalaureate degree; and 97 (21.5%) had a graduate
or professional degree. One hundred thirty-nine (30.8%) were high school
graduates or had a GED.
The majority of the respondents perceived their health status as Good
(n=207,45.8%) or Very Good (n=142,31.4%). The mean perceived health status
for the group was 3.25 (SD=0.82), which is a rating between Good and Very
Good.
The respondents had a high level of perceived competence or self-efficacy
indicating that they felt able to effectively manage their health outcomes. The
31
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32
mean score on the PHCS was 30.21 (SD=5.61). Cronbach’s alpha for the PHCS
in this study of older adults was r=0.80.
The subjects’ perception of the meaning of health was highest for health
defined as role performance with a mean score of 35.30 (SD=5.57) and lowest for
health defined in clinical terms as absence of disease with a mean score of 29.95
(SD=8.39). The mean score for health as adaptation was 33.98 (SD=6.08) and the
mean score for the eudaimonistic view of health was 33.26 (SD=6.16).
The LHCS demonstrated a high degree of internal consistency in this older
adult group (r=0.95). Each of the individual health conception definitions also
demonstrated a high degree of internal consistency with Cronbach’s alphas of
r=0.89 for the clinical definition, r=0.91 for the role performance, r=0.91 for the
eudaimonistic definition, and r=0.92 for the adaptation definition.
Overall, the respondents participated in a fairly high level of health
promotion behaviors with a mean score of 2.90 (SD=0.44). Health promoting
behaviors for spiritual growth were practiced most frequently (Mean=3.28,
SD=0.53). Health promotion behaviors related to physical activity were practiced
least frequently (Mean=2.39, SD=0.80). Table 1 shows the mean scores and
standard deviations for each subscale of the HPLPII.
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Table 1
Mean Scores and Standard Deviations of the HPLPII Subscales
SUBSCALE MEAN* STANDARDDEVIATION
Physical Activity 2.39 0.80Health Responsibility 2.77 0.64Nutrition 2.93 0.58Stress Management 2.95 0.54Interpersonal Relations 3.21 0.50Spiritual Growth 3.28 0.53
♦Scale 1.00 to 4.00
33
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34
Research Question 1
The extent of the internal consistency of the HPLPII in older adults was
first examined by calculating a Cronbach’s alpha for the 52-item instrument and
each of the subscales. Cronbach’s alpha is beneficial to use because it addresses
sampling of content and sampling of situational factors that accompany individual
items (Mishel, 1998). The HPLPII demonstrated a high degree of internal
consistency with a Cronbach’s alpha of 0.91. The Cronbach’s alpha for the
subscales was lower indicating a lesser degree of internal consistency. The alphas
for the subscales were: Spiritual Growth=0.87; Health Responsibility=0.84;
Interpersonal Relations=0.80; Nutrition=0.79; Physical Activity=0.7l; and Stress
management=0.55.
The extent of the reliability and validity of the HPLPII in older adults was
further examined using structural equation modeling (SEM). The measurement
models for each latent construct were tested first using LISREL 8.30. Then the
entire measurement model was tested. Maximum Likelihood was the estimation
method used in model testing. Fit of the model to the data was determined by
using the Normal Theory Weighted Least Squares Chi-Square, the Root Mean
Square Error of Approximation (RMSEA), the 90% Confidence Interval for the
RMSEA, and the Goodness of Fit Index (GFI). The R2 for each item was
examined to evaluate reliability of the item. Items with R2 > 0.50 were considered
reliable since less than half the variance in the item is due to error. The
magnitude, consistency, and significance of the T-values for the parameter
coefficients were examined to determine the construct validity of the hems.
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35
The measurement models were tested using two different methods of
estimation, Maximum Likelihood (ML) and the robust ML with the Satorra-
Bentler scaled chi-square. The second method of estimation was used because the
assumption of multivariate normality for ML was violated. Violation of this
assumption may lead to inflated chi-square and parameter estimates and
underestimation of model fit.
For the first testing, the data for the 52 items together were screened
through PRELIS. The data were categorized as continuous (Johnson & Creech,
1983) and a covariance matrix was requested. The total effective sample size for
the study was 327. The Relative Multivariate Kurtosis was 1.27 and the Skewness
and Kurtosis Chi-square was 18788.46, p-value<0.001 indicating the assumption
of multivariate normality was violated. Examination of the histograms indicated
floor effects for several items and ceiling effects for many items. In LISREL,
Maximum Likelihood was used as the estimation method. The sample size was
too small to calculate an asymptotic covariance matrix therefore, an Asymptotic
Distribution Free estimator nor the robust ML with the Satorra-Bentler chi-square
could not be used.
For the second testing, the data consisting of eight or nine items for each
latent construct were screened separately through PRELIS. The data were
categorized as continuous (Johnson and Creech, 1983) and a covariance matrix
was requested. The sample size met the requirement of k(k+l)/2 subjects per item
and an asymptotic covariance matrix was calculated for each latent construct. In
LISREL, the robust ML with the Satorra-Bentler scaled statistics was used as the
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36
method of estimation because the assumption of multivariate normality was
violated for each latent construct. Table 2 presents the results of the testing for the
assumption of multivariate normality.
Table 2
Results of Multivariate Normality Testing
LatentConstruct
Effective Sample Size
RelativeMultivariate
Kurtosis
Skewness and Kurtosis
Chi-SquareHealth
Responsibility 410 1.110 147.83*Interpersonal
Relations 394 1.217 439.51*Nutrition 432 1.093 236.66*
Physical Activity 378 7.358 15563.21*Spiritual Growth 411 1.348 696.20*
StressManagement 418 4.750 11338.62*
*p<0.05
The testing of the measurement models using the robust ML with the
Satorra-Bentler scaled chi-square method of estimation produced results that were
not that different from the results obtained when using ML as the method of
estimation. Therefore, ML was chosen as the method of estimation as it is a more
conservative and consistent estimator. The results that are reported are those
obtained using ML as the estimation method.
The measurement model for the nine items measuring the latent construct
of Health Responsibility was tested. The chi-square was 118.55, df=27 (p<0.001).
The RMSEA=0.11 (p<0.001) with a 90% Confidence Interval o f0.089,0.13. The
GFI was 0.92 indicating that 92% of the variance in the data was explained by the
model. The hypothesized model did not fit the data. All items except two (Items
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37
27 and 39) had R2 values less than O.SO. The T-values were all significant and
ranged from 7.39 (Item 9) to 17.31 (Item 39).
An attempt to improve the reliability and construct validity of the
measurement model was made by deleting, one by one, items with low R2 and T-
values. After deletion of each item, the measurement model was retested using
LISREL. Five additional measurement models were tested. The measurement
model with the best fit to the data had four items. The chi-square was 1.74, df=2
(p=0.42). The RMSEA was 0.00 (p=0.66) with a 90% Confidence Interval of
0.00, 0.11. The GFI was 1.00. This indicates a good fit of the model to the data.
Although the R2 for items 3 and IS were less than O.SO, these items were retained
because of the significant T-values. Deleting these items created under
identification. Table 3 summarizes the testing of the measurement models for the
latent construct of Health Responsibility.
The T-values for the parameter coefficients for all items were significant
ranging from 12.93 (Item 9) to 19.60 (Item 27). The measurement model
demonstrated strong construct validity because of the significance of all the T-
values. Figure 1 shows the T-values for the four items in the Health responsibility
measurement model.
The measurement model for the nine items measuring the latent construct
of the Spiritual Growth was tested. The chi-square was 70.11, df=27 (p<0.001).
The RMSEA was 0.07 (p=0.05) with a 90% Confidence Interval o f0.050,0.090.
The GFI was 0.95 indicating that 95% of the variance in the data was explained
by the model. Only four hems (12, 18,30,36) had R2 values greater than 0.50
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Table 3
Health Responsibility Measurement Models
RUN 1 RUN 2 RUN 3 RUN 4 RUN 5 RUN 6CHI- 118.55* 77.81* 48.23* 30.18* 14.24* 1.74
SQUARE df=27 df=20 df=14 df=9 df==5 df=2RMSEA 0.11* 0.11* 0.09* 0.09* 0.08 0.00
GFI 0.92 0.95 0.96 0.97 0.98 1.00
ITEM FI23 0.42 0.43 0.44 0.45 0.46 0.469 0.17 Deleted Deleted Deleted Deleted Deleted15 0.44 0.45 0.45 0.45 0.46 0.4521 0.38 0.37 0.35 0.33 0.31 Deleted27 0.72 0.74 0.76 0.78 0.79 0.8333 0.27 0.26 0.26 0.25 Deleted Deleted39 0.67 0.67 0.66 0.64 0.63 0.5945 0.27 0.25 Deleted Deleted Deleted Deleted51 0.32 0.31 0.29 Deleted Deleted Deleted
*p<0.05
38
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Figure 1
T-values for Hypothesized Health Responsibility Measurement Model
resp
13.22 12.93 15.2619.60
Item 3 Item 15 Item 27 Item 39
Chi-square=1.74, df=2, p-value=0.42 RMSEA=0.00, p-value=0.66
39
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40
(Table 4). The T-values for all items were significant and ranged from 8.99 (Item
48) to 16.94 (Item 36).
In an attempt to improve the fit of the model and the reliability and
construct validity of the measure, items with the lowest R2 and T-values were
deleted one at a time. After each item deletion, the measurement model was
retested. Five measurement models were tested. The measurement model with the
best fit to the data had four items. The chi-square was 3.93, df=2 (p=0.14). The
RMSEA was 0.05 (p=0.36) with a 90% Confidence Interval of 0.00,0.13. The
GFI was 0.99. The R2 values for the four-item measure were all greater than 0.50
and ranged from 0.50 (Item 30) to 0.66 (Item 36) indicating good reliability in
these items. Table 4 summarizes the testing of the Spiritual Growth measurement
models.
The four item Spiritual Growth measurement model demonstrated strong
construct validity. The T-values were all significant. The T-values were similar
ranging from 13.65 (Item 30) to 16.56 (Item 36). Figure 2 shows the T-values for
the final Spiritual Growth measurement model.
The testing of the measurement model for the nine items measuring the
latent construct of Interpersonal Relations produced a model that did not fit the
data. The chi-square was 57.60, df=27 (p<0.001). The RMSEA was 0.06 (p=0.22)
with a 90% Confidence Interval o f0.038, 0.080. The GFI was 0.96 indicating
96% of the variance in the data was explained by the model. The R2 values ranged
from 0.17 (Items 1 and 37) to 0.60 (Item 25) with only two of the nine items
having R2 values greater than 0.50. The T-values were all significant and ranged
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Table 4
Spiritual Growth Measurement Models
Run 1 Run 2 Run 3 Run 4 Run 5 Run 6Chi- 70.11* 55.55* 36.49* 21.66* 8.22 3.93
Square df=27 df=20 df=14 d£=9 df=5 df=2RMSEA 0.07 0.07* 0.07 0.07 0.04 0.05
GFI 0.95 0.96 0.97 0.98 0.99 0.99
Item FI26 0.36 0.36 0.37 Deleted Deleted Deleted12 0.62 0.62 0.60 0.60 0.63 0.6018 0.18 0.57 0.59 0.58 0.59 0.5924 0.24 0.47 0.46 0.44 0.45 Deleted30 0.30 0.50 0.50 0.50 0.48 0.5036 0.36 0.65 0.65 0.68 0.65 0.6642 0.42 0.31 Deleted Deleted Deleted Deleted48 0.48 Deleted Deleted Deleted Deleted Deleted52 0.52 0.40 0.41 0.40 Deleted Deleted
*p<0.05
41
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Figure 2
T-values for Hypothesized Spiritual Growth Measurement Model
spirgr
16.5615.56 13.6515.31
Item 36Item 18Item 12 Item 30
Chi-square=3.93, d£=2, p-value=0.14 RMSEA=0.05, p-value=0.36
42
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43
from 7.11 (Item 1) to 1S.43 (Item 25). Strong construct validity was shown by the
significant T-values.
Items with low R2 and T-values were deleted one by one in an attempt to
improve the fit of the model to the data and reliability and construct validity of the
measure. After deletion of each item, the measurement model was rerun through
LISREL. Four additional measurement models were tested. The measurement
model with five items produced a model that best fit the data and had the best R2
and T- values. The chi-square for the five item model was 4.75, df=5 (p=0.45).
The RMSEA was 0.00 (p=0.79) with a 90% Confidence Interval o f0.00, 0.075.
The GFI was 0.99.
The R2 values ranged from 0.33 (Item 7) to 0.63 (Item 25).
Item 7 with an R2 of 0.33 was deleted from the model in an attempt to further
improve the R2 values of the other items. The fit of the model to the data was
worse and the R2 values did not improve so Item 7 was left in the model. Table 5
provides a summary of the testing of the Interpersonal Relations measurement
models.
The T-values for the final measurement model were all significant. The
T-values ranged form 10.40 (Item 1) to 15.52 (Item 25). Figure 3 shows the
T-values for the parameter coefficients in the final model.
The measurement model with eight items measuring the latent construct of
Physical Activity was tested. The hypothesized eight-item model did not fit the
data. The chi-square was 106.99, df=20 (p<0.001). The RMSEA was 0.11
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Table 5
Interpersonal Relations Measurement Models
Run 1 Run 2 Run 3 Run 4 Run 5 Run 6Chi- 57.60* 38.66* 28.04* 19.63* 4.75 3.60
Square df=27 df=20 df=14 df=9 df=5 df=2RMSEA 0.06 0.53 0.06 0.06 0.00 0.049
GFI 0.96 0.97 0.98 0.98 0.99 0.99
Item I1 0.17 Deleted Deleted Deleted Deleted Deleted7 0.29 0.30 0.30 0.31 0.33 Deleted13 0.51 0.52 0.51 0.52 0.50 0.5019 0.40 0.39 0.40 0.41 0.40 0.4025 0.60 0.61 0.63 0.63 0.63 0.6231 0.46 0.44 0.44 0.44 0.43 0.4437 0.17 0.17 Deleted Deleted Deleted Deleted43 0.33 0.32 0.31 0.30 Deleted Deleted49 0.25 0.24 0.23 Deleted Deleted Deleted
*p<0.05
44
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Figure 3
T-values for Hypothesized Interpersonal Relations Measurement Model
_ i P r/ I
10.40 13.35 11.68 15.52
Item 19 Item 25Item 13Item 7
Chi-square=4.75, <tf=5, p-value=0.43 RMSEA=0.00, p-value=0.79
12.08
XItem 31
45
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46
(p<0.001) with a 90% Confidence Interval 0.092, 0.14. The GFI was 0.93
indicating 93% of the variance in the data was explained by the model.
The R2 values ranged from 0.061 (Item 22) to 0.61 (Item 4) with only two
items (Items 4 and 16) having R2 values greater than O.SO. The T-values were all
significant but widely ranged from 4.15 (Item 22) to 15.42 (Item 4).
Items with low R2 and T-values were deleted one at a time to try to
improve the reliability and construct validity of the measure. After deletion of
each item, the measurement model was retested in LISREL. Four additional
measurement models were tested. The measurement model with four items
produced a model that best fit the data and had the highest R2 and T-values. The
chi-square for the four item model was 0.36, dfr=2 (p=0.84). The RMSEA was
0.00 (p=0.92) with a 90% Confidence Interval o f0.00,0.063. The GFI was 1.00.
Table 6 summarizes the testing of the Physical Activity measurement models.
Strong construct validity was supported with T-values for the parameter
coefficients that were all significant and had fairly similar values. The T-values
ranged from 10.43 (Item 28) to 16.15 (Item 4). Figure 4 shows the T-values for
the four-item measurement model for the latent construct of Physical Activity.
The eight-item measurement model that measured the latent construct of
Stress Management was tested. The eight-item hypothesized model did not fit the
data. The chi-square was 67.91, df=20 (p<0.001). The RMSEA was 0.09
(p<0.001) with a 90% Confidence Interval of 0.06,0.11. The GFI was 0.95
indicating that 95% of the variance in the data was explained by the model. The
R2 values ranged from 0.02 (Item 35) to 0.48 (Item 47) with no items having an
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Table 6
Physical Activity Measurement Models
Run 1 Run 2 Run 3 Run 4 Run 5Chi- 106.69* 100.28* 94.58* 86.02* 0.36
Square df=20 d£=14 df=9 df=5 df=2RMSEA 0.11* 0.14* 0.17* 0.22* 0.00
GFI 0.93 0.92 0.91 0.90 1.00
Item R24 0.61 0.61 0.59 0.60 0.7210 0.17 0.17 Deleted Deleted Deleted16 0.50 0.50 0.49 0.46 0.5122 0.06 Deleted Deleted Deleted Deleted28 0.35 0.36 0.35 0.35 0.3434 0.23 0.23 0.23 Deleted Deleted40 0.27 0.27 0.29 0.30 Deleted46 0.49 0.48 0.51 0.51 0.39
*P<0.05
47
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Figure 4
T-values for Hypothesized Physical Activity Measurement Model
physact
16.15 13.19 10.43 11.25
Item 4 Item 16 Item 28 Item 46
Chi-square=0.36, df=2, p-value=0.84 RMSEA=0.00, p-value=0.92
48
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49
R2 greater than O.SO. The T-values were all significant but ranged from 2.24 (Item
35) to 11.53 (Item 47).
Three items with low R2 and T-values were deleted on at a time in an
attempt to improve the fir of the model and the reliability and validity of the
measure. The measurement model was rerun each time an item was deleted. A
five-item measurement model provided the best fit of the model to the data. The
chi-square was 12.32, df=5 (p=0.03). The RMSEA was 0.07 (p=0.23) with a 90%
Confidence Interval of 0.019, 0.12.
The R2 values continued to be low ranging from 0.19 (Item 11) to 0.51
(Item 47). Item 47 was the only item to have an R2 greater than 0.50. A furtherA
attempt to improve the model was made by deleting item 11 which had a low R .
The fit of the model got worse and the R2 values did not improve so item 11 was
left in the model. Table 7 summarizes the testing of the Stress Management
measurement models.
The T-values for the parameter coefficients were all significant and ranged
form 6.95 (Item 11) to 11.35 (Item 47). The significant T-values do support strong
construct validity in the measurement model. Figure 5 shows the T-values for the
five-item Stress Management measurement model.
The nine-item measurement model that measured the latent construct of
Nutrition was tested. The hypothesized model did not fit the data. The chi-square
was 109.03, df=27 (p<0.001). The RMSEA was 0.097 (p<0.001) with 90%
Confidence Interval o f0.078,0.12. The GFI was 0.93 indicating that 93% of the
variance in the data was explained by the model. The R2 values ranged from 0.06
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Table 7
Stress Management Measurement Models
Run 1 Run 2 Run 3 Run 4 Run 5Chi-
Square67.91*df=20
58.55*df=14
34.90*df=9
12.32*df=5
7.00*df=2
RMSEA 0.09* 0.10* 0.07 0.07 0.088GFI 0.95 0.95 0.98 0.99 0.99
RJItem5 0.07 0.07 Deleted Deleted Deleted11 0.22 0.23 0.21 0.19 Deleted17 0.21 0.22 0.19 0.20 0.1823 0.39 0.39 0.38 0.40 0.4629 0.21 0.20 0.22 0.20 0.2235 0.02 Deleted Deleted Deleted Deleted41 0.14 0.13 0.14 Deleted Deleted47 0.48 0.48 0.50 0.51 0.46
*p<0.05
50
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Figure 5
T-values for Hypothesized Stress Management Measurement Model
stress
6.95 7.12 10.15 7.14 11.35
Item 47Item 11 Item 29Item 23Item 17
Chi-square=12.32, df=5, p-value=0.03 RMSEA=0.07, p-value=0.23
51
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52
(Item 50) to 0.46 (Item 2). No items had an R2 greater than 0.50. Table 8 provides
the R2 values for all items. The T-values were all significant and ranged from 4.17
(Item 50) to 12.60 (Item 2) demonstrating a low degree of construct validity.
In an attempt to obtain a model that fit the data and to improve the
reliability and construct validity of the Nutrition subscale, items with low R2 and
T-values were deleted one at a time. Four items were deleted and the
measurement model was rerun in LISREL after each item was deleted. No fit of
the model to the data was obtained in the Nutrition measurement model. The
closest fitting model was a six-item model tested during the fourth run. The chi-
square for the six-item model was 35.68, df=9 (p<0.001). The RMSEA was 0.095
(p=0.01) with a 90% Confidence Interval o f0.064,0.13. The GFI was 0.96.The
R2 values ranged from 0.24 (Item 38) to 0.56 (Item 2). Only item 2 (R2=0.56) had
an R2 greater than 0.50. Table 8 summarizes the testing of the five measurement
models for the latent construct of Nutrition.
Strong construct validity was supported in the Nutrition measurement
model as evidenced by the significant T-values. The T-values were all significant
but ranged from 8.54 (Item 38) to 14.20 (Item 2). Figure 6 shows the T-values for
the parameter coefficients for the Nutrition measurement model.
The testing of the latent construct measurement models individually
reduced the instrument from 52 items to 28 items. The measurement models for
the six latent constructs with 28 items were tested together. The non-significant
RMSEA indicated that the hypothesized model did fit the data. The chi-square
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Table 8
Nutrition Measurement Models
Run 1 Run 2 Run 3 Run 4 Run 5Chi- 109.03* 97.74* 70.55* 35.68* 26.22*
Square d£=27 df=20 dfH4 df=9 df=5RMSEA 0.097* 0.11* 0.11* 0.95* 0.11*
GFI 0.093 0.93 0.94 0.96 0.97
Item R12 0.46 0.47 0.51 0.56 0.598 0.29 0.29 0.30 0.31 0.3214 0.20 0.20 Deleted Deleted Deleted20 0.43 0.42 0.41 0.38 0.3626 0.45 0.46 0.42 0.38 0.3532 0.20 0.20 0.17 Deleted Deleted38 0.28 0.28 0.27 0.24 Deleted44 0.41 0.42 0.44 0.47 0.4850 0.06 Deleted Deleted Deleted Deleted
*p<0.05
53
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Figure 6
T-values for Hypothesized Nutrition Measurement Model
nutrition
8.54 12.6314.20 9.89 11.02 11.10
Item 2 Item 44Item 8 Item 20 Item 26 Item 38
Chi-square=35.68, df=9, p-value<0.001 KMSEA=0.095, p-value=0.01
54
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55
was 671.44, df=335 (p<0.001). The RMSEA was 0.056 (p=0.069) with a 90%
confidence interval o f0.049,0.062. The GFI was 0.87 indicating that 87% of the
variance in the data was explained by the model.
The R2 values ranged from 0.14 (Item 11, Stress Management) to 0.79
(Item 27, Health Responsibility). Ten hems had R2 values greater than 0.50. The
T-values were all significant and ranged from 6.22 (Item 11, Stress Management)
to 19.42 (Item 27, Health Responsibility).
Five items (Items 7,8,11, 29,38) had R2 values less than 0.32. These
items were deleted one at a time in an attempt to improve the fit of the data to the
model and increase the reliability of the hems. The measurement models were
retested in LISREL after deletion of each item. The measurement model with 23
items had the best fit to the data. The chi-square was 415.62, df^215 (p<0.001).
The RMSEA was 0.054 (p=0.22) with a 90% Confidence Interval o f0.046,0.061.
The GFI was 0.90. The R2 values ranged from 0.31 (Item 47, Stress Management)
to 0.79 (Item 27, Health Responsibility). Eleven hems had R2 values greater than
0.50 with an additional six hems having R2 values from 0.40 to 0.49. Table 9
summarizes the testing of the six measurement models together.
The T-values were all significant indicating strong construct validity. The
T-values ranged from 9.53 (Item 47, Stress Management) to 19.34 (Item 27,
Health Responsibility). Table 9 provides the T-values for the 23 hem
measurement model.
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Table 9
Six Latent Construct Measurement Models
Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 6Chi-
Square671.44*df=335
618.95*df=309
522.56*df=284
485.53*df=260
452.09*df=237
415.62*df=215
RMSEA 0.056 0.055 0.051 0.052 0.053 0.054GFI 0.87 0.88 0.89 0.89 0.90 0.90
RJT-
ValueItem2 0.54 0.54 0.54 0.56 0.54 0.54 13.733 0.46 0.46 0.46 0.47 0.46 0.46 13.334 0.69 0.69 0.69 0.70 0.70 0.70 16.387 0.32 0.32 0.32 0.32 0.32 Deleted Deleted8 0.31 0.31 0.31 0.31 Deleted Deleted Deleted11 0.14 Deleted Deleted Deleted Deleted Deleted Deleted12 0.67 0.67 0.67 0.67 0.67 0.67 17.0713 0.57 0.57 0.57 0.57 0.57 0.58 15.1715 0.47 0.47 0.47 0.48 0.47 0.47 13.4816 0.50 0.50 0.50 0.50 0.50 0.50 13.3217 0.27 0.27 0.33 0.33 0.33 0.33 9.8018 0.57 0.57 0.57 0.57 0.57 0.58 15.3619 0.39 0.39 0.39 0.37 0.39 0.39 11.5920 0.35 0.35 0.35 0.34 0.35 0.35 10.5223 0.44 0.43 0.42 0.42 0.42 0.42 11.1925 0.60 0.60 0.60 0.60 0.60 0.58 15.1826 0.39 0.39 0.39 0.36 0.38 0.38 11.1427 0.79 0.79 0.79 0.79 0.79 0.79 19.3428 0.34 0.34 0.34 0.34 0.34 0.34 10.5729 0.25 0.25 Deleted Deleted Deleted Deleted Deleted30 0.48 0.48 0.47 0.47 0.47 0.48 13.5031 0.41 0.41 0.41 0.40 0.41 0.41 11.9736 0.62 0.62 0.62 0.62 0.62 0.62 16.2638 0.26 0.26 0.26 Deleted Deleted Deleted Deleted39 0.61 0.61 0.61 0.61 0.62 0.62 16.1644 0.49 0.50 0.50 0.51 1 0.51 0.51 13.3046 0.42 0.42 0.41 0.41 0.41 0.41 11.8347 0.39 0.35 0.32 0.32 0.32 0.31 9.53
*p<0.05
56
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57
There was a significantly high correlation between the latent variables of
Spiritual Growth and Interpersonal Relations (r=0.79), Spiritual Growth and
Stress Management (r=0.74), and Interpersonal Relations and Stress Management
(r=0.76). The modification indices suggested adding a path from Item 17 (Stress
Management latent variable) to the Spiritual Growth latent variable. This was
done because the item was theoretically related to both latent variables. The
modification indices suggested adding paths between numerous other items in
these latent variables to another one of the highly correlated variables. This was
not done because the items were not theoretically relevant. The Modification
Indices suggested adding error covariance between numerous items. This was not
done because it was not theoretically appropriate.
The measurement model was retested in LISREL. The chi-square was
389.77, df=214 (p<0.001). The RMSEA was 0.050 (p=0.47) with a 90%
Confidence Interval of 0.042,0.058. The GFI was 0.91. Item 17 (Stress
Management) continued to have a low R2 value of 0.31. The T-values for this
item were 5.34 for the Spiritual Growth path and 2.44 for the Stress Management
path. Because these T-values were so much lower than the rest of the items, item
17 was deleted from the model (See Table 9 for T-values).
The 22-item measurement model was retested in LISREL. The non
significant RMSEA and the GFI, indicated the model had the best fit to the data.
The chi-square was 345.09, df=194 (p<0.001). The RMSEA was 0.049 (p=0.58)
with a 90% Confidence Interval o f0.040,0.051. The GFI was 0.91. The R2 values
ranged from 0.34 (Item 28, Physical Activity) to 0.79 (Item 27, Health
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58
Responsibility). The T-values were all significant and ranged from 9.96 (Item 47,
Stress Management) tol9.30 (Item 27, Health Responsibility). Table 10 shows the
R2 and T-values for the 22 items.
The higher order model was tested using the robust ML with the Satorra-
Bentler scaled statistics as the method of estimation. The effective sample size of
369 met the requirement of k(k+l)/2 subjects needed to calculate an asymptotic
covariance matrix. Screening of the data through PRELIS indicated a violation of
multivariate normality. The Relative Multivariate Kurtosis was 1.173 and the
Skewness and Kurtosis chi-square was 880.66 (p<0.001).
The higher order measurement model tested the 22 items that were found
to fit the model in the testing of the six latent constructs. The latent constructs of
Health Responsibility, Interpersonal Relations, Spiritual Growth, Nutrition, and
Physical Activity each had four items. The latent construct of Stress Management
had two items. These factors are modeled to be measures of health promotion.
The hypothesized 22-item, six latent construct higher order measurement
model had the best fit to the data as indicated by the non-significant RMSEA. The
chi-square was 448.40, df=203 (p<0.001). The RMSEA was 0.057 (p=0.05) with
a 90% Confidence Interval o f0.050,0.064. The GFI was 0.88 indicating that 88%
of the variance in the data was explained by the model. The modification indices
suggested adding a path from numerous items to other latent constructs and
adding paths between latent constructs.
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Table 10
Twenty-two Item Measurement Model
Run I Run 2 Run 2Chi- 389.77* 345.09*
Square df=214 dP=194RMSEA 0.050 0.049
GFI 0.91 0.91
Item Subscale R2 T-value2 Nutrition 0.54 0.54 13.733 Health Responsibility 0.46 0.46 13.314 Physical Activity 0.69 0.69 16.3412 Spiritual Growth 0.68 0.66 16.9313 Interpersonal Relations 0.58 0.57 15.0115 Health Responsibility 0.47 0.47 13.5116 Physical Activity 0.50 0.50 13.3217 Stress Management,
Path to Spiritual Growth0.31 Deleted Deleted
18 Spiritual Growth 0.57 0.58 15.3519 Interpersonal Relations 0.38 0.38 11.5620 Nutrition 0.35 0.35 10.5123 Stress Management 0.49 0.51 11.3125 Interpersonal Relations 0.58 0.59 15.2126 Nutrition 0.38 0.38 11.1427 Health Responsibility 0.79 0.79 19.3028 Physical Activity 0.34 0.34 10.6030 Spiritual Growth 0.46 0.48 13.6131 Interpersonal Relations 0.40 0.41 12.0636 Spiritual Growth 0.62 0.62 16.2439 Health Responsibility 0.62 0.62 16.1944 Nutrition 0.51 0.51 13.3246 Physical Activity 0.41 0.42 11.8947 Stress Management 0.39 0.37 9.96
*p<0.05
59
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60
The suggestions were not theoretically relevant and were not done. Adding a
large number of error covariances between items was also suggested. This was
not done because the use of ML as an estimator assumes that the error is normally
distributed and not correlated (Rubio and Gillespie, 1995).
The R2 values for the items ranged from 0.34 (Item 28, Physical Activity)
to 0.77 (Item 27, Health Responsibility). Eleven items had R2 values greater than
0.50 and an additional eight items had R2 values between 0.40 and 0.49 (See
Table 11). Sixteen items had significant T-values. The T-values ranged from 7.69
(Item 47, Stress Management) to 14.65 (Item 36, Spiritual Growth). The
remaining six items, one from each latent construct, were assigned as reference
variables by LISREL and T-values were not given for them. Table 11 provides the
T-values for each item.
Nineteen percent of the variance in health promoting behaviors is
explained by the latent construct of Physical Activity. Sixty-eight percent of the
variance in health promoting behaviors is explained by the latent construct of
Interpersonal Relations. Table 12 summarizes the percent of variance in health-
promoting behaviors explained by each latent construct.
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Table 11
Higher Order Measurement Model of HPLPII
Chi-Square^ 440.40, df=203, p<0.001RMSEA=0.057, p=0.05
GFI=0.88
Item Subscale T-value Ra2 Nutrition RV* 0.533 Health Responsibility RV 0.464 Physical Activity RV 0.7012 Spiritual Growth RV 0.6113 Interpersonal Relations RV 0.5415 Health Responsibility 10.91 0.4616 Physical Activity 14.41 0.5118 Spiritual Growth 13.04 0.6019 Interpersonal Relations 9.71 0.4520 Nutrition 8.96 0.3623 Stress Management RV 0.4925 Interpersonal Relations 12.12 0.5826 Nutrition 9.67 . 0.4027 Health Responsibility 13.56 0.7728 Physical Activity 11.39 0.3430 Spiritual Growth 12.64 0.5231 Interpersonal Relations 9.35 0.4636 Spiritual Growth 12.02 0.6239 Health Responsibility 11.95 0.6144 Nutrition 10.18 0.4946 Physical Activity 12.45 0.4047 Stress Management 8.14 0.36
*RV=Reference variable assigned by LISREL
61
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62
The T-values for each of the structural equation coefficients were significant and
ranged from 6.9S (Physical Activity) to 11.26 (Spiritual Growth). The T-values
for the structural equation coefficients for each of the latent constructs are
reported in Table 12.
Table 12
Variance Explained and T-values in Health-Promoting Lifestyle
Latent Construct T-value ' "r1.........Physical 6.95 0.19Nutrition 9.48 0.38
Health Responsibility 10.70 0.49Spiritual Growth 10.70 0.63
Stress Management 11.26 0.65Interpersonal Relationships 10.76 0.68
Cronbach’s alpha was recalculated for the 22-item instrument to re
examine the extent of the internal consistency of the revised instrument. The
Cronbach’s alpha for the 22 items was 0.89. The alphas for the six subscales were
Spiritual Growth=0.83; Health Responsibility^. 82; Interpersonal
Relations=0.80; Physical Activity=0.78; Nutrition=0.76; and Stress
Management=O.SO.
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63
Research Question 2. 3. and 4
Path analysis and multiple regression were used to test the theoretical
relationships among the select components of the HPM. The composite scores on
the LHCS and PHCS were used in the analysis because of the sample size. The
sample size was not large enough to meet the requirement of five subjects per
parameter needed to test a full structural model (Raykov and Widaman, 199S).
The direct effects from the individual characteristics on health promotion
behaviors were examined. The direct effect of self-efficacy on health promoting
behaviors was examined. Lastly, the effects of the individual characteristics
through the mediating variable of self-efficacy were examined to determine their
effect on health promotion behaviors. The path coefficients produced by the
model were tested for significance to determine which variables had a significant
relationship with the practice of health promotion behaviors in older adults.
The data were screened through PRELIS. The variables gender, race,
marital status, and education were categorized as ordinal. The variables
perception of health, age, the scores on the four dimensions of the LHCS, the
scores on the PHCS, and the scores on the HPLPII were categorized as
continuous. The total effective sample size was 376. For the ordinal variables, the
assumption of underlying bivariate normality was not violated. Examination of
the chi-square and RMSEA values showed that a great number of them were
small with p-values greater than 0.0S. The percentage of tests exceeding 5.0%
significance level was 7.9%. For the continuous variables, the Skewness and
Kurtosis chi-square was 285.49 (p<0.001) and the Relative Multivariate Kurtosis
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64
was 1.21. This indicates that the assumption of multivariate normality was
violated. Maximum Likelihood was used as the method of estimation in LISREL.
Since multivariate normality was violated, use of maximum likelihood as an
estimator may lead to inflated chi-square values and parameter estimates that
might produce an underestimated fit of the model to the data.
Research question 2 examined the direct effects of the individual
characteristics on health promoting behaviors. The individual characteristics of
gender, age, race, marital status, education, perception of health, and the four
definitions of health were placed in the model. The individual characteristics
combined explained 26% of the variance in health promoting behaviors. Age and
the role performance definition of health had no significant effect on health
promotion behaviors. Gender, race, marital status, and the clinical definition of
health had significant negative effects on health promoting behaviors. Education,
perception of health, health defined as adaptation, and the eudaimonistic
definition of health had significant positive effects on health promoting behaviors.
Figure 7 shows the path diagram with T-values for the direct effects of the
individual characteristics on health promoting behaviors.
Research question 3 and question 4 were examined together. Research
question three examined the direct effect of self-efficacy on health promoting
behaviors in older adults using path analysis.
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Figure 7
T-values for Direct Effects of Individual Characteristics On Health Promoting Behaviors
-3.99
0.97
-3.38
-2.25
3.60
4.31
-4.37
-1.93
2.86
2.86
Gender
Age
Education
EudaimonisticDefinition
ClinicalDefinition
Marital status
Race
AdaptationDefinition
Healthpromotingbehaviors
Perception of health
RoleperformanceDefinition
65
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66
Research question 4 examined the effects of the individual characteristics on
health promoting behaviors through the mediating variable of self-efficacy. Figure
8 shows the hypothesized model. The hypothesized model did not fit the data. The
chi-square was 64.53, df=10, p<0.001. The RMSEA was 0.12 (p<0.001) with a
90% Confidence Interval o f0.079, 0.12.
The modification indices suggested adding direct paths from gender, race,
education, clinical definition of health and eudaimonistic definition of health to
health promoting behaviors. This was done because Pender’s model indicated that
the individual characteristics have both direct and indirect effects on health-
promoting behaviors. Retesting after adding the additional paths produced a
model that fit the data. The chi-square was 10.64, dfr=5, p=0.06. The RMSEA was
0.056 (p=0.36) with a 90% Confidence Interval of 0.00,0.10. The GFI was 1.00
indicating that 100% of the variance in the data is explained by the model.
The model explained 29% of the variance in health promoting behaviors.
Age (T-value=1.17), marital status (T-value=-1.62), and health defined as
adaptation (T-value=1.58) had no significant effect on health promoting behaviors
either directly or through self-efficacy. Race had a significant negative effect on
health promoting behaviors directly (T-value=-2.82) but not through self-efficacy
(T-vaIue=-1.20). Eudaimonistic definition of health had a significant positive
relationship to health promoting behaviors directly (T-value=4.78) but not
through self-efficacy (T-value =-0.50). Perception of health rating had a
significant positive relationship on health promoting behaviors
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Figure 8
Hypothesized Model of Individual Characteristics Effects on Health Promoting Behaviors Mediated Through Self-Efficacv
Education
Age
Marital status
Race
Perception of health
Gender
Clinical
Roleperformance
Adaptation
Eudaimonistic
Self-efficacy
Healthpromotingbehaviors
67
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68
through self-efficacy (T-value=12.14). Gender and clinical definition of health
had significant negative relationships with health promoting behaviors directly
(T-value=-2.51; T-value=-3.26) and indirectly through self-efficacy
(T-value=-3.09; T-value=-4.01). Education had a significant positive relationship
with health promoting behaviors directly (T-value=3.26) and indirectly through
self-efficacy (T-value=2.89). Self-efficacy had a significant direct effect on health
promoting behaviors (T-value=6.82). Figure 9 shows the T-values for the path
coefficients for the model.
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Figure 9
Health Promotion Model Testing
12.14
-2.51
-3.09
2.89
-2.826.82
3.26-3.26
Healthpromotingbehaviors
-4.01
4.78
Gender
Race
Eudaimonistic
Perception of health
Education
Clinical
Self-efficacy
Chi-square=10.64, d£=5, p-value=0.06 RMSEA=0.06, p-value=0.36
69
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CHAPTER V
DISCUSSION
Summary of Research Findings
Health Conception. The older adult population in this study defined health
most frequently as role performance or the ability to perform societal roles. The
clinical definition of health or perception of health as absence of disease received
the lowest score among this older adult group. These findings support research
that shows as persons get older, they perceive their health to be closely related to
their ability to function and carry out specified roles (Kumpusalo et al., 1992).
Health-promoting Lifestyle. This study found that older adults scored in
health promotion activities related to spiritual growth, interpersonal relations, and
stress management higher than in the areas of nutrition, health responsibility, and
physical activity. Numerous studies support these findings (Dufify, 1993; Huck &
Armer, 1996; Riffle et al., 1989; Speake et al., 1989; Walker et al., 1988).
Physical activity received the lowest score, indicating that this group practiced
fewer health promotion behaviors in this area. Many of the respondents left the
questions on the HPLPII related to participation in physical activities and exercise
blank. These items have been criticized as not matched to the population and too
difficult, resulting in low scores and a floor effect (Frank-Stromborg & Olsen,
1997). For this or other reasons, the low score for physical activity and exercise is
a common finding in studies examining health promotion activities in older adults
(Dufify, 1993; Huck & Armer, 1996; Riffle et al., 1989; Speake et al., 1989;
Walker et al., 1988). Exercise items that were most reliable in the
70
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71
instrument for older adults were related to having a planned exercise program and
participating regularly in moderate exercise.
Research Question 1. The extern of the reliability and validity of the
HPLPO in an older adult population was examined using structural equation
modeling. Cronbach’s alpha was used to examine internal consistency. Using
structural equation modeling, the hypothesized measurement models for each of
the six latent constructs were tested. All of latent constructs except Nutrition had
measurement models that fit the data. The testing of the measurement models
individually reduced the original HPLPII from 52 items to 28.
The content of the items in the Nutrition latent construct may have
contributed to the poor fit of the model to the data. The items were related to
eating the recommended number of servings of each of the food groups from the
food pyramid. Four respondents indicated that the number of servings were too
high and that they did not eat the amount of food recommended by the items in
this subscale.
Testing of the latent construct measurement models together further
reduced the instrument from 28 to 22 items. The 22 item, hypothesized six latent
construct model was tested. This hypothesized model did fit the data and
produced the best R2 and T-values for the HPLPII in the older adult population
studied.
In this measurement model, twelve of the items (Items 2,4,12,13,16,18,
23,25,27,30,36, and 39) had R2 values greater than 0.50 indicating that these
items are highly reliable measures of the construct health-promoting lifestyle
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72
since over half the variance in each item was not due to error. Ten items (Items 3,
15, 19, 20,26, 28, 31,44,46, and 47) had R2 values less than 0.50. Even though
these items explained a smaller percentage of the variance in health-promoting
lifestyle, they contributed significantly to the model and the measurement of
health-promoting lifestyle as indicated by the significant T-values of the
parameter estimates.
The 22-item HPLPII demonstrated a strong magnitude of construct
validity as indicated by the items having significant T-values for the parameter
estimates. In the testing of the measurement model of the six latent constructs
together, there was a significant positive correlation between the constructs of
Spiritual Growth, Interpersonal Relations, and Stress Management. This was
anticipated as the health promotion behaviors on these scales are focused on the
promotion of psychological health of an individual. The modification indices
suggested adding paths from five items within these constructs to one of the three
remaining constructs such as nutrition, physical activity or health responsibility.
This was not done because, theoretically, the wording of the items did not relate
to the suggested construct.
Cronbach’s alpha showed the 22-item HPLPII to have high internal
consistency (r=0.89). This is an acceptable level of internal consistency for an
instrument. The Cronbach’s alpha for a developed instrument ought to be 0.80 or
higher (Frank-Stromborg & Olsen, 1997; Nunnally & Bernstein, 1994; Strickland,
1996). Each of the six subscales showed lower degrees of internal consistency
although the Cronbach’s alphas were all greater than 0.75 except for the Stress
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73
Management subscale. The lower alpha levels for the subscales may be due to the
fewer number of items in each subscales, two to four items (Frank-Stomborg &
Olsen). A copy of the revised 22 item Health Promoting Lifestyle Profile is found
in Appendix F.
Research Questions 2. 3. and 4. Several individual characteristics from the
Health Promotion Model were found to directly impact health promotion
behaviors in the older adult population, impact health promotion behaviors
through the mediating variable of self-efficacy, or both. The findings from this
study support the revised Health Promotion Model postulated by Pender (1996).
Persons who perceived their health to be good to excellent practiced more
health promoting behaviors than those who perceived their health to be poor or
fair. This is consistent with other studies that demonstrated a positive relationship
between perceived health and health promotion behaviors (Brown & McCreedy,
1986; Duffy, 1993; Dufify & MacDonald, 1990; Padula, 1997: Speake, 1987;
Speake et al., 1989; Speake et al., 1991; Riffle et al., 1989).
Gender had a significant direct effect on health promoting behaviors and
also affected health-promoting behaviors through self-efficacy. Females were
found to practice more health promotion behaviors than males. Similar results
were found in a study conducted by Rakowski et al. (1987). Padula (1997) found
gender to have no effect on health promotion activities when studying health
promotion in elderly couples.
Education correlated with the practice of health promoting behaviors.
Persons with higher levels of education practiced more health-promoting
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74
behaviors. The findings from this study are supported by previous studies
conducted with an older adult population (Padula, 1997; Riffle et al., 1989;
Speake, 1987; Speake et al., 1989).
Race was found to have a significant direct effect on health promotion
behaviors. White, Non-Hispanic subjects practiced more health promoting
behaviors than subjects of other races. This finding must be interpreted most
cautiously due to the fact that the sample was predominantly, White, Non-
Hispanic (n=443, 99.3%).
In this study, the practice of health promotion behaviors was related to a
person’s definition of health. The eudaimonistic definition of health (health as
exuberant well-being directed toward fulfillment and development of one’s
potential) directly affected health promotion behaviors. The older adults who
defined health eudaimonistically practiced more health promotion behaviors.
Older adults who defined health clinically or as the absence of disease practiced
fewer health promotion behaviors. Similar findings are documented in other
studies (Laffrey, 198S; Frauman & Nettles-Carlson, 1991; Palank, 1991; Pender,
1996).
Self-efficacy was found to have a positive direct effect on health
promotion behaviors. Persons with higher levels of self-efficacy practiced more
health promotion behaviors. Pender (1996) identified self-efficacy in the model as
a behavior-specific cognition and a major source of motivation for practice of
health promotion behaviors. Self-efficacy has correlated with health promotion
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75
behaviors of older adults in other studies (Conn, 1998; Resnick, 2000; Resnick &
Spellbring, 2000; Waller & Bates, 1991).
Implications for Nursing Practice
To study health promotion behaviors in older adults, it is necessary of
have a highly reliable and valid instrument. The original 52-item HPLPII might
be problematic for older adults because of its length. Fatigue in filling out a
lengthy questionnaire may be problematic and not the best for older adults who
are physically or mentally frail (Frank-Stromborg & Olsen, 1997). Three of the
499 respondents returned the forms partially completed indicating that the original
form was too long. The shortened version of the HPLPII provides an instrument
that is more user-friendly, yet highly reliable and valid in measuring health
promotion behaviors in an older adult population.
Evaluation of the content of items in the Nutrition subscale is needed. The
items need to be reflective of actual eating patterns of healthy older adults. The
serving amounts recommended in the food pyramid may exceed the amount of
food normally eaten by older adults. The content of the items for the Physical
Activity scale need to be revised to reflect activity levels appropriate for healthy
older adults. Items that were related to strenuous physical activity were often not
answered by respondents. These items were also deleted in the testing of the
measurement model in LISREL.
Self-efficacy, the belief that one is able to carry out a behavior or achieve
an outcome, has been found to be associated with the practice of health behaviors.
It is here that nursing need to focus its efforts. Interventions to improve self
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76
efficacy in older adults need to be tested. An increase in self-efficacy may lead to
positive health changes such as the practice of more health promotion behaviors.
Improved health promotion behaviors help older adults maximize their health,
minimize the effects of aging, experience full functional capacity, remain
independent, and increase the quality as well as the quantity of their lives (Black
& Kapoor, 1990; Mason & McGillis, 1990; Ruffing-Rahal, 1991; Stanley &
Beare, 1999; Young, 1996).
In this study and another (Rakowski et al., 1989), men have been shown to
practice fewer health promotion behaviors then women. Nursing interventions and
education to improve health promotion behaviors need to be targeted on men.
Improvement in the health promotion activities will help men live longer and
healthier lives with less need for medical services (Bandura, 1997; Fries et al.,
1993).
In this study, persons who perceived their health to be poor or fair rather
than good or excellent and persons who defined health clinically rather than
eudaimonistically practiced fewer health promoting activities. These two groups
are prime targets for the development of nursing interventions and education to
improve health promotion behaviors. Improvement in the health promotion
activities of these groups will help them maintain healthy lifestyles (Fowler,
1996).
Limitations
The sample size for the testing of the six measurement models together
prohibited the use of the Robust Maximum Likelihood with the Satorra-Bentler
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77
scaled chi-square as an estimator because an asymptotic covariance matrix could
not be calculated. The recommended five subjects per parameter in the model
were not met with the 28-item instrument and a total effective sample size of 327.
Raykov and Widaman (1995) indicate that sample-to-parameter estimate ratios
less than 5:1 are considered small. With small and moderate samples, the actual
distribution of chi-square values, standard errors, and descriptive indices may
differ from those that might be found with a large sample (Raykov & Widaman,
1995). The covariances calculated are not stable and may be sample dependent
(Raykov & Widaman, 1995). The use of the robust maximum likelihood estimator
may produce a smaller chi-square and a better fit of the six measurement models
to the data.
Multivariate normality is an underlying assumption of the Maximum
Likelihood estimator used in LISREL. If this assumption is violated, the chi-
square is increased and there is an underestimation of the fit of the model. The
PRELIS data screening indicated that multivariate normality was violated. This
violation of multivariate normality may have inflated the chi-square and
contributed to the problem of a poor fit of the hypothesized model to the data for
the latent construct of Nutrition. This may have contributed to the lack of a non
significant chi-square when testing the six measurement models together and the
higher order model.
Convenience sampling techniques produced a sample that was not
representative of the greater population. Bias yielded a sample predominantly
White, Non-Hispanic, female, and well educated.
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78
The response rate of only 55% may have resulted in a response bias. Polh
and Hungler (1995) state that a response rate of greater than 60% is probably
sufficient to make the risk of response bias minimal. A mechanism to determine
if respondents and nonrespondents are similar to each other was not in place for
this study. The use of a self-report instrument also presented the possibility of a
social desirability response bias as respondents may have indicated a higher
frequency of behaviors than actually practiced (Polit & Hungler, 1995).
Implications for Future Research
Further research is the area of health promotion activities in older adults is
recommended. The extent of the reliability and validity of the revised 22-item
HPLPII in an older adult population needs to be reexamined with a larger sample
size using structural equation modeling. The larger sample size will allow the use
of the robust Maximum Likelihood or Weighted Least Squares estimator. The use
of one of these estimators may produce a smaller chi-square and a better fit of the
hypothesized model to the data.
The instrument needs to be tested in a more diverse sample in relation to
race, culture, and education to eliminate the problem of sampling bias. This will
also help determine the reliability and validity of the HPLPII in racially and
culturally diverse populations. Studying specific age groups such as the younger
old (65-74 years), the middle old (75-84 years), and the older old (85 years and
older) is recommended to get a better understanding of health promotion in these
groups. A difference in practice of health promotion behaviors may be present in
the different age groups.
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79
Further studies on the influence of the behavior-specific cognitions and
their effect in the Health Promotion Model in older adults needs to be conducted.
These factors are major sources of motivation for health promotion behaviors and
prime target areas for intervention (Pender, 1996). Further research on scaling
self-efficacy responses into low, medium, and levels needs to be done. This will
allow nurses to develop tailored interventions that focus on improvement in the
specific levels of self-efficacy.
Research also needs to focus on measuring health promotion activities in
persons who are experiencing the same objective level of health. The majority of
the subjects in this study perceived their health to be good or very good (n=349,
77.2%) but their objective level of health was not measured. The question that is
raised is how much of the variance in perception of health is explained by
objective health. Further investigation into health promotion activities related to
objective health levels is needed.
Conclusions
Health promotion remains a priority for nursing. Health promotion
practices in persons, especially older adults, have been found to be beneficial in
maximizing health potential and increasing longevity and quality of life. The
challenge facing nursing is how to increase health promotion practices in the older
adult population. To do this, a valid and reliable instrument is needed to assess
health promotion behaviors and evaluate outcomes.
Testing of the validity of health promotion constructs in the HPLPII
revealed that Nutrition and Physical Activity items need to be reexamined for two
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80
reasons. The first is to make the items consistent with the actual practices and
activities of healthy, older adults. The second reason is to increase the construct
validity of these two health promotion categories.
A shortened form of the HPLPII demonstrated a high degree of reliability
and construct validity in an older adult population in this study. This instrument
needs to be further tested in other subgroups of the older adult population.
The findings from this study showed that older adults define health as role
performance ability and eudaimonistically. This older adult population had high
levels of self-efficacy. Health promoting behaviors practiced most frequently
were in the areas of spiritual growth, interpersonal relations, and stress
management. The older adults practiced health promotion activities related to
physical activity least frequently.
This study supported the determinants of health promoting behaviors as
identified by Pender (1996). Self-efficacy had a direct effect on health promoting
behaviors. Gender, education, clinical definition of health, and eudaimonistic
definition had both a direct effect and an indirect effect through self-efficacy on
health promoting behaviors. Perception of health had an indirect effect on health
promoting behaviors through the mediating variable of self-efficacy. Older adults
who were female, had higher levels of education, defined health
eudaimonistically, perceived their health to be good, and had higher levels of self-
efficacy practiced more health promoting behaviors. The race effect could not be
properly evaluated because there were too few non-White respondents. As a
result, these findings are not reliable.
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Appendix A
Informed Consent Letter for Subjects
81
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82
Dear Reader,My name is Patricia Stockert. I am a doctoral nursing student at Saint Louis University, Graduate School Department of Nursing. I am conducting a research study to determine what factors influence healthy behaviors in older adults. The official name of my research study is The Determinants of a Health-Promoting Lifestyle in Older Adults.
I am requesting your help with this research study because you are an adult 60 years of age or older. You received these questionnaires because you are known to me, you answered the advertisement, your senior group distributed this packet at their meeting or included it in their mailing to members, or it was placed it in your mailbox at your senior residence. The purposes of this study are to test the accuracy and completeness of the questions in the questionnaire that measures health behaviors and to see which factors most influence health behaviors in older adults.
As a participant in this research study, you will be asked to fill out four questionnaires:
The Participant Profile Laffrey Health Conception Scale Perceived Health Competence Scale Lifestyle Profile n
This should take you approximately 30 to 45 minutes to complete. You may skip any questions on the questionnaires that you do not want to answer.
Every study has some risks. Completing these questionnaires could make you feel anxious about your health. If this side effect does occur, feel free to contact me at
or 4.1 will be more than happy to discuss this with you. Another option is for you to put the questionnaires aside and withdraw from the study. In this case, there is no need to contact me.
Please do not put your name or any identifying information on the questionnaires. In this way your responses will be anonymous. No one will know how you responded. I am the only one who will have a copy of the mailing list, and it will be destroyed at the end of the study. The results of the study may be published but your name or identity will not be revealed in any way. If you have any questions about this aspect of the study, also feel free to contact me at or
Participating in this research study has some possible benefits. These are: 1) helping nurses better understand health behaviors in older adults; 2) helping nurses use this information to improve health in older adults; and 3) becoming more aware of healthy behaviors yourself.
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83
Your participation is completely voluntary. Should you decide at any time not to participate in this study, destroy the questionnaires. If you decide not to participate in the research study, there is no penalty, loss of any benefits to which you are entitled, or prejudice.
This study has approval of Saint Louis University’s Institutional Review Board and the Community Institutional Review Board in Peoria, Illinois. If you have any questions about your rights as a research subject or if you believe you have suffered an injury as a result of participation in this research study, you may contact the Chairperson of the Saint Louis University Institutional Review Board at . The Chairperson will discuss your questions with you or will be able to refer you to the individual who will review the matter with you, identify other resources that my be available to you, and provide further information as to how to proceed. You may also contact Dr. Frank Gold of the Community Institutional Review Board in Peoria, Illinois at 3 .
If you decide to participate in the study, complete the four questionnaires. After completing the questionnaires, please place them in the self-addressed stamped envelope provided, seal it, and mail it to me. Please do not place your return address on the envelope. Your completion and return of the anonymous questionnaires in the self-addressed stamped envelope indicates your consent to participate in this research study.
Thank you for your participation in this research study. If you would like a summary of the results of the study, sometime after you have mailed the questionnaires call me and leave your name and address. I will then send a summary to you after the study is finished.
Sincerely,
Patricia A. Stockert, R.N., M.S.
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Appendix B
Participant Profile
84
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85
PARTICIPANT PROFILE
Please provide the following information about yourself. All information obtained will remain confidential.
1. Gender:___________Female Male
2. Age: _____Years
3. Race:_____________White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Other, please specify
4. MaritalStatus: _____Single
Married
Divorced or Separated
Widowed
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86
5. Highest Level of Education Completed:
Less than 9th grade
9m to 12th grade, No diploma
High school graduate or GED
Some college, No degree
Associate or Bachelor’s Degree
Graduate or Professional Degree
6. How do you rate your overall health? Circle one response.
Poor Fair Good Very good1 2 3 4
Excellent5
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Appendix C
Laffrey Health Conception Scale
87
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88
HEALTH CONCEPTION SCALE
DIRECTIONS: Below are 28 statements to describe the meaning the “health” or “being healthy” may have for an individual. Depending on your personal conception of health, you may agree or disagree with the statements. Beside each statement is a scale which ranges from strongly disagree (1) to strongly agree (6). For each item, circle the number which best represents, the extent to which you disagree or agree with the statement. The more strongly you disagree with a statement, then the lower will be the number you circle. The more strongly you agree with a statement, then the higher will be the number you circle. Please make sure that you circle only one number per item. This is a measure of your personal conception of health; there are no right or wrong answers.
“Health” or “being healthy” means:
1. Feeling great • on top of the world
2. Being able to adjust to changes in my surroundings
3. Fulfilling my daily responsibilities
4. Being free from symptoms of disease
5. Being able to do those things I have to do
6. Not requiring a doctor’s services
7. Creatively living life to the fullest
8. Adjusting to life’s changes
9. Not requiring pills for illness or disease
StronglyDisagree
StronglyAgree
2
2
2
2
5 6
5 6
5 6
5 6
5 6
S 6
5 6
5 6
5 6
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89
10. Being able to function as expected
11. Not being under a doctor’s care for illness
12. Facing each day with zest and enthusiasm
13. Being able to cope with stressful events
14. Being able to change and adjust to demands made by the environment
15. Not being sick
16. Actualizing my highest and best aspirations
17. Adequately carrying out my daily responsibilities
18. Living at top level
19. Adapting to things as they really are, not as I’d like them to be
20.1 do not require medications
21. Carrying on the normal functions of daily living
22. Coping with changes in my surroundings
StronglyDisagree
1 2
2
2
2
2
2
2
5
5
5
5
5
5
5
5
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StronglyAgree
6
6
6
6
6
6
6
6
6
6
6
6
6
90
Strongly StronglyDisagree Agree
23. Realizing my full potential 1 2 3 4 5 6
24. Fulfilling my responsibilities as 1 2 3 4 5 6as a husband/wife/son/daughter/friend/worker, etc.
25. Having no physical or mental 1 2 3 4 5 6incapacities
26. Performing at the expected level 1 2 3 4 5 6
27. Not collapsing under ordinary 1 2 3 4 5 6stress
28. My mind and body function at 1 2 3 4 5 6their highest level
© 1983 by Shirley CloutierLaffrey, PhD, RN The University of Texas at Austin
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Appendix D
Perceived Health Competence Scale
91
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92
PERCEIVED HEALTH COMPETENCE SCALE
DIRECTIONS: For each statement, please circle the response that best indicates your belief in your ability to manage your health. The responses range from (1) strongly disagree to (5) strong agree. Please circle only ONE response for each statement. This is a measure o f your perceived competence to manage your health; there are no right or wrong answers.
Strongly StronglyDisagree Agree
1. I handle myself well with respect to my health.
2. No matter how hard I try, my health just doesn’t turn out the way I wouldlike.
3. It is difficult for me to find effective solutions to the health problems that come my way.
4. I succeed in the projects I undertake to improve my health.
5. I’m generally able to accomplish my goals with respect to my health.
6. I find my efforts to change things I don’t like about my health are ineffective.
7. Typically, my plans for my health don’t work out well.
8. I am able to do things for my health as well as most other people.
3
1 2
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Appendix E
The Lifestyle Profile Q
93
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94
LIFESTYLE PROFILE IIDIRECTIONS: This questionnaire contains statements about your present way of life or personal habits. Please respond to the items as accurately as possible. Only circle one number for each item. Indicate the frequency with which you engage in each behavior by circling:
1 for Never, 2 for Sometimes, 3 for Often, or 4 for Routinely
Never
1. Discuss my problems and concerns with people close to me.
2. Choose a diet low in fat, saturated fat, and cholesterol.
3. Report any unusual signs or symptoms to a physician or other health professional.
4. Follow a planned exercise program.
5. Get enough sleep.
6. Feel I am growing and changing in positive ways.
7. Praise other people easily for their achievements.
8. Limit use of sugars and food containing sugar (sweets).
9. Read or watch TV programs about improving health.
10. Exercise vigorously for 20 or more minutes at least three times a week (such as brisk walking, bicycling, aerobic dancing, using a stair climber).
11. Take some time for relaxation each day.
2
2
2
Routinely
3
3
3
2 3
2 3
2 3
2 3 4
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95
1 for Never, 2 for Sometimes, 3 for Often, or 4 for Routinely
Never
12. Believe that my life has purpose. 1 2
13. Maintain meaningful and fulfilling 1 2relationships with others.
14. Eat 6-11 servings of bread, cereal, rice and 1 2 pasta each day.
15. Question health professionals in order to 1 2 understand their instructions.
16. Take part in light to moderate physical activity 1 2(such as sustained walking 30-40 minutes 5 ormore times a week).
17. Accept those things in my life which I can not 1 2change.
18. Look forward to the future. 1 2
19. Spend time with close friends. 1 2
20. Eat 2-4 servings of fruit each day. 1 2
21. Get a second opinion when I question my 1 2health care provider’s advice.
22. Take part in leisure-time (recreational) 1 2physical activities (such as swimming,dancing, bicycling).
23. Concentrate on pleasant thoughts at bedtime. 1 2
24. Feel content and at peace with myself. 1 2
25. Find it easy to show concern, love and warmth 1 2to others.
Routinely
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
3 4
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96
1 for Never, 2 for Sometimes, 3 for Often, or 4 for Routinely
Never Routinely
26. Eat 3-5 servings of vegetables each day.
27. Discuss my health concerns with health professionals.
28. Do stretching exercises at least 3 times per week.
29. Use specific methods to control my stress.
30. Work toward long-term goals in my life.
31. Touch and am touched by people I care about.
32. Eat 2-3 servings of milk, yogurt or cheese each day.
33. Inspect my body at least monthly for physical changes/danger signs.
34. Get exercise during usual daily activities (such as walking during lunch, using stairs instead of elevators, parking car away from destination and walking).
35. Balance time between work and play.
36. Find each day interesting and challenging.
37. Find ways to meet my needs for intimacy.
38. Eat only 2-3 servings from the meat, poultry, fish, dried beans, eggs, and nuts group each day.
39. Ask for information from health professionals about how to take good care of myself.
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
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97
1 for Never, 2 for Sometimes, 3 for Often, or 4 for Routinely
Never Routinely
40. Check my pulse rate when exercising.
41. Practice relaxation or meditation for 15-20 minutes daily.
42. Am aware of what is important to me.
43. Get support from a network of caring people.
44. Read labels to identify nutrients, frits, and sodium content in packaged food.
45. Attend educational programs on personal health care.
46. Reach my target heart rate when exercising.
47. Pace myself to prevent tiredness.
48. Feel connected with some force greater than myself.
49. Settle conflicts with others through discussion and compromise.
50. Eat breakfast.
51. Seek guidance or counseling when necessary.
52. Expose myself to new experiences and challenges.
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
© S.N. Walker, K. Sechrist, N. Pender, 1995. Reproduction without the author’s express consent is not permitted. Permission to use this scale may be obtained from Susan Noble Walker, College of Nursing, University of Nebraska Medical Center, Omaha, NE 68198-5330.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Appendix F
Twenty-Two Item Lifestyle Profile II
98
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99
Twenty-Two Item Lifestyle Profile II
Nutrition2. Choose a diet low in fat, saturated fat, and cholesterol
20. Eat 2-4 servings of fruit each day.
26. Eat 3-5 servings of vegetables each day.
44. Read labels to identify nutrients, fats, and sodium content in packaged
food.
Health Responsibility3. Report any unusual signs or symptoms to a physician or other health
professional.
15. Question health professional in order to understand their instructions.
27. Discuss my health concerns with health professionals.
39. Ask for information from health professionals about how to take good care
of myself.
Physical Activity4. Follow a planned exercise program.
16. Take part in light to moderate physical activity (such as sustained walking
30-40 minutes 5 or more times a week).
28. Do stretching exercises at least 3 times per week.
46. Reach my target heart rate when exercising.
Interpersonal Relations13. Maintain meaningful and fulfilling relationships with others.
19. Spend time with close friends.
25. Find it easy to show concern, love and warmth to others.
31. Touch and am touched by people I care about.
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100
Spiritual Growth12. Believe that my life has purpose.
18. Look forward to the future.
30. Work toward long-term goals in my life.
36. Find each day interesting and challenging.
Stress Management23. Concentrate on pleasant thoughts at bedtime.
47. Pace myself to prevent tiredness.
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VITA AUCTORIS
Patricia A. Stockert was born in Joliet, Illinois. She attended Saint Patrick
Grade School and St. Francis Academy in Joliet. She attended Illinois Wesleyan
University, Bloomington, Illinois from 1972-1976. She graduated Magna Cum
Laude with a Bachelor of Science in Nursing in 1976. She attended the Peoria
Regional Campus College of Nursing of the University of Illinois at Chicago from
1977 to 1982. She graduated from the University of Illinois at Chicago in 1982
with a Master of Science in Nursing Sciences. She is a candidate for the degree of
Doctor of Philosophy in Nursing at Saint Louis University.
Patricia is a member of the American Nurses’ Association and the
American Association of Critical Care Nurses. She is a member of the National
League for Nursing and served on the Board of Directors of the Illinois League
for Nursing for ten years. She is a member of Sigma Theta Tau International
Honor Society, Theta Pi Chapter. She was the recipient of the Illinois League for
Nursing Nursing Student Scholarship Award and the Faculty Research Support
Award. She also received the Saint Louis University Graduate School Tuition
Scholarship.
Ms. Stockert is currently employed at Saint Francis Medical Center
College of Nursing in Peoria, Illinois. She is an Associate Professor and was
recently promoted to senior year Coordinator. Primary teaching responsibilities
include senior level medical-surgical nursing, pharmacology, nursing research,
and adult critical care nursing. Future plans include holding a joint appointment at
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the College of Nursing in both the baccalaureate and newly developed master s in
nursing program.
Patricia is married to Drake W. Stockert and currently lives in Mapleton,
Illinois. She has two children, Sara and Kelsey. In her spare time, she enjoys
reading and camping with her family.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
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