DOT/FAA/AM-00/3

U.S. Department

of Transpor tation

Federal Aviation

Administration

Thomas F. Hilton

FAA Civil Aeromedical Institute

Oklahoma City, OK 73125

I. “Sam” Hart

FAA Office of Aviation Medicine

Washington, DC 20591

William L. Farmer

FAA Civil Aeromedical Institute

Oklahoma City, OK 73125

Jennifer J. Thompson

College of Public Health

University of Oklahoma Health Sciences Center

Oklahoma City, OK 73104

Lydia D. Behn

Environmental Protection Agency

Dallas, TX 75202

February 2000

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, Virginia 22161.

Office of Aviation Medicine

Washington, DC 20591

The FAA Health Awareness

Program: Results of the 1998

Customer Service

Assessment Survey
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents thereof.
i

Technical Report Documentation Page

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No.

DOT/FAA/AM-00/3

4. Title and Subtitle 5. Report Date

The FAA Health Awareness Program: Results of the 1998 Customer Service

Assessment Survey

February 2000

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.

Hilton, T.F.1, Hart, I.S.2, Farmer, W.L.1, Thompson, J.J.3, Behn, L.D.4

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

1

FAA Civil Aeromedical Institute, Oklahoma City, OK 73125

2

FAA Office of Aviation Medicine, Washington, DC 20591

3

College of Public Health, University of Oklahoma Health Sciences Center,

Oklahoma City, OK 73104

11. Contract or Grant No.

4

Environmental Protection Agency, Dallas, TX 75202

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered

Office of Aviation Medicine

Federal Aviation Administration 14. Sponsoring Agency Code

800 Independence Ave., S.W.

Washington, DC 20591

15. Supplemental Notes

This work was performed under task # AAM-A -98-HRR-500.

16. Abstract

This report presents the results of an agency-wide survey of employee health and wellness to determine

workforce involvement in and satisfaction with the Federal Aviation Administration’s Health Awareness

Program (HAP). Surveys were received from 3,262 employees, representing a 45% response rate. Results

indicated that about half the workforce had heard about HAP and that about half the workforce had

participated in one or more HAP events (even if they did not realize that the event was HAP-sponsored). In

terms of attendance, the most popular HAP information programs were health fairs, health awareness lectures,

and stress management awareness programs. Likewise, annual flu shots, cholesterol screening, blood chemistry

screening, and blood pressure screening were the most popular HAP service programs. Analyses found a

consistent relationship between HAP participation and employee exercise rates, involvement in healthy lifestyle

behaviors, and overall wellness. These findings may have been influenced to some extent by respondent

characteristics, which were somewhat disproportionately over age 45, female, and managerial. However, the

respondents’ backgrounds matched previous study results, indicating that Federal Aviation Administration

respondents accurately represent the HAP customer base -- that segment of the workforce most interested in

health and wellness.

17. Key Words 18. Distribution Statement

Health Awareness Programs, Survey Methodology, FAA Document is available to the public through the

National Technical Information Service,

Springfield, Virginia 22161

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price

Unclassified Unclassified 41

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

iii

ACKNOWLEDGMENTS

Thanks to Janine King, OMNI Corporation, for proofreading this manuscript and

for helping with the final design of the questionnaire. Thanks to the presidents of FAA’s

labor unions for their support of this survey. Their support was labor-management partnership in action, which was crucial to gaining employee participation. Thanks also to

G. J. Salazar, M.D., FAA Southwest Regional Flight Surgeon, for taking time from his

busy schedule to provide a thoughtful review of this manuscript. Finally, thanks to the

FAA Health Awareness Program Team for their guidance in developing survey content.

1

THE FAA HEALTH AWARENESS PROGRAM: RESULTS OF THE

1998 CUSTOMER SERVICE ASSESSMENT SURVEY

INTRODUCTION

By the middle 1980s, a large number of work

organizations had instituted employee health awareness programs (HAPs; (Caldwell, 1995; Office of

Disease Prevention and Health Promotion, 1993).

The goal of these programs was to promote wellness

activities and lifestyle changes that might help to

reduce the frequency and severity of illness. Additional goals included reducing work-related stress

and improving employees’ general quality of life and

sense of well-being. Beginning in the early 1990s,

articles began to appear with increasing frequency

that called for studies to determine what impact the

HAPs were having on workplace wellness (e.g., Pirie,

Stone, Assaf, Flora, & Maschewsky; Schneider, 1994;

Schaeffer, Snelling, Stevenson, & Karch, 1994). This

report presents the results of an assessment of the

Federal Aviation Administration’s Health Awareness

Program (FAA-HAP).

The number of organizations that offer HAPs to

their employees continues to increase each year. Not

surprisingly, authors have noted a coincident rise in

HAP popularity with an escalating trend in health

care costs to employers (Chenoweth, 1994; Data

Watch, 1991; Witmer, 1995). Although containing

the growth of employer-sponsored health benefits

costs is a key focus for management, there are other

economic benefits that work organizations can derive

from HAPs. Shephard (1994) showed that HAP

programs can reduce lost time from work by preventing or ameliorating the effects of illness and injury.

Edwards, Tindale, Heath, and Posavac (1990) as well

as James and Colwell (1997) indicate that HAPs can

enhance job performance by increasing participant

self-efficacy and sense of energy and well being. Baun

(1994) reported an association between reduced absenteeism and HAP participation. Justifications other

than direct economic benefits have been noted for

supporting employee HAPs. These include enhanced

corporate image (Pencak, 1991), and the ability to

attract high- quality job applicants (Baun, Bernacki,

& Tsai, 1986).

Overview of FAA-HAP

FAA-HAP was inaugurated on October 1, 1988,

with a one-year prototype project based at the FAA

Washington, DC headquarters health clinic. In 1990,

the program was expanded to include all nine of

FAA’s geographical regions and its two major centers

(Mike Monroney Aeronautical Center and the William J. Hughes Technical Center). Although many

HAPs now offer a limited variety of programs FAAHAP offers a broad range of health awareness information programs and services. To demonstrate the

scope of these offerings, we categorized its programs

according to taxonomy developed by Pencak in 1991

to indicate how comprehensive a HAP may be. According to Pencak, HAPs that offer a variety of

programs in each of three levels of lifestyle intrusiveness are the most comprehensive. Level I programs

offer events to raise health awareness through health

fairs, posters, newsletters, education classes, and health

screening without follow-up. Level II programs offer

events that help employees to change lifestyle behaviors such as reducing tobacco use and alcohol consumption, exercising regularly, and by offering

programs to help modify behaviors. Level III programs work with management to institute policies

and practices that promote and sustain a healthy

lifestyle as a business practice. Table 1 presents a

breakdown of FAA-HAP program offerings for each

of Pencak’s three levels. As Table 1 shows, FAA-HAP

offers a balanced program that spans all three levels.

To ensure a comprehensive program, a national

team of occupational health nurses develops and

submits an annual FAA-HAP activity plan to the

Federal Air Surgeon. Medical supervision and support of these FAA-HAP activities are provided by the

flight surgeons at FAA’s 12 regional units, which

include its national and nine regional headquarters,

as well as the Mike Monroney Aeronautical Center

and the William J. Hughes Technical Center. FAAHAP activities may differ slightly from location to

location in the timing and manner of presentation,

but they conform to the national plan. FAA-HAP’s

objective is to ensure that all agency employees have
2

the opportunity to receive health-related information and to participate in health screening services

and activities throughout the work year. Therefore,

the two primary goals of this study were to estimate

HAP participation levels within the FAA workforce

and to identify common barriers to broad program

participation.

Evaluating Health Awareness and Workplace

Wellness

As HAP program budgets become a larger part of

an organization’s budget, it is reasonable to expect

challenges to the continuation of HAPs. Therefore,

regular program evaluations become a vital factor in

justifying HAP continuation and/or expansion. The

evaluation of HAPs is challenging. The American

Evaluation Association (AEA) devoted an entire issue

of its journal, New Directions for Program Evaluation

(Braverman, 1989) to the topic. AEA articles identified many barriers to successfully evaluating HAPs,

and offered ideas about how to deal with them.

One of the most troublesome challenges to HAP

evaluation identified by AEA and others (Schaeffer et

al., 1994) is the unreliability of job-related outcome

measures. Many organizations do not consistently or

systematically report such job behaviors as performance levels, absence from work, absence due to

illness/injury, etc. A second challenge stems from the

fact that HAPs are not the only source of wellness

intervention to which employees are exposed (Pirie et

al., 1994). Therefore, within-organization evaluations may quantify changes that are difficult to attribute directly to HAP interventions. A third

challenge, related to the second one, is that stress,

illness, and injury also can be caused by or influenced

by factors outside the workplace, thereby making it

difficult to control for effects unlikely to be influenced by HAP interventions. A fourth challenge is

how to make credible conversions of valid job-related

HAP outcomes into dollar amounts (Lynch, 1994;

Raju, Burke, & Normand, 1990; Schmidt, Hunter,

& Pearlman, 1982). Finally, a fifth challenge stems

from the typically long time lags associated with

healthful lifestyle interventions and HAP participant

health outcomes such as reduced heart disease, diabetes, or stroke (Blair, 1995).

This study inaugurates the evaluation of FAAHAP. All of the evaluation challenges reviewed here

apply to the FAA. Therefore, it was necessary to design

an assessment strategy that would immediately provide

information useful to program sponsors that also

might enable more definitive evaluation in the future. Reviews of the effects of wellness programs by

authors like Pelletier (1991, 1993, 1996) demonstrate wide variability in evaluation designs. Still,

Pelletier shows that most-common is the withinorganization pre-post design that compares aggregated summaries of employee health outcomes at two

or more points in time, assigns a cost to the employer

in terms of lost time from work, insurance costs, etc.;

and then associates health changes with HAP participation. The current study will enable follow-up research using such a pre-post design (e.g., Cook &

Campbell, 1979; page 99). However, this study was

not intended to establish a direct cost-benefit link

between FAA-HAP and the agency’s budget.

The goals of the present study, in order of priority,

were to:

1. assess the level of employee participation in FAAHAP,

2. identify barriers to participation in FAA-HAP,

3. discover possible ways to enhance participation in

FAA-HAP,

4. create an aggregate baseline estimate of employee

fitness and wellness for future evaluation,

5. produce data to enable linking fitness, wellness,

and lifestyle changes to FAA-HAP participation.

Because all data were collected via questionnaire,

detection of significant participation effects was not

expected to emerge from self-reported exercise,

wellness, and lifestyle change activity. They were

included with the expectation that future surveys

would enable trend analyses to examine for HAP

effects on health-related activities and outcomes for

the FAA workforce.

METHOD

Participants

A 15% random sample (7,309) of the FAA’s

workforce of more than 48,000 was selected to receive questionnaires at their offices. Because corporate HAP programs are a service to employees, the

FAA-HAP evaluation survey was presented as a customer service questionnaire. Studies of customer

service often indicate very low response rates to

customer surveys. Therefore, an effort was made to

obtain endorsements from the seven unions that
3

represented most FAA employees. Union officials cosigned the cover letter requesting participation, and

each union’s logo was prominently displayed under

the FAA letterhead. Completed surveys were received

from 3,262 employees, yielding a 45% response rate.

Table 2 summarizes the demographic breakdown of

respondents, compared to the FAA population.

Table 2 shows that survey respondents differed

significantly from the FAA population as a whole.

Respondents were disproportionally older (over 45),

female, and in management positions. Likewise,

among the 12 regional units sampled; four did not

match population parameters. Central and Northwest Mountain regions were over-represented by 25%

and 21% respectively, whereas the William J. Hughes

Technical Center and Washington HQ were underrepresented by 40% and 18%, respectively.

Analyses were conducted to test the hypothesis

that survey participation might have been influenced

by HAP participation. Past studies have shown that

women, older people, and people of relatively high

occupational status are generally more health conscious than males younger people, and people without college degrees; and therefore, are more likely to

participate in health awareness activities (e.g.,

Furnham & Kirkcaldy, 1997; Heckler, 1985;

Nakazono, Davidson, & Anderson, 1997). Correlations were run on all four background factors with

self-reported participation in FAA-HAP. A small but

significant relationship was found between each factor and reports of participation in FAA-HAP. The

relationship between age and FAA-HAP participation was low but significant1

 (g = .15, p < .001) with

HAP participants being older. The correlation between gender and HAP participation was j= .11, p <

.0012

, with female respondents being somewhat more

likely to participate in HAP. The correlation between

manager status and FAA-HAP participation was j=

.15, p < .001, with managers being more likely than

nonmanagers to participate. Finally, there was a significant relationship between region and participation rates (p = .24, p < .001).2

 This latter relationship

may reflect the fact that FAA-HAP programs are

administered regionally, and can vary in quality and

accessibility by region. Therefore, results support a

conclusion that differences in survey participant background might be due to greater response rates from

HAP participants.

The Survey

The FAA-HAP Customer Service Survey is a 36-

item checklist-type questionnaire with an additional

five items addressing personal background (e.g., sex,

age; see Appendix A). Because delivery of FAA-HAP

program and service is not designed or marketed

using the Pencak (1991) three-level taxonomy used

to demonstrate program breadth and depth, a different taxonomy was used for organizing this report.

FAA-HAP divides programs into two groups: information programs and service programs. Information

programs increase participant awareness of healthful

behaviors or educate participants about how to change

their lifestyles to include behaviors that promote

good health. Service programs provide immunizations against common illnesses such as flu, and screen

for biological evidence of common diseases.

Survey items asked respondents to indicate their

past participation in any of 13 information programs

and 12 services programs. Participation items were

followed by requests to identify barriers to participation in each of the two types of programs. Respondents were also asked to indicate how they first

learned about FAA-HAP, about the specific program

that was their first HAP experience, and what type of

promotion activities most appeals to them. Respondents also completed a healthstyle activity matrix (see

Appendix A, page A1) on which they reported the

duration of involvement in any of eight exercise/

fitness regimes and five lifestyle changes. The matrix

included a space to indicate whether participation in

each exercise form or lifestyle change was still actively

being pursued. The survey ended with a brief selfassessment of well being during the previous 12

months followed by a few questions about the respondents’ characteristics and background. Assurances of anonymity were made in this final section.

1 Gamma (g) is a measure of association similar to a correlation coefficient. It is used when examining relationships among

categorical and ordinal variables (Mueller, Schuessler, & Costner, 1970).

2 Phi (j) is a measure of association between two categorical variables. In the two by two contingency table, j is identical

to the Pearson correlation coefficient (r) (Hays, 1973).
4

RESULTS

HAP Participation

Forty-two percent (1,274) of the respondents indicated that they had previously heard about FAAHAP. Additionally, another 15% (473) of

respondents, who reported no previous knowledge

about HAP, indicated participation in at least one

HAP program. Therefore, 57% of respondents had

either heard about HAP and/or had participated in an

FAA-HAP program.

Respondents were asked to indicate how they first

learned about FAA-HAP and in which program they

first participated. Results presented in column 1 of

Table 3 show respondents were most likely to hear

first about HAP from a local FAA medical representative (20.3%). Flyers announcing health screening,

newsletters, and the Center for Management Development (CMD) were the second-most common way

in which respondents first learned about HAP. All

three were relatively equal at about 14% each. Results

in column 2 summarize the HAP programs in which

respondents first participated. The annual flu shot

program and health fairs were the most commonly

identified events in which employees first encountered FAA-HAP, each accounting for over 20% of

reported first encounters. These programs were followed by blood chemistry screening (12.7%) and

cholesterol screening (9.8%).

Overall, 49.2% (1,605) of respondents indicated

participating in at least one HAP program, while over

half of the participants (58%; or 27% overall) had

attended two or more programs. Overall, 33% of respondents overall reported participating in one or more

information programs, with 42.7% overall reporting

participation in one or more service programs.

Table 4 summarizes program participation for the

various information and service programs. Data summarize the percentage of total respondents and of

respondents who participated in one or more information program(s)/service program(s). Consistent

with the first contact with HAP results listed in Table

3, health fairs were the most popular information

program and the flu shot program was the most

popular service program. In relative order of popularity, health fairs, health awareness lectures, stress management awareness, and fitness awareness were the

top four information programs in which respondents

participated. These programs represented 71% of all

2,282 HAP participants responding to information

items. The annual flu shot program, cholesterol screening, blood chemistry screening, and blood pressure

screening were the top four service programs in which

respondents participated, which represented 70% of

all 3,860 participants responding to service items.

Program Participation Influence on Subsequent

HAP Participation

If HAP participation actually achieves its goal of

increasing health awareness, then one logical outcome of participation in a HAP wellness program

should be an increased likelihood that employees

would participate in other wellness programs. Figure

1 presents a simple recursive model depicting the

hypothesized likelihood that HAP participation leads

to increased participation in other types of HAP

wellness programs.

A test of the model was limited to four programs

with high levels of participation to ensure sufficient

variance to meet assumptions for analyses using dichotomous data. In addition, the model was limited

to examining participation across the two types of

wellness program (i.e., information and services) to

help control for temporal coincidence. For example,

it was necessary to control for the possibility that

blood pressure testing (a service) might be made

available coincident with cholesterol screening or

blood chemistry testing (other types of services).

Because of the recursive nature of the participation

model, the direction of causality is not relevant. The

magnitude of the relationship is of primary concern.

Therefore, various analytic approaches for testing the

model in Figure 1 were considered. The use of simultaneous structural equations was not selected because, even for a simple recursive model, the number

of coefficients generated would be numerous and the

presentation of results would be complex. Also, general unfamiliarity with interpretation of goodness of

fit indicators might be confusing. Had the model

been nonrecursive, implying temporal causality, then

structural equations would have been preferred regardless of the complexity of results. Two other

methods were considered, logistic regression and

multiple discriminant analysis (MDA). Both methods were applied to test the model, and both yielded

almost identical results. Presented here are the MDA

results, because the statistical coefficients from MDA

should be familiar to most readers.
5

MDAs were conducted to analyze the participation levels of HAP participants and nonparticipants

in the two most popular information programs (health

fairs and health awareness lectures) and the two most

popular service programs (flu shots and cholesterol

screening) on attendance levels for all the other types

of information or service programs. MDA is equivalent to a single-factor multivariate analysis of variance in which participation/nonparticipation in a

particular service or information program is made the

dependent variable (Kerlinger & Pedhazur, 1973;

Stevens, 1986). The independent variables comprise

participation levels in all of the programs not in the

same type as the dependent variable. For example,

independent variables might be all service programs

vis-a-vis the dependent variable: participation in an

information program like health fairs.

MDA produces a multivariate F-test of the chisquare c2

) for between-group differences on all independent variables analyzed. An indication of strength

of relationship between participant/nonparticipant

status and participation in the other types of programs

is provided by the canonical correlation coefficient,

Rc

. A significant Rc permits direct interpretation of

univariate (1-Way) F-tests comparing the group averages for each independent variable, without concern for Type I error caused by repeated testing.

MDA also yields weights that order, the independent

variables, relative to their contribution to betweengroup differences. The larger the function weight, the

larger the difference between participants and nonparticipants in the dependent variable group (Harris,

1975; McKay & Campbell, 1982).

Tables 5 and 6 summarize the results of the MDAs.

All equations were significant in the multivariate

sense (considering all variables together). For ease of

interpretation, only six programs with the greatest

between group differences were listed. Between-group

average differences were found to be significant for all

six program variables. The four equations of Tables 5

and 6 accounted for between 10 and 25% of variance

in program participation, based upon the square of

the canonical correlations.

Tabled in columns are the associated function weights

for each variable in the equation, converted to correlation coefficients (i.e., structure loadings); the averages

for the two groups (participants and nonparticipants);

and the univariate (1-Way) significance levels for between-group differences. Note that the averages are in

decimal form because responses were dichotomous due

to the checklist nature of the items.3

Table 5 shows that, compared to respondents who

did not attend health fairs, health fair attendees were

relatively more likely to participate in blood pressure

screening, followed successively by cholesterol screening, blood chemistry screening, health risk assessment,

body fat measurements, and annual flu shots. Employees who reported attending health awareness lectures

were more likely to participate in cholesterol screening,

blood pressure screening, health risk assessment, body

fat measurement, blood chemistry screening, and blood

sugar/diabetes screening. Similarly, the results of Table

6 show that respondents who had flu shots and/or

cholesterol screening were also more likely to have

participated in a number of information programs. The

pattern of information program participation levels

were almost the same, showing that respondents who

participated in either of the two service programs were

more likely to attend health fairs, health awareness

lectures, and stress management awareness programs in

addition to fitness awareness, weight management, and

the personal viewing of medical information videos.

Barriers and Enticements to HAP Participation

Results reported so far indicate that about half of

FAA respondents indicated participation in HAP. Also,

once an employee participates in one HAP event, it was

shown that they are more likely to participate in other

types of HAP events. These results lead to a conclusion

that getting employees to participate in HAP initially

can be an important method for increasing the workforce

participation level. Knowing what employees consider

to be barriers to their participation should offer clues

regarding how to increase employee involvement in

FAA-HAP. Respondents were asked: “Regardless of

whether or not you have participated, please indicate

your BIGGEST BARRIER to participation...” for each

of the two types of programs. The list of 15 barriers was

based primarily upon 13 factors identified in published

research (Shephard, 1983, 1994; Wilson, 1990 and

several explanations volunteered to occupational health

professionals within the FAA.

3 Because all items were coded 0 and 1 (selected or not selected), averages must fall between 0 and 1. Dichotomization normally

reduces variance, thereby masking true relationships and differences. This is discussed at length in Nunnally (1978).
6

Table 7 summarizes the barriers identified by

respondents. The percentages for each type of HAP

program are very similar. In fact, the cross-tabular

contingency coefficient was high, r = .93, indicating

a high degree of consistency in how respondents

reported barriers to HAP participation across the two

program types. From these results, one can conclude

that the barriers to participation are unlikely to be

influenced by the type of HAP program (information

or service). Approximately one quarter of respondents reported no barriers to participation in the

FAA-HAP. Thus, nearly 75% of respondents reported a barrier. Over 40% of respondents reported

never or rarely hearing about HAP programs. The

next largest barrier, program never or rarely available

in the respondent’s local area, was mentioned by

almost 10% of respondents. Excluding the “no barrier” respondents, never/rarely hearing or never/rarely

available, account for over 70% of reported barriers.

In addition to barriers to participation, the survey

also asked respondents which health/wellness promotion activities “most” appealed to them. Seven

options were provided. Table 8 summarizes the promotional activities endorsed by respondents.

As Table 8 shows, health screening and high involvement activities (e.g., exercising and dieting)

were most appealing to the 2,457 employees who

responded to the item. Among those who selected the

“other” category, 20% (18) mentioned creating a

fitness room/exercise center at work or subsidizing

membership in a nearby health club. The remaining

options were rarely mentioned by more than one

respondent.

Baseline Wellness Activities

An additional goal of the HAP survey was to

establish a national baseline for employee wellness

activities against which to gauge changes in the future. Respondents were asked, regardless of HAP

participation, to indicate their involvement in eight

exercise programs and five lifestyle changes advocated by FAA-HAP. Employees were asked:

Please indicate which wellness activities you have

begun during the past 24 months, regardless of

HAP’s influence. In addition, please estimate how

long you have been able to stay with the activity, and

whether you are still doing the activity. [Mark all

that apply]

The duration options ranged from a few days to 19

to 24 months.

Baseline Exercise Program Participation

Two tables summarize the exercise data. Table 9A

presents participation levels for all respondents in the

FAA. Table 9B presents participation levels limited

to those reporting participation in some type of

exercise. Table 9A results enable identifying the most

popular types of exercise among agency employees

based upon self-reported participation levels. Table 9B

results enable identifying types of exercise that attract

the greatest amount of employee participation.

Table 9A lists each type of exercise down the first

column. Subsequent columns, moving left to right,

indicate the total number and percentage of all respondents participating in each type of exercise. For

example, 1,460 respondents indicated participation

in walking, which was 44.8% of 3,262 respondents.

Respondents were free to indicate participation in

more than one type of exercise. Participation was

followed by the percentage of respondents in each of

seven duration categories expressed as a percentage of

the total sample (N = 3,262). For example, 6.1% of

all respondents indicated that the duration of their

walking extended only a few days, whereas 18.5%

indicated 19 to 24 months’ participation. The final

three columns indicate the number and percentage of

the total sample continuing each program of exercise,

and the percentage who reported continuing in each

program of exercise for more than 18 months. For

example, 1,144 respondents reported that they are

continuing to walk, which is 35% of the total sample,

thereby indicating that 316 respondents had stopped

walking. The last column shows that 17% of the total

sample reported walking for over 18 months, and

that they are continuing to walk for exercise. Finally,

the last row summarizes reports of participation in

one or more type of exercise.

Following the format of Table 9A, Table 9B limits

results to those reporting participation in some type

of exercise. These results enable generalization to the

population of exercising employees. One notable

difference from Table 9A is that the relative percentage column sums to 100% of all 4,498 instances of

self-reported exercise. This column of data enables

relative comparisons of the popularity of each type of

exercise among exercising employees. As Table 9A

shows, almost 70% of respondents reported engaging

in some form of exercise during the past 24 months.
7

Compared to other exercise types, a higher percentage of those involved in walking, a mixture of fitness

activities, or gym/fitness center workouts were continuing after 18 months. In fact, the average canonical correlation between exercise level and continuation

was Rc = .76 (c2 = 1405.8; p<.0001). The relative

popularity of programs is demonstrated on both

Tables 9A and 9B, with the greatest percentage of all

respondents engaged in walking (44.8%) and the

fewest engaging in skating (3.7%). Among exercisers,

32.5% walk, whereas only 2.7% skate. The columnar

data of Table 9A cannot present a clear picture of

relative dropout trends because they include respondents who did not report any exercise (31%). Dropout trends can help to identify programs capable of

generating high initial interest, yet fail to sustain that

interest. Programs with substantial dropout trends

may need revision, or reduced resources may be

justified in cases where HAP influence may be limited. Dropout trends can best be inferred from the

data presented in Table 9B.

Looking at the relative percentage of exercisers

who stay with a program for at least 18 months, it can

be seen in Table 9B that swimming and skating had

the lowest adherence levels. Moreover, inspection

across the matrix for swimming and skating shows

that a relatively large percentage of respondents participated for only a few days or weeks. This pattern

suggests that respondents found it difficult to adhere

to a regular pattern of exercise for these two exercise

types, with most dropping out within 18 months. It

is interesting to note that a somewhat reverse pattern

is shown for the three programs with the highest

continuation levels (walking, mix of activities, and

gym/fitness center). The initial participation levels

for the three are relatively small but show substantial

levels of adherence beyond 18 months. Moreover,

although walking was relatively the most popular

exercise program in terms of the numbers of participants, participation levels beyond 18 months were

about average. Three forms of exercise were higher

than walking at 19 to 24 months (a mix of activities,

gym/fitness center workouts, and jogging) and four

forms were higher among those continuing after 18

months. Therefore, while the popularity of different

exercises may vary, adherence to regular exercise over

time (measured in terms of either participation or

continuation after 18 months) is similar for six of the

eight exercise types studied.

The results of Tables 9A and 9B do not shed light

on whether or not exercise levels are related to FAAHAP participation. To determine if there is a relationship between HAP participation and exercise,

exercise levels for FAA-HAP participants and nonparticipants were compared using MDA. Data were

coded on a seven-point ordinal scale ranging from 0

(no participation) to 7 (19 to 24 months’ duration)

consistent with the categories in Tables 9A and 9B.

Because most people who exercise are limited by time

to only a few forms, the data set will necessarily

contain many zero values, even among exercisers.

This situation does not affect the validity of betweengroup tests, but it does constrain the range of values

and associated group averages. Two analyses were

conducted. The first MDA equation examined participation levels, and the second examined continuation levels. Table 10 presents the overall results for

the MDAs in terms of the size of the canonical

correlations and multivariate significance of the chisquares. Tabled in columns are the associated function weights for each variable in the equation,

converted to correlation coefficients (sometimes called

structure loadings), the averages for the two groups

(participants and nonparticipants), and the univariate

significance levels for between-group differences. Only

univariate significant differences should be interpreted and only when the overall MDA achieves

multivariate significance McKay & Campbell (1982).

As Table 10 shows, there was a multivariate significant difference between FAA-HAP participants and

nonparticipants with FAA-HAP participants exercising

significantly more than nonparticipants. Inspection of

each univariate analysis of variance shows statistically

significant differences between FAA-HAP participants

and nonparticipants for average walking and jogging

levels. The remaining differences were not statistically

different from one another. However, exercise levels

were greater for FAA-HAP participants (16% greater on

average) with the exception of a mix of fitness activities.

Although the amount of variance accounted for in the

exercise equation is small this is partly due to the

restriction of range among independent variables. In

addition, 31% of respondents (regardless of FAA-HAP

participation) reported no participation in exercise during the past two years. Results of the second equation

show that FAA-HAP participation did not significantly

increase the likelihood of continuation in exercise.

However, the averages were in a direction consistent

with results for exercise participation.
8

Baseline Lifestyle Changes

Two tables summarize the lifestyle change data.

Table 11A presents participation levels for all respondents in the FAA. Table 11B presents participation

levels limited to lifestyle changers. Table 11A results

enable identifying the most popular types of lifestyle

change among respondents upon self-reported participation levels. Table 11B results enable identifying

types of lifestyle change that attract the greatest

amount of employee participation. Table 11A lists

each type of lifestyle change down the first column.

Subsequent columns, moving left to right, indicate

the total number and percentage of all respondents

participating in each type of lifestyle change. For

example, 1,208 respondents indicated participation

in dieting to improve nutrition, which was 37% of

3,262 respondents. Respondents were free to indicate

participation in more than one type of lifestyle change.

Participation was followed by the percentage of respondents in each of seven duration categories expressed as a percentage of the total sample (N =

3,262). For example, 2.4% of all respondents indicated that the duration of their diet for nutrition

participation extended only a few days, whereas 16.9%

indicated 19 to 24 months’ participation. The final

three columns indicate the number and percentage of

the total sample continuing each program of lifestyle

change and the percentage who reported continuing

in each program of lifestyle change for more than 18

months. For example, 993 respondents reported that

they are continuing to diet, which is 30.4% of the

total sample, thereby indicating that 215 respondents had stopped dieting for nutrition. The last

column shows that 15.7% of the total sample reported dieting for nutrition for over 18 months, and

that they are continuing to do so. Finally, the last row

summarizes reports of participation in one or more

type of lifestyle change.

Following the format of Table 11A, Table 11B

limits results to those reporting participation in some

type of lifestyle change. These results enable generalization to the population of lifestyle changing employees. One notable difference from Table 11A is

that the relative percentage column sums to 100% of

all 3,643 instances of self-reported lifestyle change.

This column of data enables relative comparisons of

the popularity of each type of lifestyle change among

employees who made changes.

As Table 11A shows, over half (58.8%) of the

respondents reported engaging in one or more forms

of lifestyle change during the past 24 months. Consistent with the exercise results, Table 11A also shows

a pattern that, the longer respondents stayed with a

lifestyle change, the greater the likelihood that they

are still adhering to that lifestyle change after 18

months. The average canonical correlation between

lifestyle change level and continuation was Rc = .78

(c2 = 2353.3; p<.0001). The relative popularity of

programs is also demonstrated in Table 11A, with the

greatest percentage of all respondents changing their

diet to improve nutrition (37%), and weight loss/diet

(28.9%), and the fewest trying to reduce smoking

(12.3%). The columnar data of Table 11A cannot

present a clear picture of relative dropout trends

because they include respondents who did not report

any lifestyle changes (41%). Dropout trends can help

to identify programs capable of generating high initial interest, yet fail to sustain that interest. Programs

with substantial dropout trends may need revision, or

reduced resources may be justified in cases where

HAP influence may be limited. Dropout trends can

best be inferred from the data presented in Table 11B.

Unlike the exercise results, the pattern of results

for lifestyle changes is more complex. Table 11B

shows that smokers were most likely to try changing

for only a few days (11.4%). However, smokers were

third-most likely to maintain reduced smoking after

18 months. Weight loss/diet, on the other hand, also

had high participation levels for categories under six

months but, relatively speaking, had the lowest adherence of all types of lifestyle change after 18 months.

Moreover, participation levels across categories suggest that drop-out levels beyond six months were

relatively greater at nearly all stages for weight loss/

dieters when compared to other lifestyle changes.

The most popular lifestyle change begun by respondents was diet to improve nutrition, with 37%

of all respondents reporting some degree of participation, and 33% of lifestyle changers indicating attempts to improve nutrition. However, continuance

in nutrition programs after 18 months, while highest

for all respondents (15.7%; Table 11A) was thirdlowest among changers (38.7%; Table 11B). Stress

reduction, on the other hand, was third-most popular

among respondents and changers but it had the secondhighest adherence level among all respondents at 18
9

months (20.2%; Table 11A) and the highest adherence level among changers at 18 months (46.1%;

Table 11B).

The results of Tables 11A and 11B do not shed

light on whether or not lifestyle change levels are

related to FAA-HAP participation. To determine if

there is a relationship between HAP participation

and lifestyle change, lifestyle change levels for FAAHAP participants and nonparticipants were compared using MDA. The first equation examined

participation levels, and the second examined continuation levels. The data presented in Table 12

indicate that there was a multivariate significant

tendency for HAP participants to participate and

continue in lifestyle change relative to nonparticipants. The function weights order the lifestyle change

programs according to the amount of difference

between FAA-HAP participants and nonparticipants.

Univariate significance levels indicate significant differences between FAA-HAP participants and nonparticipants. HAP participant average scores were

higher for improved nutrition, stress reduction, and

weight loss, respectively. Differences in continuation

levels were significant for improved nutrition, stress

reduction, and reduced alcohol intake, respectively.

The amount of variance accounted for in the equations is small. However, this is to be expected when

analyzing independent variables with restriction of

range because of the checklist style of the items and

because 41% of respondents reported no attempts at

lifestyle change during the past two years.

Baseline Health Status

The survey also baselined self-reported health status. These data provide a baseline for future studies

while providing some immediately useful information about possible wellness outcomes related to

HAP participation. Nine questions addressed health

changes in the past 12 months. Table 13 summarizes

the percentage of respondents who indicated positive

health changes. As can be seen, at least 30% of

respondents indicated improvement in general well

being, either through weight loss, being less ill, reducing medication usage and/or dosage, or becoming

more muscular.

Three questions addressed physical examinations

and their results. Employees were asked if they had

undergone a thorough physical examination in the

past 18 months. Also, they were asked if they had

discovered a previously unknown medical condition

as a result of participating in HAP information or

screening service programs. Almost 70% (68.5) of

respondents indicated that they had undergone a

thorough physical in the past 18 months. However,

that percentage dropped to 61% after eliminating

respondents who indicated that a routine physical

was an FAA job requirement. A small percentage of

the respondent sample, 1.5% (51 respondents) reported that participation in FAA-HAP information

programs led to discovery of a previously unknown

medical condition. A slightly higher percentage, 3.4%

(110 respondents) indicated discovery of a previously

unknown condition because they participated in

screening service programs.

Table 14 presents a summary of results of an MDA

examining responses from FAA-HAP participants and

nonparticipants. Scores ranged from a low of 0 to a high

of 1. Because of missing data, only 2,244 respondents

could be analyzed. Of these 50.3% were FAA-HAP

participants and 49.7% were nonparticipants.

As Table 14 shows, FAA-HAP participants were

significantly more likely to have reported a positive

change in health status during the past 12 months. The

three areas in which HAP participants reported the most

improvement was less illness, reduced stress, and reduced use of prescription medication, respectively. Only

aerobic fitness and weight loss were not significantly

different between the two groups. As with previously

reported survey results, the magnitude of betweengroup variance explained is small. This is likely due to

the dichotomous nature of the data.

Respondents were also asked if they would be

willing to participate in a health monitoring program

over a number of months to help them develop a

healthier lifestyle. As the pie chart in Figure 2 shows,

36.6% of respondents expressed “great” or “very

great” interest in participating, while another 30.5%

indicated “some” interest in monitoring.

Background Differences

Results showed significant differences between

respondents and the target population. Therefore,

analyses were run to determine the extent to which

background affected HAP participation and reported

wellness behaviors and outcomes. There is a considerable literature reporting differences in wellness behaviors due to age, gender, and even occupational

factors (Anspaugh, Hunter, & Savage, 1996; Crump,

Earp, Kozma, & Hertz-Picciotto, 1996; Donaldson

& Blanchard, 1995; Powell, 1998). These studies
10

have shown that each of these three background

factors significantly affect HAP participation and

various wellness behaviors. Therefore, MDAs were

run on FAA-HAP information program participation, service program participation, exercise levels

and continuation levels, lifestyle change levels and

continuation levels, and self-reported wellness. Generally, small but significant effects were found for

age, gender, and managerial status. For the sake of

brevity, only multivariate significance levels will be

reported for the five MDA equations for each background factor. Variable-by-variable between-group

averages will only be summarized in narrative. Only

significant results will be reported.

Age. Older respondents (over age 45) were significantly different from younger respondents on all topic

areas. Table 15 summarizes results for each topic area

analyzed. As can be seen, canonical correlation coefficients were all significant, although accounting for only

between one and four percent of multivariate betweengroup variance. Examining univariate analyses of variance, it was found that older respondents were more

likely than younger respondents to participate in health

fairs, health awareness lectures, medical information

videos, walking for health, and stress management; but

less likely to participate in cancer awareness programs.4

Older respondents were also more likely than younger

respondents to participate in all health services, except

body fat, sickle cell screening, colorectal screening, and

mammograms. Exercise showed that older respondents

scored lower than younger respondents on all exercises

except walking and swimming. The same pattern was

found for continuation in exercise programs. Lifestyle

changes presented mixed results. Older respondents

were more likely than younger respondents to score high

on diet changes (losing weight and improving nutrition) and reduced smoking, but they were less likely

than younger respondents to reduce alcohol consumption. A similar pattern was found for continuation in

lifestyle changes except that there was no difference in

continuation levels for dieting for nutrition. Finally,

older respondents reported lower levels of wellness

than younger respondents in only three areas: feeling

more energetic, being more muscular, and being

more aerobically fit.

Gender. Female respondents were significantly

different from male respondents on all topic areas

analyzed. Table 16 summarizes results for each topic.

Gender specific items (e.g., mammograms) were not

included. As can be seen, canonical correlation coefficients were all significant, although accounting for

only between one and four percent of between-group

variance. Examining univariate analyses of variance,

it was found that female respondents were more likely

than male respondents to participate in all information programs except stop-smoking, and alcohol

awareness. Similarly, female respondents were more

likely than male respondents to participate in all

health services except health risk assessment, sickle

cell screening, colorectal screening, blood sugar screening, and hearing tests. Exercise showed mixed results

with female respondents scoring higher than male

respondents on walking and aerobics, but lower than

males respondents on jogging, bicycling, and gym

exercise. The same pattern was found for continuation in exercise programs. Lifestyle changes also presented mixed results. Female respondents were more

likely than male respondents to score high on diet

changes (losing weight and improving nutrition) but

were less likely than male respondents to stop smoking. The same pattern was found for continuation in

lifestyle changes. Lastly, female respondents reported

higher levels of wellness during the past 12 months

than did male respondents. Compared to male respondents, female respondents reported that they

were ill less often, exercised more, reduced stress

more, reduced use of prescribed medication more,

and stayed away from work due to illness less often.

Managerial Status. Managerial respondents were

significantly different from non-managerial respondents on five of seven topic areas analyzed. Table 17

summarizes results for each topic area analyzed. As

can be seen, canonical correlation coefficients accounted for between one and seven percent of between-group variance. Examining univariate analyses

of variance, it was found that managerial respondents

were more likely than non-managerial respondents to

participate in all information programs except breast

self-examination training, stop smoking, World AIDS

4

 If variables are not reported to be significantly different between one group and another (i.e., more/less likely), then the

two groups were the same. This applies to the remainder of the report.
11

day, and cancer awareness. Similarly, managerial

respondents were more likely than non-managerial

respondents to participate in all health services except

sickle cell screening and mammograms. Neither exercise levels nor exercise continuation showed significant between-group differences. Managers engaged

in lifestyle changes significantly more than nonmanagers in all five areas. However, managerial respondents were more likely than non-managerial

respondents to continue those changes only in reducing alcohol consumption and smoking. Finally, managerial and nonmanagerial respondents did not differ

in self-reported wellness during the past 12 months

except that non-managers reported a greater reduction

in the use of prescribed medication than managers did.

DISCUSSION

The brevity of the survey instrument used in this

study precludes addressing many questions regarding

the efficacy and utility of HAPs per se. The answers

to such questions were of secondary importance to

the FAA. Assessing the level of participation in the

various programs in FAA-HAP, identifying barriers

to HAP participation, uncovering possible ways to

enhance participation, and establishing a baseline

estimate of FAA wellness all took precedence over

linking wellness to HAP participation. Nevertheless,

results presented a pattern of evidence indicating that

HAP participants are healthier, exercise more, and

lead healthier lifestyles than are nonparticipants. Such

results, derived from baseline data, cannot establish

the efficacy of FAA-HAP until confirmed by followup research.

Although participation included less than half of

the employees randomly sampled, the 45% who

responded to the survey provided ample statistical

power to test hypotheses relevant to primary study

goals. Because primary goals did not address differences in participant background, significant differences between the background characteristics of survey

respondents (i.e., gender, age, region, and managerial

status) and the FAA workforce are not problematic.

However, readers might wish to draw conclusions

about participant background factors. We pointed

out that, although significant, background differences were small in magnitude. It is even possible,

though not yet validated, that HAP participation

may be a function of background characteristics, and

that this might affect survey participation as well. We

cited several studies reporting that HAP participants

were generally older, more senior in position and

tenure, and disproportionately female. Until this

issue is more fully resolved in future research, we

recommend caution when drawing conclusions based

upon respondent background.

Generalizations about group differences not involving reference to respondent background characteristics should be accurate for guiding national

resource, policy, and practice decisions. FAA-HAP

participant/nonparticipant between-group differences

reported here are also not likely to be affected by

sampling error because both groups were almost

evenly represented. Having both a sufficiently large

sample and a 50-50 split on HAP participation minimizes statistical bias in testing between-group differences (Hays, 1973; Winer, 1971), and if anything,

may be more likely to underestimate differences

(Wilcox, 1998). Moreover, limiting factors normally

associated with surveys, such as poor memory, tendencies to characterize one’s health favorably or unfavorably, misinterpreting item meaning, should equally

affect both HAP participants and nonparticipants.

Results are valid for meeting the sponsor’s goals

for this study. However, comparisons with results

from any future surveys should take respondent characteristics into account. Managerial status, gender,

age, and region should be used as covariates in comparisons to the baseline reported in this study.

Program Participation

Overall, 49.2% of respondents indicated participating in at least one HAP program. These results

compare favorably with data reported by the National Coordinating Committee on Worksite Health

Promotion (Office of Disease Prevention and Health

Promotion, 1993) rates of 47% for a large chemical

company (N=30,000), 32% for a large regional phone

company (N=20,500), and 60% for a large manufacturer (N=43,500).

According to Baker, Israel, and Schurman, (1994),

only about 15 to 20% of employees regularly participate in HAPs. The current study covered a period

ranging from 8 to 10 years for most respondents

depending on when FAA regional HAPs became

functional. Because the survey did not inquire about

the recency of participation in FAA-HAP, it is not

possible to compare FAA data to the rate of Baker et

al. However, at 49% participation over a 10-year

period, the odds are that FAA’s results are consistent
12

with Baker and associates’ findings. Estimates of

near-term participation rates will be possible at follow-up by limiting respondents to the time period

since the baseline survey.

The generally low level of HAP participation in

most organizations has led to calls for increased

attention regarding how HAPs are marketed to employees (Powell, 1998). Such advice seems appropriate in the present case. The need for increased

marketing of FAA-HAP is bolstered by results showing that the number one reason given for not participating in HAP was “never/rarely hear about HAP

programs” (48% for information programs and 43.6%

for service programs; Table 7). Factoring in the

approximately 3% of respondents who indicated not

getting sufficient notice about HAP events, as many

as half of FAA employees may not be getting enough

information about FAA-HAP.

Respondents who reported knowing about FAAHAP first learned about the program from FAA

medical personnel (20.3%) followed by printed flyers

(14.6%), newsletters (14.2%), and lectures coincident with management training at CMD (13.4%).

The remaining 37% of respondents heard about the

program from any of 11 other sources. According to

the data of Table 3, the most popular venues for first

FAA-HAP participation were the flu shot program

(23.9%), health fairs (22%), blood chemistry screening (12.7%), cholesterol screening (9.8%), and health

awareness lectures (5.6%). Not surprisingly, Table 4

indicates that these were the programs in which

respondents were most likely to participate.

 Program Participation Influence on Subsequent

HAP Participation

Analyses demonstrated that participation in either

of the two most popular FAA-HAP information or

service programs listed in Table 4 increased the

likelihood of subsequent participation in another

type of HAP program (Tables 5 and 6). These results

validated the recursive model for HAP participation

(Figure 1). These four programs also accounted for

more than half of the venues in which respondents

first participated in the HAP (Table 3). Anspaugh,

Hunter, and Savage, (1996) report a list of popular

HAP programs that is very similar to the FAA results.

They recommend using popular programs as a vehicle

for promoting other HAP programs and services.

Barriers and Enticements to HAP Participation

Broader participation in FAA-HAP among the

workforce requires not only understanding why people

participate in HAP, but also why they do not participate. Although fully a quarter of respondents reported no barriers to participation, almost half

indicated “Never/rarely” hearing about HAP programs. Data presented in Table 7 shows that the type

of HAP program was not a factor. Barriers were the

same for both information and service programs. The

FAA results were consistent with Wilson (1990), who

identified promotion as key to employee HAP participation. On the other hand, other studies

(Shephard, 1985; Serfass and Gerberich, 1984) have

presented evidence that personal interest, time, social

support, convenience of facilities, work conflicts,

and self-perceived health can also be significant barriers to HAP participation. Although these latter

factors are likely salient for some FAA employees,

present results unambiguously support Wilson’s position. Moreover, management exercises more control over promotional activities than it does over such

factors as time availability and social support.

The second most common barrier to FAA-HAP

participation (less than 9%) was lack of program

availability in the respondents’ geographic area. However, limited availability may be unavoidable given

the large number of small facilities throughout each

geographical region. The third most common barrier

mentioned was workload (less than 6%). Consistent

with Dishman (1986), workload barriers may be

unavoidable and may not be cost effective to remedy.

Such a small percentage suggests that most employees

are able to arrange time away from work to participate

in HAP. Nevertheless, the workload factor can be a

double bind for those who could benefit from stress

management workshops. They are most likely to

perceive themselves as being too busy to participate.

Stress management program coordinators might wish

to consider how to accommodate to participant work

demands when planning marketing strategies.

Employees were also asked to indicate which health/

wellness promotion activities were most appealing.

More than 50% of the respondents indicated health

screening and high-involvement activities such as

exercise and diets (Table 8), whereas only about 10%

indicated health fairs and hands-on training were

most appealing. Based upon reported participation
13

levels in Table 4, high-involvement activities do not

seem to be consistent with stated customer preferences. Only about 25% of respondents reported

attending high-involvement, hands-on training-type

programs. This apparent paradox signals a need for

caution when considering whether or not to reengineer

FAA-HAP programs to increase personal involvement and more hands-on activities. The relationship

between program attributes and participation rates is

weak, and program changes might not be cost-effective. Lovato and Green (1990) have pointed out that

changes in an organization’s culture are most likely to

increase employee participation in HAP activities.

All things considered, marketing improvements are

more likely to influence commonly held workforce

values and beliefs (culture) than reengineering FAAHAP. Therefore, a second wave of survey results

should be obtained before significant reengineering

efforts are considered.

Baseline Wellness Activities

In addition to learning about factors that influence customer participation, the survey also attempted

to baseline workforce health and healthful activities.

As stated earlier in this report, participant characteristics limit generalizability of baseline workforce health

to current and future samples. However, these data

are valid for testing hypotheses about the influence of

FAA-HAP participation on wellness activities.

The data of Tables 9A and 9B suggest that, regardless of HAP participation, most FAA employees are

exercising and engaging in healthful lifestyle activities. Nearly 70% of the respondents indicated involvement in some form of exercise program. Only

17% who started or continued in an exercise program

dropped out during the past 24 months. These respondent exercise levels relate favorably to the 53%

rate reported by Lamb and Brodie (1990) in their

study of 1,677 adult sports participants and nonparticipants.

Results also show the important dynamic effect of

beginning exercise on sustaining wellness behaviors.

Overall continuation levels showed a strong relationship with whether or not a respondents had exercised

beyond 18 months. Walking was by far the most

popular activity, with 44.8% of respondents participating. Both swimming and skating (6.6% and 3.7%

respectively) appeared to be “joiner” activities in that

most people who started them dropped out within six

months. Even activities exhibiting lower initial participation levels, for example, mix of activities, gym/

fitness center, and walking (7 to 13.7%) had relatively high continuation levels beyond 18 months

(53.4 to 67.5%). This pattern generally held for all

activities, except skating and swimming. Such results

invite speculation that skating and swimming may be

less popular and sustain less involvement because

they require special facilities that might impede enduring participation. Other forms of exercise with

high levels of continuation (e.g., gym/fitness center),

also involve special facilities, but swimming and

skating facilities may be less available and less convenient. Another explanation would be that skating and

swimming are more popular with an age group that is

underrepresented in the FAA workforce.

Results presented in Table 10 indicate a significant relationship between FAA-HAP participation

and respondent exercise activities. Although betweengroup differences were small, the trend was consistent. FAA-HAP participants reported approximately

16% higher exercise levels (on average). However,

there was no significant relationship between FAAHAP participation and continuation in exercise programs. This result does not mean that a relationship

does not exist. A significant relationship can be

masked by a substantial percentage of respondents

having a long history of regular exercise even before

HAP was introduced. Alternatively, it may be too

early for the influence of HAP participation to be

manifested in differences in adherence to a program

of regular exercise.

The significant relationship between continuation

in exercise and having exercised for longer than 18

months raises the possibility that exercise results can

be affected by lifestyle activities established prior to

the 24-month window examined in this study. However, long-term exercise participation seems unlikely

to negate the relationships found between exercise

rates and HAP participation. One might expect that

people with a long history of regular exercise would

have less motivation than non-exercisers to participate in FAA-HAP because they already maintain a

high level of health awareness.

Shifting attention to lifestyle changes, results show

that nearly 60% of respondents engaged in some

form of healthful lifestyle change during the previous

24 months, and most (53.9%) were persevering. As

was the case for exercise, results show that, the longer

respondents adhered to a lifestyle changes, the greater

the likelihood that they would continue adherence
14

after 18 months. Changes in food intake, both quantity (dieting) and quality (nutrition) were most commonly reported. The distribution of respondents

across every level of participation was flatter for

lifestyle activities than for exercise activities. Stress

reduction had the second-highest lifestyle adherence

level beyond 18 months (56.2%), and the highest

level of continuation (95.2%). Consistent with the

exercise results, HAP participants reported significantly more lifestyle change and continuation of

change than did nonparticipants. Again, as was the

case for exercise, these results might be influenced by

changes begun prior to the 24-month window of the

survey. However, if prior changers were less likely to

participate in FAA-HAP, then participant levels

should be lower than nonparticipants, not higher. If

prior changers were more likely to participate in

FAA-HAP, then there should have been disproportionately more participants in the high range (18 to

24 months) of the participation distribution. Given

the flatness of the participation distribution across

time periods, this simply cannot be the case.

Finally, although results indicate a significant relationship between FAA-HAP participation and selfreported exercise and lifestyle changes, the percentage

of covariance was small. The small amount of variance accounted for by HAP participation is likely due

to restriction of variance caused by the checklist

nature of most survey items (i.e., dichotomous and/

or many zero or missing responses).

Baseline Health Status

Additional evidence that FAA-HAP had an influence on agency health status would help to bolster

confidence that FAA-HAP was a contributing factor.

Results presented in Table 13 provide such evidence.

In the multivariate sense, FAA-HAP participants

were, on average, 15% more likely to have reported

positive changes in their health status during the

previous 12 months than were nonparticipants.

Changes significantly affected seven of the nine areas

addressed by the survey. Only aerobic fitness and

weight loss averages did not significantly differ between participant/nonparticipant groups in the

univariate sense.

Almost 70% of respondents reported having had a

physical examination during the previous 18 months,

although 7.5% were line-of-duty physicals. These

results compare favorably with a 1993 report from

the National Health Interview Survey (cited in Woolf,

Jonas, & Lawrence, 1995) that reported 78% of

respondents of working age (18 to 64 years) had a

routine physical during the previous 36 months.

Perhaps the most positive results are those that indicated potentially life-saving outcomes for 51 respondents. Those respondents discovered a previously

unknown medical problem because of an FAA-HAP

health screening. Although detection of a medical

problem occurred for a very small percentage of total

respondents, the preservation of health and wellness

presents a powerful justification for continuation of

FAA-HAP.

Background Differences

Results indicate that survey participation was affected by age, gender, and managerial status. Although 39.3% of the workforce were over age 45,

another 25% of the respondents (49.5%) were over

45. Only 24.1% of the workforce were female, but

17% more of the respondents (28.3%) were female.

Finally, managers comprised 10.7% of the workforce,

but 50% more of the respondents (15.7%) were

managers. These background differences may not be

due as much to sampling error as factors associated

with varying interest in health awareness due to

background.

The characteristics of respondents to an earlier

survey of federal employee HAP programs (Crump et

al., 1996) tended to be older than the target population. They also appeared to be more managerial in

that their educational achievements were disproportionally higher than the general workforce

(Carter, Omenn, Martin, Crump, Grunbaum, and

Williams, 1995). Similarly, participation in the

AT&T health Total Life Concept program (Holt,

McCauley, & Paul, 1995) was disproportionally female, older (c. 40), and managerial. Study participants in the Duke University employee Live for Life

HAP (Goetzel, Kahr, Aldana, & Kenny, 1996) were

older (75% over age 35), and more were female

(70%). Finally, a review of HAP participants by

Wilson (1990) concluded that, compared to the

general workforce, they were more educated, had

higher incomes (ergo, more likely to be managerial),

were older, and more likely to be female.

Authors such as Furnham and Kirkcaldy (1997)

and Edington, Sharp, Vreeken, Yen, and Edington

(1997) have attributed differences in the backgrounds
15

of HAP participants to differences in their health

interests. For example, as employees move into middle

age, they and their associates are more likely to

experience illnesses such are coronary thrombosis,

skin cancer, colon cancer, etc. This phenomena may

increase interest in personal health behaviors among

older employees. Likewise, females of childbearing

age are generally more likely than males to consult

with physicians on a regular basis (Centers for Disease Control, 1995). Frequent physician visits might

lead to increased interest in health behaviors among

female employees.

Small, but significant relationships were found to

exist between all three background characteristics

and HAP participation. This would indicate that

survey participation may have been influenced by

interest in wellness and health issues, and at least to

some extent HAP in particular. Sampling differences

due to age and gender appear to be consistent with the

cited literature indicating a relationship between

background and health interests. Although managerial status differences might be influenced by educational achievement, as previous studies have found,

they may also be caused by the FAA’s mandatory

health awareness training for supervisory personnel

who attend the Center for Management Development (CMD).

With respect to specific subgroup differences, it

must be pointed out that the amount of betweengroup variance explained in the analyses reported

here for age, gender, and managerial status is quite

small. Nevertheless, results indicate that younger

employees, male employees, and nonmanagers are

less likely to participate in FAA-HAP information

and service programs. Older employees were less

likely to engage in exercise programs than younger

ones, although both age groups were similar for

walking and swimming. Nonmanagerial employees

seem to engage in lifestyle changes less than managers. Younger employees, male employees, and nonmanagerial employees seem to diet less. Older

employees and nonmanagers were less likely than

younger employees and managers to report efforts to

reduce alcohol consumption. Nonmanagerial employees and female employees were less likely to

report efforts to reduce smoking than managers and

male employees.

CONCLUSIONS

HAP participation in FAA seems to be typical for

most worksite programs. Participation appears to

have reached only about half of the workforce since

1989. Participation levels in FAA-HAP are due, in

part, to HAP marketing being insufficient to allow

employees to know what HAP programs are available

and when they are scheduled. Survey respondents

were different from the workforce in that they were

somewhat older, disproportionately female, and more

likely to hold managerial positions. These differences

may be caused the representativeness of the respondent pool, or (as argued here), they may typify those

segments of the workforce most interested in health

and wellness. Several significant differences in health

and wellness results related to age, gender, and managerial status, as was participation in HAP.

When examining health outcomes, HAP participants were significantly more likely to exercise, engage

in healthy lifestyle changes, and report a high level of

wellness. Though hardly a definitive evaluation of HAP,

this baseline study found a positive effect for HAP

participation for every factor examined and nearly every

variable tested. This initial finding still needs to be

further validated in a follow-up study.

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18
19

Figure 1. Causal model depicting the likelihood that participation in one type of HAP program

may influence participation in another type of HAP program.

Recursive model for HAP program participation on participation in other programs

HAP information program

participation:

• Health fairs

• Health awareness lectures

HAP service program participation:

• Flu shots

• Cholesterol screening

FIGURES

Figure 2. Interest in health monitoring participation

Missing

3.7%

To a small extent

11.3%

Not at all

17.9%

To a great extent

21.5%

To a very great

extent

15.1%

To some extent

30.5%
20

TABLES

Table 1

Summary of FAA-HAP activities and interventions.1

Level I Level II Level III

• Health Fair

• Health Awareness Lectures

(e.g., lowering cholesterol,

managing stress, healthy

lifestyle)

• Medical Information Videos

(e.g., living with diabetes,

heart attack, first aid)

• Medical Information

Pamphlets

• Breast Self-examination

Training for Early Cancer

Detection

• Testicle Self-examination

Training for Early Cancer

Detection

• World AIDS Day

• Alcohol Awareness Programs

• Cancer Awareness Programs

• Stress Management

Awareness Programs

• Walking for Health Programs

• Weight Management

Programs/Nutrition

Awareness Programs

• Stop Smoking Programs

• Blood Pressure Screening

• Cholesterol Screening

• Blood Chemistry Screening

• Body Fat Measurement

• Sickle Cell Screening

• Colorectal (Occult Blood)

Screening

• Mammogram

• Annual Flu Shot Program

• Glaucoma & Vision Testing

• Blood Sugar/Diabetes

Screening

• Hearing Tests

• Fitness Awareness Programs

• Health Risk Assessment

• Incorporation of Wellness

Topics in Managerial and

Technical Training Curricula

• Requiring "Healthy Choice"

Cafeteria Options

• Posting Nutrition Information

in Cafeterias

• Employee Fitness Centers

(limited to larger facilities)2

• Employee Assistance

Program2

• On-site Clinics (limited to

larger facilities)

1. Levels, following Pencak's 1991 model. 2. Sponsored by the Office of Human Resources or local facility managers
21

Table 2

FAA-HAP survey participants described by age, gender, managerial status, and

geographical region (N = 3,262).

Respondents

(%)

FAA Population

(%) χ

2 Sig.

Age

Under 45 51.5 60.7 117.52 p < .001

Over 45 49.5 39.3

Gender

Male 71.7 75.9 31.60 p < .001

Female 28.3 24.1

Managerial Status

Manager 15.7 10.7 85.50 p < .001

Nonmanager 84.3 89.3

Regional Sampling Units 50.96 p< .001

Alaskan 3.1 3.1

Central 6.4 5.1

Eastern 10.7 10.8

Great Lakes 13.8 13.3

New England 3.8 3.9

Northwest Mountain 10.4 8.6

Southern 14.9 16.0

Southwest 10.9 10.9

Western-Pacific 12.1 11.4

Aeronautical Center 5.8 5.9

Washington HQ 6.2 7.6

Hughes Technical Center 1.9 3.2
22

Table 3

Ways by which respondents first learned about FAA-HAP and the first program in which

they participated.

Percent

(N = ,193) First Learned of HAP from:

Percent

(N = 1,369) First Participation

20.3 My local FAA medical representative

(e.g., occupational nurse, flight

surgeon, local clinic staff)

14.6 A flyer announcing screening

14.2 In an agency newsletter (Intercom,

Regional Newsletter, HOPE, Vitality,

etc.)

13.4 CMD (e.g., Health Risk Assessment,

wellness lectures)

8.1 A pamphlet

7.2 Some other source

6.0 Coworkers

5.2 Health Fair

4.9 A flyer announcing

lectures/information/training

2.8 My supervisor/manager

1.4 A Volunteer Field HAP

Representative/Contact

1.0 Local agency-sponsored fitness

program/center referral

.6 Employee Assistance Program

(EAP)referred me

.1 My safety coordinator

.1 Video/HAP Video library

23.9 Annual Flu Shot Program

22.0 Health Fair

12.7 Blood Chemistry Screening

9.8 Cholesterol Screening

5.6 Health Awareness Lectures

4.5 Blood Pressure Screening

4.1 Health Risk Assessment

3.6 Body Fat Measurement

3.2 Hearing Tests

2.3 Stress Management Awareness

1.4 Medical Information Videos)

1.3 Stop Smoking Programs

1.0 Fitness Awareness Programs

.8 Weight Management

.8 Glaucoma / Vision Testing

.7 Walking For Health Programs

.6 Mammogram

.4 Blood Sugar/Diabetes

.3 Breast Self-examination

.3 Testicle Self-examination

.3 Alcohol Awareness Programs

.3 Cancer Awareness Programs

.2 World Aids Day

.1 Colorectal Screening

.0 Sickle Cell Screening
23

Table 4

HAP program participation levels described by program type (information and service).

Percent of

Total

(N = 3,262)

Percent of

Program

Participants*

(N = 1,082) Information Programs

21.5 64.7 Health Fair

12.9 38.9 Health Awareness Lectures (e.g., lowering cholesterol, managing

stress, healthy lifestyle)

7.9 23.8 Stress Management Awareness Programs

5.5 16.5 Fitness Awareness Programs

4.4 13.2 Weight Management Programs/Nutrition Awareness Programs

3.9 11.6 Medical Information Videos (e.g., living with diabetes, heart attack, first

aid)

3.2 9.6 Walking For Health Programs

2.4 7.3 Stop Smoking Programs

2.2 6.7 Alcohol Awareness Programs

1.9 5.8 Cancer Awareness Programs

1.7 5.1 Breast Self-examination Training for Early Cancer Detection

1.3 3.8 Testicle Self-examination Training for Early Cancer Detection

1.3 3.9 World AIDS Day

Percent of

Total

(N = 3,262)

Percent of

Program

Participants*

(N = 1,082) Service Programs

23.4 54.9 Annual Flu Shot Program

20.8 48.8 Cholesterol Screening

17.5 40.2 Blood Chemistry Screening

17.1 41.0 Blood Pressure Screening

12.7 29.7 Body Fat Measurement

7.6 17.8 Health Risk Assessment (questionnaire and feedback)

7.4 17.2 Hearing Tests

4.5 10.6 Glaucoma Screening/Vision Testing

4.3 10.0 Blood Sugar/Diabetes Screening

1.5 3.6 Mammogram

1.3 3.1 Colorectal (Occult Blood) Screening

.2 .4 Sickle Cell Screening

*Note: N refers to all respondents who indicated participation in 1 or more HAP information or

service programs.
24

Table 5

Multivariate discriminant analyses examining the interrelationship of participation in the

two most popular information programs on participation in service programs.

Service Program Effects on Information

Programs Rc χ

2 Sig.

Health Fairs .45 746.84 .000

(700 participants/2562 nonparticipants)

Participant

Average

Function

Weights

Nonparticipant

Average

1-Way

Sig.

Blood pressure .43 .75 .10 .000

Cholesterol .48 .71 .14 .000

Blood chemistry .42 .69 .11 .000

Health risk assessment .21 .56 .04 .000

Body fat Measurement .28 .50 .08 .000

Annual flu shot .43 .49 .18 .000

Health Awareness Lectures .51 961.34 .000

(421 participants/2841 nonparticipants)

Participant

Average

Function

Weights

Nonparticipant

Average

1-Way

Sig.

Cholesterol .63 .74 .15 .000

Blood pressure .55 .71 .12 .000

Health risk assessment .32 .65 .04 .000

Body fat .42 .62 .08 .000

Blood chemistry .44 .47 .14 .000

Blood sugar/diabetes screening .17 .42 .02 .000
25
26
27
28
29
30
31
32
A1

APPENDIX A

FAA Health Awareness Program

Customer Service Assessment
A2
A3
A4
A5
A6