1. Health Behaviors of Operating Engineers
Sonia A. Duffy, Ph.D., R.N., FAAN
The University of Michigan

2. Research Team Investigators Sonia Duffy, PhD, RN David Ronis, PhD
Andrea Waltje, RN, MS
Lee Ewing, MPH
Seung Hee Choi, PhD, RN
Students
Cody Carey
Samantha Louzon
Corinne Lee, RN, MSN
Seung Hee, please check your degrees

3. What is an Operating Engineer (OE)
An OE is responsible for the operation and maintenance of heavy earthmoving equipment used in the construction of buildings, bridges, roads, and other facilities (Stern & Haring-Sweeney, 1997).

4. Three Studies of Operating Engineers
Study 1: Cross-sectional Study of Health Behaviors of Operating Engineers (funded by NINR)
Study 2: A Randomized Control Trial of the Tobacco Tactics Website for Operating Engineers vs QUIT NOW (funded by Blue Cross/Blue Shield of Michigan Foundation and NIH R21)
Study 3: A Randomized Control Trial of Sun Protection Interventions for Operating Engineers (funded by Blue Cross/Blue Shield of Michigan Foundation)

5. Study 1: Health behaviors of Operating Engineers
Cross-sectional survey Winter of 2008
Convenience sample of 498 Operating Engineers in MI (return rate: 90%)
Variables included health behaviors (smoking, alcohol use, diet, physical activity, BMI, & sleep quality), health conditions (medical comorbidities & depressive symptoms), health-related quality of life, and demographics

6. Description of Sample Mean (SD) Frequency (%) Age (n=476) 42.95 (9.38)
Sex (n=482)
Male
Female
445 (92.3)
37 (7.7)
Race (n=472)
White
Non-White
436 (92.4)
36 (7.6)
Marital Status (n=485)
Married
Non-married
329 (67.8)
156 (32.2)
Educational levels (n=485)
High school or lower
College or higher
295 (60.8)
190 (39.2)
Medical comorbidities (n=482)
None
One or more
239 (49.6)
243 (50.4)

7. Health behaviors of the Sample
Mean (SD)
Frequency (%)
Significant depressive symptoms on CES-D (Population 21%)
220 (46.8)
Smoking (n=487) (Population19.3%)
Yes No
142 (28.5)
270 (54.2)
Problem Drinking (n=476) (Population10%)
156 (32.8)
320 (67.2)
Physical Activity (n=472) (Population 40.8)
42.65 (5.34)
Diet (n=485)
Fruit Intake (4 or more/day)
Vegetable Intake (4 or more/day)
6 (1.2)
10 (2.1)
BMI (n=478)
Overweight (BMI )
Obese (BMI ≥ 30) (Michigan Population 28%)
192 (40.2)
213 (44.6)
Sleep Quality (n=487) (Population Mean for Medical Clinic72 )
70.32 (17.36)

8. Background: Smoking
Disparities in smoking prevalence between white collar workers (20.3%) and blue collar workers (35.4%)
Blue collar workers do not benefit from worksite anti-smoking legislation as much as white collar workers (Rachiotis et al., 2009)
Blue collar workers have relatively limited accesses to health promoting programs (Okechukwu et al., 2009)
Few studies on smoking and smoking interventions have been conducted among blue collar workers (Lee et al., 2004)

9. Factors Associated With Smoking Behavior
Odds Ratio
P-Value
Age
.96
.002
Marital status
Separated/Widowed/Divorced
Never married
Married
1.81
.49 1
.007
.049
.029
Medical comorbidities
.76
.216
AUDIT (Problem Drinking)
1.08
.000
Vegetable intake
0-1 per week
2-4 per week
5-6 per week
1 per day (Reference)
.65
.51
.41
.012
.167
.013
.003
Physical activity
.94
BMI
.025

10. Background: Smokeless Tobacco Use
Blue collar workers showed higher prevalence in smokeless tobacco compared to white collar workers (Lee et al., 2007)
13.6% of the sample reported past month smokeless tobacco use (Population 3.5%, Dietz et al., 2011)

11. Factors Associated With Smokeless Tobacco Use
Odds Ratio
P-Value
Age
.951
.002
Male
5.06
.119
White
1.78
.448
High school or less
1.44
.224
Past month cigarette use
.402
.017
AUDIT (Problem drinking)
1.67
.082

12. Background: Obesity
Blue collar workers are less likely to have recommended fruit and vegetable intake and rank among the lowest in leisure time physical activity (Beydoun & Wang, 2009)
40.2% of the sample were overweight and 44.6% were obese

13. Factors Associated With Obesity
Odds Ratio
P-Value
Age (in 5 year increments)
.862
.016
Female
.263
.022
White
1.653
.273
Married
1.331
.250
High school or less
1.195
.428
Pain (SF-36)
.997
.589
Medical comorbidities
2.167
.001
Depression
.966
.888
Smoking
.550
.010
Alcohol problem
.912
.706

14. Factors Associated With Obesity (Cont.)
Odds Ratio
P-Value
Vegetable intake
0-1 per week
2-4 per week
5-6 per week
1 per day (Reference)
1.208
.729
.743 1
.382
.602
.299
.360
Fruit intake
0 – 2-4 per week
5-6 per week or more
.867
.574
Fried food intake
.678
.076
Physical activity (in 5 point increments)
.769
.013

15. Background: Sleep Quality
Blue collar workers are exposed to high job stress, loud noises at work, and more prevalent in smoking and problem drinking, all of which are associated with poor sleep quality (Deatherage et al., 2009).
33.9% of the sample showed interest in health service for better sleep quality.

16. Factors Associated With Sleep Quality
Beta
P-Value
Age
.158
.001
Sex (Female)
-.100
.035
Race (White)
-.055
.226
Marital status (Married)
.067
.151
Educational Level (High school or less)
-.068
.130
Pain
.238
.000
Number of medical comorbidities
-.146
.003
Depressive symptoms
-.322
Alcohol problem
-.056
.233
Smoking
Non-smoker
Smoker without nicotine dependence
Smoker with nicotine dependence 1
.042
-.124
.367
.009
Physical activity
-.058
.206
Obesity
.025
.601

17. Background: Sun Exposure Behaviors
While outdoor workers are exposed to high UV levels and at greater risk of developing skin cancer, the rates of receiving skin examination and the use of sun protection are lower (LeBlanc et al., 2008)
Over 80% reported spending 4-5 hours in the sun during weekdays and about ⅔ spent 4-5 hours in the sun on weekends
While 50% reported 2 or more sunburns in summer, 37% never used sunscreen and 38% rarely used sunscreen
22.8% of the sample showed interest in sun protection guidance

18. Factors Associated With Sunburns
Beta
P-Value
Perceived Skin
Always to Usually burn
.602
.000
Sometimes burn
.317
Rarely burn
Smoking
-.039
.401
Alcohol Problems
.077
.095
Fruit Intake
-.008
.861
BMI
.110
.020
Physical Activity
.092
.048
Sleep Quality
-.027
.584
Depressive symptoms
.045
.359
Number of Medical Comorbidities
-.030
.539
Age
.998
Sex (Female)
.034
.477
White
-.004
.930
Married
-.019
.683
High School or Less
.035
.441

19. Factors Associated With Blistering
Beta
P-Value
Perceived Skin
Always to Usually burn
.343
.000
Sometimes burn
.252
Rarely burn
Smoking
.023
.644
Alcohol Problems
.107
.031
Fruit Intake
.005
.920
BMI
.137
.007
Physical Activity
-.025
.618
Sleep Quality
-.107
.046
Depressive symptoms
.071
.170
Number of Medical Comorbidities
-.062
.236
Age
.177
.001
Sex (Female)
.161
White
.152
.002
Married
.034
.492
High School or Less
.043
.367

20. Factors Associated With Use of Sun block
Beta
P-Value
Perceived Skin
Always to Usually burn
.305
.000
Sometimes burn
.121
.038
Rarely burn
Smoking
-.089
.078
Alcohol Problems
.115
.022
Fruit Intake
.180
BMI
-.005
.926
Physical Activity
-.044
.379
Sleep Quality
-.040
.468
Depressive symptoms
-.030
.568
Number of Medical Comorbidities
-.066
.218
Age
-.010
.853
Sex (Female)
.197
White
-.061
.224
Married
.045
.373
High School or Less
-.009
.857

21. Background: Health-related Quality of Life
Blue collar workers are more likely to have depressive symptoms and engage in poor health behaviors, such as smoking, problem drinking, unhealthy diet, and low physical activity level, which deteriorate health-related quality of life.

22. Factors Associated With Health-related Quality of LifePF RP BP GH VT SF RE MH
PCS
MCS
Age
-.201
-.163
-.151
-.145
-.174
Marital status (Married)
-.094
-.113
-.087
-.085
-.105
Depressed
-.100
-.148
-.109
-.222
-.124
# Medical comorbidities
-.126
-.101
-.229
-.281
-.214
-.182
Smoking
-.131
-.091
-.120
Alcohol problems
Vegetable intake
-.122
-.119
-.097
Fruit intake
-.112
-.108
-.104
-.090
Physical activity
.105
.100
.092
BMI
-.166
-.088
-.171
.090
Sleep quality
.125
.237
.272
.353
.502
.416
.389
.549
.159
.552

23. Background: Occupational Exposures and Cigarette Smoking
Blue collar workers smoke more and are exposed to occupational hazards at work, which have a synergic effect of developing lung cancer with smoking.
Majority of the sample were exposed to various occupational hazards: heat stress (75.7%), concrete dust/milling (75.5%), welding fumes (71.4%), asphalt fumes (63.6%), solvents (58.0%), silica (56.8%), asbestos (51.2%), lead/lead paint (40.3%), and benzene (37.9%).

24. Occupational Exposures as Predictors of Cigarette Smoking
Odds ratio
P-Value
Occupational Exposure Factor 1a
.99
.956
Occupational Exposure Factor 2b
.79
.033
Age
.97
Marital Status
.009
Married (Reference)
Separated/Widowed/Divorced
2.24
.013
Never married
.61
.163
Medical Comorbidities
None (Reference)
One or more
.76
.269
Alcohol Use
1.07
.001
BMI
.95
.015
a Occupational Exposure Factor 1: Lead/Lead paint + Benzene + Asbestos + Solvents + Silica
b Occupational Exposure Factor 2: Asphalt fumes + Heat stress + Concrete dust + Welding fumes

25. Conclusions Poor Health behaviors cluster together. Examples:
Smoking: problem drinking, physical inactivity, low BMI
Sleep Quality: smoking with nicotine dependence
Risky Sun Exposure Behaviors: problem drinking, high BMI, poor sleep quality
Health-Related Quality of Life: smoking, diet (less fruit/vegetable intake), physical inactivity, poor sleep quality

26. Conclusions
Health behaviors are poor among Operating Engineer’s increasing the risk of developing chronic diseases.
0 % Operating Engineer’s met the criteria of healthy lifestyle (3% general population).
Health behavior interventions are needed for Operating Engineer’s.

27. STUDY 2: TOBACCO TACTICS WEBSITE FOR OPERATING ENGINEERS

28. Aims
Aim 1: Compare the efficacy of the Tobacco Tactics website intervention to the state sponsored QUIT-NOW telephone line in improving cessation including: a) 30-day and 6-month quit rates; b) 6-month cotinine levels; c) 30-day and 6-month cigarettes smoked/day; d) 30-day and 6-month number of quit attempts; and e) 30-day and 6-month nicotine addiction.
Aim 2: Compare Operating Engineers randomized to the Tobacco Tactics website to those randomized to the QUIT-NOW telephone quit line in terms of: a) number of contacts with the intervention; b) medications used; and c) satisfaction with the intervention.
The setting is the Operating Engineers Local 324 Training Center. Included are 195 Operating Engineers who: 1) were attending a safety training course provided by Local 324 Education Center; 2) were greater than 18 years of age; 3) currently smoked; and 4) were interested in participating in a cessation program.

29. Methods RCT of Tobacco Tactics versus 1-800-Quit Now
Convenience sample of 146 Operating Engineers recruited at training center
Baseline, 1 month and 6 month follow up surveys
Tobacco Tactics Intervention
Nurses introduces website at training center
Nurse calls to arrange for nicotine replacement therapy which is then mailed
Nurse makes 4 follow up counseling calls
Nurse-moderated chat room 3 times per week
Control group counseled and given card for Quit- Now state-supported phone line
Operating Engineer calls the phone line
Is assigned a counselor that makes 4 calls
Can be mailed NRT if it is not covered by their insurance
The setting is the Operating Engineers Local 324 Training Center. Included are 195 Operating Engineers who: 1) were attending a safety training course provided by Local 324 Education Center; 2) were greater than 18 years of age; 3) currently smoked; and 4) were interested in participating in a cessation program.

30. http://bcbsm-operatingengineers.nursing.umich.edu/ User Name: Guest
The website will be linked in presentation mode – just move your cursor across the picture or across the address and click.
User Name: Guest
Password: Test

31. Description of Sample All (N=146) Intervention (N=67) Control (N=79)
Mean (SD)
Frequency (%)
P-Value
Age (n=146)
42.0 (9.5)
42.1 (9.3)
41.8 (9.7)
.837
Sex (n=146)
Male
Female
116 (79.5)
30 (20.5)
58 (86.6)
9 (13.4)
58 (73.4)
21 (26.6)
.050
Race (n=146)
White
Non-White
125 (85.6)
21 (14.4)
60 (89.6)
7 (10.4)
65 (82.3)
14 (17.7)
.212
Marital Status (n=145)
Married
Non-married
81 (55.5)
63 (43.2)
38 (57.6)
28 (42.4)
43 (55.1)
35 (44.9)
.768
Educational levels (n=145)
High school or lower
College or higher
89 (61.9)
42 (63.6)
24 (36.4)
47 (59.5)
32 (40.5)
.610
Recruitment and Retention: Initial recruitment began in 2010 funded by a small grant from the Blue Cross/Blue Shield of Michigan Foundation to develop a Tobacco Tactics website for Operating Engineers. Over the course of three years, there were 34 classes randomized with average size of 6 participants per group, resulting in 92 participants in the intervention group and 104 participants in the control group (N=196). Of these, 42 (21%) participants joined the study twice and 9 (5%) participants joined the study three times, resulting in 145 first attendees.

32. Description of Sample All (N=146) Intervention (N=67) Control (N=79)
Mean (SD)
Frequency (%)
P-Value
Nicotine Depend. (n=141)
55 (37.7)
27 (42.1)
28 (35.9)
.400
Alcohol Problems (n=134)
60 (41.1)
26 (41.3)
34 (47.9)
.442
BMI (n=145)
29.0 (5.7)
30.1 (6.0)
28.0 (5.3)
.028
Physical Activity (n=109)*
vs (gen. population)
41.1 (5.2)
40.0 (4.4)
42.1 (5.6)
.036
Sleep Quality (n=109)*
vs. 72 (gen. population)
70.1 (18.9)
72.4 (14.9)
68.1 (21.5)
.218
Never using Sun block (n=109)*
55 (50.5)
23 (46.9)
32 (53.3)
.518
Physical Activity
Sleep
Sun
 based on 6-month FU surveys, we didn’t ask at baseline.
* Based on 6-month survey findings

33. Aim 1: 1-month Efficacy of the Tobacco Tactics Website versus 1-800-QUIT-NOW
Baseline
30-day Follow Up
Intervention
(N=67)
Control
(N=79)
(N=45)
(N=59)
Mean (SD)
N (%)
Quit Rate (all follow-up survey completers)
P-Value
18 (40)
6 (10.2)
(n=104) .000
Quit Rate (intention to treat)
18 (26.9)
6 (7.7)
(n=145) .002
Able to Quit for over 24 hours
32 (86.1)
15 (31.9)
Nicotine Dependence Score
5.1 (2.4)
4.4 (2.7)
(n=140) .149
2.9 (2.7)
3.5 (2.8)
(n=103) .262
Nicotine Dependence Changea
-2.3 (3.0)
-0.8 (2.1)
(n=98) .006
Cigarettes Smoked/Day
20.4 (12.9)
18.3 (12.8)
(n=145) .336
11.4 (10.5)
17.4 (13.9)
(n=105)b .018
Cigarettes Smoked/Day Changea
-9.7 (14.9)
.1 (14.1)
(n=105)b .001
At 30-day follow-up, Operating Engineers in the intervention group had
significantly higher quit rates,
significantly higher rates of persons being able to quit for any length of time,
greater decreases in nicotine dependence at the 30-day follow-up time point, and
significantly greater decreases in cigarettes per day.
At 6-month follow-up, the groups were more equal to each other. Except for the quit-rate, the trends remained in favor of the intervention group. Six-month data collection is still ongoing.
 The findings suggest that clinical maintenance should be continued after the 1-month intervention phase.
a Values for both assessment points
b Includes results from Mini-Survey

34. Aim 2: Process Measures Intervention (N=45) Control (N=59) N (%)
Contacts with the intervention
P-Value
45 (100)
7 (11.9)
(N=104) .000
At least one contact with the website
66 (98.5) NA
NRTs
34 (75.6)
2 (3.4)
NRT - Patches
20 (44.4)
1 (1.7)
NRT - Gum
27 (60.0)
NRT - Lozenges
5 (11.1)
(N=104) .009
NRT – Both
17 (37.8)
Both - meaning patches AND gum OR lozenges

35. Aim 2 (cont) Intervention (N=45) Control (N=59) Mean (SD)
Visits to the website
2.7 (3.7)
Range: 0-26 NA
Satisfaction with the website
3.7 ( .7)
Helpfulness of the coach/nurse
P-Value
4.3 ( .8)
2.9 (1.1)
(N=52) .000
Recommend to someone else
4.9 ( .7)
4.0 ( .6)
(N=52) .935
Satisfaction, helpfulness, recommend all on a scale from 1-5

36. Conclusions Operating Engineers in the intervention group had:
significantly better quit rates,
significantly higher rate of contacts with the intervention,
significantly higher rates of NRT use.
Six-month data collection is still ongoing.
Once a web-based intervention has been built, the cost of reaching a million smokers is not much more than reaching a 1000 smokers. The goal is for high reach, high efficacy, and a low cost.
"The project described was supported by Grant Number 1465.RFP from the Blue Cross Blue Shield of Michigan Foundation and by Grant Number R21CA from the National Cancer Institute.”

37. STUDY 3: A RANDOMIZED CONTROLLED TRIAL OF 4 SUN PROTECTION INTERVENTIONS FOR OPERATING ENGINEERS

38. Aims
Aim 1: Determine differences in changes in sunscreen use and sun burning among Operating Engineers randomized to four sun protection interventions:
a. education only;
b. education and mailed sunscreen;
c. education and text message reminders; and,
d. education, mailed sunscreen, and text message reminders.
Aim 2: Explore if particular subgroups of Operating Engineers (e.g., problem drinkers or job type subgroups) differ in changes in sunscreen use and sun burning pre-and post-intervention.

39. methods RCT of 4 interventions conducted at OE training center 2012
Convenience sample of 231 Operating Engineers
All given 1 hour of educational ppt, then randomized to nothing more, sunscreen, text messages, or both
Text messages sent 3 times per week on random days from May thru Sep.
2 large containers of sunscreen mailed twice May and July
Half received spray and half received lotion
Baseline surveys, mini-surveys each month, and larger follow up survey in October

40. Sample of 60 unique Text Messages
Smile and put on sunscreen today
Your family and friends love you - put on sunscreen!
Oh boy, it’s a hot one— use sunscreen
Yikes it’s hot—put on sunscreen
Only 10% of OE’s use sunscreen – do you?
Look young – use sunscreen
Catch some rays...with sunscreen
Big muscles need strong sunscreen. Wear a 30!
Got sunscreen?
It’s a sin to neglect your skin – USE SUNSCREEN!
Looking good with sunscreen! 
Don't be a prune!  Use sunscreen

41. Description of Sample Mean (SD) Frequency (%)
More than one sunburn in past summer (n=231)
188 (81.39)
Four or more sunburn in past summer (n=231)
48 (20.78)
Using sunscreen sometimes or never when working outside (n=230)
162 (70.44)
# Sunburns severe enough to blister (n=228)
6.65
Range: 0-100
Most participants (81.39%) reported more than one sunburn during the past year, 20.78% reported four times or more, and the mean score for severe lifetime sun burning was Seventy percent reported using sunscreen sometimes or never when working outside.

42. RESULTS RELATED CHANGES IN CONSTRUCTS OF THE HEALTH BELIEF MODEL (self-efficacy, perceived barriers, perceived benefits, susceptibility, and perceived severity) BEFORE AND AFTER EDUCATION
Pre-Education
Post-Education
Mean Difference
Wilcoxon signed-ranked Test Statistic
 p-value
How confident are you that you can apply sun protection regularly?
 2.99
 3.20
 0.211
1087.5
0.0009
How difficult will it be to apply sun protection regularly?
1.86
2.01
0.158
728.5
0.0055
How important is it that you prevent sun burning?
3.32
3.87
0.533
2991.5
<.0001
How important is it that you prevent skin cancer?
4.44
4.63
0.192
451
0.0002
How likely do you think you are to sun burn next summer?
2.89
2.71
-0.186
-834
0.0102
How likely do you think you are to develop skin cancer?
2.45
2.30
-0.128
-571
0.0434
How bad would it be for you to get sunburned?
2.60
3.16
0.557
2734.5
How bad would it be for you to get skin cancer?
4.51
4.57
0.080
133
0.1077

43. What they told us Sunscreen makes hands slippery on steering wheel.
Sunscreen smudges glasses when driving.
Don’t want to smell like coconut oil.
Spray might be better.

44. Lesson Learned
Computerized text messaging program by law must tell participant that they may be charged for these texts and they can reply “STOP” to cancel
20% dropped out of the text messaging arm within minutes of the first text.
Many were contacted and if they had free texting came back on, but many were lost

45. This study is ongoing
The project described is supported by Grant Number 1899.II from the Blue Cross Blue Shield of Michigan Foundation.

46. Publications
Duffy, S.A., Missel, A.L., Waltje, A.H., Ronis, D.L., Fowler, K.E., Hong, O. (2011). Health Behaviors of Operating Engineers. American Association of Occupational Health Nurses Journal. 59 (7),
Duffy, S.A., Ronis, D.L., Richardson, C., Waltje, A.H., Ewing, L.A., Noonan, D., Hong, O., Meeker, J. (2012). Protocol of a randomized control trial of the Tobacco Tactics website for Operating Engineers. BMC Public Health, 12:335.
Duffy, S.A., Cohen, K.A., Choi, S.H., McCullagh, M.C., Noonan, D. (2012). Predictors of Obesity in Michigan Operating Engineers. Journal of Community Health. 37,
Duffy, S.A., Choi, S.H., Hollern, R., Ronis, D.L. (2012). Factors Associated With Risky Sun Exposure Behaviors Among Operating Enginners. American Journal of Industrial Medicine. 55 (9),
Noonan, D., Duffy, S.A. (2012). Smokeless Tobacco Use Among Operating Engineers. Journal of Addictions Nursing. 23 (2),
Choi, S.H., Redman, R.W., Terrell, J.E., Pohl, J.M., Duffy, S.A.: Factors associated with health-related quality of life among Operating Engineers. In press. Journal of Occupational and Environmental Medicine.