Health Behaviors of Operating Engineers Sonia A Duffy

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

Research Team Investigators n Sonia Duffy, Ph. D, RN n David Ronis, Ph. D n Andrea Waltje, RN, MS n Lee Ewing, MPH n Seung Hee Choi, Ph. D, RN Students n Cody Carey n Samantha Louzon n Corinne Lee, RN, MSN

What is an Operating Engineer (OE) n 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).

Three Studies of Operating Engineers n Study 1: Cross-sectional Study of Health Behaviors of Operating Engineers (funded by NINR) n Study 2: A Randomized Control Trial of the Tobacco Tactics Website for Operating Engineers vs. . 1 -800 -QUIT NOW (funded by Blue Cross/Blue Shield of Michigan Foundation and NIH R 21) n Study 3: A Randomized Control Trial of Sun Protection Interventions for Operating Engineers (funded by Blue Cross/Blue Shield of Michigan Foundation)

STUDY 1: HEALTH BEHAVIORS OF Operating Engineers Cross-sectional survey Winter of 2008 n Convenience sample of 498 Operating Engineers in MI (return rate: 90%) n 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 n

DESCRIPTION OF SAMPLE Mean (SD) Age (n=476) Frequency (%) 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)

HEALTH BEHAVIORS OF THE SAMPLE Mean (SD) Significant depressive symptoms on CES-D (Population 21%) Frequency (%) 220 (46. 8) Smoking (n=487) (Population 19. 3%) Yes No 142 (28. 5) 270 (54. 2) Problem Drinking (n=476) (Population 10%) Yes No 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 25 -29. 9) Obese (BMI ≥ 30) (Michigan Population 28%) 192 (40. 2) 213 (44. 6) Sleep Quality (n=487) (Population Mean for Medical 70. 32 (17. 36) Clinic 72 )

Background: Smoking • Disparities in smoking prevalence between white collar workers (20. 3%) and blue collar workers (35. 4%) ü Blue collar workers do not benefit from worksite anti-smoking legislation as much as white collar workers (Rachiotis et al. , 2009) ü Blue collar workers have relatively limited accesses to health promoting programs (Okechukwu et al. , 2009) ü Few studies on smoking and smoking interventions have been conducted among blue collar workers (Lee et al. , 2004)

Factors Associated With Smoking Behavior Age Odds Ratio P-Value . 96 . 002 Marital status Separated/Widowed/Divorced Never married Married . 007. 049. 029 1. 81. 49 1 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) . 012. 167. 013. 003 . 65. 51. 41 1 Physical activity . 94 . 003 BMI . 96 . 025

Background: Smokeless Tobacco Use ü Blue collar workers showed higher prevalence in smokeless tobacco compared to white collar workers (Lee et al. , 2007) ü 13. 6% of the sample reported past month smokeless tobacco use (Population 3. 5%, Dietz et al. , 2011)

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

Background: Obesity ü 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) ü 40. 2% of the sample were overweight and 44. 6% were obese

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

Factors Associated With Obesity (Cont. ) Odds Ratio Vegetable intake 0 -1 per week 2 -4 per week 5 -6 per week 1 per day (Reference) Fruit intake 0 – 2 -4 per week 5 -6 per week or more 1. 208. 729. 743 1 P-Value. 382. 602. 299. 360. 574 . 867 1 Fried food intake 0 – 2 -4 per week 5 -6 per week or more . 076. 678 1 Physical activity (in 5 point increments) . 769 . 013

Background: Sleep Quality ü 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). ü 33. 9% of the sample showed interest in health service for better sleep quality.

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 . 000 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

Background: Sun Exposure Behaviors ü ü 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 (Le. Blanc 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

Factors Associated With Sunburns Beta P-Value Always to Usually burn . 602 . 000 Sometimes burn . 317 . 000 Rarely burn 0 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 . 000 . 998 Sex (Female) . 034 . 477 White -. 004 . 930 Married -. 019 . 683 High School or Less . 035 . 441 Perceived Skin

Factors Associated With Blistering Beta P-Value Always to Usually burn . 343 . 000 Sometimes burn . 252 . 000 Rarely burn 0 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) . 071 . 161 White . 152 . 002 Married . 034 . 492 High School or Less . 043 . 367 Perceived Skin

Factors Associated With Use of Sun block Beta P-Value Always to Usually burn . 305 . 000 Sometimes burn . 121 . 038 Rarely burn 0 Smoking -. 089 . 078 Alcohol Problems . 115 . 022 Fruit Intake . 180 . 000 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 . 000 White -. 061 . 224 Married . 045 . 373 High School or Less -. 009 . 857 Perceived Skin

Background: Health-related Quality of Life ü 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.

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

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. ü 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%). ü

Occupational Exposures as Predictors of Cigarette Smoking Odds ratio P-Value Occupational Exposure Factor 1 a . 99 . 956 Occupational Exposure Factor 2 b . 79 . 033 Age . 97 . 033 Marital Status . 009 Married (Reference) Separated/Widowed/Divorced 2. 24 . 013 Never married . 61 . 163 One or more . 76 . 269 Alcohol Use 1. 07 . 001 BMI . 95 . 015 Medical Comorbidities None (Reference) 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

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

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

STUDY 2: TOBACCO TACTICS WEBSITE FOR OPERATING ENGINEERS

AIMS n Aim 1: Compare the efficacy of the Tobacco Tactics website intervention to the state sponsored 1 -800 -QUITNOW 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. n Aim 2: Compare Operating Engineers randomized to the Tobacco Tactics website to those randomized to the 1 -800 -QUIT-NOW telephone quit line in terms of: a) number of contacts with the intervention; b) medications used; and c) satisfaction with the intervention.

METHODS n n 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 n n 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 1 -800 -Quit- Now state-supported phone line n n n 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

http: //bcbsm-operatingengineers. nursing. umich. edu/ User Name: Guest Password: Test

DESCRIPTION OF SAMPLE Age (n=146) Sex (n=146) Male Female Race (n=146) White Non-White Marital Status (n=145) Married Non-married Educational levels (n=145) High school or lower College or higher All (N=146) Intervention (N=67) Control (N=79) Mean (SD) Frequency (%) P-Value 42. 0 (9. 5) 42. 1 (9. 3) 41. 8 (9. 7) . 837. 050 116 (79. 5) 30 (20. 5) 58 (86. 6) 9 (13. 4) 58 (73. 4) 21 (26. 6). 212 125 (85. 6) 21 (14. 4) 60 (89. 6) 7 (10. 4) 65 (82. 3) 14 (17. 7). 768 81 (55. 5) 63 (43. 2) 38 (57. 6) 28 (42. 4) 43 (55. 1) 35 (44. 9). 610 89 (61. 9) 63 (43. 2) 42 (63. 6) 24 (36. 4) 47 (59. 5) 32 (40. 5)

DESCRIPTION OF SAMPLE All (N=146) Intervention (N=67) Control (N=79) Mean (SD) Frequency (%) PValue 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)* 41. 1 (5. 2) 40. 0 (4. 4) 42. 1 (5. 6) . 036 70. 1 (18. 9) 72. 4 (14. 9) 68. 1 (21. 5) . 218 55 (50. 5) 23 (46. 9) 32 (53. 3) . 518 vs. . 40. 8 (gen. population) Sleep Quality (n=109)* vs. 72 (gen. population) Never using Sun block (n=109)* * Based on 6 -month survey findings

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) Intervention (N=45) Control (N=59) Mean (SD) N (%) 18 (40) 6 (10. 2) (n=104). 000 Quit Rate (intention to treat) P-Value 18 (26. 9) 6 (7. 7) (n=145). 002 Able to Quit for over 24 hours P-Value 32 (86. 1) 15 (31. 9) (n=104). 000 2. 9 (2. 7) 3. 5 (2. 8) (n=103). 262 -2. 3 (3. 0) -0. 8 (2. 1) (n=98). 006 11. 4 (10. 5) 17. 4 (13. 9) (n=105)b. 018 -9. 7 (14. 9) . 1 (14. 1) (n=105)b. 001 Quit Rate (all follow-up survey completers) P-Value Nicotine Dependence Score P-Value 5. 1 (2. 4) 4. 4 (2. 7) (n=140). 149 Nicotine Dependence Changea P-Value Cigarettes Smoked/Day Change a P-Value a Values for both assessment points b Includes results from Mini-Survey 20. 4 (12. 9) 18. 3 (12. 8) (n=145). 336

AIM 2: PROCESS MEASURES Intervention (N=45) Control (N=59) N (%) 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) (N=104). 000 20 (44. 4) 1 (1. 7) (N=104). 000 27 (60. 0) 1 (1. 7) (N=104). 000 5 (11. 1) 0 (N=104). 009 17 (37. 8) 0 (N=104). 000 Contacts with the intervention P-Value NRT - Patches P-Value NRT - Gum P-Value NRT - Lozenges P-Value NRT – Both P-Value

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) NA Helpfulness of the coach/nurse 4. 3 ( . 8) 2. 9 (1. 1) (N=52). 000 4. 9 ( . 7) 4. 0 ( . 6) (N=52). 935 P-Value Recommend to someone else P-Value

CONCLUSIONS Operating Engineers in the intervention group had: n significantly better quit rates, n significantly higher rate of contacts with the intervention, n 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 R 21 CA 152247 from the National Cancer Institute. ”

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

AIMS n n 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 preand post-intervention.

METHODS n n n n 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

SAMPLE OF 60 UNIQUE TEXT MESSAGES n n n 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

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 6. 65 blister (n=228) Range: 0 -100

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 signedranked 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 1. 86 protection regularly? 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 2. 89 sun burn next summer? 2. 71 -0. 186 -834 0. 0102 How likely do you think you are to 2. 45 develop skin cancer? 2. 30 -0. 128 -571 0. 0434 How bad would it be for you to get 2. 60 sunburned? 3. 16 0. 557 2734. 5 <. 0001 How bad would it be for you to get 4. 51 skin cancer? 4. 57 0. 080 133 0. 1077

WHAT THEY TOLD US Sunscreen makes hands slippery on steering wheel. n Sunscreen smudges glasses when driving. n Don’t want to smell like coconut oil. n Spray might be better. n

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 n 20% dropped out of the text messaging arm within minutes of the first text. n Many were contacted and if they had free texting came back on, but many were lost n

THIS STUDY IS ONGOING The project described is supported by Grant Number 1899. II from the Blue Cross Blue Shield of Michigan Foundation.

PUBLICATIONS n. 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), 293 -301. n. 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. n. Duffy, S. A. , Cohen, K. A. , Choi, S. H. , Mc. Cullagh, M. C. , Noonan, D. (2012). Predictors of Obesity in Michigan Operating Engineers. Journal of Community Health. 37, 619 -625. n. 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), 786 -792. n. Noonan, D. , Duffy, S. A. (2012). Smokeless Tobacco Use Among Operating Engineers. Journal of Addictions Nursing. 23 (2), 132 -136. n. 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.