Health Behaviors of Operating Engineers

Health Behaviors of Health Behaviors of Operating EngineersOperating Engineers

Sonia A. Duffy, Ph.D., R.N., FAANSonia A. Duffy, Ph.D., R.N., FAAN

The University of Michigan The University of Michigan

Research TeamResearch Team

InvestigatorsInvestigators Sonia Duffy, PhD, RNSonia Duffy, PhD, RN David Ronis, PhDDavid Ronis, PhD Andrea Waltje, RN, MSAndrea Waltje, RN, MS Lee Ewing, MPHLee Ewing, MPH Seung Hee Choi, PhD, RNSeung Hee Choi, PhD, RN

StudentsStudents Cody CareyCody Carey Samantha LouzonSamantha Louzon Corinne Lee, RN, MSNCorinne Lee, RN, MSN

What is an What is an Operating Engineer (OE)Operating Engineer (OE)

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

Three Studies of Three Studies of Operating EngineersOperating Engineers

Study 1: Study 1: Cross-sectional Study of Cross-sectional Study of Health Health Behaviors of Operating Engineers (funded by Behaviors of Operating Engineers (funded by NINR)NINR)

Study 2: Study 2: A Randomized Control Trial of the A Randomized Control Trial of the Tobacco Tactics Website for Operating Tobacco Tactics Website for Operating Engineers vs.. 1-800-QUIT NOW Engineers vs.. 1-800-QUIT NOW (funded by Blue (funded by Blue Cross/Blue Shield of Michigan Foundation and NIH R21)Cross/Blue Shield of Michigan Foundation and NIH R21)

Study 3: Study 3: A Randomized Control Trial of Sun A Randomized Control Trial of Sun Protection Interventions for Operating Protection Interventions for Operating Engineers Engineers (funded by Blue Cross/Blue Shield of Michigan (funded by Blue Cross/Blue Shield of Michigan Foundation)Foundation)

STUDY 1: HEALTH BEHAVIORS STUDY 1: HEALTH BEHAVIORS OF OF Operating EngineersOperating Engineers

Cross-sectional survey Winter of 2008Cross-sectional survey Winter of 2008 Convenience sample of 498 Operating Convenience sample of 498 Operating

Engineers in MI (return rate: 90%) Engineers in MI (return rate: 90%) Variables included health behaviors Variables included health behaviors

(smoking, alcohol use, diet, physical (smoking, alcohol use, diet, physical activity, BMI, & sleep quality), health activity, BMI, & sleep quality), health conditions conditions (medical comorbidities & depressive (medical comorbidities & depressive

symptoms)symptoms), health-related quality of life, and , health-related quality of life, and demographicsdemographics

DESCRIPTION OF SAMPLEDESCRIPTION OF SAMPLEMean (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)

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%) 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 Clinic72 )

70.32 (17.36)

HEALTH BEHAVIORS OF THE SAMPLEHEALTH BEHAVIORS OF THE SAMPLE

Background: SmokingBackground: Smoking

• Disparities in smoking prevalence between white Disparities in smoking prevalence between white collar workers (20.3%) and blue collar workers collar workers (20.3%) and blue collar workers (35.4%)(35.4%) Blue collar workers do not benefit from Blue collar workers do not benefit from

worksite anti-smoking legislation as much as worksite anti-smoking legislation as much as white collar workers white collar workers (Rachiotis et al., 2009)(Rachiotis et al., 2009)

Blue collar workers have relatively limited Blue collar workers have relatively limited accesses to health promoting programs accesses to health promoting programs (Okechukwu et al., 2009)(Okechukwu et al., 2009)

Few studies on smoking and smoking Few studies on smoking and smoking interventions have been conducted among interventions have been conducted among blue collar workers blue collar workers (Lee et al., 2004)(Lee et al., 2004)

Odds Ratio P-Value

Age .96 .002

Marital status Separated/Widowed/Divorced Never married Married

1.81.491

.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

.411

.012

.167

.013

.003

Physical activity .94 .003

BMI .96 .025

Factors Associated With Smoking BehaviorFactors Associated With Smoking Behavior

Blue collar workers showed higher Blue collar workers showed higher prevalence in smokeless tobacco prevalence in smokeless tobacco compared to white collar workers (Lee compared to white collar workers (Lee et al., 2007)et al., 2007)

13.6% of the sample reported past 13.6% of the sample reported past month smokeless tobacco use month smokeless tobacco use (Population 3.5%, Dietz et al., 2011)(Population 3.5%, Dietz et al., 2011)

Background: Background: Smokeless Tobacco Use 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

Factors Associated With Factors Associated With Smokeless Tobacco UseSmokeless Tobacco Use

Blue collar workers are less likely to have Blue collar workers are less likely to have recommended fruit and vegetable intake and recommended fruit and vegetable intake and rank among the lowest in leisure time physical rank among the lowest in leisure time physical activity activity (Beydoun & Wang, 2009)(Beydoun & Wang, 2009)

40.2% of the sample were overweight and 44.6% 40.2% of the sample were overweight and 44.6% were obesewere obese

Background: ObesityBackground: 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 ObesityFactors Associated With Obesity

Odds Ratio P-Value

Vegetable intake 0-1 per week 2-4 per week 5-6 per week 1 per day (Reference)

1.208.729.7431

.382

.602

.299

.360

Fruit intake 0 – 2-4 per week 5-6 per week or more

.8671

.574

Fried food intake 0 – 2-4 per week 5-6 per week or more

.6781

.076

Physical activity (in 5 point increments) .769 .013

Factors Associated With Obesity (Cont.)Factors Associated With Obesity (Cont.)

Blue collar workers are exposed to high job Blue collar workers are exposed to high job stress, loud noises at work, and more prevalent stress, loud noises at work, and more prevalent in smoking and problem drinking, all of which in smoking and problem drinking, all of which are associated with poor sleep quality are associated with poor sleep quality (Deatherage et (Deatherage et

al., 2009)al., 2009). . 33.9% of the sample showed interest in health 33.9% of the sample showed interest in health

service for better sleep quality.service for better sleep quality.

Background: Sleep QualityBackground: 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

Factors Associated With Sleep QualityFactors Associated With Sleep Quality

While outdoor workers are exposed to high UV levels While outdoor workers are exposed to high UV levels and at greater risk of developing skin cancer, the and at greater risk of developing skin cancer, the rates of receiving skin examination and the use of sun rates of receiving skin examination and the use of sun protection are lower protection are lower (LeBlanc et al., 2008)(LeBlanc et al., 2008)

Over 80% reported spending 4-5 hours in the sun Over 80% reported spending 4-5 hours in the sun during weekdays and about ⅔ spent 4-5 hours in the during weekdays and about ⅔ spent 4-5 hours in the sun on weekendssun on weekends

While 50% reported 2 or more sunburns in summer, While 50% reported 2 or more sunburns in summer, 37% never used sunscreen and 38% rarely used 37% never used sunscreen and 38% rarely used sunscreensunscreen

22.8% of the sample showed interest in sun 22.8% of the sample showed interest in sun protection guidanceprotection guidance

Background:Background:Sun Exposure BehaviorsSun Exposure Behaviors

Beta P-Value

Perceived Skin

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

Factors Associated With Factors Associated With SunburnsSunburns

Beta P-Value

Perceived Skin



Rarely burn 0

Smoking .023 .644


Fruit Intake .005 .920

BMI .137 .007

Physical Activity -.025 .618


Depressive symptoms .071 .170


Age .177 .001


White .152 .002

Married .034 .492

High School or Less .043 .367

Factors Associated With Factors Associated With BlisteringBlistering

Beta P-Value

Perceived Skin



Rarely burn 0

Smoking -.089 .078


Fruit Intake .180 .000

BMI -.005 .926

Physical Activity -.044 .379


Depressive symptoms -.030 .568


Age -.010 .853


White -.061 .224

Married .045 .373

High School or Less -.009 .857

Factors Associated With Factors Associated With Use of Sun blockUse of Sun block

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

Background: Background: Health-related Quality of LifeHealth-related Quality of Life

PF 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 -.105

Fruit intake -.100 -.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

Factors Associated With Factors Associated With Health-related Quality of LifeHealth-related Quality of Life

Blue collar workers smoke more and are exposed Blue collar workers smoke more and are exposed to occupational hazards at work, which have a to occupational hazards at work, which have a synergic effect of developing lung cancer with synergic effect of developing lung cancer with smoking. smoking.

Majority of the sample were exposed to various Majority of the sample were exposed to various occupational hazards: heat stress (75.7%), occupational hazards: heat stress (75.7%), concrete dust/milling (75.5%), welding fumes concrete dust/milling (75.5%), welding fumes (71.4%), asphalt fumes (63.6%), solvents (71.4%), asphalt fumes (63.6%), solvents (58.0%), silica (56.8%), asbestos (51.2%), (58.0%), silica (56.8%), asbestos (51.2%), lead/lead paint (40.3%), and benzene (37.9%).lead/lead paint (40.3%), and benzene (37.9%).

Background: Occupational Exposures and Background: Occupational Exposures and Cigarette SmokingCigarette Smoking

Odds ratio P-Value

Occupational Exposure Factor 1a .99 .956

Occupational Exposure Factor 2b .79 .033

Age .97 .033

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 + Silicab Occupational Exposure Factor 2: Asphalt fumes + Heat stress + Concrete dust + Welding fumes

Occupational Exposures as Predictors of Occupational Exposures as Predictors of Cigarette SmokingCigarette Smoking

CONCLUSIONSCONCLUSIONS

Poor Health behaviors cluster together. Examples:Poor Health behaviors cluster together. Examples: Smoking: Smoking: problem drinking, physical inactivity, problem drinking, physical inactivity,

low BMIlow BMI Sleep Quality: Sleep Quality: smoking with nicotine smoking with nicotine

dependencedependence Risky Sun Exposure Behaviors:Risky Sun Exposure Behaviors: problem problem

drinking, high BMI, poor sleep qualitydrinking, high BMI, poor sleep quality Health-Related Quality of Life: Health-Related Quality of Life: smoking, diet smoking, diet

(less fruit/vegetable intake), physical inactivity, (less fruit/vegetable intake), physical inactivity, poor sleep qualitypoor sleep quality


Health behaviors are poor among Operating Health behaviors are poor among Operating Engineer’s increasing the risk of developing chronic Engineer’s increasing the risk of developing chronic diseases.diseases.

0 % Operating Engineer’s met the criteria of healthy 0 % Operating Engineer’s met the criteria of healthy lifestyle (3% general population).lifestyle (3% general population).

Health behavior interventions are needed for Health behavior interventions are needed for Operating Engineer’s.Operating Engineer’s.

STUDY 2:STUDY 2:

TOBACCO TACTICS WEBSITE TOBACCO TACTICS WEBSITE FOR OPERATING ENGINEERSFOR OPERATING ENGINEERS

AIMSAIMS

Aim 1: Aim 1: Compare the efficacy of the Tobacco Tactics Compare the efficacy of the Tobacco Tactics website intervention to the state sponsored 1-800-QUIT-website intervention to the state sponsored 1-800-QUIT-NOW telephone line in improving cessation including: a) NOW telephone line in improving cessation including: a) 30-day and 6-month quit rates; b) 6-month cotinine 30-day and 6-month quit rates; b) 6-month cotinine levels; c) 30-day and 6-month cigarettes smoked/day; d) levels; c) 30-day and 6-month cigarettes smoked/day; d) 30-day and 6-month number of quit attempts; and e) 30-30-day and 6-month number of quit attempts; and e) 30-day and 6-month nicotine addiction.day and 6-month nicotine addiction.

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

METHODSMETHODS

RCT of Tobacco Tactics versus 1-800-Quit NowRCT of Tobacco Tactics versus 1-800-Quit Now Convenience sample of 146 Operating Engineers Convenience sample of 146 Operating Engineers

recruited at training centerrecruited at training center Baseline, 1 month and 6 month follow up surveysBaseline, 1 month and 6 month follow up surveys Tobacco Tactics InterventionTobacco Tactics Intervention

Nurses introduces website at training centerNurses introduces website at training center Nurse calls to arrange for nicotine replacement therapy which is Nurse calls to arrange for nicotine replacement therapy which is

then mailedthen mailed Nurse makes 4 follow up counseling callsNurse makes 4 follow up counseling calls Nurse-moderated chat room 3 times per weekNurse-moderated chat room 3 times per week

Control group counseled and given card for 1-800-Quit- Control group counseled and given card for 1-800-Quit- Now state-supported phone lineNow state-supported phone line Operating Engineer calls the phone lineOperating Engineer calls the phone line Is assigned a counselor that makes 4 callsIs assigned a counselor that makes 4 calls Can be mailed NRT if it is not covered by their insuranceCan be mailed NRT if it is not covered by their insurance

User Name:User Name: Guest Password:Password: Test

http://bcbsm-operatingengineers.nursing.umich.edu/

DESCRIPTION OF SAMPLEDESCRIPTION OF SAMPLEAll(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)63 (43.2)

42 (63.6)24 (36.4)

47 (59.5)32 (40.5)

.610

DESCRIPTION OF SAMPLEDESCRIPTION OF SAMPLE

All(N=146)

Intervention(N=67)

Control(N=79)




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.. 40.8 (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

* Based on 6-month survey findings

AIM 1: 1-MONTH AIM 1: 1-MONTH Efficacy of the Tobacco Efficacy of the Tobacco Tactics Website versus 1-800-QUIT-NOW 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 (%)

Mean (SD)N (%)

Mean (SD)N (%)

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)P-Value

18 (26.9) 6 (7.7)(n=145) .002

Able to Quit for over 24 hoursP-Value

32 (86.1) 15 (31.9)(n=104) .000

Nicotine Dependence ScoreP-Value

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

P-Value-2.3 (3.0) -0.8 (2.1)

(n=98) .006

Cigarettes Smoked/DayP-Value

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

P-Value-9.7 (14.9) .1 (14.1)

(n=105)b .001

a Values for both assessment pointsb Includes results from Mini-Survey

AIM 2: PROCESS MEASURESAIM 2: PROCESS MEASURESIntervention(N=45)

Control(N=59)

N (%) N (%)

Contacts with the interventionP-Value

45 (100) 7 (11.9)(N=104) .000

At least one contact with the website 66 (98.5) NA

NRTsP-Value

34 (75.6) 2 (3.4)(N=104) .000

NRT - PatchesP-Value

20 (44.4) 1 (1.7)(N=104) .000

NRT - GumP-Value

27 (60.0) 1 (1.7)(N=104) .000

NRT - LozengesP-Value

5 (11.1) 0(N=104) .009

NRT – BothP-Value

17 (37.8) 0(N=104) .000

AIM 2 (cont)AIM 2 (cont)

Intervention(N=45)

Control(N=59)

Mean (SD) 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/nurseP-Value

4.3 ( .8) 2.9 (1.1)(N=52) .000

Recommend to someone elseP-Value

4.9 ( .7) 4.0 ( .6)(N=52) .935


Operating Engineers in the intervention group had: Operating Engineers in the intervention group had: significantly better quit rates,significantly better quit rates, significantly higher rate of contacts with the significantly higher rate of contacts with the

intervention, intervention, significantly higher rates of NRT use.significantly higher rates of NRT use.

Six-month data collection is still ongoing.Six-month data collection is still ongoing.

Once a web-based intervention has been built, the cost of Once a web-based intervention has been built, the cost of reaching a million smokers is not much more than reaching reaching a million smokers is not much more than reaching a 1000 smokers. The goal is for high reach, high efficacy, a 1000 smokers. The goal is for high reach, high efficacy, and a low cost. 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 R21CA152247 from the National Cancer Institute.”

STUDY 3:STUDY 3:

A RANDOMIZED CONTROLLED A RANDOMIZED CONTROLLED TRIAL OF 4 SUN PROTECTION TRIAL OF 4 SUN PROTECTION

INTERVENTIONS FOR INTERVENTIONS FOR OPERATING ENGINEERSOPERATING ENGINEERS

AIMS AIMS Aim 1: Aim 1: Determine differences in changes in sunscreen Determine differences in changes in sunscreen

use and sun burning among Operating Engineers use and sun burning among Operating Engineers randomized to four sun protection interventions: randomized to four sun protection interventions:

a. education only; a. education only;

b. education and mailed sunscreen; b. education and mailed sunscreen;

c. education and text message reminders; and, c. education and text message reminders; and,

d. education, mailed sunscreen, and text message d. education, mailed sunscreen, and text message reminders.reminders.

Aim 2: Aim 2: Explore if particular subgroups of Operating Explore if particular subgroups of Operating Engineers (e.g., problem drinkers or job type subgroups) Engineers (e.g., problem drinkers or job type subgroups) differ in changes in sunscreen use and sun burning pre-differ in changes in sunscreen use and sun burning pre-and post-intervention.and post-intervention.

METHODSMETHODS RCT of 4 interventions conducted at OE training center 2012RCT of 4 interventions conducted at OE training center 2012 Convenience sample of 231 Operating EngineersConvenience sample of 231 Operating Engineers All given 1 hour of educational ppt, then randomized to nothing more, All given 1 hour of educational ppt, then randomized to nothing more,

sunscreen, text messages, or bothsunscreen, text messages, or both Text messages sent 3 times per week on random days from May thru Sep.Text messages sent 3 times per week on random days from May thru Sep. 2 large containers of sunscreen mailed twice May and July2 large containers of sunscreen mailed twice May and July Half received spray and half received lotionHalf received spray and half received lotion Baseline surveys, mini-surveys each month, and larger follow up survey in Baseline surveys, mini-surveys each month, and larger follow up survey in

OctoberOctober

SAMPLE OF 60 UNIQUE SAMPLE OF 60 UNIQUE TEXT MESSAGESTEXT MESSAGES

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

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

DESCRIPTION OF SAMPLEDESCRIPTION OF SAMPLE

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 <.0001

How bad would it be for you to get skin cancer?

4.51 4.57 0.080 133 0.1077

RESULTS RELATED CHANGES IN CONSTRUCTS OF THE HEALTH BELIEF RESULTS RELATED CHANGES IN CONSTRUCTS OF THE HEALTH BELIEF MODEL (MODEL (self-efficacy, perceived barriers, perceived benefits, susceptibility, and self-efficacy, perceived barriers, perceived benefits, susceptibility, and

perceived severity) perceived severity) BEFORE AND AFTER EDUCATIONBEFORE AND AFTER EDUCATION

WHAT THEY TOLD USWHAT THEY TOLD US

Sunscreen makes hands slippery on Sunscreen makes hands slippery on steering wheel.steering wheel.

Sunscreen smudges glasses when driving.Sunscreen smudges glasses when driving. Don’t want to smell like coconut oil.Don’t want to smell like coconut oil. Spray might be better.Spray might be better.

LESSON LEARNEDLESSON LEARNED Computerized text messaging program by law Computerized text messaging program by law

must tell participant that they may be charged for must tell participant that they may be charged for these texts and they can reply “STOP” to cancelthese texts and they can reply “STOP” to cancel

20% dropped out of the text messaging arm 20% dropped out of the text messaging arm within minutes of the first text.within minutes of the first text.

Many were contacted and if they had free texting Many were contacted and if they had free texting came back on, but many were lostcame back on, but many were lost

THIS STUDY IS ONGOINGTHIS STUDY IS ONGOING

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

PUBLICATIONSPUBLICATIONSDuffy, 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..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, 619-625.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.Noonan, D., Duffy, S.A. (2012). Smokeless Tobacco Use Among Operating Engineers. Journal of Addictions Nursing. 23 (2), 132-136.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.

Documents

Health Behaviors of Operating Engineers