Can a workplace intervention
decrease cardiometabolic risk?
Preliminary results from the
Work, Family and Health study
Lisa F. Berkman, Ph.D.
Thomas Cabot Professor of Public Policy& Epidemiology
Director, Harvard Center for Population and Development Studies
Work, Family Health Network
• Created in 2005 by the National Institutes of
Health and the Centers for Disease Control and
Prevention
Researchers/ collaborators at nine different
universities and research sites
Provide scientific evidence about how changes in the
work environment can improve the health of workers
while benefiting organizations.
Funding
• Eunice Kennedy Shriver National Institute of Child Health and
Human Development (Grants # U01HD051217, U01HD051218,
U01HD051256, U01HD051276)
• National Institute on Aging (Grant # U01AG027669)
• Office of Behavioral and Social Sciences Research
• National Institute for Occupational Safety and Health (Grants #
U01OH008788, U01HD059773)
• Additional funding from
–
–
–
–
National Heart, Lung, and Blood Institute (Grant #R01HL107240)
William T. Grant Foundation
Alfred P. Sloan Foundation
US Department of Health and Human Services Administration for
Children and Families
Overview
•
Balancing organizational & scientific requirements
– Recruitment
– Randomization design and implementation
•
Collecting meaningful data for a variety of scientists
and policy makers
– Data collection process and burden
– Measures
•
•
Developing broad transdisciplinary analysis plans
Assessing daily processes
•
Implementing a participatory initiative
Figure from: Bray, J. W., Kelly, E. L., Hammer, L. B., Almeida D. M., Dearing, J. W., King. R.
B., & Buxton, O. M. (2013)
Balancing organizational and
scientific requirements
• Single protocol
– Work redesign, supervisor training and selfmonitoring
• Multiple industries and worksites
– Healthcare
– Telecommunications
• Group randomized field experiment
– Treatment (TX) and usual practice (UP) assignment
at the worksite or work group level
– Adaptive randomization method which balances the
UP and TX groups on pre-selected criteria
Recruitment
■ Company 1 - Leef
 Low- middle income, largely hourly workforce
 Worksites are geographically distinct at 30 physical
locations across six states in New England
■ Company 2 - Tomo
 High wages, professional workforce
 Centralized company with 56 worksites in 2
geographic locations
6
Study Design:
Group Randomized Trial
Wave 1
Baseline:
Survey and
Health Data
Collection
Spouse
Survey
Child
Survey
Daily
Diaries
Interviews
Workplace
Change
Introduced
STAR
delivered
to
sites/work
groups
randomize
d to
treatment
Wave 2
Wave 3
Wave 4
12-month:
Survey and
Health Data
Collection
6-month:
Survey and
Health Data
Collection
Spouse
Survey
Child
Survey
18-month:
Survey and
Health Data
Collection
Interviews
Daily
Diaries
Interviews
7
Hypotheses
• Intervention group will have decreased
cardiometabolic risk compared to the control
group ( other outcomes include sleep,
distress, turnover, health care utilization ROI)
• Age, baseline cardiometabolic risk and family
conditions ( # children) will moderate risk
• Intervention group will have decreased
individual cardiometabolic risk factors
compared to the control group
Methods: Randomization
Matching criteria for Leef UP and TX work sites:
the number of employees, state, and retention
rate.
Matching criteria for Tomo UP and TX work
units: job function, the vice president the unit
reported to, and the number of employees.
Methods: Randomization
Sites/groups were randomized into UP and TX as
they were ready to begin data collection.
We biased randomization odds at each
randomization of available sites to maintain overall
balance across the selected criteria.
Methods: Consort Diagram
LEEF
TOMO
Randomization
Randomization
USUAL PRACTICE
m=15 facilities
n=725 employees
INTERVENTION
m=15 facilities
n=799 employees
USUAL PRACTICE
m=29 study groups
n=400 employees
INTERVENTION
m=27 study groups
n=423 employees
BASELINE
n=687; 95%
BASELINE
n=725; 91%
BASELINE
n=333; 83%
BASELINE
n=370; 87%
USUAL
PRACTICE
INTERVENTION
IMPLEMENTATION
USUAL PRACTICE
INTERVENTION
IMPLEMENTATION
12 MONTH
n=521; 72%
12 MONTH
n=452; 57%
12 MONTH
n=277; 69%
12 MONTH
n=278;66%
Follow up
Allocation
ELIGIBLE EMPLOYEES
M=56 study groups
N=1171 Employees
Enrollment
ELIGIBLE EMPLOYEES
M=30 facilities
N=1524 Employees
Methods: Sample by industry and Tx & UP
Leef
Tomo
UP
Tx
UP
Tx
521
452
277
278
40 (12)*
38 (12)
46 (9)
47 (9)
Male
9
8
62
58
Married/ Partnered (%)
65
61
80
81
Caregiver (%)
29
31
23
24
White
62
67
67
70
Black
12
16
2
4
Hispanic
18
12
7
8
Asian/ Pacific Islander
--
--
19
12
Asian Other
--
--
5
5
8
5
1
1
Foreign-born (%)
30*
27
30
21
Post-secondary education (%)
61
57
97
96
Kids <=18 in HH (%)
47
47
50
47
n
Age at baseline (std)
Race/Ethnicity (%)
Other
Methods: Outcome variable
• Marino M, Li Y, Pencina MJ, D'Agostino RB Sr, Berkman LF,
Buxton OM. Quantifying cardiometabolic risk using modifiable
non-self-reported risk factors. Am J Prev Med. 2014
Aug;47(2):131-40.
• Develop and evaluate a cumulative cardiometabolic risk score
optimized on modifiable risk factors and building on the
Framingham risk factor score
Cardiometabolic risk score
Framingham risk score
Cardiometabolic risk
score
BMI
Blood pressure
Blood pressure
Log(systolic blood pressure), if
treated (self-report)
Systolic blood pressure
Log(systolic blood pressure), if
not treated (self-report)
(No self-report)
Smoking in previous year
(yes/no)
Smoking in previous year
(yes/no)
Diabetes (any of below)
Diabetes
Blood glucose ≥126 mg/dL
Measured HbA1c levels (%)
Use of insulin or oral
hypoglycemic medication (selfreport)
(No self-report)
Log(total cholesterol, mg/dL)
Total cholesterol, mg/dL
Log(HDL cholesterol, mg/dL)
HDL cholesterol, mg/dL
Methods: exposure assessment-Intervention
• Increase employees’ control over their work
time
• Increase supervisor and coworker support for
employees’ family and personal lives (Kossek,
Hammer, & Kelly, 2012)
• Includes participatory work redesign activities
that identify new work practices and processes
15
STAR Overview
• Participatory workshops – role play & discussions
about work practices, team processes with dual goals
of getting the work done and meeting personal needs
• Training supervisors – identify supportive
behaviors (personal support, professional support),
track own behaviors using iPod Touch, compare with
peers
• 8 hours for employees, 12 hours for managers
• Family Supportive Supervisor Training
• Materials at www.workfamilyhealthnetwork.org
STAR Intervention
• Participatory workshops
– Destabilize current culture, identify new work practices, processes to
increase schedule control while meeting business needs
– 8 hours for employees, 12 hours for managers
– Mean of 74% sessions attended
• Training for supervisors with behavioral tracking
–
–
–
–
Statement of executive support
Importance of supervisors showing support for family/personal life
Identify supportive behaviors (performance support, personal support)
2 tracking periods (iPods that beeped 2x day for 1 week)
• Company pilot, professional facilitators
• STAR:Office materials at
www.workfamilyhealthnetwork.org
STAR Rollout : IT
Wave 1
survey
Leadership
Education Session
Do
Something
Scary Poll
Culture
Clinic Session
Mgr
tracking
Managers
Only Session
18
Mgr
training
&
tracking
Sludge
Eradication
Poll
Forum Session
Kick-Off
Session
Sludge Session
Wave 2
survey
Shifts with STAR in IT Workplace
Identifying…
• Rewards for hours (per
se)
• Rewards for availability,
instant responsiveness
• Approval of (ad hoc) flex
for certain reasons
• Meeting overload
Prompting…
• Clearer focus on results
• Acceptance of “heads
down” time, personal time
• Expectation that
employee decides work
location, schedule with
role in mind
• Customized
documentation &
communication plans;
invite fewer people
Customization of STAR for Health Care
• Reflect that work is happening at site 7 days per
week (e.g., changed ‘Every day feels like
Saturday’ to ‘Every day feels like your day off’)
• Added guiding principle for any change:
Safe, Legal, and Cost Neutral
• Focused on handling schedule conflicts, “call
outs” and shift trades in a fair, transparent way
• Often unit or committee pursued change once it
was identified in sessions
Also customized process…
20
WFH Study results from TOMO: Intervention
improves sleep
Olson R, et al, A workplace
intervention improves
sleep: results from the
randomized controlled
Work, Family, and Health
Study, Sleep Health (2014).
Result – Main Effects for LEEF and TOMO
Percentage points (95% CI)
Leef
Tomo
Leef
Tomo
0.1
(-0.2, 0.4)
12 Month
--
--
0.2
(-0.1, 0.4)
Treatment
-1.0
(-2.3, 0.3)
-0.3
(-2.6, 2.1)
-0.8
(-2.1, 0. 6)
-0.1
(-2.4, 2.2)
--
--
-0.3
(-0.6, 0.1)
-0.2
(-0.6, 0.2)
973
555
1946
1110
Tx*12 Month
N
Results: Moderation by baseline
cardiometabolic risk
12 Month
Percentage points (95% CI)
Leef
Tomo
0.17 (-0.17, 0.51)
0.17 (-0.31, 0.65)
Treatment
-0.01 (-0.40, 0.39)
0.13 (-0.42, 0.68)
Baseline CRS
1.00 (0.98, 1.02)
1.00 (0.97, 1.03)
Tx * 12 Month
0.33 (-0.16, 0.82)
0.47 (-0.20, 1.14)
Tx * Baseline CRS
0.00 (-0.03, 0.03)
0.00 (-0.04, 0.04)
Baseline CRS * 12 Month
0.00 (-0.03, 0.03)
-0.01 (-0.05, -0.03)
-0.08 (-0.13, -0.04)
-0.07 (-0.12, -0.01)
Tx*12 Month *Baseline CRS
Results: Intervention effects on individual
cardiometabolic risk factors, LEEF
Leef
BMI
SBP
HDL
Total Chol
HgbA1C
-.8 -.7 -.6 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 .6 .7 .8
DID estimate
Results: Intervention effects on individual
cardiometabolic risk factors, TOMO
Tomo
BMI
SBP
HDL
Total Chol
HgbA1C
-.8 -.7 -.6 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 .6 .7 .8
DID estimate
Conclusion
• No main effects for worksite intervention
on overall cardiometabolic risk
• Moderated by baseline cardiometabolic
risk with intervention having an effect on
those with higher cardiometabolic risk
• Average intervention effects for individual
cardiometabolic risk outcomes indicate
possible reduced risk
26
Methods: Analytical approach
We used the following GLMM model :
Yij:k:l = β0 + β1Cl + β2Tj + β3TjCjl + β4Xij:k:l + β5Randomizationk
+ εij:k:l
Where Yij:k:l is the outcome for person i observed at time j, nested
within site k, which is in condition l; εij:k:l is an iid error or residual,
Cl is a dichotomous variable indicating whether the individual is in
the TX group, Tj is a dichotomous variable indicating whether it is
the12 month follow-up, TjCjl is the interaction between the TX and
time indicator variables, and Randomizationk is a vector of
randomization factors.
Results: LEEF
25
20
Predicted 15
CRS
10
5
0
0
5
10
15
20
Baseline CRS
Treatment
Control
25
Results: TOMO
25
20
Predicted 15
CRS
10
5
0
0
5
10
15
20
Baseline CRS
Treatment
Control
25
Barriers faced in Health Care
• Attendance issues
– Great difficulty getting people off the floor
• Regulatory environment
– Little room for innovation regarding scheduling;
staffing levels are dictated by government
• Overworked supervisors felt they were
being asked for one more thing
• Employees mistrusted managers in some
sites
30