ArticlePDF Available

Abstract

Purpose: Report the results of a randomized, controlled trial of Live Healthy, Work Healthy (LHWH), a worksite translation of the Chronic Disease Self-Management Program (CDSMP). Design: 14 worksites were randomly assigned to LHWH, standard CDSMP (usual care) or no-intervention (control) group. Setting: The diverse set of work organizations centered around a rural community in SE US. Subjects: 411 participants completed baseline data with 359 being included in the final analyses. Intervention: LHWH had been adapted to fit the unique characteristics of work organizations. This translated program consists of 15 sessions over 8 weeks and was facilitated by trained lay leaders. Measures: The primary outcomes including health risk, patient-provider communication, quality of life, medical adherence and work performance were collected pretest, posttest (6 mos.) and follow-up (12 mos.). Analysis: Analyses were conducted using latent change score models in a structural equation modeling framework. Results: 79% of participants reported at least one chronic condition with an average of 2.7 chronic conditions reported. Results indicated that LHWH program demonstrated positive changes in a most outcomes including significant exercise (uD ¼ 0.89, p < .01), chronic disease self-efficacy (uD ¼ 0.63, p < .05), fatigue (uD¼�1.45, p < .05), stress (uD¼�0.98, p < .01) and mentally unhealthy days (uD ¼ �3.47, p < .001). Conclusions: The translation of LHWH is an effective, low cost, embeddable program that has the potential to improve the health and work life of employees.
Abstract
Purpose
Report the results of a randomized, controlled trial of Live Healthy, Work Healthy (LHWH), a
worksite translation of the Chronic Disease Self-Management Program (CDSMP).
Design
14 worksites were randomly assigned to LHWH, standard CDSMP (usual care) or no-
intervention (control) group.
Setting
The diverse set of work organizations including, public, private, non-profit, and healthcare
organizations centered around a rural community in S.E. U.S.
Subjects
411 participants completed baseline data with 359 being included in the final analyses.
Intervention
LHWH had been adapted to fit the unique characteristics of work organizations. This translated
program consists of 15 sessions over 8 weeks and was facilitated by trained lay leaders.
Measures
The primary outcomes including health risk, patient-provider communication, quality of life,
medical adherence and work performance were collected pretest, posttest (6 mos.) and follow-up
(12 mos.).
Analysis
Analyses were conducted using latent change score models in a structural equation modeling
framework.
Results
79% of participants reported at least one chronic condition with an average of 2.7 chronic
conditions reported. Results indicated that LHWH program demonstrated positive changes in a
most outcomes including significant exercise (uΔ=0,89, p<.01), chronic disease self-efficacy
(uΔ=0.63, p.<.05), fatigue (uΔ=-1.45, p< .05), stress (uΔ=-0.98, p<.01) and mentally unhealthy
days (uΔ=-3,47, p<.001).
Conclusions
The translation of LHWH is an effective, low cost, embeddable program that has the potential to
improve the health and work life of employees.
Key words: Interventions, employee health, peer education
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Purpose
According to the World Health Organization, 71% of all deaths worldwide are due to
non-communicable diseases, the top four of which (cardiovascular disease, cancer, respiratory
diseases and diabetes) account for 80% of all premature non-communicable disease deaths.1
90% of annual U.S. health care expenditures are for individuals with chronic and mental health
conditions.2 With over 206 million employed adults in the U.S.,3 the impact of chronic diseases
on the workforce is substantial. In an analysis of a large employer-sponsored health insurance
database (n=135,219), 31% of the employees were obese, 12% depressed, 10% with high blood
pressure, 8% with high blood glucose and 6% with high cholesterol.4 Data from a large
healthcare organization showed 75% of employees had one or more chronic conditions, 54% had
multiple and 16.5% had five or more conditions.5 Data from a study of approximately 1700
workers across multiple organizations who participated in a health screening, showed 55%
reported physical or cognitive difficulty or both in performing work tasks and the average
number of chronic conditions reported was 3.5.6
Work organizations are acutely aware of this issue and the impact of unmanaged chronic
conditions on productivity and costs. In one study examining health care costs, employees with
high glucose were 41% more expensive, obese employees were 26% more expensive, and
depressed employees were 15% more expensive than employees at low risk for those conditions.
For employees ages 18-64 years with fewer than four conditions, the average annual healthcare
claims incrementally increased by $1,700 to $2,000 per person for each additional chronic
condition, and the annual cost was $10,000 higher for those with five or more conditions
compared to those with four conditions.5 Given chronic disease management efforts may have
the largest impact on employers’ healthcare costs,4 and in light of the aging workforce,7 it is clear
2
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
why organizations are making disease management programs an important part of their
workplace health promotion efforts.
Evidence suggests that offering disease self-management interventions to employees can
result in substantial benefits to employees and employers.8 One such program, the Chronic
Disease Self-Management Program (CDSMP), is one of the most widely disseminated and
researched evidence-based programs for middle-aged and older adults in the U.S.9 CDSMP has
demonstrated efficacy in a randomized controlled trial (RCT),10,11 has been tailored for specific
conditions (i.e. diabetes, arthritis, chronic pain, cancer), and is available in approximately 17
languages. CDSMP has primarily been offered through the aging services networks and is
typically delivered in healthcare organizations, senior centers, residential facilities, and faith-
based organizations,9 but not widely implemented in worksite settings. In a 7-year national
evaluation, only 1% of CDSMP participants attended a workshop in a workplace setting9 even
though the growing burden of chronic disease among the working population suggest the
benefits of offering this program to those still in the workforce. Considering the aging
workforce and the growing burden of chronic disease among the working population, efforts are
needed to translate effective interventions for use within workplace settings.12
Using translational research processes13 and intervention adaptation principles,14,15
CDSMP was tailored for use among working-aged adults with chronic conditions.16 The new
worksite version of CDSMP was titled Live Healthy, Work Healthy (LHWH). The purpose of this
paper is to present 12-month findings from a randomized controlled trial (RCT) comparing
LHWH to the traditional CDSMP format (i.e., usual care) in worksite settings.
Methods
Study Design
3
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
The study employed a group randomized repeated measures design with a delayed-
control group. Study sites first were randomly assigned to one of three conditions: LHWH (i.e.,
Workplace CDSMP or wCDSMP), usual care (i.e., CDSMP), or delayed control. Then, after
being designated as a delayed control condition, control sites were randomly assigned to either
the LHWH or usual care groups a priori so all group assignment was known before any data was
collected. For the delayed control group, data were collected at baseline (6 months prior to
pretest), pretest, posttest (6 months), and follow-up (12 months). For the two intervention
groups, data were collected at pretest, posttest (6 months), and follow-up (12 months). At each
data collection, participants completed a self-administered questionnaire to collect socio-
demographics, employment characteristics, disease self-management measures, and health
behaviors. Participants that registered for the program were sent a link to the survey on Qualtrics
via email and completed the survey before attending the program sessions. The CONSORT
diagram (Figure 1) outlines levels of participation in the various data collection steps. A total of
411 employees completed the initial data collection, with 359 included in the final analyses.
Among former participants we were able to contact, reasons for discontinuing study participation
included personal or work conflicts and lack of time.
Participants and Settings
The study was conducted in 14 worksites within three rural Georgia counties and one
county in Tennessee. Working with a local health organization (YMCA), worksites were
identified and approached about study participation. After signing a letter of support, sites were
randomly assigned to a study condition. Sites randomized to receive LHWH included a regional
medical center, two county school systems (i.e., middle school, junior high school, high school,
central office), a community action agency, and a processing plant. Sites randomized to receive
4
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
the usual care condition (CDSMP) included the city government, county government, a
behavioral healthcare facility, a bank, two county school systems (i.e., elementary schools), and a
technical college.
Intervention workshops were made available to all full-time employees at each site.
There were no eligibility criteria for employees to participate in intervention workshops;
however, recruitment efforts aimed to enroll employees with one or more chronic condition. At
each worksite, employees were recruited using one or more recruitment strategies including
emails, flyers, break-room events, announcements at staff meetings, and notifications on pay
stubs. Participants did not receive any compensation or reward for participating in the program;
however, they received a gift card ($10 value) for completing each data collection. Overall
participation was voluntary and responses were kept confidential. The study methods were
approved by the Institutional Review Board at the University of Georgia.
Intervention
The LHWH program was a translation of the CDSMP adapted to the unique
characteristics of the workplace.12 As part of this process, the authors worked with the original
CDSMP developers to maintain fidelity to the behavior change strategies utilized in CDSMP.
Program modification consisted of significant changes to the format and minor changes to the
content.16
The modifications to the format were designed to make it easier to implement in a
worksite setting without being disruptive of the work process.16 More specifically, the LHWH
workshop sessions were split into 50-minute sessions that were held twice a week over 8 weeks,
instead of the 2.5-hour CDSMP sessions held once a week over 6 weeks. Format changes
resulted in a comparable amount of facilitator-participant interaction, but in a structure that was
5
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
more acceptable for worksites and feasible for workflow embedment. CDSMP was designed for
participants ages 50 years and older; however, we lowered this age to 40 for this study to better
serve the working population. Despite these changes, the program still targeted those with one or
more chronic conditions or caregivers for someone with a chronic condition.
Modifications to the content were designed to address work-related topics and provide
work-relevant examples. A strong emphasis was placed on work-life balance, stress
management, and communication with supervisors and coworkers. Additionally, we streamlined
information about nutrition, and reduced information about falls at home, all of which was done
with a slightly younger target audience in mind.
The lay leaders for this project were staff of the YMCA. Although we had discussions
with the various worksites to train their staff, no site was able to commit staff during the course
of the project. The training of lay leaders followed standardized CDSMP certification protocols
which included initial training on the facilitation of CDSMP, a requirement that they facilitate
one CDSMP (usual care) program and an additional cross-training specific to the LHWH
program. For this project, all lay leaders were certified to, and actually facilitated, both
programs.
Measures
Patient-provider communication. Participants were asked to reflect about their
communication with their healthcare provider using a 3-item scale developed by Lorig and used
in previous CDSMP research.17 Examples of items included “When you visit your doctor, how
often do you: Prepare a list of questions for your doctor” and “Discuss any personal problems
that may be related to your illness.” Responses were measured on a 6-point scale ranging from
6
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
never (1) to always (6). Items were averaged to create a composite score with higher scores
reflecting better communication with their doctor.
Medication non-adherence. This scale measured participants’ adherence to their
prescribed medication using a scale developed by Morisky and colleagues.18 This was a 4-item
scale with each item having dichotomous response choices (yes, no). Examples of items
included “Do you ever forget to take your medicine?” and “When you feel better, do you
sometimes stop taking your medicine?”. Items were summed to create a composite score with
higher scores reflecting higher medication non-adherence.
Pain, stress, and fatigue. These variables were measured with single-item questions
asking individuals to report the degree to which they had experienced the issue in the past
week.17 Responses were measured on an 11-point Likert scale ranging from none (0) to severe
(10).
Chronic disease self-efficacy. Participants’ self-efficacy to manage their chronic
conditions was measured using a 7-item scale used in previous CDSMP research.19 Items
included “How confident are you that you can: Keep the fatigue caused by your disease from
interfering with things you want to do?” and “Do the different tasks and activities needed to
manage your health conditions reduce your need to see a doctor?”. Responses were recorded on
an 11-point scale, which ranged from not at all confident (0) to completely confident (10).
Responses were averaged to create a composite score with higher scores reflecting higher levels
of self-efficacy.
Physical and mental unhealthy days. Unhealthy days were measured using two items
from the CDC Healthy Days Scale.20 Respondents were asked “Thinking about your physical
health, which includes physical illness and injury, for how many days during the past 30 days
7
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
was your physical health not good?” and “Thinking about your mental health, which includes
stress, depression, and problems with emotions, for how many days during the past 30 days was
your mental health not good?” with responses ranging from 0 to 30 days.
Health interference. Health interference was measured using one item from the CDC
Healthy Days Scale.20 Participants were asked “During the past 30 days, for about how many
days did poor physical or mental health keep you from doing your usual activities, such as self-
care, work or recreation?” with responses ranging from 0 to 30 days.
Eating behaviors. Participants’ eating behaviors were measured using four items taken
from Starting The Conversation - a food frequency instrument developed by Paxton and
colleagues.21 Respondents were asked “Over the past 7 days: How many times did you eat fast
food meals and snacks?”, “How many servings of fruits and vegetables did you eat each day?”,
How many soda or sugar-sweetened drinks did you drink each day?”, and “How many cups of
water do you drink on an average day?”. Responses ranged from 1 (0 servings) to 6 (5 or more
servings) for all items except water, which ranged from 1 (0 cups) to 9 (8 or more cups). Items
were analyzed individually.
Physical activity behaviors. Physical activity measures were designed to capture the
levels of aerobic activity, stretching and strengthening activities, and sedentary behaviors.17,19 To
determine aerobic activity one item asked: “How many days in the past week were you
physically active or exercising for at least 30 minutes, such as brisk walking, running, dancing,
bicycling, water exercise, etc., that may cause faster breathing or heartbeat, or feeling warmer?”.
One item was used to measure stretching or strengthening exercises: “How many days in the past
week did you do stretching or strengthening exercises, such as range of motion, using
weights/resistance, yoga, tai chi, Pilates, etc.?”. Sedentary behaviors were captured by asking:
8
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
“Estimate the total hours spent sitting on an average work day: While traveling to or from
places?”, “As part of your job?”, “While watching TV or using a computer not at work?” and
“For recreation (e.g. socializing, movies, reading)?”. Items were analyzed individually.
Sleep. Sleep was measured with a single item asking respondents to “Please indicate the
number below that describes your sleep in the past week.”22 Responses were on an 11-point scale
ranging from no sleep problems (0) to severe sleep problems (10).
Demographics. Demographic items included age, gender, race/ethnicity, education, and
income.
Analyses
For these analyses, the control group served as a true control with only the baseline and
pretest measures being analyzed. Participants in the sites assigned to delayed control were not
included in the two intervention group analyses because of concerns about statistical non-
independence. Changes during this no intervention period were compared to changes occurring
for LHWH and usual care groups during intervention (pre to 6-month post) and maintenance (6-
month post to 12-month follow-up) periods. An intent-to-treat analysis was used such that
participants that had at least two of three data points were included in the final analyses. The
outcomes analyses are primarily focused on temporal change and the differences in those
changes between the three conditions.
All analyses were undertaken using latent change score models in a structural equation
modeling framework.23 Latent change score models examine within-person changes over time
while also accounting for measurement error. In contrast to latent growth curve models, which
uses all available time points, latent change score models can, for example, model change
between time 1 and time 2 separately from change between time 2 and time 3. This feature was
9
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
desirable in the current study because of the need to analyze change in the intervention period
(baseline to 6 months) separately from change during the maintenance period (6 months to 12
months). Both periods likely have different change trajectories. Change was also operationalized
in the control condition from their baseline to 6 months – a period in which they did not receive
any intervention. Figure 2 illustrates a statistical model of this multiple-group latent change score
model.
All models were analyzed based on syntax from Newsom24 using Mplus V8.3.25 Before
undertaking the analyses, we reviewed the pattern of missingness in the data to determine
whether it was missing at random or not. All of the cases in this analysis met the criterion for
missing at random. Missing data were handled using full-information maximum likelihood
estimation procedures in Mplus. The MODEL CONSTRAINT option in Mplus was used to
create difference variables (e.g., LWHW μ∆2 – Control μ∆1) and the statistical significance of
the difference between two change scores was tested with a z-test.
While it is common to include demographic variables as control variables with some of
these dependent variables, we did not do so because there were no statistically significant
relationships between the demographic variables and the dependent variables. If they are
included in the face of non-significance, they are referred to as impotent control variables.26
Impotent control variables should be avoided as doing so can have severe negative effects such
as reducing power, and increasing the prevalence of type I error rates (concluding a relationship
exists when it doesn’t in reality).27 As noted by Edwards28 the inclusion of impotent controls
also changes the substantive meaning of the dependent variables.
Results
10
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
On average, participants were 46.4 (sd=10.7) years of age and self-reported having 2.7
(sd=2.0) chronic conditions. Participants were primarily female (83%). The majority of
participants were either White (62%) or Black/African American (36%) (Table 1). There was
considerable variability in education and income levels of participants, which was slightly
skewed toward higher education (32% with postgraduate work / degree) and income (42%
earned $60,000 or higher). No significant differences between groups were observed on any
demographic variable at baseline.
Within Group Results
Means and standard deviations for the health behavior and self-management outcomes
are presented in Table 2. For the LHWH program, exercise (+0.89), stretching (+0.68) and fruit
and vegetable intake (+0.38) significantly increased pretest to posttest while sugar sweetened
drink (-0.54) and fast food intake (-0.49) significantly decreased (Table 3). There was slight
regression toward baseline from posttest to follow-up for fruit and vegetable intake, fast food
intake, sugar sweetened drink intake, and sleep quality; however, none of those values reached
baseline levels.
For the LHWH group, changes in the self-management outcomes followed a similar
pattern and generally changed in the expected direction (Table 3). Chronic disease self-efficacy
(+.63) significantly increased from pretest to posttest but decreased slightly from posttest to
follow-up. Physician communication (+0.14 and +0.04) and perception of health (+0.07 and
+0.23) increased both time periods with the only statistically significant change occurring for
perception of health from posttest to follow-up. Medication adherence, pain perception,
mentally unhealthy days, physically unhealthy days, stress, fatigue, and health interference all
decreased as expected for the intervention from pretest to posttest, although some (physically
11
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
unhealthy days, stress, fatigue and health interference) regressed from posttest to follow-up.
However, with exception of fatigue, none regressed to baseline levels. Changes from pretest to
posttest for mentally unhealthy days (-3.47), stress (-.98), fatigue (-1.45), and health interference
(-1.7) were statistically significant. No changes for these four variables were statistically
significant posttest to follow-up.
For the usual care group (CDSMP), positive health behaviors generally increased and
negative health behaviors generally decreased. Fruit and vegetable (+.37) and fast food (-.48)
intake were both significant from pretest to posttest in the anticipated direction. Sedentary
behavior (-1.21 and -1.21) decreased significantly for both time periods while exercise (+1.22)
and stretching significantly increased for the posttest to follow-up period only (Table 3).
In the usual care group, we observed increases in chronic disease self-efficacy, physician
communication, and perception of health from pretest to posttest as well as from posttest to
follow-up. However, perception of health (+.16) (pretest to posttest) was the only statistically
significant variable. For those variables expected to decrease as a result of the program, all
decreased from pretest to posttest except health interference (Table 3). Of those, only fatigue (-
2.95) was statistically significant. However, medication adherence, pain perception, physically
unhealthy days and fatigue regressed from posttest to follow-up but not to the baseline level.
The control group reported significant decreases in exercise (-1.07), sedentary behavior
(-.83), and stretching (-.83) from baseline to pretest (listed as pretest to posttest for comparison
purposes in Table 3). The control group significantly increased water intake (+.41). For the
control group, chronic disease self-efficacy, perception of health, pain perception, and mentally
unhealthy days increased, but only perception of health (+.12) was statistically significant. Also,
12
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
while medication adherence, stress, fatigue, and health interference also decreased, only fatigue
(-1.59) was statistically significant.
Between Group Results
When comparing changes between the control group and the intervention groups over
time, statistically significant differences were observed (Table 4). Relative to the control group,
LHWH participants reported significant differences for exercise (-1.96 and +1.68) and stretching
(+1.51 and +.88) for both the intervention and maintenance periods. Compared to the control
group, LHWH participants reported significant differences for sugar sweetened drink intake
(+.57) for the intervention period. Relative to the control group, usual care participants reported
significant differences for exercise (-1.18 and +2.28) and stretching (-1.03 and +1.29) for both
the intervention and maintenance periods. Further, relative to the control group, LHWH
participants reported significantly larger improvements for mental unhealthy days (+3.62) and
stress (+.85) for the intervention period. Compared to the control group, both the LHWH and
usual care groups reported significant improvements in fatigue (+3.02 and +3.77 respectively)
for the maintenance period.
Discussion
Alongside the aging workforce, the prevalence of chronic conditions among working
individuals is on the rise, which exacerbates productivity and healthcare costs for employers. In
this context, LHWH, a workplace tailored version of CDSMP,16 was developed to improve
widespread adoption by overcoming traditional barriers associated with workplace health
intervention delivery.17 At this writing, there have been no other evaluated workplace self-
management interventions targeting workers with chronic disease, although there have been
some with an emphasis on prevention or well-being targeting all workers.29,30 As such, this RCT
13
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
was the first to examine year-long effects of a workplace-designed chronic disease self-
management program (LHWH), in a workplace setting.
Overall, both LHWH and usual care had a positive impact on employee health behaviors
and self-management of chronic conditions. We observed some significant findings for both the
translated program and usual care that differed from the control group, which suggests the
translated program was capable of producing similar findings as the traditionally-delivered
intervention. More specifically, our study findings align with those from the National Study of
CDSMP, which included participants from 22 non-worksite organizations across 17 states, in that
documented significant improvements in general health status, fatigue, pain, medication
compliance, communication, and unhealthy mental and physical days at 6- and/or 12-months
post baseline. 17,31 Of particular relevance were findings from the National Study of CDSMP that
reported working-aged participates age 50 to 64 years improved in terms of health interference,
pain, fatigue, and unhealthy mental days 12-months post baseline.32 As such, findings suggest
CDSMP’s core components (active ingredients) that determine its effectiveness12 were
maintained in the translation process. LHWH provides alternative ways of delivering CDSMP in
worksites that may not allow/permit workers being away from their job for an extended period.
The shorter format allows LHWH to be offered during lunch or other brief portions of the
workday (early morning or late afternoon), which is consistent with the way worksites provide
other health programming.
With regard to the core components of the program, those most likely to change health or
self-management behaviors, both LHWH and CDSMP were similar. The translation of CDSMP
to LHWH primarily consisted of factors related to how to self-manage their chronic disease
while at work; therefore, similar positive changes in both programs for these outcomes were
14
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
expected. As such, it is no surprise that effectiveness of LHWH in worksite settings mirrors the
effectiveness reported in other CDSMP implementations.17,32 Effective program translations
might undergo multiple iterations to maximize their efficiency and effectiveness; therefore,
future efforts are needed to identify additional opportunities for intervention refinement.
Strengths and Limitations
Previous worksite research has demonstrated the importance of considering the context of
the organization when translating and implementing programs in that organization.33,34 Both a
strength and complication of this study was the inclusion of multiple diverse organizations
during program implementation. The worksite diversity forced workshop facilitators to adjust to
multiple worksite contexts. While this diversified our sample to understand implementation
logistics and feasibility in different contexts, implementation research in worksite settings
remains in its early stages and requires more understanding.35 Data were self-reported, which
may have introduced recall and reporting biases and findings are not generalizable to the U.S.
workforce. This study benefited from partnering with a respected local health agency that
facilitated many of these adjustments and from experienced facilitators that were able to make
slight adjustments to accommodate the nuances of the organization. Strengths of the study
included randomization of worksites into conditions, intent-to-treat analyses that maximized
participation in the final analyses, and latent change analyses that enabled us to isolate change
over separate periods of time. A limitation of the study included self-report measures of
outcomes.
Public Health Implications
These results demonstrate that the LHWH translation of CDSMP was effective for self-
management of chronic conditions in worksites. The program was modified slightly based on
15
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
lessons learned from this project and is currently being disseminated nation-wide as wCDSMP
(https://www.selfmanagementresource.com/programs/small-group/workplace-chronic-disease-
self-management/ ). Recommendations for dissemination of workplace programs such as this
would include adapting implementation to the context of the organization and garnering the
support of local agencies. Future research should explore effective dissemination strategies that
could be used by local health agencies to adapt their offerings to multiple diverse constituencies
and effective implementation strategies across diverse worksites.
16
362
363
364
365
366
367
368
369
So What?
What is already known on this topic?
The Chronic Disease Self-Management Program (CDSMP) is an efficacious program that
has been implemented nationwide predominately through aging services organizations.
However, it has been seldom used in worksite settings.
What does this article add?
The Live Healthy Work Healthy program, a worksite translation of CDSMP, was adapted
to fit the unique characteristics of work organizations and evaluated in a rural setting with a
diverse set of worksites.
What are the implications for health promotion practice or research?
Worksites now have access to an effective, low cost, peer education program that can be
easily embedded in the workplace and can significantly impact the health and productivity of
their employees.
17
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
References
1. World Health Organization. Noncommunicable Diseases. 2018.
https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed
5.27.20.
2. Centers for Disease Control and Prevention. Health and economic costs of chronic
diseases. 2020. https://www.cdc.gov/chronicdisease/about/costs/index.htm. Accessed
7.16.20.
3. Bureau of Labor Statistics. Employment status of the civilian noninstitutional population
by age, sex, and race. 2019. https://www.bls.gov/cps/demographics.htm. Accessed
7.16.20.
4. Goetzel RZ, Henke RM, Head MA, Benevent R, Rhee K. Ten modifiable health risk
factors and employees’ medical costs—An update. Am J Health Promot. 2020:1-10.
DOI: 10.1177/0890117120917850.
5. Naessens J, Stroebel R, Finnie D, et al. Effect of multiple chronic conditions among
working-age adults. Am J Manag Care. 2011; 17(2): 118-122.
6. Jinnett K, Schwatka N, Tenney L, Brockbank CVS, Newman LS. Chronic conditions,
workplace safety, and job demands contribute to absenteeism and job performance.
Health Aff. 2017; 36:237-244.
7. U.S. Bureau of Labor Statistics. Older workers: Labor force trends and career options.
https://www.bls.gov/careeroutlook/2017/article/older-workers.htm . Posted May 2017.
Accessed February 26, 2020.
18
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
8. Caloyeras JP, Liu H, Exum E, Broderick M, Mattke S. Managing manifest diseases, but
not health risks, saved PepsiCo money over seven years. Health Aff. 2014;1:124-131.
doi: 10.1377/hlthaff.2013.0625.
9. Smith ML, Towne SD, Herrera-Venson A, etal. (2017). Dissemination of Chronic Disease
Self-Management Education (CDSME) Programs in the United States: Intervention
delivery by rurality. Int J of Environ Res Public Health. 2017; 16(6), E638.
doi:10.3390/ijerph14060638.
10. Lorig K, Sobel DS, Stewart AL, etal. Evidence suggesting that a chronic disease self-
management program can improve health status while reducing hospitalization: A
randomized trial. Med Care. 1999; 37, 5–14.
11. Lorig K, Ritter P, Stewart AL, etal. (2001). Chronic disease self-management program: 2-
year health status and health care utilization outcomes. Med Care. 2001; 39: 1217–1223.
12. Smith ML, Wilson MG, DeJoy DM, etal. Chronic Disease Self-Management Program
(CDSMP) in the workplace: Opportunities for health improvement. Fron Public Health.
2015 July 15; 2: 179. doi:10.3389/fpubh.2014.00179.
13. Wilson K, Fridinger F. Focusing on public Health: A different look at translating research
to practice. J Women’s Health. 2008; 17: 173-79.
14. Wilson MG, Basta T, Bynum B, DeJoy DM, Vandenberg RJ, Dishman R. Do
intervention fidelity and dose influence outcomes? Results from the Move to Improve
worksite physical activity program. Health Educ Res. 2010; 25: 294-305.
15. Harden SM, Smith ML, Ory MG, Smith-Ray RL, Estabrooks PA, Glasgow RE. RE-AIM
in clinical, community, and corporate settings: Perspectives, strategies, and
19
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
recommendations to enhance public health impact. Fron Public Health. 2018; 6: 71. doi:
10.3389/fpubh.2018.00071.
16. Smith ML, Wilson MG, Robertson M, etal. (2018). Impact of translated disease self-
management program on employee health and productivity: Six-month findings from a
randomized controlled trial. Int J Environ Res Public Health. 2018; 15: 851.
doi:103390/ijerph15050851.
17. Ory MG, Ahn S, Jiang L, etal. National study of chronic disease self-management: Six-
month outcome findings. J Aging Health. 2013; 25(7): 1258-1274.
18. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-
reported measure of medication adherence. Med Care. 1986; 1: 67-74.
19. Lorig K, Stewart A, Ritter P, Lynch J, Gonzalez V, Laurent, D. Outcome Measures for
Health Education and Other Health Care Interventions. Sage Publications: Thousand
Oaks, CA; 1996.
20. Centers for Disease Control and Prevention. (2000). Measuring healthy days, Atlanta,
GA: CDC.
21. Paxton A, Strycker L, Toobert D, Ammerman A, Glasgow R. Starting the conversations.
Performance of a brief dietary assessment and intervention tool for health professionals.
Am J Prev Med. 2011; 40: 67-71.
22. Ahn S, Jiang L, Smith ML, Ory MG. (2014). Improvements in sleep problems among
Chronic Disease Self-Management Program participants. Fam Community Health. 2014;
37(4): 327-335.
23. McArdle JJ, Grimm KJ (2010). Five steps in latent curve and latent change score
modeling with longitudinal data. In van Montfort K, Oud J, Satorra A. (Eds.).
20
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
Longitudinal research with latent variables Heidelberg, Germany: Springer: 2010: 245–
273.
24. Newsom JT. Latent difference score models. In Newsom JT. (Ed.), Longitudinal
structural equation modeling: A comprehensive introduction. New York, NY: Routledge;
2015:248-263.
25. Muthén LK, Muthén BO. Mplus User’s Guide. Eighth Edition. Los Angeles, CA:
Muthén & Muthén; 2017.
26. Becker TE. Potential problems in the statistical control of variables in organizational
research: A qualitative analysis with recommendations. Organ Res Meth. 2005;8:274-
289.
27. Aguinis H, Vandenberg RJ. An ounce of prevention is worth a pound of cure: Improving
research quality before data collection. Annu Rev Organ Psychol Organ Behav.
2014;1:569–95.
28. Edwards JR. To prosper, organizational psychology should ... Overcome methodological
barriers to progress. J Organ Behav. 2008;29:469-491.
29. Schopp LH, Clark M, Lamberson WR, Uhr DJ, Minor MA. A randomized controlled
trial to evaluate outcomes of a workplace self-management intervention and an intensive
monitoring intervention. Health Educ Res. 2017;32:219-232.
30. Goedendorp M, Steverink N. (2017). Interventions based on self-management of well-
being theory: pooling data to demonstrate mediation and ceiling effects, and to compare
formats. Aging Mental Health. 2017;21:947-53.
31. Ory MG, Ahn S, Jiang L, Smith ML, Ritter P L, Whitelaw N. Successes of a national
study of the Chronic Disease Self-Management Program: Meeting the Triple Aim of
21
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
Health Care Reform. Med Care. 2013;51:992-998.
doi:10.1097/MLR.0b013e3182a95dd1.
32. Ory MG, Ahn S, Smith ML, Jiang L, Lorig K, Whitelaw N. (2014). National Study of
Chronic Disease Self-Management: Age comparison of outcome findings. Health Educ
Behav. 2014; 41(1 Suppl): 34S-42S.
33. Wilson MG, DeJoy DM, Vandenberg RJ, Corso P, Padilla H, Zuercher H. Effect of
intensity and program delivery on the translation of Diabetes Prevention Program to
worksites. A randomized controlled trial of Fuel Your Life. J Occup Environ Med. 2016;
58: 1113-20.
34. Wilson MG, DeJoy DM, Vandenberg RJ, Padilla H, Davis M. FUEL Your Life: A
translation of the Diabetes Prevention Program to worksites. Am J Health Prom. 2016;
30: 188-197.
35. Wolfenden L, Goldman S, Stacey FG. Strategies to improve the implementation of
workplace-based policies or practices targeting tobacco, alcohol, diet, physical activity
and obesity. Cochrane Database of Systematic Reviews. 2018; 11: 1-127.
doi:0.1002/14651858.CD012439.pub2.
22
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
Table 1. Demographics of included participants at pretest
Overall
(n=411)
Control
(n=170)
Usual care
(n=111)
Worksite
(n=130)
Mean age (sd) 46.4 (10.7) 46.9 (10.4) 45.3 (8.8) 47.2 (11.4)
Gender
Female
Male
341 (83%)
70 (17%)
134 (79%)
36 (21%)
105 (95%)
6 (5%)
109 (84%)
21 (16%)
Race/ethnicity
White
Black/African American
Other
Hispanic
255 (62%)
148 (36%)
8 (2%)
13 (4%)
105 (62%)
63 (37%)
2 (1%)
3 (2%)
65 (59%)
45 (40%)
1 (1%)
2 (2%)
83 (64%)
40 (31%)
7 (5%)
13 (10%)
Education
High school graduate or GED or
less
Some college or technical/
vocational training
Associate degree
Bachelor degree
Postgraduate work / degree
53 (13%)
115 (28%)
57 (14%)
53 (13%)
133 (32%)
10 (6%)
54 (32%)
24 (14%)
26 (15%)
56 (33%)
9 (8%)
13 (12%)
12 (11%)
17 (15%)
60 (54%)
33 (25%)
35 (27%)
23 (18%)
17 (13%)
22 (17%)
Income
$0-$20,000
$20,001-40,000
$40,001-60,000
$60,001-100,000
$100,001+
41 (10%)
115 (28%)
82 (20%)
115 (28%)
58 (14%)
12 (7%)
51 (30%)
29 (17%)
53 (31%)
25 (15%)
14 (13%)
20 (18%)
26 (23%)
33 (30%)
18 (16%)
13 (10%)
39 (30%)
23 (18%)
35 (27%)
20 (15%)
Mean no. chronic conditions 2.7 3.0 2.4 3.3
23
493
494
495
496
Table 2. Means (SD) of disease management outcomes
Variables Pretest (T1)
Mean (SD)
Posttest (T2)
Mean (SD)
Follow-up (T3)
Mean (SD)
Health Behaviors
Exercise
Control
Usual Care
Worksite
2.51 (1.67)
1.60 (1.93)
1.36 (1.90)
1.44 (1.91)
1.71 (1.77)
2.25 (1.83)
---
2.92 (1.66)
2.86 (1.60)
Sedentary Behavior
Control
Usual Care
Worksite
10.83 (3.37)
8.92 (4.39)
10.65 (3.86)
10.04 (3.32)
7.78 (4.08)
9.83 (3.92)
---
8.99 (3.90)
9.91 (3.99)
Stretching
Control
Usual Care
Worksite
1.59 (1.78)
0.79 (1.54)
0.71 (1.51)
0.76 (1.52)
0.99 (1.61)
1.39 (1.71)
---
1.45 (1.55)
1.45 (1.81)
Fruit & Vegetable intake
Control
Usual Care
Worksite
2.47 (1.36)
2.93 (1.34)
2.62 (1.35)
2.73 (1.41)
3.30 (1.33)
3.00 (1.38)
---
3.24 (1.28)
2.85 (1.12)
Fast food intake
Control
Usual Care
Worksite
3.02 (1.56)
2.66 (1.39)
2.52 (1.57)
2.84 (1.45)
2.18 (1.36)
2.03 (1.32)
---
2.11 (1.34)
2.08 (1.54)
Water intake
Control
Usual Care
Worksite
4.06 (2.41)
4.32 (2.38)
4.57 (2.44)
4.46 (2.47)
4.72 (2.09)
4.84 (2.37)
---
4.68 (2.47)
5.29 (2.13)
Sugar sweetened drink intake
Control
Usual Care
Worksite
1.58 (1.61)
1.68 (1.66)
1.63 (1.76)
1.61 (1.64)
1.34 (1.37)
1.09 (1.45)
---
1.37 (1.44)
1.14 (1.25)
Sleep quality
Control
Usual Care
Worksite
8.63 (2.39)
9.01 (1.97)
8.43 (2.04)
8.63 (2.29)
9.22 (2.21)
8.74 (2.16)
---
9.34 (2.10)
8.47 (2.13)
Self-Management
Chronic Disease Self-Efficacy
Control
Usual Care
Worksite
7.43 (2.07)
7.42 (2.40)
6.61 (2.55)
7.76 (2.05)
7.66 (2.23)
7.24 (1.92)
---
7.99 (2.17)
7.10 (2.14)
Physician Communication
Control
Usual Care
Worksite
3.40 (1.16)
3.19 (1.14)
3.55 (1.32)
3.37 (1.29)
3.41 (1.22)
3.69 (1.22)
---
3.67 (1.28)
3.65 (1.16)
24
497
498
Perception of health
Control
Usual Care
Worksite
2.90 (0.84)
3.05 (0.79)
2.76 (0.83)
3.02 (0.91)
3.21 (0.80)
2.83 (0.79)
---
3.28 (0.95)
3.07 (0.71)
Medication adherence
Control
Usual Care
Worksite
1.49 (1.28)
1.29 (1.33)
1.21 (1.24)
1.41 (1.38)
1.05 (1.30)
1.07 (1.17)
---
1.15 (1.31)
0.88 (1.12)
Pain perception
Control
Usual Care
Worksite
2.07 (2.57)
2.66 (2.77)
2.98 (2.70)
2.19 (2.71)
2.18 (2.34)
2.90 (2.78)
---
2.50 (2.97)
2.37 (2.06)
Mentally unhealthy days
Control
Usual Care
Worksite
5.65 (7.15)
6.70 (7.89)
8.64 (9.34)
5.80 (7.08)
5.59 (6.86)
5.18 (6.04)
---
5.27 (7.37)
6.75 (7.86)
Physically unhealthy days
Control
Usual Care
Worksite
4.65 (7.74)
4.49 (7.27)
6.07 (8.83)
4.64 (7.63)
3.65 (7.16)
4.34 (6.28)
---
4.06 (7.48)
4.41 (7.08)
Stress
Control
Usual Care
Worksite
4.96 (2.66)
5.55 (3.03)
5.64 (2.85)
4.82 (2.64)
5.08 (2.73)
4.66 (2.48)
---
4.76 (3.05)
4.79 (2.92)
Fatigue
Control
Usual Care
Worksite
22.34 (7.03)
21.51 (6.59)
22.98 (7.87)
20.83 (7.97)
18.41 (6.80)
21.18 (6.27)
---
20.49 (6.73)
22.44 (7.52)
Health Interference
Control
Usual Care
Worksite
2.63 (5.25)
2.23 (5.14)
4.48 (7.59)
2.20 (4.82)
2.15 (4.68)
2.78 (4.85)
---
2.22 (4.60)
3.77 (6.89)
25
499
500
501
Table 3. Within-group differences in latent difference scores
Variable Control Usual Care Worksite
Health Behaviors
Exercise
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-1.07c
---
0.11
1.22c
0.89b
0.61
Sedentary Behavior
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.83b
---
-1.21a
1.21a
-0.66
-0.24
Stretching
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.83c
---
0.20
0.46b
0.68b
0.05
Fruit & Vegetable intake
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.27
---
0.37a
-0.06
0.38b
-0.15
Fast food intake
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.18
---
-0.48b
-0.07
-0.49b
0.06
Water intake
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.41b
---
0.40
-0.04
0.27
0.45
Sugar sweetened drink intake
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.03
---
-0.34
-0.03
-0.54b
0.04
Sleep quality
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.00
---
0.21
0.12
0.31
-0.28
Self-Management
Chronic disease self-efficacy
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.33
---
0.24
0.33
0.63a
-0.14
Physician Communication
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.02
---
0.21
0.26
0.14
0.04
Perception of health
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.12a
---
0.16a
0.06
0.07
0.23a
Medication adherence
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.07
---
-0.24
0.10
-0.14
-0.19
Pain perception
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.12
---
-0.48
0.32
-0.08
-0.53
26
502
503
Mentally unhealthy days
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
0.16
---
-1.11
-0.33
-3.47c
-1.57
Physically unhealthy days
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.01
---
-0.83
0.41
-1.72
0.07
Stress
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.14
---
-0.47
-0.32
-0.98b
0.13
Fatigue
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-1.59b
---
-2.95c
2.19c
-1.45a
1.44
Health interference
Change pretest to posttest (T2 – T1)
Change posttest to follow-up (T3 – T2)
-0.43
---
-0.08
0.07
-1.70a
0.99
a = p < .05
b = p < .01
c = p < .001
27
504
505
506
507
Table 4. Between-group differences in latent difference scores
Variable Usual Care vs
Control
(intervention)
Usual Care vs
Control
(maintenance
)
LHWH vs
Control
(intervention)
LHWH vs
Control
(maintenance
)
Health Behaviors
Exercise -1.18c2.28c-1.96c1.68c
Sedentary Behavior 0.37 2.05b-0.17 0.59
Stretching -1.03c1.29c1.51c0.88a
Fruit & Vegetable intake -0.10 -0.33 -0.12 -0.42
Fast food intake 0.30 0.11 0.31 0.23
Water intake 0.01 -0.44 0.14 0.04
Sugar sweetened drink
intake
0.37 0.00 0.57a0.01
Sleep quality -0.21 0.12 -0.31 -0.27
Self-Management
Chronic disease self-
efficacy
0.09 0.00 -0.30 -0.46
Physician
Communication
-0.23 0.28 -0.16 -0.01
Perception of health -0.04 -0.06 0.05 0.11
Medication adherence 0.17 0.18 0.07 -0.12
Pain perception 0.60 0.20 0.20 -0.65
Mentally unhealthy days 1.27 -0.48 3.62b1.41
Physically unhealthy
days
0.83 0.41 1.72 0.08
Stress 0.34 -0.19 0.85a0.27
Fatigue 1.36 3.77c0.14 3.02b
Health interference -0.35 0.50 1.26 1.43
a = p < .05
b = p < .01
c = p < .001
28
508
509
510
511
512
513
Figure 1. Consort diagram of participant flow through study.
29
130 completed pretest
94 analyzed
76 completed follow-up
94 completed posttest
data collection
(6 new participants)
111 completed pretest data
collection
181 analyzed
138 completed posttest
(11 new participants)
170 completed
pretest
84 completed posttest
(all returning)
84 analyzed
40 completed follow-up
14 worksites randomly
assigned
Usual Care
(4 sites)
Control
(5 sites)
LHWH
(5 sites)
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
Figure 2. Statistical model of multiple-group latent change score model.
30
537
538
... Previous work on transportation-related physical activity has largely focused on encouraging active transportation through bike use, public transit, and walking school buses [58][59][60]. These efforts have largely occurred through policy changes, suggesting that vigorous activity promotion strategies may be most effective at this level [61][62][63][64][65][66][67][68]. ...
Article
Full-text available
Background: This present study experimentally evaluated the Pasos Para Prevenir Cancer (PPPC) program to determine whether participation was associated with improved physical activity engagement. Evidence suggests that obesity prevention programs improve physical activity (PA) engagement and lead to healthier weights, which substantially impacts cancer and cardiometabolic disease risk. There is a shortage of knowledge on the effectiveness of programs tailored to populations along the U.S.-Mexico border. Methods: We collected demographic, nutrition, and physical activity data at baseline, 6 months, and 12 months using the Research Electronic Data Capture (REDCap) for 209 participants. We analyzed the average metabolic equivalents (METS) per week for all physical activity levels and types and the achievement of the recommended METS per week to determine the demographic characteristics most associated with a change between baseline, 6 months, and 12 months. Results: Light activity was the most common activity at all three points, and it slightly increased at 6 months in work settings. Subjects conducted moderate physical activity primarily at home and work, and moderate physical activity increased more compared to vigorous physical activity. Conclusions: Intervention tailoring might improve PA engagement in Mexican Americans residing on the U.S.-Mexico border; however, larger studies that are more diverse are required.
... As such, effective prevention and management programs to enhance self-management and promote healthy behaviors among non-Hispanic Black and Hispanic younger men with chronic conditions are key to reducing racial/ethnic health disparities. For example, interventions such as the Workplace Chronic Disease Self-Management Program [51,52] can be offered to working-aged men to improve access to community resources for self-management support, promote more productive interactions with healthcare providers, and ultimately increase patient activation while decreasing disease symptomatology (see CCM components in Figure 1). The Centers for Disease Control and Prevention's Racial and Ethnic Approaches to Community Health (REACH) Program [53] provides funding to support culturally-tailored interventions across the U.S. to reduce tobacco use, poor nutrition, and physical inactivity, while also increasing community-clinical linkages for enhanced disease management. ...
Article
Full-text available
Middle-aged and older men of color with chronic conditions have low utilization of preventive health services. In the context of the Chronic Care Model (CCM), the objective of this study was to identify perceptions about being informed, activated patients and having productive interactions in healthcare settings among non-Hispanic Black and Hispanic middle-aged and older men with chronic health conditions in the United States. Using an internet-based survey deployed nationally using a Qualtrics panel, data were collected from a sample of non-Hispanic Black and Hispanic men aged 40 years and older with one or more self-reported chronic conditions (n = 2028). Chi-square tests and one-way ANOVAs were used to describe this national sample by race/ethnicity and age group (40–64 years and ≥65 years). Results suggest that most health-related factors differed more on age than by race/ethnicity. Younger age groups reported less preventive care, greater barriers to self-care, mental health issues, and risky behavior. Findings from this study provide insight into the health status and healthcare utilization of racial/ethnic men with one or more chronic conditions. Results may help inform prevention and treatment interventions for middle-aged and older men of color.
... The researchers found that the "work healthy" arm participants not only produced improvements on nearly all health outcomes measured, participants in the environmentally abetted arm of the study also showed significantly better chronic disease self-efficacy. 16 It seems this contemporary research affirms the ancient proverb that when we give a person a fish they eat for a day, but, well, Wilson and his colleagues' paper explains the rest. ...
Article
Each year the editorial team of the American Journal of Health Promotion selects our “Best of the Year List” of health promotion studies from the prior year. This editorial features the Editor’s Picks Awards, the Editor in Chief Awards, the Michael P. O’Donnell Award and the Dorothy Nyswander Award for the research and writing published in 2021 in this journal. Our criteria for selection includes: whether the study addresses a topic of timely importance in health promotion, the research question is clearly stated and the methodologies used are well executed; whether the paper is often cited and downloaded; if the study findings offer a unique contribution to the literature; and if the paper is well-written and enjoyable to read. Awardees in 2021 offered new insights into addressing discrimination against race or sexual identity, preferred sources of information about COVID-19 and the impact of community and workplace interventions on healthy lifestyles. This year’s award winning research spans from character to culture relative to improving well-being.
Article
Full-text available
The prevalence of chronic health conditions is increasing, with over half the current workforce attempting to manage one or more chronic conditions. The Live Healthy, Work Healthy (LHWH) program is a version of the Chronic Disease Self-Management Program translated to the workplace, with the goal of improving and sustaining the health, well-being, and productivity of employees living with chronic health conditions. Using organizational support theory as a theoretical framework and a clustered randomized controlled trial design, this paper demonstrates how the LHWH program positively impacts work-related quality of life, orientations toward the organization, and organizational cognitions and behaviors. Participants in the program experienced increases in perceived organizational support (POS), with a large intervention effect. Direct intervention effects were also found for burnout, work engagement, work ability, affective organizational commitment, and organizational citizenship behaviors. Within-person changes in POS during the intervention was a key mechanism through which participants of the program experienced changes in organizationally-relevant outcomes. Finally, offering the program on work time strengthened these effects indirectly through greater changes in POS during the intervention period. This paper provides evidence to researchers and organizational decision-makers that offering the LHWH program not only improves the health and well-being of employees but also improves important organizational outcomes.
Article
Full-text available
Background: Given the substantial period of time adults spend in their workplaces each day, these provide an opportune setting for interventions addressing modifiable behavioural risk factors for chronic disease. Previous reviews of trials of workplace-based interventions suggest they can be effective in modifying a range of risk factors including diet, physical activity, obesity, risky alcohol use and tobacco use. However, such interventions are often poorly implemented in workplaces, limiting their impact on employee health. Identifying strategies that are effective in improving the implementation of workplace-based interventions has the potential to improve their effects on health outcomes. Objectives: To assess the effects of strategies for improving the implementation of workplace-based policies or practices targeting diet, physical activity, obesity, tobacco use and alcohol use.Secondary objectives were to assess the impact of such strategies on employee health behaviours, including dietary intake, physical activity, weight status, and alcohol and tobacco use; evaluate their cost-effectiveness; and identify any unintended adverse effects of implementation strategies on workplaces or workplace staff. Search methods: We searched the following electronic databases on 31 August 2017: CENTRAL; MEDLINE; MEDLINE In Process; the Campbell Library; PsycINFO; Education Resource Information Center (ERIC); Cumulative Index to Nursing and Allied Health Literature (CINAHL); and Scopus. We also handsearched all publications between August 2012 and September 2017 in two speciality journals: Implementation Science and Journal of Translational Behavioral Medicine. We conducted searches up to September 2017 in Dissertations and Theses, the WHO International Clinical Trials Registry Platform, and the US National Institutes of Health Registry. We screened the reference lists of included trials and contacted authors to identify other potentially relevant trials. We also consulted experts in the field to identify other relevant research. Selection criteria: Implementation strategies were defined as strategies specifically employed to improve the implementation of health interventions into routine practice within specific settings. We included any trial with a parallel control group (randomised or non-randomised) and conducted at any scale that compared strategies to support implementation of workplace policies or practices targeting diet, physical activity, obesity, risky alcohol use or tobacco use versus no intervention (i.e. wait-list, usual practice or minimal support control) or another implementation strategy. Implementation strategies could include those identified by the Effective Practice and Organisation of Care (EPOC) taxonomy such as quality improvement initiatives and education and training, as well as other strategies. Implementation interventions could target policies or practices directly instituted in the workplace environment, as well as workplace-instituted efforts encouraging the use of external health promotion services (e.g. gym membership subsidies). Data collection and analysis: Review authors working in pairs independently performed citation screening, data extraction and 'Risk of bias' assessment, resolving disagreements via consensus or a third reviewer. We narratively synthesised findings for all included trials by first describing trial characteristics, participants, interventions and outcomes. We then described the effect size of the outcome measure for policy or practice implementation. We performed meta-analysis of implementation outcomes for trials of comparable design and outcome. Main results: We included six trials, four of which took place in the USA. Four trials employed randomised controlled trial (RCT) designs. Trials were conducted in workplaces from the manufacturing, industrial and services-based sectors. The sample sizes of workplaces ranged from 12 to 114. Workplace policies and practices targeted included: healthy catering policies; point-of-purchase nutrition labelling; environmental supports for healthy eating and physical activity; tobacco control policies; weight management programmes; and adherence to guidelines for staff health promotion. All implementation interventions utilised multiple implementation strategies, the most common of which were educational meetings, tailored interventions and local consensus processes. Four trials compared an implementation strategy intervention with a no intervention control, one trial compared different implementation interventions, and one three-arm trial compared two implementation strategies with each other and a control. Four trials reported a single implementation outcome, whilst the other two reported multiple outcomes. Investigators assessed outcomes using surveys, audits and environmental observations. We judged most trials to be at high risk of performance and detection bias and at unclear risk of reporting and attrition bias.Of the five trials comparing implementation strategies with a no intervention control, pooled analysis was possible for three RCTs reporting continuous score-based measures of implementation outcomes. The meta-analysis found no difference in standardised effects (standardised mean difference (SMD) -0.01, 95% CI -0.32 to 0.30; 164 participants; 3 studies; low certainty evidence), suggesting no benefit of implementation support in improving policy or practice implementation, relative to control. Findings for other continuous or dichotomous implementation outcomes reported across these five trials were mixed. For the two non-randomised trials examining comparative effectiveness, both reported improvements in implementation, favouring the more intensive implementation group (very low certainty evidence). Three trials examined the impact of implementation strategies on employee health behaviours, reporting mixed effects for diet and weight status (very low certainty evidence) and no effect for physical activity (very low certainty evidence) or tobacco use (low certainty evidence). One trial reported an increase in absolute workplace costs for health promotion in the implementation group (low certainty evidence). None of the included trials assessed adverse consequences. Limitations of the review included the small number of trials identified and the lack of consistent terminology applied in the implementation science field, which may have resulted in us overlooking potentially relevant trials in the search. Authors' conclusions: Available evidence regarding the effectiveness of implementation strategies for improving implementation of health-promoting policies and practices in the workplace setting is sparse and inconsistent. Low certainty evidence suggests that such strategies may make little or no difference on measures of implementation fidelity or different employee health behaviour outcomes. It is also unclear if such strategies are cost-effective or have potential unintended adverse consequences. The limited number of trials identified suggests implementation research in the workplace setting is in its infancy, warranting further research to guide evidence translation in this setting.
Article
Full-text available
Disease management is gaining importance in workplace health promotion given the aging workforce and rising chronic disease prevalence. The Chronic Disease Self-Management Program (CDSMP) is an effective intervention widely offered in diverse community settings; however, adoption remains low in workplace settings. As part of a larger NIH-funded randomized controlled trial, this study examines the effectiveness of a worksite-tailored version of CDSMP (wCDSMP [n = 72]) relative to CDSMP (‘Usual Care’ [n = 109]) to improve health and work performance among employees with one or more chronic conditions. Multiple-group latent-difference score models with sandwich estimators were fitted to identify changes from baseline to 6-month follow-up. Overall, participants were primarily female (87%), non-Hispanic white (62%), and obese (73%). On average, participants were age 48 (range: 23–72) and self-reported 3.25 chronic conditions (range: 1–16). The most commonly reported conditions were high cholesterol (45%), high blood pressure (45%), anxiety/emotional/mental health condition (26%), and diabetes (25%). Among wCDSMP participants, significant improvements were observed for physically unhealthy days (uΔ = −2.07, p = 0.018), fatigue (uΔ = −2.88, p = 0.002), sedentary behavior (uΔ = −4.49, p = 0.018), soda/sugar beverage consumption (uΔ = −0.78, p = 0.028), and fast food intake (uΔ = −0.76, p = 0.009) from baseline to follow-up. Significant improvements in patient–provider communication (uΔ = 0.46, p = 0.031) and mental work limitations (uΔ = −8.89, p = 0.010) were also observed from baseline to follow-up. Relative to Usual Care, wCDSMP participants reported significantly larger improvements in fatigue, physical activity, soda/sugar beverage consumption, and mental work limitations (p < 0.05). The translation of Usual Care (content and format) has potential to improve health among employees with chronic conditions and increase uptake in workplace settings.
Article
Full-text available
The RE-AIM Framework is a planning and evaluation model that has been used in a variety of settings to address various programmatic, environmental, and policy innovations for improving population health. In addition to the broad application and diverse use of the framework, there are lessons learned and recommendations for the future use of the framework across clinical, community, and corporate settings. The purposes of this article are to: (A) provide a brief overview of the RE-AIM Framework and its pragmatic use for planning and evaluation; (B) offer recommendations to facilitate the application of RE-AIM in clinical, community, and corporate settings; and (C) share perspectives and lessons learned about employing RE-AIM dimensions in the planning, implementation, and evaluation phases within these different settings. In this article, we demonstrate how the RE-AIM concepts and elements within each dimension can be applied by researchers and practitioners in diverse settings, among diverse populations and for diverse health topics.
Article
Full-text available
Background: Alongside the dramatic increase of older adults in the United States (U.S.), it is projected that the aging population residing in rural areas will continue to grow. As the prevalence of chronic diseases and multiple chronic conditions among adults continues to rise, there is additional need for evidence-based interventions to assist the aging population to improve lifestyle behaviors, and self-manage their chronic conditions. The purpose of this descriptive study was to identify the geospatial dissemination of Chronic Disease Self-Management Education (CDSME) Programs across the U.S. in terms of participants enrolled, workshops delivered, and counties reached. These dissemination characteristics were compared across rurality designations (i.e., metro areas; non-metro areas adjacent to metro areas, and non-metro areas not adjacent to metro areas). Methods: This descriptive study analyzed data from a national repository including efforts from 83 grantees spanning 47 states from December 2009 to December 2016. Counts were tabulated and averages were calculated. Results: CDSME Program workshops were delivered in 56.4% of all U.S. counties one or more times during the study period. Of the counties where a workshop was conducted, 50.5% were delivered in non-metro areas. Of the 300,640 participants enrolled in CDSME Programs, 12% attended workshops in non-metro adjacent areas, and 7% attended workshops in non-metro non-adjacent areas. The majority of workshops were delivered in healthcare organizations, senior centers/Area Agencies on Aging, and residential facilities. On average, participants residing in non-metro areas had better workshop attendance and retention rates compared to participants in metro areas. Conclusions: Findings highlight the established role of traditional organizations/entities within the aging services network, to reach remote areas and serve diverse participants (e.g., senior centers). To facilitate growth in rural areas, technical assistance will be needed. Additional efforts are needed to bolster partnerships (e.g., sharing resources and knowledge), marketing (e.g., tailored material), and regular communication among stakeholders.
Article
Full-text available
An aging workforce, increased prevalence of chronic health conditions, and the potential for longer working lives have both societal and economic implications. We analyzed the combined impact of workplace safety, employee health, and job demands (work task difficulty) on worker absence and job performance. The study sample consisted of 16,926 employees who participated in a worksite wellness program offered by a workers' compensation insurer to their employers-314 large, midsize, and small businesses in Colorado across multiple industries. We found that both workplace safety and employees' chronic health conditions contributed to absenteeism and job performance, but their impact was influenced by the physical and cognitive difficulty of the job. If employers want to reduce health-related productivity losses, they should take an integrated approach to mitigate job-related injuries, promote employee health, and improve the fit between a worker's duties and abilities.
Article
Purpose To estimate the relationship between employees’ health risks and health-care costs to inform health promotion program design. Design An observational study of person-level health-care claims and health risk assessment (HRA) data that used regression models to estimate the relationship between 10 modifiable risk factors and subsequent year 1 health-care costs. Setting United States. Participants The sample included active, full-time, adult employees continuously enrolled in employer-sponsored health insurance plans contributing to IBM MarketScan Research Databases who completed an HRA. Study criteria were met by 135 219 employees from 11 employers. Measures Ten modifiable risk factors and individual sociodemographic and health characteristics were included in the models as independent variables. Five settings of health-care costs were outcomes in addition to total expenditures. Analysis After building the analytic file, we estimated generalized linear models and conducted postestimation bootstrapping. Results Health-care costs were significantly higher for employees at higher risk for blood glucose, obesity, stress, depression, and physical inactivity (all at P < .0001) than for those at lower risk. Similar cost differentials were found when specific health-care services were examined. Conclusion Employers may achieve cost savings in the short run by implementing comprehensive health promotion programs that focus on decreasing multiple health risks.
Article
The purpose of this study was to determine and compare outcomes of two voluntary workplace health management methods: an adapted worksite self-management (WSM) approach and an intensive health monitoring (IM) approach. Research participants were randomly assigned to either the WSM group or the IM group by a computer-generated list (n = 180; 92 WSM and 88 IM). Participants completed baseline, 3 and 12-month follow-up surveys. Individuals receiving workplace WSM and IM improved in self-efficacy and nearly all health behaviors and health status variables after the intervention, compared to before the intervention. Individuals in the WSM group improved in depression symptoms at 3 and 12 months (P < 0.0001, P < 0.0001), and individuals in the IM group did not improve at either time period (P < 0.1488, P < 0.0521). Participants in the WSM group reported more improvement in physical activity and energy, health interfering less with personal life and daily activities and fewer depression symptoms at follow up, compared to participants in the IM group. This study provided additional support for worksite-based health promotion programs to promote healthy lifestyles and improve health status, and documented effectiveness of both methods, with superior performance and greater scalability for the WSM program.
Article
Objective: The aim of this study was to evaluate the effectiveness of the Fuel Your Life program, an adaptation of the Diabetes Prevention Program (DPP), utilizing implementation strategies commonly used in worksite programs-telephone coaching, small group coaching, and self-study. Methods: The primary outcomes of body mass index and weight were examined in a randomized control trial conducted with city/county employees. Results: Although the majority of participants in all three groups lost some weight, the phone group lost significantly more weight (4.9 lb), followed by the small groups (3.4 lb) and the self-study (2.7 lb). Of the total participants, 28.3% of the phone group, 20.6% of the small group, and 15.7% of the self-study group lost 5% or more of their body weight. Conclusions: Fuel Your Life (DPP) can be effectively disseminated using different implementation strategies that are tailored to the workplace.