Engagement Indicators Predict Health Changes in a Lifestyle Intervention Jessica L. Thomson, PhD; Lisa M. Tussing-Humphreys, PhD, RD, LDN; Melissa H. Goodman, PhD; Jamie M. Zoellner, PhD, RD Objective: To evaluate the utility of several participant engagement indicators for predicting health changes in a church-based lifestyle intervention shown effective for improving dietary, physical activity, and clinical outcomes. Methods: Descriptive indicators were constructed using 2 participant engagement measures – education session attendance (EDA) and exercise class attendance (EXA) – separately and combined. Relationships of 6 engagement indicators to health outcomes were tested using generalized linear mixed models.

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articipant non-adherence or lack of engagement can have deleterious effects on the success of behavioral lifestyle interventions for achieving positive health outcomes.1 Adherence and engagement can determine the degree to which an intervention is received by participants and can aid in understanding relationships between program elements and participant outcomes.2 More positive health outcomes, such as improved mental and physical health, have been associated with higher levels of intervention implementation, including adherence, dosage, fidelity, and reach.3 Hence, measures of participant engagement can help identify intervention components associated with the most significant health outcomes. Understanding participant fidelity to or engagement in multicomponent behavioral lifestyle interventions is important because strategies are designed to affect changes in several aspects of an individual’s daily habits, notably diet and physical activity. Accordingly, diverse measures are needed

Jessica L. Thomson, US Department of Agriculture, Agricultural Research Service, Stoneville, MS. Lisa M. Tussing-Humphreys, Assistant Professor, Department of Medicine and Cancer Center, University of Illinois at Chicago, Chicago, IL. Melissa H. Goodman, US Department of Agriculture, Agricultural Research Service, Stoneville, MS. Jamie M. Zoellner, Associate Professor, Department of Human Nutrition, Foods and Exercise, Virginia Tech, VA. Correspondence Dr Thomson; [email protected]

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Results: EDA predicted 5 dietary and 1 clinical outcome, whereas EXA predicted one physical activity and one clinical outcome. The combined indicator predicted the same 7 outcomes. Conclusion: Use of single engagement indicators specific to each intervention component is advocated for predicting relevant health outcome. Key words: diet; nutrition education; physical activity; African American; participant engagement Am J Health Behav. 2015;39(3):409-420 DOI: http://dx.doi.org/10.5993/AJHB.39.3.13

to track participant engagement and/or adherence in all components of an intervention. However, lifestyle intervention studies often report the effects of a single adherence/engagement measure on health outcomes, commonly frequency of class attendance at educational events.1,4,5 Encouragingly, the effects of multiple or multicomponent adherence/engagement indicators on health outcomes are being reported with increasing frequency. In 4 lifestyle interventions, relationships between multiple or multi-dimensional participant adherence/ engagement indicators and weight, dietary, or physical activity outcomes were highlighted.6-9 In 3 of these studies, relationships between intervention adherence/engagement and post-intervention changes in health outcomes were based upon combined adherence/engagement measures treated in categorical form.6,7,9 The fourth study compared several adherence/engagement indicators in categorical and continuous forms with one another in terms of utility to predict health outcomes.8 However, the results were somewhat inconclusive as no adherence/engagement indicator emerged as the best overall predictor. Hence, questions still remain concerning whether measures of participant adherence or engagement are more useful as individual or multifactorial indicators for predicting health outcomes in behavioral interventions. Behavioral interventions targeting diet and physical activity may help alleviate the higher burden of chronic disease risk that is exacerbated by un-

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Engagement Indicators Predict Health Changes in a Lifestyle Intervention healthy diet and limited physical activity in African Americans.10 However, recognition of the imbedding of unhealthy behaviors, such as overeating and physical inactivity, into core social and cultural processes and environments of day-to-day life in racial and ethnic minority populations is necessary for the success of such interventions.11 For example, community-based settings, such as African-American churches, have proven effective in implementing diet and physical activity interventions.10 Additionally, the use of culturally-tailored intervention components, such as modifying but not eliminating cultural foods to improve diet and promoting culturally acceptable modes of physical activity, have been suggested.10 The primary analysis of Delta Body and Soul III, a church-based, moderate dose intensity, lifestyle intervention conducted with Southern, primarily African-American adults, examined the intervention’s effectiveness in achieving improvements in dietary, physical activity, anthropometric, and clinical outcomes. Results indicated that the intervention was effective in improving diet quality and blood lipids, and increasing physical activity in this cohort.12 Given the limitations of the literature, the objective of this secondary analysis was to evaluate the utility of several participant engagement indicators for predicting changes in dietary, physical activity, anthropometric, and clinical outcomes in Delta Body and Soul III. We hypothesized that participants with higher engagement in intervention components would have larger improvements in health outcomes as compared to control participants and intervention participants, both with lower levels of engagement. Further, we anticipated that a combined engagement indicator would be the most useful in terms of ability to predict multiple health outcome changes. METHODS Design Delta Body and Soul III was a 6-month, churchbased, multicomponent educational program designed to improve diet quality and increase physical activity in rural, Southern, primarily AfricanAmerican adults residing in the Lower Mississippi Delta region of the US. Church enrollment occurred on a rolling basis with baseline data collected between August and October 2011 and follow-up data between March and May 2012. Study participants provided informed written consent. Church recruitment across 4 Lower Mississippi Delta counties occurred via mailed study invitation letters, followed by telephone contact to schedule an informational study presentation. Churches qualified for study participation if able to pre-register at least 20 eligible congregational members. Individual participant eligibility criteria included being at least 18 years of age and not currently pregnant. A further restriction was placed on participation in the exercise classes such that participants with baseline blood pressures greater than

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160/110 mm Hg or resting heart rates greater than 110 beats/minute were required to obtain written medical clearance before being allowed to participate in these classes. Nine churches were identified and contacted by research staff active in the local church community. The first 5 churches were assigned to the intervention group, the last 3 to the control group, and 1 declined to participate. The intentional assignment of more churches to intervention increased the statistical power for detecting changes within this group as previous experience showed more variability within the intervention group as compared to the control group.13 Figure 1 illustrates the CONSORT diagram. Of the 287 participants enrolled in the intervention group and 122 enrolled in the control group, the retention rates were 76% (N = 219) and 84% (N = 102), respectively. Intervention Delta Body and Soul III was an adaptation of the original theory- and evidence-based Body and Soul program14 and built upon the 2 earlier adapted, lower dose intensity interventions.4,15 Similar to the psychosocial constructs underlying the development, implementation, and evaluation of these interventions, the current study targeted and assessed changes in psychosocial constructs from the Transtheoretical Model of Behavioral Change (decisional balance and self-efficacy)16 and the construct of social support.17 Enhancements included in the current intervention sought to inform decisional balance and improve self-efficacy, as well as increase social support for health behavior change. Modifications included replacing peer counseling with counseling by trained research staff (telephone motivational interviewing; up to 2 calls), broadening the dietary focus, adding 3 more nutrition education sessions, adding one didactic physical activity education session, and including approximately weekly supervised exercise classes (N = 20). Although the motivational interviewing component may be regarded as an indicator of participant engagement, it was not included in the analyses because it was highly dependent upon the research staff’s ability to contact the participants. The 60-minute nutrition and physical activity education sessions consisted of a total of 9 sessions – 8 focused on nutrition and 1 focused on physical activity. These education sessions were held approximately every 3 weeks and emphasized increasing consumption of fruits, vegetables, whole grains, and low-fat dairy foods; decreasing consumption of solid fats, added sugars, and sodium; eating a healthy breakfast; meal planning and healthy food substitutions, including regional and cultural foods; weight and portion control; reading food labels; and childhood obesity. Presentations and activities (eg, cooking demonstrations) were developed by the research staff and delivered collaboratively with trained church liaisons who also called participants to remind them of upcoming

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Figure 1 CONSORT Diagram of Church Contact, Enrollment, and Participation Rates for Delta Body and Soul III Churches  from  4  Lower  Mississippi  Delta  coun7es   contacted  for  study  par7cipa7on  (N  =  9)  

Declined  to  par7cipate   (N  =  1  church)  

Expressed  interest  and  met  inclusion   criterion  (N  =  8  churches)  

Assigned  to  interven7on  arm   (N  =  5  churches)  

Assigned  to  control  arm   (N  =  3  churches)  

Completed  informed  consent,  baseline   assessment,  and  enrolled  in  Delta  Body  and   Soul  III  (N  =  287  par7cipants)  

Completed  informed  consent,  baseline   assessment,  and  enrolled  in  Delta  Body  and   Soul  III  (N  =  122  par7cipants)  

Completed  6-­‐month  assessment   (N  =  219  par7cipants)  

Completed  6-­‐month  assessment   (N  =  102  par7cipants)    

sessions. Healthful foods and beverages consistent with lesson themes were served at these events. The single didactic physical activity session was centered on the benefit of, recommendations for, and strategies for overcoming barriers to physical activity. A trained, certified fitness instructor co-led this session and taught the 60-minute supervised exercise classes that incorporated approximately equal proportions of aerobic and strength/flexibility activities. All study related events were held at the churches, except in the case of the smallest church for which a nearby US Department of Agriculture facility was used. Intervention participants received binders consisting of the 9 educational lessons, healthy recipes, and other nutrition, chronic disease prevention, and physical activity related handouts. Intervention participants also received a Delta Body and Soul cookbook and monthly newsletters that featured nutrition and physical activity topics, healthy recipes, and dates and times for upcoming education sessions and exercise classes. Additionally, intervention participants had access to their church’s health and fitness station which

consisted of a digital scale, digital blood pressure monitor, body mass index (BMI) and blood pressure monitoring charts, culturally appropriate fitness DVD library, and a television with a built-in DVD player. These stations were given to the intervention churches as an incentive for participation in the study. However, use of these stations was not monitored by research staff. Participants in the control churches received bimonthly newsletters containing information pertaining to cold and influenza, food safety, and minimizing stress. Further details regarding study methodology are published elsewhere.12

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Measures Surveys were interviewer administered and data included demographic characteristics, self-report medical diagnoses, medications, and smoking. Dietary intake for the previous 6 months was measured using the Delta Food Frequency Questionnaire (FFQ).18 The FFQ data were used to generate Healthy Eating Index-2005 (HEI-2005) total and component scores. HEI-2005 measures adherence to the 2005 Dietary Guidelines for Americans

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Engagement Indicators Predict Health Changes in a Lifestyle Intervention (2005 DGA). The 12 components of HEI-2005 are summed to create a total score with a maximum value of 100.19 For each component, higher scores reflect better adherence to 2005 DGA recommendations. Only valid (< 5 missing questionnaire items) and plausible (daily intake between 500 and 6000 kcal) FFQs were used in the analyses. Physical activity was measured using the Rapid Assessment of Physical Activity (RAPA) survey20 that allows for classification of aerobic physical activity into 1 of 5 categories – sedentary, underactive, underactive regular light, underactive regular, and active. Anything less than active is considered suboptimal. The tool also allows for classification of strength and flexibility physical activity into 1 of 4 categories – none, strength only, flexibility only, and both strength and flexibility. Anthropometric variables included height, measured using a vertical stadiometer (Shorr Production, Olney, MD) and weight, measured using a calibrated digital scale (model BWB-500, Tanita Corp., Tokyo, Japan). BMI was calculated as weight (kg) divided by height (meters) squared. Blood pressure was measured using an automatic blood pressure monitor (HEM780, Omron Healthcare Inc., Kyoto, Japan). Non-fasting blood lipids (high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDLC], total cholesterol, and triglycerides) and glucose were measured using a Cholestech LDX Analyzer (Alere Inc, Waltham, MA). Anthropometric and clinical measures were assessed by trained research staff. Intervention and control participants were given their clinical values at both time points. The study nurse contacted participants with abnormal values, advising them to meet with their healthcare provider for further evaluation. To track education session attendance (EDA) and exercise class attendance (EXA), participants signed in upon arrival at these events. Two participant engagement measures, EDA and EXA, and several methodological approaches to computing descriptive indicators were explored and included continuous and categorical as well as single (EDA and EXA alone) and combined (EDA/ EXA) variables. Six engagement indicators were analyzed – 3 continuous (2 single and 1 combined) and 3 categorical (2 single and 1 combined). The continuous indicators included EDA with a range of 0-9 sessions, EXA with a range 0-20 classes, and combined EDA/EXA with a range of 0-29 contacts. Hence, continuous EDA/EXA was a composite indicator that represented the total number of exposures to the education and exercise components. It was computed by summing the values of the individual indicators (EDA + EXA). The categorical indicators included EDA at 3 levels (no = 0 sessions, low = 1-4 sessions, and high = 5-9 sessions), EXA at 3 levels (no = 0 classes, low = 1-10 classes, and high = 11-20 classes), and combined EDA/EXA with 3 levels (no, mixed, and high). The first (no) level of the categorical EDA/EXA indicator was defined as no engagement in either of the

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intervention components; the third (high) level as high engagement in both components; and the second (mixed) level as all other combinations. Median splits were used to define the low versus high levels of the single categorical variables due to their intuitive interpretation and for consistency with adherence/engagement reports in other behavioral studies.4,5 Similar to others’ work, the high level of the categorical combined indicator was defined to represent “excellent” or “super” adherers/engagers.9,21 Statistical Analyses Statistical analyses were performed using SAS® software, version 9.4 (SAS Institute Inc., Cary, NC). Generalized linear mixed models, in which church was modeled as a random effect, were used to determine associations between participant engagement indicators and health outcome changes (baseline to follow-up). Health outcomes included diet quality (HEI-2005 total and component scores), aerobic and strength/flexibility physical activity, and clinical measures (BMI, blood pressure, blood lipids, and glucose). Aerobic physical activity was modeled both in categorical (suboptimal or optimal) and continuous (5 levels) forms. Strength/flexibility physical activity was modeled in categorical form [none (no strength and no flexibility) or some (some strength and/or some flexibility)]. Selection of health outcomes included in the multivariable analysis was based upon significant bivariate relationships with the continuous engagement indicators. Group comparisons for the categorical indicators were based upon least squares means with Tukey-Kramer adjusted p-values used for multiple comparisons. Generalized linear mixed models also were used to determine the predictive ability of the engagement indicators for health outcomes in the presence of covariates. Baseline covariates included age, sex, marital status, education level, employment status, vehicle ownership, smoking status, BMI, and outcome value of interest (eg, baseline diet quality). Marital status was categorized as married (including common law) or not married (widowed, separated, divorced, and never married). Education level was categorized as less than or equal to high school (including GED) or greater than high school (some college, vocational/technical degree, and associate’s degree or higher). Employment status was categorized as not employed (unemployed, retired, student, and disabled) or employed (full time, part time, and self-employed). The significance level of the tests was set at .05. Additional sensitivity analyses were conducted with the physical activity data due to the blunt nature of the RAPA tool used to measure physical activity. In these analyses, participants who were categorized as active based on their aerobic activity level at both baseline and follow-up were excluded from the aerobic physical activity models. Similarly, participants who reported both strength

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Table 1 Baseline Characteristics for and Comparisons between Control and Intervention Participants: Delta Body and Soul, Mississippi, 2011-2012 Control (N = 122) Characteristic

N

Intervention (N = 287) %

N

%

p

Female

78

63.9

216

75.3

.014

African Americana

120

98.4

282

98.3

.739

Married/living with Significant Otherb

55

45.5

129

45.1

.924

> High School Education

50

41.7

163

56.8

.157

Employedc

56

46.7

154

54.6

.247

Health Insurance

85

72.6

196

72.3

.938

Own Vehicle

85

69.7

235

81.9

.089

Smoker

24

19.7

40

13.9

.182

Diabetes

25

21.0

59

21.3

.897

Hypertension

65

53.7

154

54.0

.760

High cholesterol

33

27.7

63

22.3

.291

41

33.9

102

36.4

.643

Chronic Health Condition

Physical Activity Optimal aerobic Some strength/flexibility

61

50.8

105

37.5

.013

Mean

SD

Mean

SD

p

Age (years)

47.0

16.40

47.3

14.43

.140

Body Mass Index (kg/m2)

32.6

8.24

34.5

8.33

.152

Systolic Blood Pressure (mm Hg)

135.1

22.56

132.6

19.54

.364

Diastolic Blood Pressure (mm Hg)

82.0

11.66

78.7

11.35

.142

HDL-C (mg/dL)

49.6

14.67

.138

55.3

18.71

LDL-C (mg/dL)

99.1

29.99

109.4

32.32

.060

Total Cholesterol (mg/dL)

182.8

40.95

182.4

39.15

.920

Triglycerides (mg/dL)

136.8

92.58

134.4

86.40

.806

Glucose (mg/dL)

109.8

52.08

108.1

37.06

.675

Healthy Eating Index-2005 score

56.0

11.00

56.0

10.20

.892

Note. HDL-C = high density lipoprotein cholesterol; LDL-C = low density lipoprotein cholesterol. a Other category included white, Hispanic, other, and multiracial . b Not married category included never married, widowed, divorced, or separated. c Employed (full time, part time, and self-employed) vs not employed (unemployed, retired, student, and disabled).

and flexibility activities at both baseline and follow-up were excluded from the strength/flexibility models. These participants were excluded because no increase in physical activity (eg, duration or intensity) could be captured given their activity increased during the course of the intervention. RESULTS The majority of participants in both groups were

women, non-smokers, and persons who owned a vehicle. Mean BMI was 35 and 33 kg/m2 in the intervention and control groups, respectively, whereas mean age was 47 years for both groups (Table 1). Control and intervention participants did not differ at baseline with the exception of a higher proportion of women in the intervention group and a higher proportion of participants engaging in some strength/flexibility physical activity in the control

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Engagement Indicators Predict Health Changes in a Lifestyle Intervention

Table 2 Summary Statistics for Intervention Participant Engagement Measures Overall and by Study Completion: Delta Body and Soul, Mississippi, 2011-2012 Measure

N

Mean

Median

SD

Non-attendees included EDA

287

EXA

268

3.0

2.0

3.19

4.5

1.0

6.02

Non-attendees excluded EDA

179

4.8

4.0

2.78

EXA

149

8.2

7.0

5.98

Overall N

%

Non-completersa N

%

Completersa N

%

EDA

pb < .001

0 sessions

108

37.6

52

76.5

56

25.6

1-4 sessions

91

31.7

12

17.6

79

36.1

5-9 sessions

88

30.7

4

5.9

84

38.4

0 classes

119

44.4

47

77.0

72

34.8

1-10 classes

92

34.3

11

18.0

81

39.1

11-20 classes

57

21.3

3

4.9

54

26.1

EXA

< .001

Note. EDA = education session attendance (0-9 sessions); EXA = exercise class attendance (0-20 classes; excludes 19 ineligible participants). a Study non-completers defined as completing data collection at baseline only; completers defined as completing data collection at baseline and follow-up. b Represents p-value for comparison between study non-completers and completers.

group. Nineteen (7%) intervention participants were ineligible to partake in the exercise classes due to their failure to obtain medical clearance. Table 2 presents summary statistics for the participant engagement measures. Mean EDA was 3.0 sessions with 31% of the intervention participants attending at least 5 of the 9 educational sessions. Mean EXA was 4.5 classes with 21% of the intervention participants partaking in at least 11 of the 20 exercise classes. Study completion, defined as completing the follow-up data collection, was significantly associated with higher EDA and EXA. A relatively large and positive correlation (r = 0.83, p < .001) was observed between EDA and EXA (data not shown). Significant associations between the single categorical engagement indicators and participants’ baseline characteristics were found (data not shown). Proportionally more women were in the high EDA group (88%) as compared to the no and low EDA groups (68% and 73%, p = .004), and in the low and high EXA groups (79% and 91%) as compared to the no EXA group (61%, p < .001). Participants classified as low EDA or low EXA were

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more likely to be employed (65% and 67%) than those classified as no EDA or no EXA (44% and 46%, p = .010 and .007, respectively). Proportionally more vehicle owners were in the low EXA (90%) as compared to no EXA group (74%, p = .007), and proportionally more smokers were in the no EDA (21%) as compared to the high EDA group (8%, p = .017). Participants in the high EDA or high EXA were older by 4-5 years than those in the low EDA or low EXA groups (p < .001 and = .033, respectively). Associations between Health Outcomes and Categorical Participant Engagement Indicators Table 3 presents bivariate relationships between categorical engagement indicators and health outcomes. In the interest of brevity, only health outcomes with significant within or between group differences are included. Additionally, we did not report associations between EXA and diet quality because expecting dietary changes to result from physical activity changes alone does not make conceptual sense. Significant between group differences for the categorical EDA indicator were ap-

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Table 3 Associations between Health Outcomes and Categorical Participant Engagement Indicators and in Comparison to Control Participants: Delta Body and Soul, Mississippi, 2011-2012 Controla (N = 122)

Outcome HEI-2005

No EDA (N = 108)

Low EDA (N = 91)

High EDA (N = 88)

LSM

SEM

LSM

SEM

LSM

SEM

LSM

SEM

p

Total fruit

0.1

0.22

0.3

0.28

0.6

0.23

0.6

0.21

.430

Whole fruitb

0.2

0.24

-0.2

0.30

0.5

0.25

0.8

0.23

.042

Total vegetable

0.2

0.17

-0.1

0.23

0.3

0.19

0.7

0.17

.083

DGOV&L

0.2

0.24

0.5

0.29

0.5

0.24

0.6

0.22

.564

Total grain

-0.2

0.11

-0.1

0.15

0.0

0.13

-0.1

0.12

.643

Whole grainb

-0.1

0.19

-0.3

0.26

0.2

0.22

0.6

0.20

.034

SoFAAS

0.2

0.69

-0.4

0.80

1.0

0.68

2.4

0.63

.019

0.9

1.38

-1.3

1.58

3.8

1.35

7.0

1.26

< .001

Aerobic PA (5 levels)d

-0.6

0.16

-0.4

0.22

-0.1

0.18

0.0

0.17

.045

Body Mass Index

0.1

0.23

0.1

0.27

-0.6

0.23

-0.3

0.22

.093

HDL-C

-3.0

1.62

-4.1

1.79

-1.3

0.39

-3.0

1.48

.523

LDL-C

1.5

3.19

-3.1

4.43

-4.6

3.43

-9.9

3.33

.104

Total Cholesterol

-2.6

2.84

-1.1

3.78

-6.9

3.14

-6.6

3.04

.506

b

Totalc

No EXAe (N = 119)

Low EXA (N = 92)

High EXA (N = 57)

LSM

SEM

LSM

SEM

LSM

SEM

p

Aerobic PA (5 levels)d

-0.2

0.19

-0.1

0.18

0.2

0.21

.023

LDL-Cf

-2.1

3.63

-7.6

3.44

-11.2

3.84

.049

Total Cholesterol

0.3

3.28

-7.6

3.04

-8.5

3.68

.189

No EDA/EXA (N = 83) HEI-2005

LSM

Mixed EDA/EXA (N = 130)

High EDA/EXA (N = 55)

SEM

LSM

SEM

LSM

SEM

p

Total fruit

0.4

0.34

0.5

0.20

0.6

0.27

.613

Whole fruit

-0.2

0.37

0.6

0.22

0.8

0.30

.128

Total vegetable

0.1

0.27

0.4

0.15

0.8

0.21

.115

DGOV&L

0.7

0.34

0.5

0.20

0.7

0.27

.469

SoFAAS

0.1

0.95

1.5

0.57

1.8

0.76

.215

Total

-0.3

1.90

5.0

1.16

5.4

1.53

.015

Aerobic PA (5 levels)d

-0.2

0.27

-0.2

0.15

0.3

0.21

.017

Strength/Flex PA (%)

15.2

6.24

31.8

4.44

34.6

6.60

.319

HDL-C

-3.4

2.10

-2.6

1.30

-2.2

1.72

.958

LDL-C

-1.1

5.00

-6.5

2.87

-11.0

3.89

.061

Total Cholesterol

2.9

4.50

-6.3

2.54

-8.5

3.75

.188

Triglyceridesg

43.0

19.74

8.4

11.64

15.7

16.09

.295

Note. EDA = education session attendance (No = 0 attended; Low = 1-4 attended; High = 5-9 attended); EXA = exercise class attendance (No = 0 attended; Low = 1-10 attended; High = 11-20 attended); HEI-2005 = Healthy Eating Index-2005; LSM = least squares mean (bolded values = significant within group change at .05 level); SEM = standard error of mean; DGOV&L = dark green and orange vegetables and legumes; SoFAAS = solid fats, alcoholic beverages, and added sugars; PA = physical activity; HDLC = high density lipoprotein cholesterol; LDL-C = low density lipoprotein cholesterol; EDA/ EXA = combined (No = no EDA and no EXA, Mixed = some EDA and/or some EXA, High = High EDA and High EXA). a Values for EDA comparison; slight variations (hundredths decimal) for EXA and EDA/EXA comparisons. b High EDA > No EDA. c High EDA > Control; Low and High EDA > No EDA. d High EDA, High EXA, and High EDA/EXA > Control e Associations between EXA and diet quality outcomes are illogical and therefore not reported. f None of the EXA group comparison reached .05 level of significance. g Corresponding control values are LSM = -1.4 and SEM = 13.32.

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Engagement Indicators Predict Health Changes in a Lifestyle Intervention parent for HEI-2005 whole fruit; whole grain; solid fats, alcoholic beverages, and added sugars (SoFFAS); and total diet quality scores, as well as aerobic physical activity. Although these group differences did not reach statistical significance, within group changes also were present for total fruit, total vegetable, dark green and orange vegetables and legumes (DGOV&L), and total grain diet quality components, as well as clinical (BMI, HDL-C, LDL-C, total cholesterol) outcomes. Significant between group differences for the categorical EXA indicator were apparent for aerobic physical activity and LDL-C. Although the between group difference was not significant, within group changes were present for total cholesterol (low and high EXA groups). Results from the sensitivity analyses, which excluded participants categorized as having optimal aerobic activity or performing both strength and flexibility activities at baseline and follow-up, were not substantively different from the full sample analyses. That is, no within or between group differences changed significance in the excluded sample as compared to the full sample analyses (data not shown). With engagement measures combined, significant between group differences for the categorical EDA/EXA indicator were apparent only for HEI2005 total diet quality and aerobic physical activity, although within group changes were present for several diet quality components (total fruit, whole fruit, total vegetable, DGOV&L, and SoFAAS), as well as strength/flexibility physical activity, and clinical (HDL-C, LDL-C, total cholesterol, and triglycerides) outcomes. Again, results from the sensitivity analyses were not substantively different from the full sample analyses (data not shown). Associations between Health Outcomes and Continuous Participant Engagement Indicators Table 4 presents bivariate relationships between continuous participant engagement indicators and health outcomes. Again in the interest of brevity, only health outcomes with significant correlations are included. Significant correlations were observed between the continuous EDA indicator and HEI-2005 whole fruit, total vegetable, whole grain, discretionary fat and oil, SoFAAS, and total diet quality scores (0.14 ≤ r ≤ 0.28). Significant correlations were observed between the continuous EXA indicator and strength/flexibility physical activity only (r = 0.03). With engagement measures combined, significant correlations were observed between the continuous EDA/EXA indicator and HEI-2005 whole fruit, total vegetable, whole grain, discretionary fat and oil, SoFAAS, and total diet quality scores; and aerobic physical activity and LDL-C (-0.46 ≤ r ≤ 0.24). Results from the sensitivity analyses were not substantively different from the full sample analyses (data not shown). Generalized linear mixed model results also are presented in Table 4. When controlling for covariates, a 1-unit increase in EDA (additional session

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attended) resulted in 0.06, 0.12, 0.10, 0.28, and 0.67 point increases (improvements) in HEI-2005 whole fruit, whole grain, discretionary fat and oil, SoFAAS, and total scores, respectively. Similarly, a 1-unit increase in EDA/EXA resulted in 0.02, 0.04, 0.04, 0.09, and 0.22 point increases in HEI-2005 whole fruit, whole grain, discretionary fat and oil, SoFAAS, and total scores. One-unit increases in EXA and EDA/EXA resulted in 0.04 and 0.02 level increases in aerobic physical activity (5 levels). Finally, 1-unit increases in EDA, EXA, and EDA/ EXA resulted in 1.0, 0.6, and 0.4 unit decreases in LDL-C. Again, results from the sensitivity analyses were not substantively different from the full sample analyses (data not shown). DISCUSSION The objective of this secondary analysis was to evaluate the utility of several participant engagement indicators, based on EDA and EXA, for predicting changes in dietary, physical activity, anthropometric, and clinical outcomes in Delta Body and Soul III. Results indicated that several engagement indicators were useful for predicting changes in diet quality, aerobic physical activity, and blood lipids. In addition, group differences that failed to reach statistical significance in the primary analyses, in which the intervention group as a whole was compared to the control group,12 were significant when the intervention group was categorized by engagement level. Specifically, changes (increases) in the diet quality components whole fruit, whole grain, and SoFAAS, as well as total diet quality were larger for the high EDA engagement group as compared to the no EDA or control groups. Further, the strong predictive ability of the continuous engagement indicators for health outcomes is evidenced by their retaining significance in the presence of demographic and baseline characteristics known to moderate engagement or affect changes. Coupled with the magnitude of the significant increases observed in the high engagement groups, these findings indicate that the observed changes in health outcomes in Delta Body and Soul III were largely driven by level of participant engagement to components of the intervention, namely attendance at education and exercise events. In the 2 earlier iterations of Delta Body and Soul, participant engagement in EDA also affected changes in diet quality such that more positive changes were observed in the high engagement groups.4,15 In another dietary and physical activity intervention conducted in a similar population, both single (EDA and pedometer diary submission) and combined engagement/adherence indicators were significantly associated with changes in physical activity, anthropometric (BMI, % body fat, and fat mass), and clinical (blood pressure and LDL-C), but not dietary outcomes.8 However, significant positive associations between adherence to dietary goals (study outcomes) and attendance at counseling sessions for nutrition and behavior

Thomson et al

Table 4 Bivariate Correlations and Generalized Linear Mixed Model Results for Health Outcomes and Continuous Participant Engagement Indicators: Delta Body and Soul Mississippi, 2011-2012 Single

Combined

EDA Outcome

EXAa

r

p

Whole fruit

0.14

Total vegetable Whole grain

r

EDA/EXA p

β1

p

.047

0.03

.049

0.21

.004

0.03

.016

0.14

.044

0.03

.022

Discretionary fat & oil

0.16

.027

0.04

.044

SoFAAS

0.22

.002

0.08

.018

Total

HEI-2005

0.28

< .001

0.24

< .001

Aerobic PA (5 levels)

0.04

.244

0.03

.062

0.03

.005

Strength/flexibility PA (%)

0.05

.135

0.03

.043

0.05

.172

LDL-C

-0.13

.096

-0.13

.106

-0.46

.014

Multivariable regression modelsb β1 HEI-2005

SE

β1

SE

β1

SE

Covariatesc

 

Whole fruit

0.06

0.029

0.02

0.011

Sex, Ed, BOV

Whole grain

0.12

0.027

0.04

0.010

Age, BOV

Discretionary fat & oil

0.10

0.047

0.04

0.018

Age, Ed, BOV

SoFAAS

0.28

0.070

0.09

0.027

Em, BOV

Total

0.67

0.155

0.22

0.059

Age, Ed, BOV

Aerobic PA (5 levels) LDL-C

NS

--

0.04

0.013

0.02

0.009

Sex, Ed, Em, BOV

-1.01

0.463

-0.61

0.257

-0.42

0.173

BOV

Note. EDA = education session attendance (0-9 sessions); EXA = exercise class attendance (0-20 classes); EDA/EXA = combined indicator (EDA + EXA; 0-29 contacts); r = correlation coefficient; β1 = regression slope (models include control group with value = 0); HEI-2005 = Healthy Eating Index-2005; SoFAAS, solid fats, alcoholic beverages, and added sugars; PA = physical activity; LDL-C = low density lipoprotein cholesterol; SE = standard error; NS = not significant at .05 level. a Associations between EXA and diet quality outcomes are illogical and therefore not reported. b No engagement indicator retained significance in total vegetable and strength/flexibility models. c Significant at .05 level; covariates included age, sex, marital status, education level (Ed), employment status (Em), vehicle ownership, smoking status, body mass index, and baseline outcome value (BOV; eg, baseline diet quality score).

modification were reported in the Polyp Prevention Trial, a dietary intervention targeting individuals at risk for colorectal cancer.9 Whereas significant associations between EDA and EXA and changes in anthropometric outcomes were not found in the present study, attendance at both educational and exercise sessions was associated with reductions in waist circumference, fat mass, and blood pressure in a lifestyle intervention targeting overweight/obese individuals.22 Taken together, these results provide strong evidence for the utility of

participant engagement/adherence indicators for predicting a multitude of health outcomes changes. In the present study, both singular and combined engagement indicators were useful for predicting health outcome changes. However, the magnitude of health outcome changes were generally greater in the high engagement groups categorized by a single measure as compared to the combined categorical engagement group. Similarly, based on the relative size and significance of the regression coef-

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Engagement Indicators Predict Health Changes in a Lifestyle Intervention ficients, no additional predictive power was gained by using the combined versus single continuous engagement indicators. This may be due to the high degree of colinearity between EDA and EXA. Possible exceptions to these conclusions are blood lipids for which larger and more significant results were observed using the combined engagement indicators as compared to the single indicators. It is not clear why the combined indicators were arguably more predictive, though it may be due to the “downstream” quality of blood lipids versus the more direct nature of dietary and physical activity outcomes. Similar to others, we used common (ie, quantile) values for categorical indicator splits due to their ease of interpretability. In the Polyp Prevention Trial, categorical adherence groups were based on tertile splits for 3 dietary goals.9 In Project PRIME, a 2-arm physical activity intervention, adherence to intervention components was based on 3 dichotomized markers of engagement/adherence – completion of homework (0-7 versus 8-20 assignments), daily self-monitoring (none versus some), and class attendance (0-7 versus 8-20 classes; first arm) or telephone call completion (0-3 versus 4-6 calls; second arm).7 These studies and the present one provide evidence that “low” and “high” engagement/adherence are study-specific designations, and thus, may not allow for direct comparison as across studies, particularly if multicomponent indicators are used. Hence an argument can be made for assessing engagement and adherence in a continuous fashion, especially because this method generally has more statistical power than a categorical approach.3 However, the use of continuous indicators does not solve the problem of comparability between studies as the range of and measures used to create engagement indicators may differ. Thus, it is difficult to make an “across the board” recommendation for the form of engagement indicators as this choice is largely driven by study characteristics and researcher preference. The low proportion of intervention participants classified as having high engagement to both intervention components was discouraging, although the relatively large increases in the whole fruit and total vegetable diet quality scores as well as the decreases in LDL-C and total cholesterol observed in this group were encouraging. Further, the relatively low proportion of intervention participants classified as having no engagement in either of the intervention components was reassuring, and suggests that most participants engaged in the intervention at some level. Similar results were reported in the Polyp Prevention Trial, a multicomponent dietary intervention in which participants were classified as poor (met 0-3 goals; 30%), inconsistent (met 4-8 goals; 45%), or super (met 9-12 goals; 25%) compliers to 3 dietary intervention goals over a period of 4 years.9 Conversely, in a combined behavioral intervention plus medication trial to treat abstinent alcohol dependent patients, 3 behavioral adherence

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groups were defined – adherers (71%), late non-adherers (11%), and early non-adherers (12%).21 The differences in proportions of patients in these 3 adherence groups as compared to the present study and the Polyp Prevention Trial is likely due to the method used to identify the groups – longitudinal trajectory patterns, which is based on a statistical approach that takes temporal data into account, versus traditional summary measures.23 The unexpected reductions in HDL-C observed in the high EDA and mixed EDA/EXA groups bear mentioning as they are not in the hypothesized direction for healthful changes. It is not clear why these reductions occurred in the more engaged groups, although it is possible that they resulted from decreased total fat intake as suggested by the improved SoFAAS diet quality scores and decreases in LDL-C. This study has several strengths, including the use and comparison of 2 observational measures to create engagement indicators and the variety of indicators created and compared. It has been suggested that observational data are more likely to be linked to outcomes than are self-report data.24 Some authorities might argue that our engagement indicators are simple measures of attendance, and therefore, not reflective of active participation. However, based upon anecdotal accounts by research staff, participants attending the educational sessions and exercise classes were involved (or engaged) in these events. Validated measures were used for dietary and physical activity variables. However, the ceiling effect for the RAPA tool is a limitation of the physical activity data as increases in activity could not be determined for participants achieving the highest levels at baseline. Further, generalizability of the results is limited because our participants were primarily African-American women who were residents of rural Southern communities. Overrepresentation of women in Southern African-American churches is common and reflects the low percentage of male church members. The relatively short length of the intervention and lack of follow-up to determine if changes were maintained after the active intervention phase may be viewed as limitations. However, as is often the case with community-based research, pragmatic and financial constraints limited our ability to extend the study length or follow up with participants. Finally, participating churches represented a convenience sample, and their nonrandom assignment to treatment group may have resulted in more motivated churches (ie, those first agreeing to participate) assigned to the intervention group. As in our previous work,8 none of the engagement indicators emerged as the “best” overall predictor of dietary, physical activity, and clinical outcomes in this cohort of rural, Southern, primarily African-American adults. Largely due to the combined indicators’ general lack of additional predictive power, we advocate the use of single engagement indicators specific to each component of behav-

Thomson et al ioral lifestyle interventions for predicting relevant outcomes (eg, attendance at nutrition education sessions for dietary outcomes). However, we also advocate for the use of multifactorial indicators for predicting anthropometric and clinical outcomes that may be directly or indirectly affected by all intervention components. An overall or combined indicator may have the most power to predict changes in “downstream” outcomes. The results of these analyses yielded useful information for researchers and practitioners engaged in multicomponent lifestyle interventions. However, future analyses exploring relationships between attendance at specific nutrition education sessions and corresponding diet quality components also may prove useful for determining nutrition topics to target. Identifying engagement trajectories to estimate predictive and moderating effects of these trajectories on health outcomes may prove informative as well.21 Finally, questions concerning optimal intervention dose or minimum level of participant engagement that will achieve significant and clinically relevant changes in health behaviors and outcomes need to be addressed to inform program planning and resource allocation, particularly in health disparate regions such as the Lower Mississippi Delta.

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 3. Durlak JA, DuPre EP. Implementation matters: a review of research on the influence of implementation on program outcomes and the factors affecting implementation. Am J Community Psychol. 2008;41(3-4):327-350.  4. Tussing-Humphreys L, Thomson JL, Mayo T, Edmond E. A church-based diet and physical activity intervention for rural, lower Mississippi Delta African American adults: Delta Body and Soul effectiveness study, 20102011. Prev Chronic Dis. 2013;10:E92.  5. Zoellner J, Hill JL, Grier K, et al. Randomized controlled trial targeting obesity-related behaviors: Better Together Healthy Caswell County. Prev Chronic Dis. 2013;10:E96.  6. Akers JD, Cornett RA, Savla JS, et al. Daily self-monitoring of body weight, step count, fruit/vegetable intake, and water consumption: a feasible and effective longterm weight loss maintenance approach. J Acad Nutr Diet. 2012;112(5):685-692, e682.  7. Heesch KC, Masse LC, Dunn AL, et al. Does adherence to a lifestyle physical activity intervention predict changes in physical activity? J Behav Med. 2003;26(4):333-348.  8. Thomson JL, Landry AS, Zoellner JM, et al. Participant adherence indicators predict changes in blood pressure, anthropometric measures, and self-reported physical activity in a lifestyle intervention: HUB City Steps. Health Educ Behav. 2015;42(1):84-91.  9. Wanke KL, Daston C, Slonim A, et al. Adherence to the Polyp Prevention Trial dietary intervention is associated with a behavioral pattern of adherence to nondietary trial requirements and general health recommendations. J Nutr. 2007;137(2):391-398. 10. Lemacks J, Wells BA, Ilich JZ, Ralston PA. Interventions for improving nutrition and physical activity behaviors in adult African American populations: a systematic review, January 2000 through December 2011. Prev Chronic Dis. 2013;10:E99. 11. Kumanyika SK, Whitt-Glover MC, Gary TL, et al. Expanding the obesity research paradigm to reach African American communities. Prev Chronic Dis. 2007;4(4):A112. 12. Thomson JL, Goodman MH, Tussing-Humphreys L. Diet quality and physical activity outcome improvements resulting from a church-based diet and supervised physical activity intervention for rural, Southern, African American adults: Delta Body and Soul III. Health Promot Pract. In press. DOI: 10.1177/1524839914566851. 13. Dumville JC, Hahn S, Miles JN, Torgerson DJ. The use of unequal randomisation ratios in clinical trials: a review. Contemp Clin Trials. 2006;27(1):1-12. 14. Resnicow K, Campbell M, Carr C, et al. Body and Soul. A dietary intervention conducted through African-American churches. Am J Prev Med. 2004;27(2):97-105. 15. Tussing-Humphreys LM, Thomson JL, Onufrak SJ. A church-based pilot study designed to improve dietary quality for rural, Lower Mississippi Delta, African American adults. J Relig Health. In press. DOI: 10.1007/ s10943-014-9823-5. 16. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol. 1992;47(9):1102-1114. 17. Israel BA, McLeroy KR. Social networks and social support: implications for health education. Health Educ Q. 1985;12(1):65-80. 18. Tucker KL, Maras J, Champagne C, et al. A regional food-frequency questionnaire for the US Mississippi Delta. Public Health Nutr. 2005;8(1):87-96. 19. Guenther PM, Reedy J, Krebs-Smith SM. Development of the Healthy Eating Index-2005. J Am Diet Assoc. 2008;108(11):1896-1901. 20. Topolski TD, LoGerfo J, Patrick DL, et al. The Rapid Assessment of Physical Activity (RAPA) among older adults. Prev Chronic Dis. 2006;3(4):A118. 21. Gueorguieva R, Wu R, Krystal JH, et al. Temporal patterns of adherence to medications and behavioral treat-

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Human Subjects Statement Procedures followed in Delta Body and Soul III were in accordance with ethical standards for human research, and approval was obtained from the Institutional Review Board of Delta State University, Cleveland, Mississippi (document number 09002). Conflict of Interest Statement All authors declare they have no conflicts of interest to report. Acknowledgements We thank the original investigators who developed the intervention, Delta Body and Soul III research team, and Delta Health Alliance. We are particularly grateful to the church leaders, committee members, and study participants for their tremendous support. This research was supported by the US Department of Agriculture, Agricultural Research Service Project 640151000-001-00D and US Department of Health and Human Services, Health Resources Services Administration grant no. 6 U1FRH07411. The views expressed are solely those of the authors and do not reflect the official policy or position of the US government. References

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Engagement Indicators Predict Health Changes in a Lifestyle Intervention ment and their relationship to patient characteristics and treatment response. Addict Behav. 2013;38(5):21192127. 22. Mazzeschi C, Pazzagli C, Buratta L, et al. Mutual interactions between depression/quality of life and adherence to a multidisciplinary lifestyle intervention in obesity. J Clin Endocrinol Metab. 2012;97(12):E2261-2265. 23. Nagin DS. Analyzing developmental trajectories: a semi-

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