BBA312 DECISION ANALYSIS Mid Term Assignment Task brief & rubrics

Task

Students are required to develop a report addressing the specific exercises set out in the guideline below. Your report should be 2000-2500 words, excluding Title, Abstract, Table of contents, Bibliography and Appendices.

Exercise 1 (30 points).

All complex organizations must be broken down into manageable pieces to ensure that roles and responsibilities for making and executing critical decisions are clear. A good way to determine what the important decisions are in your company is to look at the sources of value in your business and then organize the macrostructure around them.

Take the case of British Gas, a division of the multinational energy and utility company Centrica. In 2006, faced with a serious performance crisis, the company’s new leadership team started looking at the sources of value in its business. Managers began by examining differences in profitability by service, by geographic area, and by customer segment. They discovered that profitability and growth varied much more by customer segment than by any other variable.

One segment used large amounts of gas or electricity and paid regularly through guaranteed direct debits. Decisions that helped the company retain these customers, such as how to handle home moves and how best to offer additional services, were most important for this segment. A second customer segment used less energy and paid regularly through a system of prepayment cards. Here the key decisions related to controlling costs, particularly those associated with processing additional payments and with meter reading. A third segment wasn’t as consistent in keeping up payments. For that group, the critical decisions related to managing receivables.

Recognizing those different sources of value, managing director Phil Bentley decided that the best way to structure the company was by customer segment. He established three separate businesses: Premier Energy, Energy First, and Pay-As-You-Go Energy. This new structure allowed him to place accountability for decisions that directly affected customers, such as service levels, positioning, and product bundling, in the business units. Corporate headquarters could focus on noncustomer-facing matters such as IT and finance. The alignment of structure and decisions helped British Gas improve its performance significantly. It reduced customer attrition from about 20% to less than 10%. Its bad debt fell, and the business began growing for the first time in years.

Source: Harvard Business Review. (2010). The Decision-Driven Organization.

(a) What was the objective of British Gas Leadership? (b) What is the information they collected? (c) How they analyzed this information? (d) What are the decisions taken and what are their impacts on British Gas?

Exercise 2 (40 points).

An advertising company has a proposal to make to its customer who wants to launch a new cosmetic product.

The client is motivated by the potential profits. Therefore, the advertising company should evaluate the potential sales that each marketing strategy can generate:

Marketing strategy 1: Advertise on the TV;

Marketing strategy 2: Advertise in TV and in supermarkets;

Marketing strategy 3: Advertise in TV, in supermarkets and via internet.

For simplicity, the demand the new cosmetic product is categorized as low, medium or high.

The table below shows how the sales the company will generate for each option depends on the level of demand in the market.

Marketing strategy

Low sales Medium sales High sales

1 - 4 000 - 8 000 20 000 2 - 12 000 - 4 000 12 000 3 - 20 000 -12 000 4 000

It is estimated that if strategy 1 is adopted the probabilities of low, medium and high demand are 0.3, 0.4 and 0.2, respectively. For strategy 2 the respective probabilities are 0.2, 0.4 and 0.4 while for strategy 3 they are 0.1, 0.2 and 0.7.

Determine the marketing strategy that the advertising company should recommend to its client based on expected sales. Draw the decision tree.

Exercise 3 (30 points).

You are the director of a hotel and check the level of costumer’s satisfaction with a questionnaire that the hosts answer when they leave the hotel and evaluate with a grade (from 0 to 10) the service given. The results of the last ones are:

8.5 8 7.5 6 9 7 7.5 8.5 5 9 8 7.5 7 9 6 9.5 8.5 8.5

6.5 7.5 7.5 9 7.5 8.5 8 8.5 8 8 8 9 7 8 7 7.5 8.5 8

If the goal is to reach, at least, a grade of 8, do you think the hotel is offering a good service?

After analyzing the most common causes of customers’ unsatisfaction and their frequency:

N Complaints Frequency 1 Small rooms 3

2 Bad quality food in restaurant 30 3 Receptionists do not speak English 15 4 Too far from city center 12 5 Very old TVs in rooms 4 6 Wi-fi does not work properly 25 7 Very high prices 5 8 A/C does not work in rooms 17

a. Sort the eight causes in a Cause-and-Effect diagram. b. Determine the most important causes and define an Action Plan for those ones.

Formalities:

• Word count: 2000-2500 words. • Cover, Table of Contents, References and Appendix are excluded of the total word count. • Font: Arial 12,5 pts. • Text alignment: Justified. • The in-text References and the Bibliography have to be in Harvard’s citation style.

Submission:

• Week 8: Via Moodle (Turnitin).

• Deadline: 27th November 2022 at 23:59 CEST.

Weight: This task is a 40% of your total grade for this subject.

Rubrics

Exceptional 90-100

Good 80-89

Fair 70-79

Marginal Fail 60-69

Knowledge & Understanding (20%)

Student demonstrates excellent understanding of key concepts and uses vocabulary in an entirely appropriate manner.

Student demonstrates good understanding of the task and mentions some relevant concepts and demonstrates use of the relevant vocabulary.

Student understands the task and provides minimum theory and/or some use of vocabulary.

Student understands the task and attempts to answer the question but does not mention key concepts or uses minimum amount of relevant vocabulary.

Application (30%) Student applies fully relevant knowledge from the topics delivered in class.

Student applies mostly relevant knowledge from the topics delivered in class.

Student applies some relevant knowledge from the topics delivered in class. Misunderstanding may be evident.

Student applies little relevant knowledge from the topics delivered in class. Misunderstands are evident.

Critical Thinking (30%) Student critically assesses in excellent ways, drawing outstanding conclusions from relevant authors.

Student critically assesses in good ways, drawing conclusions from relevant authors and references.

Student provides some insights but stays on the surface of the topic. References may not be relevant.

Student makes little or none critical thinking insights, does not quote appropriate authors, and does not provide valid sources.

Communication (20%) Student communicates their ideas extremely clearly and concisely, respecting word count, grammar and spellcheck

Student communicates their ideas clearly and concisely, respecting word count, grammar and spellcheck

Student communicates their ideas with some clarity and concision. It may be slightly over or under the wordcount limit. Some misspelling errors may be evident.

Student communicates their ideas in a somewhat unclear and unconcise way. Does not reach or does exceed wordcount excessively and misspelling errors are evident.

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Leader Behaviors, Group Cohesion, and Participation in a Walking

Group Program

Betty T. Izumi, PhD, Amy J. Schulz, PhD, Graciela Mentz, PhD, Barbara A. Israel, DrPH,

Sharon L. Sand, MPP, Angela G. Reyes, MPH, Bernadine Hoston, MA, ED, Dawn Richardson, DrPH, Cindy Gamboa, Zachary Rowe, BBS, Goya Diaz

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Introduction: Less than half of all U.S. adults meet the 2008 Physical Activity Guidelines. Leader behaviors and group cohesion have been associated with increased participation or adherence in sports team and exercise class settings. Physical activity interventions in community settings that encompass these factors may enhance intervention adherence. The purpose of this study is to examine the impact of Community Health Promoter leader behaviors and group cohesion on participation in a walking group intervention among racially/ethnically diverse adults in low to moderate–income communities in Detroit, Michigan.

Design: Data for the current study were drawn from the Walk Your Heart to Health (WYHH) data set. WYHH was a multisite cluster RCT with a lagged intervention and outcome measurements at baseline and 4, 8, and 32 weeks. Pooled survey data from both intervention arms were used for the current study. Data were analyzed between August 2013 and October 2014.

Setting/participants: A total of 603 non-Hispanic black, non-Hispanic white, and Hispanic adults across five cohorts that began the 32-weekWYHH intervention between March 2009 and October 2011.

Intervention: The intervention was a 32-week walking group program hosted by community- and faith-based organizations and facilitated by Community Health Promoters. Walking groups met three times per week for 90 minutes per session. To promote participation in or adherence to WYHH, Community Health Promoters used evidence-based strategies to facilitate group cohesion. Group members assumed increasing leadership responsibility for facilitating sessions over time.

Main outcome measures: Participation in WYHH as measured by consistency of attendance.

Results: Community Health Promoter leader behaviors were positively associated with participation in WYHH. Social but not task cohesion was significantly associated with consistent participation. Social cohesion may mediate the relationship between leader behaviors and walking group participation.

Conclusions: Providing leaders with training to build socially cohesive groups may help motivate individuals to continue participation in community-based physical activity programs. (Am J Prev Med 2015;49(1):41–49) & 2015 American Journal of Preventive Medicine

ol of Community Health (Izumi, Richardson), Portland State rtland, Oregon; School of Public Health (Schulz, Mentz, oston, Gamboa, Diaz), University of Michigan, Ann Arbor; nic Development Corporation (Reyes); and Friends of e), Detroit, Michigan rrespondence to: Betty T. Izumi, PhD, School of Commu- ortland State University, 506 SW Mill St., Portland OR : [email protected]. $36.00 i.org/10.1016/j.amepre.2015.01.019

rican Journal of Preventive Medicine � Published by Else

Introduction

Thehealth benefits associated with regular physical activity include reduced risk for chronic diseases such as cardiovascular disease, type 2 diabetes,

metabolic syndrome, and some cancers.1–6 Yet, less than half of all adults meet the 2008 Physical Activity Guide- lines,7 which include at least 150 minutes per week of aerobic (e.g., brisk walking) and muscle-strengthening activities that involve all major muscle groups, on 2 or

vier Inc. Am J Prev Med 2015;49(1):41–49 41

Izumi et al / Am J Prev Med 2015;49(1):41–4942

more days per week. Furthermore, rates of physical activity and inactivity vary across race/ethnicity. Studies focusing primarily on leisure-time activity have found that more non-Hispanic white adults meet physical activity guide- lines than non-Hispanic black and Hispanic adults.8–10 In addition, adults with more education and whose family incomes are above the poverty level are more likely to meet physical activity guidelines than those with less education and whose family incomes are at or below the poverty level.5,11 To date, physical activity intervention research among such underserved populations has been limited.12

Therefore, effective programs that reach low-income and racially/ethnically diverse groups are needed. Over the past two decades, interventions based on

group dynamics principles have successfully been used to promote physical activity among adults.13,14 Such inter- ventions have used a wide range of strategies to influence the group environment, process, and structure to increase cohesion among members. Although the mech- anisms underlying intervention effectiveness are poorly understood, studies have shown that group cohesion is positively associated with physical activity outcomes, including intervention adherence,15–19 physical activ- ity,20–22 and cardiorespiratory fitness.23 Group cohesion in the physical activity context has been defined as a construct that includes the following dimensions: indi- vidual attraction to the group task (e.g., walking); individual attraction to the social dimensions of the group (e.g., opportunities to interact with others); perception of integration of the group around its task (e.g., shared commitment to walking); and perception of integration of the group around social concerns (e.g., social bonding within the group).13,24

A small body of research25–29 suggests that group leader behaviors may be crucial factors for developing and maintaining group cohesion in physical activity interventions. Recently, for example, Caperchione and colleagues28 reported that in women’s walking groups, participant perceptions of leader enthusiasm, ability to motivate, and availability outside of the group were positively related to task and social dimensions of group cohesion. In a qualitative study of adults in a Danish community-based intervention, Christensen et al.29

found that, in addition to the exercise activity itself and the composition of the group, the teaching ability of the instructor was critical for forming cohesive groups. To date, few studies have applied group dynamics

principles to physical activity interventions outside of exercise class or sports team settings or in community- based settings that reach individuals from diverse racial/ ethnic and socioeconomic backgrounds. Furthermore, although research has shown that both leader behaviors and group cohesion are related to positive outcomes, only

one study has considered their joint effects on physical activity.30 In that study, Loughead and colleagues found that, among older adults involved in exercise classes (e.g., tai chi, line dancing) for 1–120 months, the relationship between leader behaviors and exercise program attend- ance or perceived exertion was mediated by task but not social dimensions of group cohesion.30 Thus, although group dynamics–based interventions have been associ- ated with positive physical activity outcomes, further research on the mechanisms underlying intervention effectiveness is warranted. The current study examines the impact of a group dynamics–based intervention on walking group participation (i.e., physical activity adher- ence) among predominantly non-Hispanic black and Hispanic adults participating in Walk Your Heart to Health (WYHH), a walking group program in low to moderate–income communities in Detroit, Michigan. WYHH is part of a larger study, Community Approaches to Cardiovascular Health, designed to increase active living and improve heart health among Detroit residents at increased risk for cardiovascular disease.31,32 This study was conducted by the Healthy Environments Partnership (HEP), a community-based participatory research partnership established in 2000 to examine and develop interventions to reduce cardiovascular inequities in Detroit. HEP is overseen by a Steering Committee, which meets monthly and is responsible for oversight of all aspects of the Partnership’s work (partner organizations listed in the Acknowledgments). Previously published results from the WYHH intervention have demonstrated its effectiveness in increasing physical activity and reducing multiple indicators of cardiovascular risk.32 The current study investigates the role of leader behaviors and group cohesion in shaping adherence to the WYHH intervention. Specifically, the hypotheses that group leader behaviors and group cohesion were positively associated with participation in WYHH and that associ- ations between group leader behaviors and participation in WYHH were mediated by group cohesion were tested.

Methods Design and Setting

Data for the current study were drawn from the WYHH data set.32

TheWYHH intervention was a multisite cluster RCT with a lagged intervention group. It was conducted in Detroit, Michigan, where residents experience excess mortality due to cardiovascular disease compared to the state and the nation.33,34 The sample consisted of 603 participants, enrolled across five cohorts that began the 32- weekWYHH intervention betweenMarch 2009 and October 2011. Individuals were recruited by HEP Steering Committee members, staff, and the Community Health Promoters who facilitated the walking groups. Individuals interested in participating in WYHH were given a flier describing the intervention and completed an

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Izumi et al / Am J Prev Med 2015;49(1):41–49 43

interviewer-administered modified version of the Physical Activity Readiness Questionnaire35 to determine eligibility. Those who were eligible completed the baseline Health Risk Assessment and were randomly assigned into one of two groups: intervention or lagged intervention (control). Those enrolling with one or more friends or family members were randomized as clusters to ensure that they were in the same walking group. Walking groups were facilitated by Community Health Promoters. Following tests for statistical differences, the data from the intervention and the lagged intervention groups were pooled for the current study. Data were analyzed between August 2013 and October 2014. The University of Michigan IRB approved all study procedures on January 31, 2008. The Clinical Trials registration number is NCT02036593. Further detail on the WYHH intervention is described in Schulz and colleagues (Figure 1).32

Intervention

WYHH was a 32-week long walking group program facilitated by Community Health Promoters and hosted by community- and faith-based organizations located in Detroit neighborhoods. The

Figure 1. CONSORT flow diagram for Walk Your Heart to Health

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organizations received a rental fee for use of their space, which included a room large enough for warm-up and cool-down exercises and for indoor walking in the case of inclement weather. The Community Health Promoters were paid staff members who were also residents of Detroit. In spring 2009, the health promoters received 60 hours of initial

training, which focused on study procedures (e.g., recruitment, data collection); walking group facilitation; benefits of physical activity; nutrition for heart health; and strategies to promote group cohesion. Throughout the study period, the health promoters met weekly for additional training and for technical and social support. Each Community Health Promoter facilitated two walking

groups (intervention and lagged intervention groups) per cohort. The average group size was 15 members. For the first 8 weeks in each group, the health promoter facilitated three 90-minute sessions per week. Each session included a warm-up period, 50 minutes of walking in the neighborhood, and a cool-down period. The health promoters used evidence-based strategies13,14,36–38 to influence the group environment, processes, and structure to promote group cohesion (Table 1). In addition to promoting group cohesion, these strategies also encouraged group members

Table 1. Examples of Evidence-Based Strategies Used to Facilitate Group Cohesion in Walk Your Heart to Health32

Components Strategies

Group environment

Distinctiveness Encourage members to wear Walk Your Heart to Health T-shirts and use Walk Your Heart to Health water bottles14

Identify group name14

Group processes

Collective goals

Set group goals for number of steps walked37

Cooperation Organize carpools for members to travel to and from walking group location23

Interaction Facilitate peer sharing and problem solving on topics related to nutrition and physical activity38

Encourage members to attend events (e.g., Thanksgiving dinner, concert) organized by other members

Group structure

Roles Request volunteers to assume responsibility for walking group facilitation tasks (e.g., attendance, warm-up, cool-down)36

Norms Establish group norms (e.g., arrive on time)14

Izumi et al / Am J Prev Med 2015;49(1):41–4944

to assume increasing responsibility for facilitating the sessions. Over the initial 8-week period, the health promoters gradually reduced their roles and encouraged group members to assume more responsibility for session facilitation. This process was tailored to the group: in some groups, by the end of 8 weeks, group members had assumed most of the responsibility for facilitating the sessions, including identifying walking routes, taking attendance, and leading warm-up and cool-down exercises. In other groups, the process unfolded over a longer period of time, with, for example, the health promoter attending the first 30 minutes during two sessions per week whereas group members assumed responsibility for facilitating the third session.

Measures

Items assessing age (years); gender; self-reported race or ethnicity (Hispanic, non-Hispanic black, non-Hispanic white); education; and annual household income were drawn from the Health Risk Assessment.

A modified version of the Physical Activity Group Environ- ment Questionnaire (PAGE-Q)39 was used to measure group cohesion. The 21-item PAGE-Q measures four dimensions of group cohesion: (1) attraction to group task (ATG-T); (2) attraction to group as a social unit (ATG-S); (3) perception of group integration around task factors (GI-T); and (4) perception of group integration around social factors (GI-S). All 21 items were modified for use in the current study by replacing physical activity group and program with walking group program. The items were rated on a 5-point Likert-type scale, with 1 indicating strongly disagree and 5 indicating strongly agree. The modified items were included in a self-administered survey completed at Week 4 and interviewer-administered surveys completed at Weeks 8 and 32. The possibility of reducing the dimensionality of the cohesion measure using exploratory factor analysis techniques was investigated. Two factors were identified: task cohesion (ATG-T, GI-T; Cronbach’s α¼0.87) and social cohesion (ATG-S, GI-S; Cronbach’s α¼0.85). Models were subsequently run using these factors for task and social cohesion. Table 2 provides examples of survey items used to measure task and social cohesion.

A 21-item survey was developed to measure group members’ perceptions of community health promoters’ leader behaviors. The development of the survey was iterative and involved several steps, including reviewing relevant literature and tools,25,40 conducting key informant interviews with HEP Steering Committee members, pre-testing measures, and con- sulting experts. Four dimensions of leader behaviors were assessed in the survey, including three proposed by Chemers40

and applied to the physical activity context by Estabrooks et al.25: image management (i.e., leader qualities that result in trust and credibility to facilitate walking groups); relationship development (i.e., ability of leader to develop relationships with individual members); and resource deployment (i.e., ability to use knowledge, skills, and resources within the group to achieve group goals). A fourth dimension, community commitment, was added to reflect the importance of com- munity health and community improvement, themes identi- fied by members of the HEP Steering Committee. All items were rated on a 5-point Likert-type scale, with 1 indicating strongly disagree and 5 indicating strongly agree. The leader behavior survey was self-administered at Week 4 and inter- viewer administered at Weeks 8 and 32. Using similar dimension reduction techniques as those described for group cohesion, one leader behaviors factor was identified (Cron- bach’s α¼0.88). Models were subsequently run using this factor for leader behaviors. Table 2 provides examples of survey items used to measure leader behaviors.

The dependent variable used in these analyses was walking group participation, as a measure of intervention adherence. Walking group participation was defined as the number of weeks in which the participant attended at least one walking group session (i.e., consistency of participation). Attendance was obtained from records kept by Community Health Promoters.

Statistical Analysis

Exploratory data analysis techniques were used to assess the distribution of adherence to WYHH. Q-Q plots and histograms were constructed to confirm the normal assumptions; thus, Gaussian models were used to assess the research questions.

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Table 2. Items Used to Measure Group Cohesion and Leader Behaviors in Walk Your Heart to Health32

Measure Item Factor loading

Task cohesion

I like how much physical activity I get in this walking group. This walking group provides me with a good opportunity to improve my health in areas that are important to me. I am happy with the intensity of the physical activities in this program. I like the different types of physical activities done in this walking group. I feel safe walking on the routes.

0.70645 0.99421

0.99175 0.70561 0.72311

Social cohesion

This walking group is an important social group for me. I enjoy my social interactions within this walking group. I like meeting the people who come to this walking group. If this walking group was to end, I would miss my contact with the other members. In terms of the social experiences in my life, this walking group is very important. The social interactions I have in this walking group are important to me.

0.73401 0.77318 0.74925 0.79841 0.70225 0.73869

Leader behaviors

Our community health promoter creates opportunities for us to help out with organizing our group sessions. Our community health promoter creates walking routes that match my abilities. Our community health promoter is committed to helping our group achieve our goals. Our community health promoter gives public recognition when group members help out with the sessions. Our community health promoter cares about my well-being. Our community health promoter encourages everyone to participate in our discussions. Our community health promoter encourages discussion between group members when there is conflict. Our community health promoter finds creative ways to solve problems. Our community health promoter motivates us to work hard to achieve our goals. Our community health promoter has taken the time to get to know me. Our community health promoter would understand if I had to miss a session. Our community health promoter is a good listener. Our community health promoter makes me feel like I am an important member of our group.

0.70704 0.66704 0.77677 0.60722

0.77425 0.77196 0.63566

0.63673 0.77258 0.62993 0.73394 0.79818 0.74830

Izumi et al / Am J Prev Med 2015;49(1):41–49 45

Statistics including frequencies, means, and SDs were used to identify basic characteristics of potential predictors. Independent and joint effects of leader behaviors, task cohesion, and social cohesion on physical activity were assessed using generalized estimating equation (GEE) models, controlling for race/ethnicity, age, gender, education, and household income. The GEE approach with normal distribution and identity link with exchangeable correlation structure was used to account for the clustering and imbalance of the longitudinal data. Initial models test for the individual effect of the leader behaviors factor and the two group cohesion factors on walking group participation. Next, two models to assess the joint effects of the leader behaviors factor with each of the group cohesion factors on physical activity participation were run. Owing to high correlations between the group cohesion factors, a model to assess the effect of the leader behaviors factor and the two group cohesion factors on physical activity partic- ipation was not run. A formal mediation test41 was run to confirm the extent to which group cohesion factors mediated associations between leader behaviors and participation. Women-specific anal- yses were also conducted to assess sensitivity of the models. Similar patterns were found. Models presented here include the full sample.

Results The average age of participants was 47.5 years, and 90% were women. Approximately 35.5% of participants were Hispanic and 61.2% were Non-Hispanic black, 54.7% had more than 12 years of education, and 42.6 had a

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mean annual income o$20,000. Retention among those who attended one or more sessions per week was 91% at 8 weeks and 65% at 32 weeks. Those who remained active in WYHH at 8 and 32 weeks were older, 48.6 and 49.6 years, respectively, compared to 47.5 years at baseline (po0.05) (Table 3). Week 4 means (SDs) for leader behaviors, task cohesion, and social cohesion were 4.8 (0.4), 4.7 (4.7), and 4.3 (0.6), respectively. At 8 weeks, on average, participants had attended at least one walking group session in 6.6 (SD¼2.1) of the 8 weeks. At 32 weeks, on average, participants had attended at least one walking group session in 19.6 (SD¼9.4) of the 32 weeks. As shown in Table 4, leader behaviors were positively

associated with walking group participation (β¼2.71, po0.001) (Model 1). Individual effects of task and social cohesion on walking group participation are shown in Models 2 and 3, respectively. Task cohesion was not significantly associated with walking group participation (β¼0.28, p¼0.63). However, social cohesion was posi- tively and significantly associated with walking group participation (β¼1.53, po0.001). When task cohesion was added to Model 1, the association between leader behaviors and walking group participation was strength- ened (β¼3.83, po0.001) (Model 4). When social cohe- sion was added to Model 1, associations between leader

Table 3. Demographic Characteristics of Walk Your Heart to Health32 Study Participants (N¼603)

Characteristics Baseline

Age, M (SD) 47.5 (13.6)

Female (%) 90.0

Race/ethnicity (%)

Hispanic 35.5

Non-Hispanic black 61.2

Non-Hispanic white 3.3

Education 412 years (%) 54.7

Annual household income, $ (%)

r9,999 18.0

10,000–19,999 24.6

20,000–34,999 25.2

Z35,000 32.2

Employed (%) 28.0

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behaviors and walking group participation were attenu- ated but remained significant (β¼1.81, p¼0.02) (Model 5). Results from a formal mediation test (results not shown) were suggestive of a partial mediation effect of social cohesion on the association between leader behav- iors and walking group participation (c-c/se¼1.622, p¼0.083); the effect of task cohesion on the association between leader behaviors and walking group participa- tion was not significant (c-c/se¼–1.748, p¼0.983).

Discussion There are three main findings from the results presented here. First, participants who perceived that their Com- munity Health Promoters developed relationships with individual group members and harnessed the group’s knowledge, skills, and resources to achieve group goals

Table 4. Walking Group Participation Regressed on Leader Beh Characteristicsa**

Model 1 Model 2 β (SE) β (SE)

Intercept –7.04 (3.28) 4.74 (2.88)

Leader behaviors 2.71** (0.60)

Task cohesion 0.28 (0.58)

Social cohesion

Note: Boldface indicates statistical significance (*po0.05; **po0.001). aIndividual characteristics include race/ethnicity, age, gender, education, an

had more consistent participation in WYHH. To date, few quantitative studies have tested the effect of leader behaviors on adherence in community-based interventions to promote physical activity.28,30,42 Loughead and col- leagues30 reported that among older adults participating in group exercise classes, leader motivation, availability, and enthusiasm were related to adherence. In a study of university students enrolled in exercise classes for course credit, Remers et al.42 found that instructor behavior did not influence adherence. In both studies, leader behavior was measured using four statements assessing partic- ipants’ perceptions of their exercise instructors’ enthusi- asm, ability to motivate, availability outside class, and ability to provide personal instruction.30,42 Neither study included measures of leader ability to develop relation- ships with individual group members and to mobilize resources within the group. As described in qualitative studies, however, effective physical activity leaders also show personal interest in and concern for participants and facilitate opportunities for participants to make contribu- tions to the group.25,29 In addition to personally recruiting neighborhood residents to participate in WYHH, Com- munity Health Promoters showed an interest in and concern for their group members by, for example, calling participants to remind and encourage them to come to walking group sessions, facilitating carpools to attend walking group sessions for participants with transportation issues, and creating walking routes for participants with varying levels of fitness. Community Health Promoters also drew on group member knowledge, skills, and interests as an important strategy to sustain their walking groups beyond the initial 8 weeks of the intervention period. Second, social but not task cohesion was associated with

more consistent participation in WYHH. This finding is somewhat inconsistent with sport psychology research18,43,44

in which task dimensions of cohesion have been most strongly associated with physical activity adherence. How- ever, the nature of the relationship between group cohesion and physical activity outcomes may be situation specific and

aviors, Task, and Social Cohesion, Adjusting for Individual

Model 3 Model 4 Model 5 β (SE) β (SE) β (SE)

–0.79 (1.80) –5.70 (3.28) –7.43* (3.24)

3.83** (0.91) 1.81* (0.76)

–1.42 (0.72)

1.53** (0.37) 1.05* (0.42)

d household income.

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Izumi et al / Am J Prev Med 2015;49(1):41–49 47

differ across settings.44 Most group cohesion studies have been conducted in settings such as fitness classes in which individuals typically have little structured opportunity to interact with others. It may be that in such settings, task cohesion motivates physical activity participation. In WYHH, participants had multiple opportunities to socialize with their peers during each of the 90-minute sessions. In addition, the neighborhood-specific location of the walking groups and faith- and community-based host organizations may have facilitated interaction between participants outside of the sessions, and contributed to the importance of social cohesion in facilitating participation. Finally, the findings presented here suggest that

Community Health Promoters fostered social cohesion within their groups, which in turn led to more con- sistent participation in the walking groups. This result differs from findings reported by Loughead and col- leagues,30 in which task but not social cohesion medi- ated the relationship between leader behaviors and exercise class adherence. The inconsistent findings between the current study and the study conducted by Loughead et al. may reflect the design of the WYHH intervention, which directed Community Health Pro- moters to use their leadership positions to implement strategies aimed at building cohesive groups. In the study conducted by Loughead and colleagues, it is unclear whether or to what degree such strategies were used. Further research on the mediational role of social cohesion in community-based physical activity inter- ventions is needed. There are a number of strengths associated with this

study. First, WYHH is among few community-based physical activity interventions based on group dynamics principles. Of particular importance is identification of strategies that may be used to promote physical activity among racially/ethnically diverse adults in low to moderate–income urban settings. Some of these strat- egies include collaborating with faith- and community- based organizations to increase opportunities for par- ticipants to develop and strengthen social bonds outside of walking group sessions, peer sharing, and problem solving to overcome challenges associated with consis- tent walking, and facilitating opportunities for members to contribute to group goals. Second, the cluster ran- domization allowed us to maintain social support between friends and family who enrolled in the study together while simultaneously using a randomized study design. Third, the community-based process used to develop and implement WYHH increased the relevance of the intervention and the likelihood of identifying and addressing challenges (e.g., identifying safe walking routes) that may erect barriers to meeting physical activity recommendation in urban areas.

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Limitations There are also several limitations of this study that should be noted. First, the focus of this study was on associations between leader behaviors and group cohesion on con- sistent participation in walking groups. The association between consistent participation and increases in ped- ometer steps over time as an indicator of physical activity has been demonstrated elsewhere.32 In the current study, associations between leader behaviors and group cohe- sion on steps were not examined. Future studies should investigate the impact of leader behaviors and group cohesion on physical activity outcomes, including, for example, changes in steps or other indicators of physical activity. Second, this study did not assess the impact of weather on walking group participation. Sensitivity analyses conducted to evaluate seasonal effects on walk- ing group adherence found no significant differences in participation across seasons. In addition, sensitivity analyses to determine effects of indoor versus outdoor walking on walking group adherence also found no significant differences in participation. Further research to better understand how weather and walking locations affect participation in walking group interventions would be useful for assessing factors that influence adherence. Finally, this study analyzed participation in a walking group program designed to promote physical activity. The implications of the findings presented here for interventions designed to promote muscle-strength- ening exercises, as another important component of overall physical activity, were not assessed.

Conclusions The results reported here suggest benefits of physical activity interventions based on group dynamics princi- ples. Given high rates of physical inactivity among underserved populations, interventions facilitated by leaders trained to use group dynamics strategies to build group cohesion may be particularly effective in socio- economically and racially/ethnically diverse areas.

The Healthy Environments Partnership (HEP) is an affiliated partnership of the Detroit Community–Academic Urban Research Center. We thank the members of the HEP Steering Committee at the time this study was conducted for their contributions to the work presented here, including representa- tives from Brightmoor Community Center, Eastside Commun- ity Network, Institute for Population Health, Detroit Hispanic Development Corporation, Friends of Parkside, Henry Ford Health System, University of Michigan School of Public Health and Survey Research Center, and community members at large. We also thank the four anonymous peer reviewers for their insights and detailed suggestions for improving our manuscript.

Izumi et al / Am J Prev Med 2015;49(1):41–4948

The work presented here was supported through a grant from the National Institute of Minority Health and Health Disparities (number R24 MD001619) and through the W.K. Kellogg Foundation’s Kellogg Health Scholars Program.

Portland State University, University of Michigan, Detroit Hispanic Development Corporation, Friends of Parkside, National Institute for Minority Health and Health Disparities, and W.K. Kellogg Foundation did not play any role in study design, collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.

No financial disclosures were reported by the authors of this paper.

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