Group 1 PRESENTATION SLIDES 2016 FINAL

A Faith-based Community Health
Needs Assessment
Leah Fahey, Shavena Fife, Katherin Rehn, Stephanie Richardson
Catherine Wurtzler, Amy Zoglauer
Thesis Advisor: Bonnie Beezhold, PhD, MHS, CHES

Introduction
1WHO, 2016; 2Merzel & D’Afflitti, 2003; 3Ockene et al, 2012
Socioecological
Model

Introduction
• Faith-based health promotion is an emerging strategy.
• A place of worship is considered a trusted community source.1
1Harmon et al, 2014

Introduction
• Needs assessments reveal major health and lifestyle issues.1
• Community members provide the best insight.2
– Behavioral Risk Factor Surveillance System Survey (BRFSS)
1Kumar & Preetha, 2012; 2Levy et al, 2004

Study Goal and Research Question
• To conduct a faith-based community needs assessment
in partnership with Gloria Dei Lutheran church in order
to identify and prioritize the congregation's health
needs.
• What are the major health needs and corresponding
health behaviors of the congregation?

Study Objectives
• Primary: To investigate the associations of diet and health
outcomes within the church community.
• Secondary: To investigate the associations of health, diet, or
lifestyle factors within the church community.

Study Design
• Correlational, cross-sectional
• Paper survey, physical measurements
• Voluntary sample
• Eligibility requirements:
– Member of the Gloria Dei community
– At least 19 years old
– Willing and able to complete a survey

Survey
• Extensive exploration into validated and reliable questions
and questionnaires
• BRFSS 2013 and 2014; other validated questions on exercise,
diet, spirituality, drug use, end of life treatment
• Diet questions – 5 A Day Consumption and Evaluation Tool
(FACET)
– Daily and weekly major foods (breakfast cereal, F&V, fish, etc.)

Survey Validity and Reliability
Validity
• Incorporated validated questions
– BFRSS, FACET
– Godin Leisure-Time Exercise Q
• Pilot testing and feedback
• Paper delivery only was utilized
Reliability
• Potentially unclear questions were
slightly adapted
• FACET – Cronbach’s alpha .59
• Godin Leisure-Time Exercise
Questionnaire – 0.44 Cronbach’s
alpha
• Core Dimensions of Spirituality
Questionnaire - Cronbach’s alpha
0.77

Physical Measurements
• Non-invasive physical measurements – height, weight, waist
circumference
– Stadiometer - Seca 213 portable unit
– Research grade scale – Precision Personal Health Scale Model UC
321PL
– Cloth measuring tape

Recruitment Protocol
• Info tables, announcements, flyers, emails, social media posts
• Recruitment days
• Incentives
– Benedictine logo gear – pens, window stickers, cutting boards
– Survey completion – entry into $100 Visa gift card drawing
– Physical measurements – daily $10 or $20 restaurant gift card
drawings
• Data collection days

Survey Protocol
• 15-20 minutes
• Paper survey
• Topics included
– Demographics, health perceptions, health status, health behaviors
• Variables of interest - diet and exercise variables
• Reliability
Clarified the script of this and the
measurement protocol a bit.

Measurement Protocol
• 5 minutes
• Separate room with divider for privacy
• Height, weight, waist circumference
• Handout to participants
• Reliability

Statistical Analysis
• Correlational, differential
• Parametric
• Descriptive statistics
• Multivariate analyses: Chi-square tests, Pearson and Spearman
correlations, logistic regression
• Univariate analyses: independent t-tests, ANOVA, ANCOVA
• SPSS version 24; p values < 0.05 significant

Group 1 PRESENTATION SLIDES 2016 FINAL

Population characteristics
Variables N
Females
n=101
Males
n=56
Test
Stat*
p
value*
Mean ± SD Mean ± SD
Age 157 59.41±15.00 57.86±14.21 t = 0.63 0.592
Ethnicity (White/other) 157 95 / 6 51 / 5 X2 = 0.14 0.707
Education (N/Y college
degree)
157 22 / 79 9 / 47 X2 = 0.43 0.515
Marital status (N/Y married) 157 71 / 30 45 / 11 X2 = 1.40 0.236
Work status (N/Y) 157 49 / 52 24 / 32 X2 = 0.26 0.607
Total physical activity
times/wk
149 36.37±25.64 41.23±28.28 t = -1.08 0.282
BMI 123 27.02±6.57 29.89±5.22 t = -2.53 0.013
*p < 0.05 indicates significance; independent samples t-tests or Chi-square tests for independence.

High School
Graduate, 3.8%, n=6
Some college,
15.9%, n=25
College Graduate,
40.8%, n=64
Postgraduate,
39.5%, n=62
Education

Married, n=116, 73.9%
Divorced, n=14, 8.9%
Widowed, n=16, 10.2%
Never married, n=8,
5.1%
Member of unmarried
couple, n=3, 1.9%
Marital Status

Employed for wages,
n=75, 48.1%
Self employed, n=9,
5.8%
Out of work < 1 year,
n=1, 0.6%
Homemaker, n=7, 4.5%
Student, n=4, 2.6%
Retired, n=58, 37.2%
Unable to work, n=2,
1.3%
Work Status

Disease conditions and risk indicators
Diagnoses / risk indicators N Females Males p value*
Count Count
Cancer 154 17 12 0.683
Cardiovascular disease 153 4 5 0.390
Diabetes 156 19 13 0.613
Depression 154 17 8 0.788
Musculoskeletal disorders 155 45 21 0.428
Obesity (per BMI) 123 59 26 0.024*
High cholesterol 155 37 26 0.282
High blood pressure 156 38 30 0.087
*p < 0.05 indicates significance. Chi-square test for independence.

Background: Musculoskeletal Disorders
• Musculoskeletal Disorders (MDs) affect 50% of
American adults over age 181; aging increases risk.2
• Certain diet and lifestyle factors adversely affect
joint health –inflammatory diet,3 inactivity,4 and
obesity.5
1US Bone and Joint Initiative, 2013;2Gheno et al, 2012 3Oliviero et al, 2015;
4Ciolac, 2016; 5Anandacoomarasamy, 2008

Methods
• Has a doctor, nurse or other health professional EVER told you
that you had some form of arthritis, rheumatoid arthritis,
gout, lupus, or fibromyalgia?
–1=Yes
–2=No
–3=Don't know / Not sure
• Question taken from BRFSS 2013 Questionnaire

N
No MD
n=89
Yes MD
n=66
p value*
Gender
(count M/F)
56 / 99 35 / 54 21 / 45 0.4281
Age
(mean ± SE)
155 54.48 ± 1.69 64.68 ± 1.32 <0.0012
Musculoskeletal disorders
by gender and age
*p < 0.05 indicates significance. 1Chi-square test for independence.
2Independent samples t-test; t (152) = 4.76, ƞ2 = 0.13.

Significant correlates with MD status
*p < 0.05 indicates significance; Pearson product moment and Spearman rank
order correlations.
Variables N MDs (N/Y)
r or rho / p value*
Age 155 0.342 / 0.000
Red meat intake frequency/wk 154 0.253 / 0.002
Breakfast cereal intake frequency/day 145 0.198 / 0.017
Resistance exercise times/wk 152 -0.186 / 0.022
Days of poor physical health 155 0.167 / 0.037
Days of poor concentration related to
physical health
153 -0.158 / 0.049

B ± SE
p
value*
Odds
ratio
95% CI
Red meat intake intake
frequency/wk
0.31 ± 0.10 0.002 1.36 (1.12, 1.66)
Age 0.59 ± 0.15 0.000 1.06 (1.03, 1.09)
Resistance exercise
times/wk
-0.23 ± 0.11 0.039 0.79 (0.64, 0.99)
Multivariate analysis – Predictors of reporting
musculoskeletal disorders
*p < 0.05 indicates significance; logistic regression, final model explained 20.6-27.7%
of the variance in MD status.

Comparison of significant correlates
by MD status
N No MD Yes MD
p
value*
95% CIs of
Difference
Mean ±SE Mean ±SE
Age 155 54.48 ± 1.69 64.68 ± 1.32 0.000 (-14.43, -5.97)
Red meat intake
frequency/wk
154 2.20 ± 0.20 3.18 ± 0.23 0.0021 (0.38, 1.58)
Resistance exercise
times/wk
152 2.97 ± 0.21 2.28 ± 0.20 0.0222 (-1.27, -0.10)
*p < 0.05 indicates significance; independent samples t-tests.
1ANCOVA; Adj means: 2.17 ± 0.20 vs 3.23 ± 0.24, p = 0.001, pƞ2 = 0.065.

Discussion
• More intake of meat and resistance exercise was related to
diagnosis of MDs.
• High meat consumption can be inflammatory if n-6/n-3 ratio is
unbalanced.1,2
• Resistance exercise can strengthen bones, muscles, and joints,
which helps prevent MDs.3,4
1Pattison, 2004; 2Patterson, 2012; 3Ciolac, 2016; 4Moreira, 2014

Background: Mental Health
• Major depressive disorder affects 6.7% of all US adults; anxiety
disorders affect 19.1% of US adults.1,2
• Evidence demonstrates link between diet and mood and
significance of nutritionally inadequate diets.3,4
• Exercise is an effective treatment for depression.5
1NIMH, 2014; 2Harvard Medical School, 2007; 3Payne et al, 2012; 4Blumenthal et al, 2012;
5Sharma et al, 2006

Methods
• During the past 30 days, for about how many days have you felt sad, blue or depressed?
• During the past 30 days, for about how many days have you felt worried, tense or anxious?
– 1=No days
– 2=1 or 2 days
– 3=3 or 4 days
– 4=5 or 6 days
– 5=About a week
– 6=A couple of weeks
– 7=Most of the month
– 8=Every day
– 9=Don't know / Not sure
• Questions taken from BRFSS 2014 Questionnaire

N
Sadness
No d/mo
n=71
Sadness
1-2 d/mo
n=50
Sadness
3+ d/mo
n=33
p
value*
Gender
(count M/F)
55 / 99 30 / 41 13 / 37 12 / 21 0.1841
Age
(mean ± SE)
154 61.82 ± 1.74 56.32 ± 2.23 56.18 ± 2.21 0.0682
Sadness days by gender and age
*p < 0.05 indicates significance. 1Chi-square test for independence. 2ANOVA.

Anxiety days by gender and age
2ANOVA; F = 8.43, ƞ2 = 0.14.
N
Anxiety
No d/mo
n=40
Anxiety
1-2 d/mo
n=41
Anxiety
3-6 d/mo
n=37
Anxiety
>6 d/mo
n=38
p
value*
Gender
(count M/F)
56 / 100 20 / 20 10 / 31 12 / 25 14 / 24 0.1101
Age
(mean ± SE)
156
62.63 ±
2.26
65.29 ±
1.91
51.76 ±
2.55
54.66 ±
2.11
<0.0012

Significant correlates of sadness & anxiety (d/mo)
Sadness days
rho / p / n
Anxiety days
rho / p / n
Days of poor mental health 0.702 / 0.000 / 153 0.642 / 0.000 / 155
Days feeling energetic -0.494 / 0.000 / 153 -0.410 / 0.000 / 155
Days of poor concentration related to mental health (N/Y) -0.356 / 0.000 / 152 -0.263 / 0.001 / 152
Days of debilitating mental health (N/Y) 0.395/ 0.000 / 154 0.397 / 0.000 / 156
Depression diagnosis (N/Y) 0.401/ 0.000 / 153 0.227 / 0.004 / 153
General satisfaction with life -0.429 / 0.000 / 154 -0.282 / 0.000 / 156
Social support -0.380 / 0.000 / 154 -0.232 / 0.004 / 156
Spirituality-feeling of peace -0.377 / 0.000 / 151 -0.260 / 0.001 / 153
Spirituality-aids in coping -0.197 / 0.008 / 153
Spirituality-aids in decision making -0.174 / 0.034 / 149
Total sitting times daily 0.227 / 0.005 / 151 0.210 / 0.010 / 151
Age -0.205 / 0.011 / 154 -0.309 / 0.000 / 156
Coffee and tea intake frequency/day 0.166 / 0.041 / 151
Potato intake frequency/day -0.165 / 0.048 / 145
*p < 0.05 indicates significance; Spearman rank order correlations. All variables are low to high in rank.

B ± SE p value*
Odds
ratio
95% CI
SADNESS days
Spirituality-aids in decision making 1.25 ± 0.55 0.023 3.47 (1.19, 10.18)
Total sitting times daily 0.24 ± 0.10 0.017 1.28 (1.05, 1.56)
ANXIETY days
Depression diagnosis (N/Y) 1.08 ± 0.51 0.034 2.94 (1.09, 7.95)
Total sitting times daily 0.25 ± 0.11 0.021 1.28 (1.04, 1.58)
Age -0.05 ± 0.01 0.000 0.95 (0.93, 0.98)
Multivariate analysis– Predictors of days of
sadness and anxiety
*p < 0.05 indicates significance; logistic regressions, final sadness days model explained 8.1-10.8% of the
variance in sadness status, final anxiety days model explained 19.9-26.5% of the variance in anxiety status.

Comparison of sitting times daily
by sadness and anxiety status
N
SADNESS
No days/mo
n=71
SADNESS
1+ d/mo
n=83
p
value* N
ANXIETY
0-2 d/mo
n=81
ANXIETY
3+ d/mo
n=75
p
value*
Less than 4 h
sitting/d
50 41.4% 25.9% 0.067 51 45.6% 20.8% <0.0011
5-8 h sitting/d 57 37.1% 38.3% 56 38.0% 36.1 %
More than 8 h
sitting/d 44 21.4% 35.8% 44 16.5% 43.1 %
*p < 0.05 indicates significance. 1 Chi-square tests for independence; X2 (2, n = 151) = 16.01, phi = 0.326.

Discussion
• Reliance on spirituality was strongest predictor of fewer days of
sadness. Sitting less was predictor of fewer days of sadness and
anxiety.
• Social isolation may explain link between faith and sadness.1,2
• Sedentary behavior is a category separate from physical activity
that affects both metabolic and mental health.3,4
1Croezen et al, 2015; 2 Miller et al, 2014; 3Chomistek et al, 2013;
4Teychenne et al, 2010

Background: Obesity
• Obesity epidemic – more than one-third (36.5%) of American
adults are obese.1
• Excess weight increases risk for developing major disease
conditions.1
• Strong relationship between an increased BMI and coronary
artery disease.2
1CDC, 2016; 2Labounty et al, 2013

BMI N=123
Normal weight
n=44
Overweight
n=43
Obese
n=36
p value*
Gender (count M/F) 77 / 46 38 / 6 23 / 20 16 / 20 0.0001
Age (mean ± SE) 59.34 ± 2.19 60.56 ± 2.45 55.53 ± 2.47 0.3212
BMI and WC risk categories by gender and age
*p < 0.05 indicates significance. 1Chi-square test for independence; X2 (2, n = 123) = 17.21, phi = 0.37. 2ANOVA
Waist circumference N=123
Lower risk
n=63
Higher risk
n=60
p value*
Gender (count M/F) 46 / 77 20 / 43 26 / 34 <0.001*
Age – Males (mean ± SE) 58.61 ± 1.90 58.69 ± 4.92 0.986
Age – Females (mean ± SE) 60.67 ± 5.04 57.95 ± 2.38 0.769
*p < 0.05 indicates significance; independent samples t-tests; t (121) = -6.57, ƞ2 = 0.000. check

Variables N BMI
r or rho / p value*
Waist circumference 122 0.854 / 0.000
Perception of general health 123 -0.380 / 0.000
Diabetes diagnosis (N/Y) 122 0.296 / 0.001
High blood pressure diagnosis (N/Y) 122 0.294 / 0.001
Gender (M/F) 123 0.285 / 0.001
Days of pain affecting activities 119 0.215 / 0.019
Sugary drink intake frequency/day 117 0.201 / 0.030
High cholesterol (N/Y) 121 0.201 / 0.027
Days feeling energetic 122 -0.195 / 0.031
Work status (N/Y) 122 0.187 / 0.039
Days of debilitating mental health 123 0.186 / 0.039
*p < 0.05 indicates significance; Pearson product moment and Spearman rank order
correlations. All variables are low to high in rank.
Significant correlates with BMI

Variables Males Females
N r or rho / p value* N r or rho / p value*
BMI 46 0.868 / 0.000 76 0.869 / 0.000
High cholesterol (N/Y) 45 0.393 / 0.008
Health professional visits 77 0.374 / 0.001
Days of poor concentration related to physical health 46 0.318 / 0.031
Musculoskeletal disorders diagnosis (N/Y) 75 0.271 / 0.019
Significant correlates with WC
*p < 0.05 indicates significance; Pearson product moment and Spearman rank order correlations. All variables are low
to high in rank.

Variables Males Females
N r / p value* N r / p value*
Veg-based meals frequency/wk 45 -0.423 / 0.004
Dark green leafy vegs frequency/day 37 -0.387 / 0.012
Breakfast cereal intake frequency/day 43 -0.318 / 0.038
Omega-3 fish frequency/wk 76 0.255 / 0.026
Significant diet correlates with WC
*p < 0.05 indicates significance; Pearson product moment correlation coefficients.

Variables B ± SE p value* Odds ratio 95% CI
High blood pressure
diagnosis (N/Y)
1.81 ± 0.48 0.000 6.11 (2.37, 15.75)
Work status (N/Y) 1.57 ± 0.49 0.001 4.79 (1.83, 12.57)
Multivariate analysis – Predictors of obesity
*p < 0.05 indicates significance; logistic regression, final model explained 17-24% of
variance in BMI.

Discussion
• Predictors of obesity (per BMI) were high blood pressure and being
employed.
• Diet correlates of a risky WC in males were intake of fewer vegetables
and more sugary drinks.
• Sitting time at work positively correlated with obesity.1
• Increased intake of sugary drinks related to increased BMI and WC.2, 3
• Vegetable-based diet related to smaller WC.4
1Chau et al, 2011; 2Malik et al, 2013; 3Odegaard et al, 2012; 4Rizzo et al, 2011

Background: Pain
• Pain is a result of inflammation.1‚2
• 100 million Americans experience pain³; complication of
chronic disease.²
• Lifestyle factors can influence pain.⁴-⁶
¹Dept of Pain Medicine & Palliative Care ; ²PubMed Health; ³American Academy of Pain
Medicine; ⁴Van Hecke, 2013; ⁵Goldberg, 2007; ⁶John, 2006

Methods
• During the past 30 days, for about how many days did pain make it hard
for you to do your usual activities, such as self-care, work or recreation?
– 1 = No days
– 2 = 1 or 2 days
– 3 = 3 or 4 days
– 4 = 5 or 6 days
– 5 = About a week
– 6 = A couple of weeks
– 7 = Most of the month
– 8 = Every day
– 9 = Don’t know / Not sure

Pain by gender and age
N
No Pain
n=106
Pain ≥ 1 d/mo
n=46
p
value*
Gender
(count M/F)
56 / 96 36 / 70 20 / 26 0.350¹
Age
(mean ± SE)
152 58.82 ± 1.45 57.63 ± 2.11 0.648²
*p < 0.05 indicates significance. ¹Chi-square test for independence. ²Independent samples t-test;
t (152) = 0.458, ƞ2 = 0.001.

Significant correlates with pain status
Variables N Pain (low to high)
rho / p value*
Days of debilitating physical health 152 0.385 / 0.000
Days feeling energetic 151 -0.321 / 0.000
BMI 119 0.215 / 0.019
High blood pressure diagnosis (N/Y) 151 0.194 / 0.017
*p < 0.05 indicates significance; Spearman rank order correlations. All variables are
low to high in rank.

B ± SE p value*
Odds
ratio
95% CI
BMI 0.81 ± 0.04 0.027 1.08 (1.01, 1.17)
Multivariate analysis - Predictors of
reporting pain
*p < 0.05 indicates significance; logistic regression, final model explained 4.1-
5.9% of the variance in pain status.
Reminder: this model only explains at
most 6% of the variance in BMI

Comparison of BMI by pain status
N
No Pain
n=106
Pain
n=46
p
value*
95% CIs of
difference
Mean ± SE Mean ± SE
BMI 119 26.99 ± 0.58 29.56 ± 1.00 0.023¹ (-4.79, -0.36)
*p < 0.05 indicates significance.
¹Independent samples t-test; t (119) = -2.298 , ƞ2 = 0.04.

Discussion
• BMI predicted the likelihood of reporting pain.
• Increasing BMI can cause excess weight > pressure on joints >
inflammation > pain.¹-³
• High BMI and physical inactivity are associated with chronic
pain.⁴ Modest weight reductions decrease pain.⁵
¹NIDDK, 2012; ²Zdziarski, 2015; ³Nijuis, 2009; ⁴ Nilsen, 2011; ⁵Anandacoomarasamy, 2008

Background: Sleep
• 30% of adults are reporting an
average of <6 hours of sleep.1
• Sleep restriction increases risk for
cardiovascular diseases and
diabetes.2
• National Institutes of Health
suggests that adults get 7-8 hours
of sleep a night.1
1CDC, 2016; 2Jackson, 2015
Image reference:
https://www.cdc.gov/features/dssleep/

Methods
• On average, how many hours of sleep do you get in a 24-hour
period?
– 4 or less hours
– 5-6 hours
– 7-8 hours
–9-10 hours
–More than 10 hours
–Don’t know/Not sure

Hours of sleep by gender and age
N
Sleeps 5-6 hrs
n=57
Sleeps ≥7 hrs
n=100
p
value*
Gender (count
M/F)
56 / 101 25 / 32 31 / 69
0.1491
Age (mean ± SE) 157 54.68 ± 2.39 61.23 ± 1.46 0.0072
2Independent samples t-test; t (157) = 2.09, ƞ2 = -0.13.

Significant correlates with sleep hrs/night
Variables N Sleep hrs/night
rho / p value*
Work status (N/Y employed) 157 -0.307/0.000
Age 157 0.229 / 0.004
Coffee and tea intake frequency/day 154 -0.194 / 0.016
BMI 124 -0.160 / 0.077
Fish intake frequency/wk 154 0.159 / 0.049
Sugary drink intake frequency/day 148 -0.059 / 0.472
*p < 0.05 indicates significance; Spearman rank order correlations.

B ± SE p value* Odds ratio 95% CI
Fish intake frequency/wk 0.38 ± 0.19 0.042 1.47 (1.01, 2.12)
Coffee and tea intake
frequency/day
-0.26 ± 0.10 0.013 0.77 (0.63, 0.95)
adequate amount of sleep
*p < 0.05 indicates significance; logistic regression, final model explained 15.8-21.5% of
variance in sleep status.

Comparison of significant correlates
by different sleep levels
N
7 or more hrs
of sleep
5-6 hrs of
sleep/night
p
value*
95% CIs of
Difference
Mean ±SE Mean ±SE
Fish intake frequency/wk 154 1.38 ± 1.16 0.98 ± 0.94 0.0321 (-0.76, -0.06)
Coffee and tea intake
frequency/day
154 1.40 ± 0.14 2.30 ± 0.29 0.0072 (0.25, 1.56)
*p < 0.05 indicates significance, independent samples t-tests.

Discussion
• Eating fish more frequently was the biggest predictor of getting
adequate sleep.
• Composition of fish – lean protein, polyunsaturated fats.1
• Drinking coffee and tea more frequently may adversely impact
sleep.
• Time and frequency of consuming coffee can affect the quality and
amount of sleep.2
1Hanson, 2014; 2CDC, 2016
See script for question

Background: High Blood Pressure
1American Heart Association, 2014; 2American Heart Association, 2013; 3Ettehad et al, 2016
• Goal blood pressure (BP) reading for an adult age 20 or
overis < 120/80 mm Hg.1
• 69% of people who have a first heart attack, 77% who have
a first stroke, and 74% who have congestive heart failure
have BP > 140/90 mm Hg.2
• Lowering BP reduces vascular risk across various baseline
BP levels and comorbidities.3

Methods
• Have you ever been told by a doctor, nurse, or other licensed
health professional that you have high blood pressure?
–1=Yes
–2=Yes, during pregnancy only (female)
–3=No
–4=Told borderline high or pre-hypertensive
–5=Don't know/ Not sure

N
No High BP
n= 88
Yes High BP
n= 68
p
value*
Gender
(count M/F)
56 / 100 26 / 62 30 / 38 0.0871
Age
(mean ± SE)
156 55.14 ± 1.62 63.38 ± 1.53 0.0002
2Independent samples t-test; t (154) = 3.62, ƞ2 = 0.08.
High blood pressure by gender and age

Significant correlates with BP status
Variables N High BP (N/Y)
r or rho / p value*
Age 156 0.280 / 0.000
BMI 123 0.225 / 0.012
Health professional visits 155 0.206 / 0.010
Tilapia/catfish intake frequency/wk 153 0.161 / 0.047
*p < 0.05 indicates significance; Pearson product moment and Spearman rank order
correlations. All variables are low to high in rank.

high blood pressure
B ± SE
p
value*
Odds
ratio
95% CI
Days of debilitating
mental health
1.89 ± 0.63 0.002 6.63 (1.95, 22.5)
Waist circumference 0.14 ± 0.04 0.001 1.15 (1.06, 1.24)
Age 0.05 ± 0.02 0.002 1.05 (1.02, 1.08)
*p < 0.05 indicates significance; logistic regression, final model explains 25.5-
34.1% of the variance in high BP status.

Comparison of tilapia/catfish intake frequency
by BP status
N
No BP diagnosis
n=88
BP diagnosis
n=68
p value*
No tilapia/catfish
intake/wk
114 62.3% 37.7% 0.0331
1-3 servings of
tilapia/catfish/wk
39 41.0% 59.0%
*p < 0.05 indicates significance.
1Chi-square test for independence; X2 (1, n = 153) = 4.52, phi = -0.19.

Discussion
• Days of debilitating mental health was the biggest predictor of
high BP diagnosis.
– Spirituality may serve as coping mechanism for negative emotions.1
• Eating tilapia/catfish frequently may raise BP.
– Tilapia/catfish are major sources of long chain n-6 fatty acid,
arachidonic acid (AA)2; low eicosapentaenoic acid (EPA) to AA ratio
may increase CAD risk.2,3
1Kretchy, 2014; 2Weaver, 2008; 3Nagahara, 2016

Key Findings
• Reduced physical activity  musculoskeletal disorders, sadness,
anxiety
• Higher BMI/WC  high blood pressure, pain
• Western diet  musculoskeletal disorders, inadequate sleep,
increased BMI/WC, blood pressure

Strengths and Limitations
Strengths
• Validated questions utilized
• Wide variety of questions
• Large sample size
• Consistency of student roles
• Physical measurements
obtained at data collection
• Exploration of topic with
limited research
Limitations
• Cross-sectional
correlational study
• Self-reported
retrospective survey data
• Length and personal
questions
• Food frequency only
• Low generalizability

Conclusion
• Health behaviors and predisposing demographic factors were
associated with various physical and mental health conditions
in church participants.
• Future interventions: physical activity, BMI/WC, dietary
habits.

Future Research
• Health education intervention studies aimed at reducing risk of
or helping manage identified conditions.
• More faith-based community health needs assessments.

Acknowledgements
• Gloria Dei Lutheran Church Wellness Cabinet
• Gift cards, BU gear
• MPH students
• Dr. Beezhold

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Group 1 PRESENTATION SLIDES 2016 FINAL

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