A Faith-based Community Health Needs Assessment

Leah Fahey, Shavena Fife, Katherin Rehn, Stephanie RichardsonCatherine 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

Participant Recruitment

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

Population characteristicsVariables N Females

n=101Malesn=56

TestStat*

pvalue*

Mean ± SD Mean ± SDAge 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 0.607Total 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 Grad-uate, 3.8%, n=6Some college, 15.9%, n=25College Graduate, 40.8%, n=64Postgraduate, 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 MDn=89

Yes MDn=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 disordersby 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 / 0.000Red meat intake frequency/wk 154 0.253 / 0.002Breakfast cereal intake frequency/day 145 0.198 / 0.017Resistance exercise times/wk 152 -0.186 / 0.022Days of poor physical health 155 0.167 / 0.037Days 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 correlatesby MD status

N No MD Yes MD p value*

95% CIs of Difference

Mean ±SE Mean ±SEAge 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. 2ANCOVA; Adj means: 2.30 ± 0.23 vs 2.95 ± 0.20, p = 0.042, pƞ2 = 0.028.

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

NSadnessNo d/mo

n=71

Sadness1-2 d/mo

n=50

Sadness 3+ d/mo

n=33p

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

*p < 0.05 indicates significance. 1Chi-square test for independence. 2ANOVA; F = 8.43, ƞ2 = 0.14.

NAnxiety

No d/mon=40

Anxiety1-2 d/mo

n=41

Anxiety3-6 d/mo

n=37

Anxiety>6 d/mo

n=38p

value*

Gender (count M/F) 56 / 100 20 / 20 10 / 31 12 / 25 14 / 24 0.1101

Age (mean ± SE) 156 62.63 ±

2.2665.29 ±

1.9151.76 ±

2.5554.66 ±

2.11 <0.0012

Significant correlates of sadness & anxiety (d/mo)Sadness days

rho / p / nAnxiety 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 dailyby sadness and anxiety status

NSADNESS

No days/mon=71

SADNESS1+ d/mo

n=83p

value* NANXIETY0-2 d/mo

n=81

ANXIETY3+ d/mo

n=75p

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

Methods

BMI N=123 Normal weightn=44

Overweightn=43

Obesen=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 riskn=63

Higher riskn=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 0.986Age – 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 BMIr or rho / p value*

Waist circumference 122 0.854 / 0.000Perception of general health 123 -0.380 / 0.000Diabetes diagnosis (N/Y) 122 0.296 / 0.001High blood pressure diagnosis (N/Y) 122 0.294 / 0.001Gender (M/F) 123 0.285 / 0.001Days of pain affecting activities 119 0.215 / 0.019Sugary drink intake frequency/day 117 0.201 / 0.030High cholesterol (N/Y) 121 0.201 / 0.027Days feeling energetic 122 -0.195 / 0.031Work status (N/Y) 122 0.187 / 0.039Days 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

Diabetes diagnosis (N/Y) 77 0.452 / 0.000

High cholesterol (N/Y) 45 0.393 / 0.008

Health professional visits 77 0.374 / 0.001

Perception of general health 77 -0.344 / 0.002

Days of poor concentration related to physical health 46 0.318 / 0.031

Days of pain affecting activities 73 0.299 / 0.010

Musculoskeletal disorders diagnosis (N/Y) 75 0.271 / 0.019

Days of debilitating mental health 77 0.271 / 0.017

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 / 0.004Dark green leafy vegs frequency/day 37 -0.387 / 0.012

Sugary drink intake frequency/day 43 0.323 / 0.035

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

• Question taken from BRFSS 2014 Questionnaire

Pain by gender and age

N No Painn=106

Pain ≥ 1 d/mon=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 poor physical health 152 0.455 / 0.000Days of debilitating physical health 152 0.385 / 0.000Perception of general health 152 -0.357 / 0.000

Days feeling energetic 151 -0.321 / 0.000

BMI 119 0.215 / 0.019

Waist circumference 119 0.215 / 0.019

Sugary drink intake frequency/day 144 0.200 / 0.016

High blood pressure diagnosis (N/Y) 151 0.194 / 0.017

Days of debilitating mental health 152 0.182 / 0.025

Diabetes diagnosis (N/Y) 151 0.162 / 0.047

*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

NNo Painn=106

Painn=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, 2015Image 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

• Question taken from BRFSS 2014 Questionnaire

Hours of sleep by gender and age

N Sleeps 5-6 hrsn=57

Sleeps ≥7 hrsn=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

*p < 0.05 indicates significance. 1Chi-square test for independence. 2Independent samples t-test; t (157) = 2.09, ƞ2 = -0.13.

Significant correlates with sleep hrs/nightVariables N Sleep hrs/night

rho / p value*Work status (N/Y employed) 157 -0.307/0.000Age 157 0.229 / 0.004Coffee and tea intake frequency/day 154 -0.194 / 0.016BMI 124 -0.160 / 0.077Fish intake frequency/wk 154 0.159 / 0.049Sugary 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)

Multivariate analysis – Predictors of reporting 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 correlatesby 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. 1ANCOVA; Adj means: 2.31 ± 0.28 vs 1.54 ± 0.21, p = 0.033, pƞ2 = 0.04. 2ANCOVA; Adj means: 1.10 ± 0.17 vs 1.30 ± 0.13, p = 0.346, pƞ2 = 0.01.

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 over is < 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

• Question taken from BRFSS 2013 Questionnaire

N No High BPn= 88

Yes High BPn= 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

*p < 0.05 indicates significance. 1Chi-square test for independence. 2Independent samples t-test; t (154) = 3.62, ƞ2 = 0.08.

High blood pressure by gender and age

Significant correlates with BP statusVariables N High BP (N/Y)

r or rho / p value*Waist circumference 122 0.349 / 0.000Age 156 0.280 / 0.000BMI 123 0.225 / 0.012Days of debilitating mental health 156 0.223 / 0.005Perception of general health 156 -0.209 / 0.009Health professional visits 155 0.206 / 0.010Days of pain affecting activities 151 0.194 / 0.017Days of poor physical health 156 0.167 / 0.037Tilapia/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.

Multivariate analysis – Predictors of reportinghigh 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 frequencyby BP status

N No BP diagnosisn=88

BP diagnosisn=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

References

AAPM facts and figures on pain. The American Academy of Pain Medicine Web site. http://www.painmed.org/patientcenter/facts_on_pain.aspx. Accessed August 30, 2016.

Adult Obesity Facts. Centers for Disease Control and Prevention Web site. https://www.cdc.gov/obesity/data/adult.html. Published September 1, 2016. Accessed October 7, 2016.

American Heart Association. High blood pressure. American Heart Association Web site. http://www.heart.org/HEARTORG/Conditions/HighBloodPressure/High-Blood-Pressure-or-Hypertension_UCM_002020_SubHomePage.jsp. Updated 2014. Accessed 2016.

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