DOI: 10.5061/dryad.xsj3tx9ss
title: User-centered design food is medicine intervention baseline and post intervention

description: Background: Food is medicine programs have shown potential at improving
 health outcomes, reducing food insecurity, and increasing dietary intake.
 However, there are few interventions that have sought to individually
 tailor these programs based on user preferences and constraints. This
 program utilized a screening decision tool to allocate adults to a
 tailored food is medicine program to examine process and clinical
 outcomes. Methods: Adults ages 18-64 with hypertension were screened for
 food insecurity at two large hospital systems (one rural, one urban) in
 Kentucky. Participants who screened positive and wanted assistance with
 food were referred to the Food as Health Alliance hub. Medically tailored
 meals (MTM) provided 5 meals per week for 12-weeks. The grocery Rx program
 provided $100 each month for 3-months to purchase foods consistent with
 guidelines for people with hypertension. Baseline and post-intervention
 health data were obtained from electronic medical records, and process
 measures included engagement, dose, and program acceptability. Dataset:
 Baseline variables of biomarkers, survey data are included as well as post
 intervention data of the same biomarkers and survey data. Primary outcome
 of systolic and diastolic blood pressure. Secondary outcomes of food
 insecurity, nutrition security, diet quality, general health status,
 financial strainIn addition, the dataset contains process measures of net
 promoter score, budget impact, engagement metrics. This dataset is open
 source based on American Heart Association guidelines for open source
 data. 
description: <strong>Eligibility Criteria:</strong> To be eligible
 for participating in this pilot study, patients had to be between the ages
 18-64, have a diagnosis of hypertension (ICD-10 code I10), and speak
 conversational English. Only one patient per household was eligible to
 participate. Participants were recruited over 45 days from two large
 hospital systems in rural and urban communities in Kentucky. 
 <strong>Screening:</strong> Two screening processes
 were used, based on the current structure and preferences of the health
 system partners. The automated procedure consisted of the patient
 receiving the Hunger Vital Signs screener25 via text or e-mail before
 check in and documenting their response in the electronic medical record
 (EMR). The face-to-face procedure consisted of the nurse or clinic staff
 asking the patient the Hunger Vital Signs screener in the patient room and
 documenting response in the EMR.
 <strong>Referral</strong>: If the patient expressed
 interest in participating, the healthcare clinic staff would input their
 relevant contact and eligibility information into the REDCap system for
 the Food as Health Alliance team to contact the patient for further
 enrollment procedures. <strong>Enrollment into
 the</strong> <strong>HCxF program</strong>: Staff from
 the Food as Health Alliance Hub at the University of Kentucky coordinated
 the enrollment of patients and subsequent delivery of the food package.
 Referred patients received a welcome message via text and email with a
 link and phone number to enroll. Follow-up messages were sent two days
 later to patients not enrolled, after which staff made three attempts by
 phone to assist with enrollment. Enrollment consisted of informed consent
 for the pilot study, use of a screening decision tool to match
 participants to the appropriate program, and a baseline survey. This study
 was approved by the University of Kentucky IRB (Protocol #93234). All
 participants completed informed consent.
description: # User-centered design food is medicine intervention baseline and post
 intervention Dataset DOI:
 [10.5061/dryad.xsj3tx9ss](https://doi.org/10.5061/dryad.xsj3tx9ss) ##
 Description of the data and file structure Participants were recruited
 from two large hospital systems in Kentucky to participate in a food is
 medicine intervention. Participants completed a baseline and post
 intervention survey related to household size, food security status using
 the USDA 6-item screener, nutrition security using the Gretchen Swanson
 nutrition security screener, general health status, financial strain,
 psychological stress. These self-report measures were captured again post
 intervention 3-months after the food is medicine program ended.  ### Files
 and variables #### File: AHAFIMUserCenteredDe_DATA_FullDataSetFINAL.csv
 **Description:**  ##### Variables * record_id: text * fi_screening_pos:
 text Screened positive for food insecurity? Positive = Answer of
 'Often true' or 'Sometimes true' on one or both social
 determinants of health questions about food 1= yes 0=no * fa_screening:
 text 1=yes 0=no * htn_screening: text 1=yes 0=no * htn_meds_1: 1=yes 0=no
 * screening_method: 1=yes face to face and 0=automated * q3:do you have a
 working kitchen 1=yes; 0=no * q4:do you have cooking utensils, like pans,
 knives, pots 1=yes; 0=no * q5:do you currently cook your own meals 1=yes;
 0=no * q6:do you prefer cooking you own meals 1=yes; 0=no * q7:are you
 easily able to get to the grocery store at least 2time per month 1=yes;
 0=no * q8:do you prefer picking out your own food at the grocery store
 more than having food delivered to you? 1=yes; 0=no * child_under: count
 variable; number of children under age 5 * child_2: count variable; number
 of children between 6-9 * child_3: count variable; number of children
 between 10-13 * child_4: count variable; number of children between 14-17
 * child_5: count variable; number children 18+ * food_assis___1: utilizes
 supplemental nutrition assistance program 1=yes; 0=no * food_assis___2:
 women infant and children program 1=yes; 0=no * food_assis___3:food
 pantry, food bank, soup kitchen 1=yes; 0=no * food_assis___4:none of the
 above 1=yes; 0=no * food_assis___5: prefer not to answer 1=yes; 0=no For
 all insurance_type questions insurance_type 1=yes; 0=no for answer type *
 insurancetype___1: private health insurance * insurancetype___2:medicare *
 insurancetype___3:medigap * insurancetype___4:medicaid *
 insurancetype___5:children's health insurance program *
 insurancetype___6:military related healthcare * insurancetype___7: indian
 health services * insurancetype___8: state sponsored health plan *
 insurancetype___9: other government program * insurancetype___10: no
 insurance * insurancetype___11: prefer not to answer * fin_strain:How
 often does this describe you? "I don't havemoney to pay my bills
 (including food, housing,medical care or heating)." 1=never;
 2=rarely; 3=sometimes; 4=often; 5=always; 0=don't know FSSM hh2-4
 answers are: 1=often true; 2=sometimes true; 3=never true; 4=don't
 know * fssm10_hh2:We worried whether our food would run out before we got
 money to buy more." Was thatoften, sometimes, or never true for you
 in the last30 days? * fssm10_hh3:The food that we bought just didn't
 last and wedidn't have money to get more." Was that
 often,sometimes, or never true for you in the last 30days? * fssm10_hh4:We
 couldn't afford to eat balanced meals." Wasthat often,
 sometimes, or never true for you inthe last 30 days? * fssm10_ad1:In the
 last 30 days, did you or other adults in thehousehold ever cut the size of
 your meals or skipmeals because there wasn't enough money forfood?
 1=yes; 0=no * fssm10_ad2:In the last 30 days, how many days did
 thishappen? integer value * fssm10_ad3:In the last 30 days, did you ever
 eat less than youfelt you should because there wasn't enoughmoney for
 food? 1=yes; 0=no * fssm10_ad4:In the last 30 days, did you lose weight
 becausethere wasn't enough money for food? 1=yes; 0=no *
 fssm10_ad5:In the last 30 days, did you or other adults in yourhousehold
 ever not eat for a whole day becausethere wasn't enough money for
 food? 1=yes; 0=no * gsn_n3:In the last 12 months, (I/we) worried that the
 food(I was/we were) able to eat would hurt (my/our)health and well-being.
 1=never; 2=rarely; 3=sometimes; 4=often; 5=always; 6=don't know *
 eq5d5l_q1: mobility1. The following ask to mark on a scale of 1-5 for
 mobility variable. I have no problems in walking about = 1 I have slight
 problems in walkingabout = 2 I have moderate problems in walkingabout = 3
 I have severe problems in walkingabout = 4 I am unable to walk about=5 *
 eq5d5l_q2: self-care. The following ask respondent to mark on a scale of
 1-5 for self-care variable. I have no problems washing ordressing myself =
 1 I have slight problems washing ordressing myself=2 I have moderate
 problems washingor dressing myself =3 I have severe problems washing
 ordressing myself=4 I am unable to wash or dress myself=5 * eq5d5l_q3:
 usual activities. The following ask the respondent to rank the on a scale
 of 105 on usual activities.  I have no problems doing my usual
 activities=1 I have slight problems doing my usual activities=2 I have
 moderate problems doing my usual activities=3 I have severe problems doing
 my usual activities=4 I am unable to do my usual activities=5 * eq5d5l_q4:
 pain/discomfort. The following ask the respondent to rank on a scale of
 105 related to pain or discomfort.  I have no pain or discomfort=1 I have
 slight pain or discomfort=2 I have moderate pain or discomfort=3 I have
 severe pain or discomfort=4 I have extreme pain or discomfort=5 *
 eq5d5l_q5: anxiety/depression. The following ask the respondent to rank on
 a scale of 1-5 on anxiety/depression I am not anxious or depressed=1 I am
 slightly anxious or depressed=2 I am moderately anxious or depressed=3 I
 am severely anxious or depressed=4 I am extremely anxious or depressed=5 *
 eq5d5l_q6:We would like to know how good or bad your health is TODAY. This
 scale is numbered from 0 to 100. 100 means the best health you can
 imagine. 0 means the worst health you can imagine. Move the scale to
 indicate how your health is TODAY. variable ordinal 0-100 * eq5d5l_q7: now
 mark this number on a scale * gen_health:Would you say that in general
 your health is excellent, very good, good, fair, or poor? 1=excellent;
 2=very good; 3=good; 4=fair; 5=poor; 6=don't know The following
 dietary questions are from the DSQ-10. All answers for dsq questions are
 as follows: 0=Never ; 1= 1 time last month; 2=2-3 times last month; 3=1
 time per week; 4=2 times per week; 5=3-4 times per week;6=5-6 times per
 week; 7=1 time per day; 8=2-3 times per day; 9=4-5 times per day; 10=6 or
 more times a day; 11=Don’t know/Prefer not to answer * 
 
 * **Convert all frequency responses to cups units according to the
 following table:**
 **[https://epi.grants.cancer.gov/nhanes/dietscreen/scoring/current/convert.html](https://epi.grants.cancer.gov/nhanes/dietscreen/scoring/current/convert.html)** * Download the current (recommended) necessary portion size and regression coefficient data from the zip file in this location: https://epi.grants.cancer.gov/nhanes/dietscreen/programs.html. Any of the files will do as the portion size and regression coefficient data is the same. a. Calib.equation.coeff.xlsx contains the regression coefficient data b. Calib.portion.size.xlsx contains the portion size data 6. Merge the portion size file (calib.portion.size.xlsx) by gender * **DSQfv=dsq_50** - Predicted intake of fruits and vegetables including legumes and French fries (cup equivalents) per day * **DSQfvlnf** =dsq_80- Predicted intake of fruits and vegetables including legumes and excluding French fries (cup equivalents) per day * **DSQfrt=dsq_90** - Predicted intake of fruits (cup equivalents) per day * **DSQvlall**=dsq_100- Predicted intake of vegetables including legumes and French fries (cup equivalents) per day * **DSQvlnf=dsq_110** - Predicted intake of vegetables including legumes and excluding French fries (cup equivalents) per day * **DSQdairy=dsq_120** - Predicted intake of dairy (cup equivalents) per day * **DSQsug=dsq_130** - Predicted intake of total added sugars (tsp equivalents) per day * **DSQssb=dsq_140** - Predicted intake of added sugars from sugar-sweetened beverages (tsp equivalents) per day * **DSQwhgr=dsq_150** - Predicted intake of whole grains (ounce equivalents) per day * **DSQfib=dsq_160** - Predicted intake of fiber (gm) per day 
 
 What other food did you purchase that was not covered by the intervention
 program. All answers are 1=yes; 0=no * post_purchases_other___1: snacks *
 post_purchases_other___2:meats * post_purchases_other___3: dairy items *
 post_purchases_other___4: baked goods * post_purchases_other___5: candy
 and other sweet treats * post_purchases_other___6: beverages *
 post_purchases_other___7: cooking items like oil *
 post_purchases_other___8:pantry items like crackers *
 post_purchases_payment___1: EBT * post_purchases_payment___2: credit card
 * post_purchases_payment___3: debit card *
 post_purchases_payment___4:apple pay * post_purchases_payment___5: google
 pay * post_purchases_payment___6:pay pal *
 post_purchases_payment___7:klarna * post_purchases_payment___8: after pay
 * post_purchases_payment___9: pre-paid gift card *
 post_purchases_payment___10: none of these How did the [intervention]
 program affect yourbudget? (Select all that apply) * post_budget___1: I
 was able to buy additional food for my household * post_budget___2: I was
 able to by better quality food * post_budget___3: I was able to use money
 I normally use for food on other needs * post_budget___0: none of these *
 net_promoter_score: How likely are you to refer a friend to this program
 (scale 0-10). Response is ordinal value 0-10. * engagement_q1: If you had
 to pay for this program would you participate? 1=yes; 2=no; 3=prefer not
 to answer The following questions have the same description and coding as
 the baseline variable.  * food_assis_post___1: * food_assis_post___2: *
 food_assis_post___3: * food_assis_post___4: * food_assis_post___5: *
 fin_strain_post: * fssm10_hh2_post: * fssm10_hh3_post: * fssm10_ad1_post:
 * fssm10_ad4_post: * fssm10_ad2_post: * fssm10_ad3_post: *
 fssm10_ad5_post: * fssm10_hh4_post: * gsn_n3_post: * eq5d5l_q1_post: *
 eq5d5l_q2_post: * eq5d5l_q3_post: * eq5d5l_q4_post: * eq5d5l_q5_post: *
 eq5d5l_q6_post: * eq5d5l_q7_post: * gen_health_post: * dsq_050_post: *
 dsq_080_post: * dsq_090_post: * dsq_100_post: * dsq_110_post: *
 dsq_120_post: * dsq_130_post: * dsq_140_post: * dsq_150_post: *
 dsq_160_post: ## Code/software All code was included in describing the
 data. Stata 17.0 was used in analyses and can be provided upon request.
 Data can be analyzed using R or C++ for free.  ## Human subjects data We
 have consent to publish de-identified data in the public domain. We have
 removed all identifiers in the dataset.