Improving Efficiencies on FoodAPS with 

Online Food Logs

Laurie May

July 17, 2018
FoodAPS Data Collection Goals

 Collect all food items purchased or acquired 

by all household members over a 7-day 

period 

– Both food at home (FAH) and food away from 

home (FAFH)

– Both purchased and free food

– Information at the event and item level

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FoodAPS-1 Data Collection 

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• Started and ended with an in-person 

interview 

• Provided UPC scanner (only technology 

used)

• Used paper food logs and income 

worksheets

• Items recorded on paper are reported 

during daily telephone calls 

• Reported receipt information; provide 

paper copy

• Respondent determined FAH versus FAFH

• Respondent entered addresses of event 

locations
Alternative Data Collection Method (ADCM) 

Pilot Tested Use of an Online Food Log

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Objective: Use technology to reduce burden and improve data quality

• Started and ended with an in-person 

interview 

• Provided UPC scanner, smartphone, laptop + 

Internet 

• Items recorded through an online log

• Uploaded photo of receipts

• System determined FAH versus FAFH: event 

locations
Multiple Ways to Access Web Food Log

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Computer with handheld barcode scanner Smartphone with downloaded barcode App
FoodAPS-1 versus the ADCM Data Collection 

Procedures 

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Scan UPCs

Paper food logs and income worksheets; 

report via phone

Report receipt information; provide 

paper copy

Respondent determines FAH versus FAFH

Respondent enters event locations

Scan UPCs, can verify if product 

match is correct 

Web system for food logs + income 

worksheets 

Take picture and upload receipts

System determines FAH vs. FAFH

Google map look up for location

FoodAPS-1 ACDM
Usability of Technology

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3.5%

76.0%

2.0%

0%

20%

40%

60%

80%

100%

Declined Took equipment Theft

Decline to 

participate due 

to technology

 No significant issues with the technology
 No direct measure of overall burden

– Infer from the activities

 Average total time to complete the food log is 49 minutes

 Scanning verses no scanning reduces time per item

– Scanning cuts average time per item by about 1.5 minutes 

– But found that scanning intensity is inversely related to 

quantity bought

Burden

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Data Completeness Challenges

 For items and events reported, not all 

questions are answered (item non-response)

 Appears that not all events and food items 

are reported (underreporting) 

 Item non-response and underreporting were 

issues for both FoodAP-1 as well

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 100% match of items to events and events to households

 Medium to high use of automated data entry features 

– data entered by automated methods are fairly clean

Completeness/Cleanliness of Items Reported

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64%

47%

87%

51% 56%

77%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Scanned item Uploaded

receipts

Entered price Entered

income

All Matched FAF FAFH
Evidence of Event and Item Under Reporting

 Daily reporting

– Reporting drops off over the 7 days data collection period

– Similar to FoodAPS-1 pattern

 Recall validation suggests 25 percent of respondents 

forgot to report at least one item in last 2 days

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Events Reported by Day

For Pilot, n=430 HHs, Weighted 

For FoodAPS1, n=4,826 HHs, weighted 

0.0

0.5

1.0

1.5

2.0

2.5

Day1 Day2 Day3 Day4 Day5 Day6 Day7

Number of events

FoodAPS-1 Total Events Pilot (Total Events)

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Items Reported by Day Adjusting for

Recall

0

2

4

6

8

10

12

14

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

Number of items

FoodAPS-1 Pilot Pilot adjusted for recall

Pilot: n= 419 HHs, Weighted 

FoodAPS1: n=4,826 HHs, weighted 

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How to Improve the Online Diary?

 Extract data from receipts in real-time 

– Lessens reporting time and effort

– Reduced burden could result in more complete reports

– Provides an accurate record of the actual purchase

 Won’t eliminate manual data entry but can 

substantially lessen it

– Perhaps cut it in half

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Use of Receipts in FoodAPS-2

 With current funding 

– Upload receipt images for both FAH and FAFH events

– Compare receipt information to reported usual 

shopping behavior; telephone prompt to under 

reporting households

– Use receipt in post-data collection Q/C and coding

 Explored partially automating data entry using 

receipts but currently unfunded

– Few available receipt scanning software capture item 

level information

– Found none that do this accurately in real-time

– Requires new development

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Challenges of Automating Reading of Receipts

 Need to translate receipt into data in real-time

– Defined variables

• At a minimum: Item name, item price

 Variation in receipt structure makes it hard to 

convert to data

– May need separate code for each store chain

 No common naming convention to items

– Use chain specific short hand names for items

– Only a minority of receipts contain UPCs (Walmart)

• 12 percent of the Pilot study events and 19 percent of 

expenditures were from Walmart

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Automating Reading of Receipts

 If receipt has UPCs, step 1-2 is a straight 

forward UPC match

 Without UPCs, step 1-2 requires text matching 

– Ultimately build a thesaurus that links grocery store 

names to IRI names

– Thesaurus has use beyond FoodAPS

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Covert receipt 

to data

Item name + 

price

Match to IRI

By item 

name (text 

match)

Get UPC + 

standard name+ 

quantify/weight

Match 

to 

FNDDS

1 2 3 4
Other issues using receipts 

 Removing non-food items

– These are almost always coded 

 Not all items will have a UPC match

– Some cases will require respondent identifies the item 

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Screen 1: Upload your receipt 

Provides directions

Real-time UPC match program is run. System returns list of 

unmatched food items. “12 Cy Nitril” is unmatched but is coded on 

the receipt as not food, so it is automatically removed 

Screen 2: For unmatched item, system asks respondent to provide a 

complete name and weight.

Please provide a detailed item Please provide item weight

item name (use receipt name as a guide (from package, receipt, or if produce, quantity 

bought but write out any abbreviations)

Folgers

Example: 13 items purchased, receipt has UPCs

For illustration assume “12 Cy Nitril” and “Folgers” do not 

match to a UPC

Walmart Receipt (with UPCs)

Instant coffee 8 oz.
Conclusion

 Using an online diary is an incremental 

improvement over paper

 Burden could be lessened and data quality 

potentially improved through real-time 

reading of receipts

– Non-trivial effort to set up 

– Still will require manual inputs from respondent

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