Study Results
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View full resultsBasic Information
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COMPLETED
NA
18 participants
INTERVENTIONAL
2016-04-01
2017-03-31
Brief Summary
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In developed economies, consumers are the largest source of FW via home preparation and dining out2, 8. In order to achieve FW goals, we must anticipate how consumer FW generation and handling decisions will respond to proposed policies and interventions. However, there exist substantial barriers to measuring consumer FW: existing methods have high respondent burden and yield biased measurements of FW generation9-11. In this project we will test a smartphone application (app) for measuring household FW.
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Detailed Description
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Importantly, the RFPM and SmartIntakeTM app eliminate one of the largest sources of error associated with self-report methods - subjects' inability to accurately estimate portion size, which accounts for \~50% of the error in self-report methods11, 13-15. FI estimation studies have observed that overweight subjects react to monitoring by underestimating the self-reported amounts of FI or eat less when monitored16, 17. Similarly, FW behavioral studies have observed that subjects start self-reporting smaller amounts of FW, however it is not clear if this is the result of increased awareness and less generation, or if it due to not appearing as wasteful individuals18. However, people respond less to the RFPM/SmartIntake app and experiment results show that the underestimation from RFPM does not vary with energy intake level12.
Many shortcomings that plague FI measurement likely hamper FW measurement. Biases in the opposite direction (e.g., cleaning your plate to show less FW) likely emerge when measuring FW and may be similarly mitigated with the proposed app. We believe using RFPM and SmartIntakeTM app as the base for a FW estimation app holds great promise to transform FW measurement. First, we note that FW associated with dining situations (i.e., plate waste) is directly estimated by the RFPM, hence only a simple transform of current outputs is required. To fully adapt RFPM for FW measurement, the procedures will be modified to estimate FW created during food preparation and during stored food purges (e.g., cleaning the fridge). The RFPM method already works in dine-out settings, which are currently omitted by some existing FW measurement methods. Additional functionality, such as estimating edible vs inedible FW (and hence calories wasted), estimating waste rate among food categories, and estimating rates of diversion from landfills, will also be explored so that multiple policy relevant metrics can be instantly calculated and stored.
Conditions
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Study Design
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NA
SINGLE_GROUP
OTHER
NONE
Study Groups
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Intervention
Intervention Group
Food Photography Application©
Group testing RFPM as method of recording plate waste
Interventions
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Food Photography Application©
Group testing RFPM as method of recording plate waste
Eligibility Criteria
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Inclusion Criteria
* Be able to consent
* Be within an age range of 18-72 years, inclusive
* Have a body mass index of 18.5 - 50 kg/m2
* Have an iPhone, and be willing to use it to capture photos via an app (participants must acknowledge that cellular data will be used during the course of the project)
* Be willing to use an iPhone app (SmartIntake) and a pen-and-paper food dairy during the food intake meal and for up to five days in free-living conditions.
Exclusion Criteria
* Be pregnant, assessed by self-report (this is not a safety issue and study data will not be compromised if 1-3 pregnant women enroll)
* Be planning to become pregnant while enrolled in the study
18 Years
72 Years
ALL
Yes
Sponsors
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Ohio State University
OTHER
Pennington Biomedical Research Center
OTHER
Responsible Party
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Corby K. Martin
Primary Investigator
Locations
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Pennington Biomedical Research Center
Baton Rouge, Louisiana, United States
Countries
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Other Identifiers
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PBRC 2016-043
Identifier Type: -
Identifier Source: org_study_id
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