Calorie Compensation Patterns Observed in App-Based Food Diaries
Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Nutrients |
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| Online Access: | https://www.mdpi.com/2072-6643/15/18/4007 |
| _version_ | 1797578283181670400 |
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| author | Amruta Pai Ashutosh Sabharwal |
| author_facet | Amruta Pai Ashutosh Sabharwal |
| author_sort | Amruta Pai |
| collection | DOAJ |
| description | Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual’s calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload–test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user’s compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain. |
| first_indexed | 2024-03-10T22:20:42Z |
| format | Article |
| id | doaj.art-cf690f94ceab497bb3fa1f3e0f71509f |
| institution | Directory Open Access Journal |
| issn | 2072-6643 |
| language | English |
| last_indexed | 2024-03-10T22:20:42Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nutrients |
| spelling | doaj.art-cf690f94ceab497bb3fa1f3e0f71509f2023-11-19T12:19:01ZengMDPI AGNutrients2072-66432023-09-011518400710.3390/nu15184007Calorie Compensation Patterns Observed in App-Based Food DiariesAmruta Pai0Ashutosh Sabharwal1Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USADepartment of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USASelf-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual’s calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload–test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user’s compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain.https://www.mdpi.com/2072-6643/15/18/4007MyFitnessPalcalorie compensationfood diarypreloadmhealthstatistical analysis |
| spellingShingle | Amruta Pai Ashutosh Sabharwal Calorie Compensation Patterns Observed in App-Based Food Diaries Nutrients MyFitnessPal calorie compensation food diary preload mhealth statistical analysis |
| title | Calorie Compensation Patterns Observed in App-Based Food Diaries |
| title_full | Calorie Compensation Patterns Observed in App-Based Food Diaries |
| title_fullStr | Calorie Compensation Patterns Observed in App-Based Food Diaries |
| title_full_unstemmed | Calorie Compensation Patterns Observed in App-Based Food Diaries |
| title_short | Calorie Compensation Patterns Observed in App-Based Food Diaries |
| title_sort | calorie compensation patterns observed in app based food diaries |
| topic | MyFitnessPal calorie compensation food diary preload mhealth statistical analysis |
| url | https://www.mdpi.com/2072-6643/15/18/4007 |
| work_keys_str_mv | AT amrutapai caloriecompensationpatternsobservedinappbasedfooddiaries AT ashutoshsabharwal caloriecompensationpatternsobservedinappbasedfooddiaries |