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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 17, 2019
Date Accepted: Jun 3, 2020

The final, peer-reviewed published version of this preprint can be found here:

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis

Kim J, Kam HJ, Kim Y, Lee Y, Lee JH

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis

J Med Internet Res 2020;22(8):e17521

DOI: 10.2196/17521

PMID: 32780028

PMCID: 7448179

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs

  • Junetae Kim; 
  • Hye Jin Kam; 
  • Youngin Kim; 
  • Yura Lee; 
  • Jae-Ho Lee

ABSTRACT

Background:

As mobile apps for weight loss provide users with convenient features for recording lifestyle and health indicators, they have been widely used for weight loss in recent years. Previous studies in this field generally focused on the relationship between the cumulative nature of self-reported data and the results of weight loss at the end of the diet period. However, since weight loss interventions tend to be lengthy for most participants, a closer look at how people behave throughout the actual diet process is very important. Therefore, we conducted an in-depth study to explore the relationships between self-reported data patterns and weight loss outcomes during the reduction process.

Objective:

There are three main topics in this study. First, we analyze how the adherence to self-reporting body weights and meal history changed over time. Second, we explore how weight loss outcomes depended on weight change patterns within the diet process. Third, we discuss how the response and commitment to the weight loss intervention changed over time by gender.

Methods:

We analyzed self-reported data collected weekly for 16 weeks (January 2017 to March 2018) from 684 Korean men and women who participated in a mobile weight loss intervention program provided by a mobile diet app called Noom. ANOVA and Chi-squared (χ²) tests were employed to determine whether the baseline characteristics among the groups of weight loss results were different. Based on ANOVA and slope analysis of the trend indicating participant behavior along the time axis, the relationships between self-reported data patterns and weight loss results were explored.

Results:

Adherence to self-reporting levels decreased over time, as previous studies have found. BMI change pattern (ie, absolute BMI values and BMI delta values within a week) was found to change over time and characterized in 3 time series periods. The relationship between the weight loss outcomes and both meal history and self-reporting patterns was gender dependent.

Conclusions:

This study makes academic contributions through an in-depth understanding of both the association between weight loss outcomes and the participants’ self-reporting behaviors as well as how weight changes during the diet process. These findings may contribute to the development of better weight loss interventions for mobile environments.


 Citation

Please cite as:

Kim J, Kam HJ, Kim Y, Lee Y, Lee JH

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis

J Med Internet Res 2020;22(8):e17521

DOI: 10.2196/17521

PMID: 32780028

PMCID: 7448179

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