Citation: Kim, J.; Lee, J.; Park, M.
Identification of
Smartwatch-Collected Lifelog
Variables Affecting Body Mass Index
in Middle-Aged People Using
Regression Machine Learning
Algorithms and SHapley Additive
Explanations. Appl. Sci. 2022, 12,
3819. https://doi.org/10.3390/app
12083819
Academic Editors: Keun Ho Ryu and
Agostino Forestiero
Received: 13 January 2022
Accepted: 8 April 2022
Published: 10 April 2022
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Article
Identification of Smartwatch-Collected Lifelog Variables
Affecting Body Mass Index in Middle-Aged People Using
Regression Machine Learning Algorithms and SHapley
Additive Explanations
Jiyong Kim
1
, Jiyoung Lee
2
and Minseo Park
3,
*
1
Department of Mathematics, Kwangwoon University, Seoul 01897, Korea; jiyongrock@kw.ac.kr
2
Clinical Research Coordinating Center, Seoul St. Mary’s Hospital, The Catholic University of Korea,
Seoul 06591, Korea; jlee007@outlook.com
3
Department of Data Science, Seoul Women’s University, Seoul 01797, Korea
* Correspondence: mpark@swu.ac.kr
Abstract:
Body mass index (BMI) plays a vital role in determining the health of middle-aged people,
and a high BMI is associated with various chronic diseases. This study aims to identify important
lifelog factors related to BMI. The sleep, gait, and body data of 47 middle-aged women and 71 middle-
aged men were collected using smartwatches. Variables were derived to examine the relationships
between these factors and BMI. The data were divided into groups according to height based on
the definition of BMI as the most influential variable. The data were analyzed using regression and
tree-based models: Ridge Regression, eXtreme Gradient Boosting (XGBoost), and Category Boosting
(CatBoost). Moreover, the importance of the BMI variables was visualized and examined using
the SHapley Additive Explanations Technique (SHAP). The results showed that total sleep time,
average morning gait speed, and sleep efficiency significantly affected BMI. However, the variables
with the most substantial effects differed among the height groups. This indicates that the factors
most profoundly affecting BMI differ according to body characteristics, suggesting the possibility of
developing efficient methods for personalized healthcare.
Keywords:
lifelog; wearable device; smartwatch; body mass index; machine learning; SHapley
Additive Explanations; feature importance
1. Introduction
A lifelog is an integrated digital record consisting of personal data collected from
various digital sensors [
1
] such as activity, sleep information, weight change, body mass,
muscle mass, and fat mass. With the development of wearable devices, more accurate
and precise measurements are possible. Lifelog information obtained by wearable devices,
such as gait, sleep, and weight, is now used for chronic disease occurrence monitoring
and health care [
2
–
4
]. However, healthcare services using lifelogs are currently limited to
simple records or incomplete statistics. Even if they include exercise and lifestyle feedback
functions, the feedback provided is not personalized according to user characteristics.
Therefore, this study aims to identify factors that can be used to develop personalized
healthcare through lifelog analysis. This study interprets machine learning results using
an interpretable model rather than a black box model.
Most previous studies on the correlation between BMI and weight with disease
incidence have used medical data [
5
,
6
]. In contrast, we used lifelogs of sleep, steps,
and weight in daily life. Individual analysis was subsequently performed using machine
learning algorithms.
The rest of this paper is organized as follows. Section 2 describes the use and impor-
tance of lifelog data. Section 3 analyzes the association between lifelog data and BMI using
Appl. Sci. 2022, 12, 3819. https://doi.org/10.3390/app12083819 https://www.mdpi.com/journal/applsci
