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. 2024 Jan 8:14:1213075.
doi: 10.3389/fphys.2023.1213075. eCollection 2023.

Predictors of sustained physical activity: behaviour, bodily health, and the living environment

Delia Elena Diaconașu  1   2 , Iulian Stoleriu  3 , Ioana Andreea Câmpanu  1 , Ana-Maria Andrei  1   4 , Ștefan Boncu  5 , Cezar Honceriu  6 , Veronica Mocanu  7 , Georgiana Juravle  1
Affiliations

Predictors of sustained physical activity: behaviour, bodily health, and the living environment

Delia Elena Diaconașu et al. Front Physiol. .

Abstract

This study examined the determinants of sustained physical activity. Eighty-four participants undertook a 7-weeks walking regime (i.e., a 1-h biometrically-monitored walk, at least 5 days/week), with bioelectrical impedance (BIA) and total cholesterol capillary blood measurements performed before and after programme. To investigate behavioural habit formation, 7 weeks after walking termination, all participants were interviewed and (health) re-tested. Data were modelled with an artificial neural network (ANN) cascading algorithm. Our results highlight the successful prediction of continued physical activity by considering one's physical fitness state, the environmental living context, and risk for cardiovascular disease. Importantly, those artificial neural network models also taking body mass index (BMI) and blood cholesterol as predictors excel at predicting walking continuation (i.e., predictions with 93% predictability). These results are first to highlight the type and importance of available physiological drivers in maintaining a sustained physical activity regime such as walking. They are discussed within the framework of habit formation and the nowadays health and/or wellbeing focus.

Keywords: Artificial Neural Networks (ANNs); BMI; blood cholesterol; environment; health; physical activity; risk for cardiovascular disease (CVD); walking.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Chronological study timeline, enlisting measurements recorded for the three laboratory testing times.
FIGURE 2
FIGURE 2
Artificial neural network architecture utilised in the present study Model 3, including one input layer with 6 neurons, one hidden layer with 7 neurons and one output layer with two neurons.
FIGURE 3
FIGURE 3
Real observations (targets) and predicted observations for the three analysed models. Model 1 includes four predictors, Model 2 includes 5 predictors, and Model 3 includes 6 predictors.
FIGURE 4
FIGURE 4
Confusion matrices for each of the three models conducted in this study.
FIGURE 5
FIGURE 5
The ROC curves for the two classes in the three ANN models conducted in this study.
See this image and copyright information in PMC

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