Perceptions and intentions of secondary students in rural Australia to progress to university

ABSTRACT

This article explores four critical factors in the determining of regional and remote school students’ intentions to progress to university. Three of these factors are based on the theory of planned behaviour (TPB): students’ attitudes, the opinions of their significant others (social capital), and students’ perceptions of control. A fourth factor, students’ knowledge about university, is also examined, extending the TPB. The research model tested used the responses of a survey of 620 school students from remote and regional areas in New South Wales, Australia. Results show that students’ attitudes towards university and perceptions about social capital are the most important predictors of their intentions to progress to university. In addition, students’ knowledge about university was found to be a significant contributor to students’ attitudes and perceptions of control.     

Improving energy expenditure estimates from wearable devices: A machine learning approach

ABSTRACT

A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p < 0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.