Development of a multifactorial prediction model for commute mode choice in 10 983 Finnish public sector employees: a cross-sectional study

Abstract

<p><b>Objective </b>The objective of this study is to examine the feasibility of using survey data to identify factors that predict commute mode choice. <br></p><p><b>Design </b>The study design is cross-sectional. <br></p><p><b>Setting </b>Survey data from the Finnish Public Sector study (2020) were used. <br></p><p><b>Participants </b>42 574 public sector employees, of whom 10 983 were selected for the final sample. These included employees with 5 km or less commuting distances and those working full-time onsite or partly remotely. The mean age was 46 (SD 11) years, and 84% were women. Primary outcomes Commute by (1) bike or foot (an active mode) during summer and winter weather and (2) by car (a passive mode) during summer and winter weather. <br></p><p><b>Methods </b>Using logistic Lasso (least-absolute-shrinkage-and-selection-operator) regression, we developed and tested a prediction model for short commutes of 5 km or less to identify the characteristics of employees most likely to commute actively during summer and winter weather and passively during summer and winter weather. <br></p><p>Results All models had a good predictive ability with a C-index of 0.82, 0.77, 0.72 and 0.71. Cycling and walking during summer weather were predicted by shorter commutes, higher physical activity, lower body mass index (BMI), female sex and higher team psychological safety. Predictors of cycling and walking during winter weather were shorter commute length, higher physical activity, lower BMI and higher age. Commuting by car during summer weather was predicted by longer journey length, higher BMI, lower physical activity, male sex and having children 7-18 years old living at home. Predictors of driving during winter weather were almost identical, but the male sex was replaced by having a spouse. <br></p><p>Conclusions We identified the correlates of active and passive commute choice in different weather conditions with eight variables. This information can be used to develop and target interventions to promote sustainable and healthy commuting modes.<br></p>