Quality of Life in European Adults and Older with All-Over Pain: Relationship with Frequency of Moderate and Vigorous Physical Activity and Decision Prediction Models with Cross-Sectional Data.

Abstract

Background/objectives: Quality of life (QoL) is negatively affected in people with all-over pain. Widespread pain has a negative impact on physical function, cognitive function, mental health and mood. Physical activity (PA) may help to improve the QoL in these people. This study aimed to assess the relationships between PA frequency (PAF) and QoL in middle-aged and older European people with all-over pain, in addition to developing and analyzing the performance of a classification and regression tree model (CRT) to predict QoL in this population.

Methods: A cross-sectional study of 1025 middle-aged and older European individuals with all-over pain. Relationships between moderate and vigorous PAF and QoL were assessed. A predictive algorithm for QoL was developed using CRT analysis. A cross-validation study was conducted to assess the performance of the model. In addition, a multivariate linear regression model was developed to predict QoL and compare its performance with the CRT model.

Results: Higher PAF and higher QoL were found to be related (p < 0.001). Specifically, the CRT found that depression, poor perceived health, and moderate physical activity once a week, rarely or never were the combinations of variables that predicted lower quality of life scores. Conversely, not having depression and performing moderate physical activity more than once a week predicted higher quality of life scores. The linear regression model performed better than the CRT model (R² = 38% vs. R² = 30%), and both identified depression, SPH, moderate PAF and education level as main predictors of QoL.

Conclusions: PA on a regular basis could improve the QoL of people with all-over pain. Depressive symptoms, self-perceived health, PAF and educational level are predictors of QoL in this population. Our findings provide useful information for assessing QoL in people with all-over pain, offering an easy-to-interpret visual model with similar accuracy to traditional models.