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Depression is the most prevalent mental disorder, causing an enormous burden on individuals and society. Prediction of depression is crucial for developing early mental health warning systems and enabling early interventions and thereby mitigating negative effects. Mobile sensing, i.e. data collection via smartphones and wearables, has the potential to overcome many of the problems faced by traditional approaches (e.g., recall bias, large intervals between assessments). Machine learning is a frequently used approach to analyze and make sense of the vast amounts of data that are collected via mobile sensing. Regression is a supervised machine learning technique that is used to predict continuous values, e.g., depressive symptom scores. This study will provide a systematic review and meta-analysis on regression-based machine learning models for predicting depression using sensor data collected via mobile devices. This study is part of the larger research project DAMOS (DAtabase for MObile Sensing Studies; associated OSF project osf.io/5ukt9). The resultant database will facilitate the conduct of systematic reviews addressing specific inquiries within the realm of mobile sensing in mental healthcare. Accessibility to this database will be extended to researchers active in the field, fostering further exploration and analysis. Additionally, the project will continually register sub-studies, contributing to the ongoing expansion and refinement of the database.

This article is from PLoS ONE , volume 9 . Abstract Background: After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods: We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results: Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions: The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities.

Calibration data of a wind tunnel sting balance was processed using a search algorithm that identifies an optimized regression model for the data analysis. The selected sting balance had two moment gages that were mounted forward and aft of the balance moment center. The difference and the sum of the two gage outputs were fitted in the least squares sense using the normal force and the pitching moment at the balance moment center as independent variables. The regression model search algorithm predicted that the difference of the gage outputs should be modeled using the intercept and the normal force. The sum of the two gage outputs, on the other hand, should be modeled using the intercept, the pitching moment, and the square of the pitching moment. Equations of the deflection of a cantilever beam are used to show that the search algorithm s two recommended math models can also be obtained after performing a rigorous theoretical analysis of the deflection of the sting balance under load. The analysis of the sting balance calibration data set is a rare example of a situation when regression models of balance calibration data can directly be derived from first principles of physics and engineering. In addition, it is interesting to see that the search algorithm recommended the same regression models for the data analysis using only a set of statistical quality metrics.

Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.

Depression is the most prevalent mental disorder, causing an enormous burden on individuals and society. Prediction of depression is crucial for developing early mental health warning systems and enabling early interventions and thereby mitigating negative effects. Mobile sensing, i.e. data collection via smartphones and wearables, has the potential to overcome many of the problems faced by traditional approaches (e.g., recall bias, large intervals between assessments). Machine learning is a frequently used approach to analyze and make sense of the vast amounts of data that are collected via mobile sensing. Regression is a supervised machine learning technique that is used to predict continuous values, e.g., depressive symptom scores. This study will provide a systematic review and meta-analysis on regression-based machine learning models for predicting depression using sensor data collected via mobile devices. This study is part of the larger research project DAMOS (DAtabase for MObile Sensing Studies; associated OSF project osf.io/5ukt9).