Prediction of changes in functional ability of inpatients with schizophrenia using logarithmic and linear regression modelling.

Background/objective: Few studies have addressed the type of time course regression that can predict changes in functional ability in inpatients with schizophrenia. This study investigated the possibility of predicting changes in functional ability by logarithmic and linear regression modelling when treating schizophrenia.

Methods: This longitudinal study included two analysis rounds. Analysis 1 comprised 40 inpatients (male/female: 16/24, mean age: 39.7 ± 13.5 years) for the identification of the time course of changes in functional ability based on the Activity Profile Scale for Patients with Psychiatric Disorders score from the group data. Analysis 2 comprised 17 inpatients (male/female: 9/8, mean age: 38.5 ± 9.4 years) to ensure correlation of the group data with the prediction of each individual's degree of functional ability.

Results: In Analysis 1, Activity Profile Scale for Patients with Psychiatric Disorders score was assessed at the initial occupational therapy visit, one week and one month thereafter, and at discharge; logarithmic modelling using the scores at the initial visit, one month later and at discharge was more suitable (R² = .506, p < .001) than the logarithmic and linear regression models using other score combinations. In Analysis 2, the individual's predicted Activity Profile Scale for Patients with Psychiatric Disorders scores at discharge, as calculated by logarithmic modelling using scores from the initial visit and one month later, correlated moderately with actual Activity Profile Scale for Patients with Psychiatric Disorders scores (R² = .574, p < .001; ICC = .747, p < .001).

Conclusion: Logarithmic modelling based on Activity Profile Scale for Patients with Psychiatric Disorders score accurately predicted changes in the functional ability of inpatients with schizophrenia and is sufficiently uncomplicated to be adopted in daily clinical practice.