Kawaguchi Takayuki, Matsunaga Atsuhiko, Watanabe Aki, Suzuki Makoto, Asano Etsuko, Shirakihara Yoko, Shimizu Shinobu, Sawayama Toru, Fukuda Michinari, Miyaoka Hitoshi
Kitasato University School of Allied Health Sciences, Japan.
Tokyo Kasei University, Japan.
Hong Kong J Occup Ther. 2018 Dec;31(2):76-85. doi: 10.1177/1569186118808431. Epub 2018 Oct 30.
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.
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.
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 (R = .506, < .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 (R = .574, < .001; ICC = .747, < .001).
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.
背景/目的：很少有研究探讨哪种时间进程回归类型能够预测精神分裂症住院患者功能能力的变化。本研究调查了在治疗精神分裂症时，通过对数和线性回归模型预测功能能力变化的可能性。
这项纵向研究包括两个分析阶段。分析1纳入了40名住院患者（男/女：16/24，平均年龄：39.7±13.5岁），用于根据精神障碍患者活动概况量表评分从组数据中确定功能能力变化的时间进程。分析2纳入了17名住院患者（男/女：9/8，平均年龄：38.5±9.4岁），以确保组数据与每个个体功能能力程度预测之间的相关性。
在分析1中，在首次职业治疗就诊时、此后一周和一个月以及出院时评估精神障碍患者活动概况量表评分；使用首次就诊、一个月后和出院时的评分进行对数建模比使用其他评分组合的对数和线性回归模型更合适（R = 0.506，P < 0.001）。在分析2中，通过使用首次就诊和一个月后的评分进行对数建模计算出的个体出院时预测精神障碍患者活动概况量表评分与实际精神障碍患者活动概况量表评分中度相关（R = 0.574，P < 0.001；组内相关系数ICC = 0.747，P < 0.00）。
基于精神障碍患者活动概况量表评分的对数建模准确预测了精神分裂症住院患者的功能能力变化，并且足够简单，可以在日常临床实践中采用。
