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电子健康记录中成人体力活动“生命体征”的预测效度。

Predictive Validity of an Adult Physical Activity "Vital Sign" Recorded in Electronic Health Records.

机构信息

Dept of Exercise and Sport Science, University of Utah, Salt Lake City, UT.

出版信息

J Phys Act Health. 2016 Apr;13(4):403-8. doi: 10.1123/jpah.2015-0210. Epub 2015 Oct 7.

DOI:10.1123/jpah.2015-0210
PMID:26445164
Abstract

BACKGROUND

Few have examined predictive relationships between physical activity (PA) and health using electronic health records (EHRs) of patient-reported PA.

OBJECTIVE

Assess initial predictive validity of the Physical Activity "Vital Sign" (PAVS) recorded in EHRs with BMI and disease burden.

METHODS

EHRs were from November 2011 to November 2013 (n = 34,712). Differences in not meeting Physical Activity Guidelines (PAG) were tested using chi-square analysis between being normal weight versus overweight/obese, and scoring below versus above the 50th percentile of the Charlson Comorbidity Index (CCI). Repeated measures logistic regression was used to determine odds of BMI and CCI classifications according to responses to the PAVS as not meeting PAG.

RESULTS

Patients who did not meet PAG according to the PAVS were more likely than normal weight patients to have a higher BMI (BMI 25.0-29.9, OR = 1.19, P = .001; BMI 30.0-34.9, OR = 1.39, P < .0001; BMI 35.0-39.9, OR = 2.42, P < .0001; BMI ≥ 40, OR = 3.7, P < .0001) and also higher disease burden (above 50th percentile for CCI, OR = 1.8, P < .0001).

CONCLUSIONS

The strong association of the PAVS found with patient BMI and moderately-strong association with disease burden supports initial predictive validity of the PAVS recorded in EHRs. PA recorded in EHRs may be vastly useful for assessing patient disease and cost burdens attributed independently to PA behavior.

摘要

背景

很少有人使用患者报告的活动电子健康记录 (EHR) 来检查身体活动 (PA) 与健康之间的预测关系。

目的

评估 EHR 中记录的体力活动“生命体征”(PAVS) 与 BMI 和疾病负担的初始预测效度。

方法

EHR 数据来自 2011 年 11 月至 2013 年 11 月(n = 34712)。使用卡方分析检验不满足体力活动指南 (PAG) 的差异,即正常体重与超重/肥胖之间的差异,以及 Charlson 合并症指数 (CCI) 第 50 百分位以下与以上的差异。重复测量逻辑回归用于根据 PAVS 对不满足 PAG 的反应来确定 BMI 和 CCI 分类的几率。

结果

根据 PAVS 不满足 PAG 的患者比正常体重患者更有可能具有更高的 BMI(BMI 25.0-29.9,OR = 1.19,P =.001;BMI 30.0-34.9,OR = 1.39,P <.0001;BMI 35.0-39.9,OR = 2.42,P <.0001;BMI ≥ 40,OR = 3.7,P <.0001)和更高的疾病负担(CCI 高于第 50 百分位,OR = 1.8,P <.0001)。

结论

PAVS 与患者 BMI 之间的强关联和与疾病负担之间的中度强关联支持 EHR 中记录的 PAVS 的初步预测效度。EHR 中记录的 PA 可能对评估患者因 PA 行为而导致的疾病和成本负担非常有用。

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