Data Management and Statistical Analysis of Longitudinal Big Data Collected from Mobile Health Management Applications_Chinese Journal of Evidence-Based Medicine

Authors：

 XUHai-peng ¹ , DONGChong-ya ² , YANXiao-yan ² ,  YAOChen ^1,2

1. Peking University First Hospital, Beijing 100034, China;
2. Peking University Clinical Research Institute, Beijing 100191, China;

Corresponding author：

YAOChen, Email: yaochen@hsc.pku.edu.cn

Keywords：

Mobile health; Health management; APP; Longitudinal data; Big data

DOI：

10.7507/1672-2531.20160149

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To explore the methods of data management and statistical analysis for longitudinal big data collected from mobile health management applications (APP). Methods The data management process and statistical analysis method were proposed by summarizing the characteristics of the data from mobile health management APPs. The methods would be clarified by a practical case: an APP recording female menstruation. Results The data from health management APPs belong to longitudinal big data and the original record of the APP should be reprocessed or computed before conducting statistical analysis. A two-step data cleaning procedure was suggested for data management of the original records and reprocessed data, and longitudinal models such as mixed models was recommended for statistical analysis. Conclusions The data from health management APPs could be used for medical research via specific data management and statistical analysis after removing suspicious data. Cloud computing could be a viable method to improve efficiency of the big data analysis of health management APPs.

Citation： XUHai-peng, DONGChong-ya, YANXiao-yan, YAOChen. Data Management and Statistical Analysis of Longitudinal Big Data Collected from Mobile Health Management Applications. Chinese Journal of Evidence-Based Medicine, 2016, 16(8): 972-977. doi: 10.7507/1672-2531.20160149 Copy

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1. Adibi S. Mobile Health:A Technology Road Map. Available at: http://link.springer.com/book/10.1007%2F978-3-319-12817-7.
2. 王小岩, 罗仁.美国移动医疗健康App应用状况的研究与分析.中国数字医学, 2015, 10(11):2-6.
3. 中国医药物资协会. 2014中国医药互联网发展报告. Available at: http://health.gmw.cn/yp/2015-02/03/content_14731574.htm.
4. Apple Inc. ResearchKit and CareKit. Available at:https://www.apple.com/researchkit/.
5. 余松林, 向惠云.重复测量资料分析方法与SAS程序.北京:科学出版社, 2004:136-162.
6. 陈峰, 姚晨, 孙高, 等.新药临床试验中重复测量资料的混合效应模型.中国卫生统计, 2000, 17(6):373-376.
7. 罗天娥, 刘桂芬.广义线性混合效应模型在分类重复测量资料中的应用.中国卫生统计, 2007, 24(5):486-487.
8. 高茂龙.分类重复测量资料广义估计方程应用.山西:山西医科大学, 2007:1-47.
9. 赖爱鸾, 孙艳格, 张李松, 等.性激素、年龄、月经模式变化对卵巢储备功能的预测价值.中国实用妇科与产科杂志, 2010, 26(9): 717-719.
10. 刘继龙, 林春莲, 蒙钟明, 等.年龄及性激素预测卵巢储备功能的临床研究.基层医学论坛, 2013, 17(1):3-4.

Chinese Journal of Evidence-Based Medicine

Data Management and Statistical Analysis of Longitudinal Big Data Collected from Mobile Health Management Applications

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