Making Sense of Mobile Health Data: An Open Architecture to Improve Individual- and Population-Level Health

被引:72
|
作者
Chen, Connie [2 ]
Haddad, David [3 ]
Selsky, Joshua [3 ,4 ]
Hoffman, Julia E. [5 ]
Kravitz, Richard L. [6 ,7 ]
Estrin, Deborah E. [3 ,4 ]
Sim, Ida [1 ,3 ]
机构
[1] Univ Calif San Francisco, Dept Med, Div Gen Internal Med, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, Sch Med, San Francisco, CA 94143 USA
[3] Open mHlth, San Francisco, CA USA
[4] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA
[5] VA Palo Alto Healthcare Syst, Natl Ctr Posttraumat Stress Disorder, Menlo Pk, CA USA
[6] Univ Calif Davis, Div Gen Med, Sacramento, CA 95817 USA
[7] Univ Calif Davis, Ctr Healthcare Policy & Res, Sacramento, CA 95817 USA
关键词
Mobile health; software tools; software engineering; open access to information; open architecture; open source; evaluation methodology; data analysis; data visualization; N-OF-1; TRIALS; REMINDERS;
D O I
10.2196/jmir.2152
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Mobile phones and devices, with their constant presence, data connectivity, and multiple intrinsic sensors, can support around-the-clock chronic disease prevention and management that is integrated with daily life. These mobile health (mHealth) devices can produce tremendous amounts of location-rich, real-time, high-frequency data. Unfortunately, these data are often full of bias, noise, variability, and gaps. Robust tools and techniques have not yet been developed to make mHealth data more meaningful to patients and clinicians. To be most useful, health data should be sharable across multiple mHealth applications and connected to electronic health records. The lack of data sharing and dearth of tools and techniques for making sense of health data are critical bottlenecks limiting the impact of mHealth to improve health outcomes. We describe Open mHealth, a nonprofit organization that is building an open software architecture to address these data sharing and "sense-making" bottlenecks. Our architecture consists of open source software modules with well-defined interfaces using a minimal set of common metadata. An initial set of modules, called InfoVis, has been developed for data analysis and visualization. A second set of modules, our Personal Evidence Architecture, will support scientific inferences from mHealth data. These Personal Evidence Architecture modules will include standardized, validated clinical measures to support novel evaluation methods, such as n-of-1 studies. All of Open mHealth's modules are designed to be reusable across multiple applications, disease conditions, and user populations to maximize impact and flexibility. We are also building an open community of developers and health innovators, modeled after the open approach taken in the initial growth of the Internet, to foster meaningful cross-disciplinary collaboration around new tools and techniques. An open mHealth community and architecture will catalyze increased mHealth efficiency, effectiveness, and innovation.
引用
收藏
页码:126 / 134
页数:9
相关论文
共 50 条
  • [1] Individual- and population-level personalities in a floriphilic katydid
    Tan, Ming Kai
    Tan, Hugh Tiang Wah
    [J]. ETHOLOGY, 2019, 125 (02) : 114 - 121
  • [2] Quantifying Purported Competition with Individual- and Population-Level Metrics
    Walters, Eric L.
    James, Frances C.
    [J]. CONSERVATION BIOLOGY, 2010, 24 (06) : 1569 - 1577
  • [3] Variable Individual- and Population-Level Responses to Ocean Acidification
    Vihtakari, Mikko
    Havenhand, Jon
    Renaud, Paul E.
    Hendriks, Iris E.
    [J]. FRONTIERS IN MARINE SCIENCE, 2016, 3
  • [4] Long COVID Frameworks: Examining Individual- and Population-Level Models to Assess and Improve Patient Care
    Comber, Christopher
    [J]. HEALTH PROMOTION PRACTICE, 2024, 25 (01) : 22 - 26
  • [5] Group breeding in vertebrates: linking individual- and population-level approaches
    Safran, Rebecca J.
    Doerr, Veronica A. J.
    Sherman, Paul W.
    Doerr, Erik D.
    Flaxman, Samuel M.
    Winkler, David W.
    [J]. EVOLUTIONARY ECOLOGY RESEARCH, 2007, 9 (07) : 1163 - 1185
  • [6] Should landscape variation in population status be assessed with individual- or population-level indicators?
    Gould, Philip R.
    Cecala, Kristen K.
    Drukker, Saunders S.
    McKenzie, Benjamin A.
    van de Ven, Chris H.
    [J]. JOURNAL OF WILDLIFE MANAGEMENT, 2024, 88 (02):
  • [7] A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data
    Eisaguirre, Joseph M.
    Williams, Perry J.
    Brockman, Julia C.
    Lewis, Stephen B.
    Barger, Christopher P.
    Breed, Greg A.
    Booms, Travis L.
    [J]. METHODS IN ECOLOGY AND EVOLUTION, 2023, 14 (08): : 2110 - 2122
  • [8] Masting promotes individual- and population-level reproduction by increasing pollination efficiency
    Moreira, Xoaquin
    Abdala-Roberts, Luis
    Linhart, Yan B.
    Mooney, Kailen A.
    [J]. ECOLOGY, 2014, 95 (04) : 801 - 807
  • [9] Individual- and population-level drivers of consistent foraging success across environments
    Lysanne Snijders
    Ralf H. J. M. Kurvers
    Stefan Krause
    Indar W. Ramnarine
    Jens Krause
    [J]. Nature Ecology & Evolution, 2018, 2 : 1610 - 1618
  • [10] Habitat Quality From Individual- and Population-Level Perspectives and Implications for Management
    Boves, Than J.
    Rodewald, Amanda D.
    Wood, Petra B.
    Buehler, David A.
    Larkin, Jeffrey L.
    Wigley, T. Bently
    Keyser, Patrick D.
    [J]. WILDLIFE SOCIETY BULLETIN, 2015, 39 (02): : 443 - 447