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Mobile health technology evaluation: The mHealth evidence workshop

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TLDR
Current evaluation standards are described to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.
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This article is published in American Journal of Preventive Medicine.The article was published on 2013-08-01 and is currently open access. It has received 770 citations till now. The article focuses on the topics: mHealth & Health technology.

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Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

TL;DR: It is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of JITAIs and particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.
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The emerging field of mobile health

TL;DR: W sweeping efforts are under way to establish the true capabilities and value of the evolving mHealth field, which can transform the mode and quality of clinical research and health care on a global scale.
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Demystifying theory and its use in improvement

TL;DR: It is concluded that although informal theory is always at work in improvement, practitioners are often not aware of it or do not make it explicit, and the germane issue for improvement practitioners is whether they make explicit the particular theory or theories, informal and formal, they actually use.
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Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.

TL;DR: Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions' effects, enabling creation of more effective JITAIs.
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A user-centered model for designing consumer mobile health (mHealth) applications (apps)

TL;DR: Use of the ISR framework is a potentially useful approach for the design of a mobile app that incorporates end-users' design preferences and has heuristic value for those venturing into the area of technology-based intervention work.
References
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Journal ArticleDOI

A tutorial on hidden Markov models and selected applications in speech recognition

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Proceedings Article

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Book

Experimental and Quasi-Experimental Designs for Generalized Causal Inference

TL;DR: In this article, the authors present experiments and generalized Causal inference methods for single and multiple studies, using both control groups and pretest observations on the outcome of the experiment, and a critical assessment of their assumptions.
Journal ArticleDOI

Probabilistic topic models

TL;DR: Surveying a suite of algorithms that offer a solution to managing large document archives suggests they are well-suited to handle large amounts of data.
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