Mobile Health Technology Evaluation

By Santosh Kumar, W. Nilsen, A. Abernethy, Audie A Atienza, K. Patrick, M. Pavel, W. Riley, A. Shar, B. Spring, Donna Spruijt-Metz and 9 more. American Journal of Preventive Medicine, 2013

884 citations recorded by the source corpus.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and, (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, future possibilities and set a grand goal for the emerging field of mHealth research.

Subfields: Applied Psychology, Demography, General Health Professions

View Paper: https://www.semanticscholar.org/paper/15218e510a6917ddbec44622bda3b71f0c9a2942

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

In Demography

In General Health Professions

Citations in the corpus

Mobile technology and the digitization of healthcare. (2016, methodology): “method of healthcare delivery from one that was health-systems generated to one that is remote and patient generated.(3,4) The culmination of these factors presents unparalleled opportunities”
AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “Many research works in recent years tried to leverage data from mobile sensors in an effort to revolutionize healthcare [66].”
Three Problems With Current Digital Mental Health Research … and Three Things We Can Do About Them (2017, background): “The rapid pace of technology development relative to the slow pace of research methods often results in mental health technologies that are outdated and obsolete by the time they are validated (6).”
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. (2015, background): “Commonly used experimental designs are not sufficient to support development of JITAIs because they do not enable researchers to determine empirically when a particular intervention component should be delivered and whether a JITAI that was delivered had the intended effect (Kumar et al., 2013).”
Ambulatory Assessment: New adventures in characterizing dynamic processes (2016, neutral): “An increasing number of reviews have addressed the topic of using AA for intervention (e.g., Adibi, 2015; Heron & Smyth, 2010; Kumar et al., 2013).”
IDEAS (Integrate, Design, Assess, and Share): A Framework and Toolkit of Strategies for the Development of More Effective Digital Interventions to Change Health Behavior (2016, background): “Published evaluations also contribute to the literature and evidence base, helping uncover the potential and challenges of using digital interventions to improve health outcomes [8,13].”
Demystifying theory and its use in improvement (2015, background): “13 It is in part these differences in the interests and practices of improvers and researchers that explain the underlying distinctions between improvement projects and research studies.”
Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework (2015, background): “…information obtained from the person as s/he experiences the JITAI, and “train” the decision rules to accommodate changes in what constitutes a state of vulnerability/opportunity and receptivity for that particular person (see Dallery, Kurti & Erb, 2015; Kumar et al., 2013 for further discussion).”
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016, background): “, 2013). Advantages of mHealth interventions include real-time communication between a patient and their health-care provider as well as systems for delivering training, teaching, and social support (Kumar et al., 2013). Mobile technology can also be used to collect rich longitudinal data to estimat”
A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward (2016, background): “Mobile devices can be used to provide treatment when, where, and in the amount desired (Litvin et al., 2013; Kumar et al., 2013).”