Optimizing Digital Integrated Care via Micro‐Randomized Trials

Walton, Ashley; Nahum‐Shani, Inbal; Crosby, Lori E.; Klasnja, Predrag; Murphy, Susan A.

doi:10.1002/cpt.1079

Cited by 59 publications

(38 citation statements)

References 43 publications

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“…First, research methodologies that can facilitate the evaluation of micro interventions are required. Micro randomized trials are one such methodology that could be of use here, especially when research questions are related to dynamic processes of change over time [ 66 ]. Second, methods for optimizing digital micro interventions and improving micro intervention suites need to be explored.…”

Section: Discussionmentioning

confidence: 99%

Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care

Baumel¹,

Fleming²,

Schueller³

2020

J Med Internet Res

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Although many people access publicly available digital behavioral and mental health interventions, most do not invest as much effort in these interventions as hoped or intended by intervention developers, and ongoing engagement is often low. Thus, the impact of such interventions is minimized by a misalignment between intervention design and user behavior. Digital micro interventions are highly focused interventions delivered in the context of a person’s daily life with little burden on the individual. We propose that these interventions have the potential to disruptively expand the reach of beneficial therapeutics by lowering the bar for entry to an intervention and the effort needed for purposeful engagement. This paper provides a conceptualization of digital micro interventions, their component parts, and principles guiding their use as building blocks of a larger therapeutic process (ie, digital micro intervention care). The model represented provides a structure that could improve the design, delivery, and research on digital micro interventions and ultimately improve behavioral and mental health care and care delivery.

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Section: Discussionmentioning

confidence: 99%

Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care

Baumel¹,

Fleming²,

Schueller³

2020

J Med Internet Res

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“…Typically, trail and survey schemes are for the efficiency, and data evaluation schemes are for the fidelity. As for trails and surveys, micro-randomized trials (MRTs) are tools for maintaining and improving participants' efficiency by optimizing the combinations of incentives (e.g., varying levels of monetary incentives, and virtual rewards) [251], [273], [274]. With MRTs, participants first randomly grouping to collect data under varying incentives, then in the following sensing loops, the collected data in the previous round is used to measure which combinations of incentives are optimal.…”

Section: B) Crowd Sensing For Population Healthmentioning

confidence: 99%

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Wang¹,

Xiong²,

Zhang³

et al. 2021

Preprint

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Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either (a) personalized medicine for individuals or (b) public health for populations, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms – (i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloudbased data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: (1) Sensing Task Creation & Participation, (2) Health Surveillance & Data Collection, and (3) Data Analysis & Knowledge Discovery. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.

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“…For example, a wearable device might remind the wearer to take deep breaths as an anxiety reducing therapy (see e.g., Sarker et al, 2017). With some randomization of the reminders and measurement of the physical responses, the device could optimize a breathing strategy for a given individual (see e.g., Walton et al, 2018). Finally, if the previously mentioned privacy concerns can be addressed, data from such devices could be pooled and used to determine the individual characteristics that make people most and least receptive to breathing therapy.…”

Section: Issues Related To Treatment Effect Analysismentioning

confidence: 99%

Big Data and the Precision Medicine Revolution

Hopp

Wang

2018

SSRN Journal

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The big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high-dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well-suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.

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Optimizing Digital Integrated Care via Micro‐Randomized Trials

Cited by 59 publications

References 43 publications

Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care

Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Big Data and the Precision Medicine Revolution

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