Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research

Kumar, Santosh; Nilsen, Wendy; Pavel, Misha; Srivastava, Mani

doi:10.1109/mc.2012.392

Cited by 196 publications

(149 citation statements)

References 11 publications

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“…RCTs are not well suited to accommodate this type of change, but are better suited when an app is "mature." [21] Several alternative approaches for efficacy testing exist, although these have received relatively limited attention. For example, regressionincontinuity, stepped-wedge designs, SMART trials [22], and pragmatic randomized controlled trials have been proposed as study designs that could efficiently yield some efficacy data and be more amenable to handling changes in technology features [21].…”

Section: How To Strike a Balance Between The Pace Of Research And Thementioning

confidence: 99%

“…[21] Several alternative approaches for efficacy testing exist, although these have received relatively limited attention. For example, regressionincontinuity, stepped-wedge designs, SMART trials [22], and pragmatic randomized controlled trials have been proposed as study designs that could efficiently yield some efficacy data and be more amenable to handling changes in technology features [21]. In this issue, Cobb and Poirer conduct a pragmatic controlled trial that demonstrates the ability to recruit, retain, intervene, and make data-driven intervention enhancements at low cost and on a rapid timeline [23].…”

Section: How To Strike a Balance Between The Pace Of Research And Thementioning

confidence: 99%

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How behavioral science can advance digital health

Pagoto

Bennett

2013

Behav. Med. Pract. Policy Res.

138

102

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The field of behavioral science has produced myriad data on health behavior change strategies and leveraged such data into effective human-delivered interventions to improve health. Unfortunately, the impact of traditional health behavior change interventions has been heavily constrained by patient and provider burden, limited ability to measure and intervene upon behavior in real time, variable adherence, low rates of implementation, and poor third-party coverage. Digital health technologies, including mobile phones, sensors, and online social networks, by being available in real time, are being explored as tools to increase our understanding of health behavior and to enhance the impact of behavioral interventions. The recent explosion of industry attention to the development of novel health technologies is exciting but has far outpaced research. This Special Section of Translational Behavioral Medicine, Smartphones, Sensors, and Social Networks: A New Age of Health Behavior Change features a collection of studies that leverage health technologies to measure, change, and/or understand health behavior. We propose five key areas in which behavioral science can improve the impact of digital health technologies on public health. First, research is needed to identify which health technologies actually impact behavior and health outcomes. Second, we need to understand how online social networks can be leveraged to impact health behavior on a large scale. Third, a team science approach is needed in the developmental process of health technologies. Fourth, behavioral scientists should identify how a balance can be struck between the fast pace of innovation and the much slower pace of research. Fifth, behavioral scientists have an integral role in informing the development of health technologies and facilitating the movement of health technologies into the healthcare system. KEYWORDS Digital health, mHealth, Social networksThe efficacy of traditional health behavior change interventions has long been constrained by expense, patient burden, and variable adherence. Current models of health care which limit our access to patients to face-to-face visits reduce our ability to gain an accurate understanding of the antecedents and consequences of behavior and to intervene in moments when patients most need help. Digital health technologies, including mobile phones, sensors, and online social networks, by being available in real time, are being explored as ways to enhance our ability to understand health behavior and intervene upon it more effectively. mHealth, a segment of the digital health market that leverages mobile technologies to solve health problems, has reached its tipping point. A rapidly growing body of evidence demonstrates the efficacy of mHealth approaches across a wide range of conditions, populations, and settings. mHealth has also attracted a explosion of industry attention. An extremely diverse group of companies are capitalizing on the mHealth market, which is projected to reach over $23 billion in r...

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Section: How To Strike a Balance Between The Pace Of Research And Thementioning

confidence: 99%

Section: How To Strike a Balance Between The Pace Of Research And Thementioning

confidence: 99%

How behavioral science can advance digital health

Pagoto

Bennett

2013

Behav. Med. Pract. Policy Res.

138

102

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“…The rapid proliferation of mobile technologies leading to a digital revolution in healthcare has paved the path to new health management model, "mobile health" or "mHealth" (Kumar, Nilsen, Pavel, & Srivastava, 2013). By removing locational and temporal constraints, the coverage of healthcare processes and services have been strengthened for both clinicians and patients (Varshney, 2009(Varshney, , 2014b.…”

Section: Decision Support Capabilities Of Mobile Healthmentioning

confidence: 99%

“…By removing locational and temporal constraints, the coverage of healthcare processes and services have been strengthened for both clinicians and patients (Varshney, 2009(Varshney, , 2014b. Combined with wireless sensing technologies, mobile health has excellent abilities to mediate a variety of health interventions, and to provide communications for shared decision making (Kumar et al, 2013). A variety of sensors such as electrocardiogram (ECG), pulse oximeter, and electroencephalogram (EEG) have been surveyed as real-time and continuous patients' data for daily health decision making (Pantelopoulos & Bourbakis, 2010).…”

Section: Decision Support Capabilities Of Mobile Healthmentioning

confidence: 99%

“…In 2015, 44% of worldwide patients have witnessed the use of mobile devices by health professionals in treatment and diagnosis (Mobile Ecosystem Forum, 2015). Nevertheless, designing of mobile decision support interventions remains as an open transdisciplinary research problem (Kumar et al, 2013) which underlines the need for translating theoretical concepts into practical design guidelines (Durand & Elwyn, 2009). …”

Section: Decision Support Capabilities Of Mobile Healthmentioning

confidence: 99%

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Analysis and design of mobile health interventions towards informed shared decision making: an activity theory-driven perspective

Nguyen

Poo²

2016

Journal of Decision Systems

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With the rapid advancement of mobile technologies, mobile health is going through a massive digital revolution that is reshaping the current way of health decision making. This study takes initiatives to investigate the manifestation of shared decision making model in mobile health for better decision quality and outcome. Hence, we propose an Activity Theory-driven framework for analysing mobile health management to achieve informed health choices. It establishes strong theoretical foundations and comprehensive requirements for designing mobile health interventions in the direction of supportive and collaborative decision making. The study contributes to the cumulative theoretical development of mobile health and shared decision making. It also provides an agenda for automated mobile health and a number of implications for academic bodies, healthcare practitioners, and developers of mobile health.

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Smartm‐Health Sensing

Istepanian¹,

Woodward²

2016

M‐Health: Fundamentals and Applications

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Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research

Cited by 196 publications

References 11 publications

How behavioral science can advance digital health

How behavioral science can advance digital health

Analysis and design of mobile health interventions towards informed shared decision making: an activity theory-driven perspective

Smartm‐Health Sensing

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