A wearable sensor system for medication adherence prediction

Kalantarian, Haik; Motamed, Babak; Alshurafa, Nabil; Sarrafzadeh, Majid

doi:10.1016/j.artmed.2016.03.004

Cited by 60 publications

(19 citation statements)

References 25 publications

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“…On the other hand, BLE best fits for short-range low power communications. The state-of-the-art IoT eHealth devices can be classified into two main groups: conductance sensor [80], PPG [81], elderly monitor [82], [83], [84], [85], [86], medication management [87], [88], food contamination detection device [89], early warning system [90], [91]. • Virtual sensor: using software and mobile applications as well as eHealth services, virtual sensors capture patient's health data and contextual data from the environment [92], [93], [94], [95].…”

Section: A Iot Ehealth Device Layermentioning

confidence: 99%

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani

Firouzi

Chang

et al. 2018

Future Generation Computer Systems

818

371

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Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: 1) device, 2) fog computing and 3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy.

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Section: A Iot Ehealth Device Layermentioning

confidence: 99%

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani

Firouzi

Chang

et al. 2018

Future Generation Computer Systems

818

371

View full text Add to dashboard Cite

show abstract

“…It is well known and has recently been affirmed that providers can use routine vital signs like heart rate and blood pressure to estimate patients’ adherence to certain therapies, like beta blockers [56]. There are efforts underway to leverage data from wearable activity and heart rate monitors to predict numerous health-related behaviors and outcomes, including non-adherence [57, 58]. One study even used ingestible sensors detected by a small wearable patch in combination with a mobile device application and a provider portal [59•].…”

Section: Health Team and System-related Factorsmentioning

confidence: 99%

Recent Approaches to Improve Medication Adherence in Patients with Coronary Heart Disease: Progress Towards a Learning Healthcare System

Levy¹,

Huang

et al. 2018

Curr Atheroscler Rep

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Purpose of Review Non-adherence to medications for the secondary prevention of myocardial infarction (MI) is a major contributor to morbidity and mortality in these patients. This review describes recent advances in promoting adherence to therapies for coronary artery disease (CAD). Recent Findings Two large randomized controlled trials to “incentivize” adherence were somewhat disappointing; neither financial incentives nor “peer pressure” successfully increased rates of adherence in the post-MI population. Patient education and provider engagement appear to be critical aspects of improving adherence to CAD therapies, where the provider is a physician, pharmacist, or nurse and follow-up is performed in person or by telephone. Fixed-dose combinations of CAD medications, formulated as a so-called “polypill,” have shown some early efficacy in increasing adherence. Technological advances that automate monitoring and/or encouragement of adherence are promising but seem universally dependent on patient engagement. For example, medication reminders via text message perform better if patients are required to respond. Multifaceted interventions, in which these and other interventions are combined together, appear to be most effective. Summary There are several available types of proven interventions through which providers, and the health system at large, can advance patient adherence to CAD therapies. No single intervention to promote adherence will be successful in all patients. Further study of multifaceted interventions and the interactions between different interventions will be important to advancing the field. The goal is a learning healthcare system in which a network of interventions responds and adapts to patients’ needs over time.

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“…To the best of our knowledge, no published research exists on any wearable technology exploring the effects of ease of reminding for healthcare professionals. The importance of these factors has mostly been stated in preventive healthcare research, and such studies have concentrated on patients' behavior (Kaushik et al, 2008;Peck et al, 2014;Kalantarian et al, 2016). However, reminding or warning functions of information systems have been implemented in healthcare settings (Bates and Gawande, 2003;Lluch, 2011;Menachemi and Collum, 2011;Dünnebeil et al, 2012;Pham, 2014); thus, these functions can be enhanced by smart devices such as glasses, and the analysis results support this concept.…”

Section: Discussionmentioning

confidence: 69%

Exploring adoption of augmented reality smart glasses: Applications in the medical industry

et al. 2018

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This study explores the use of augmented reality smart glasses (ARSGs) by physicians and their adoption of these products in the Turkish medical industry. Google Glass was used as a demonstrative example for the introduction of ARSGs. We proposed an exploratory model based on the technology acceptance model by Davis. Exogenous factors in the model were defined by performing semi-structured in-depth interviews, along with the use of an expert panel in addition to the technology adoption literature. The framework was tested by means of a field study, data was collected via an Internet survey, and path analysis was used. The results indicate that there were a number of factors to be considered in order to understand ARSG adoption by physicians. Usefulness was influenced by ease of use, compatibility, ease of reminding, and speech recognition, while ease of use was affected by ease of learning, ease of medical education, external influence, and privacy. Privacy was the only negative factor that reduced the perceived ease of use, and was found to indirectly create a negative attitude. Compatibility emerged as the most significant external factor for usefulness. Developers of ARSGs should pay attention to healthcare-specific requirements for improved utilization and more extensive adoption of ARSGs in healthcare settings. In particular, they should focus on how to increase the compatibility of ARSGs. Further research needs to be conducted to explain the adoption intention of physicians.

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A wearable sensor system for medication adherence prediction

Cited by 60 publications

References 25 publications

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Recent Approaches to Improve Medication Adherence in Patients with Coronary Heart Disease: Progress Towards a Learning Healthcare System

Exploring adoption of augmented reality smart glasses: Applications in the medical industry

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