Evolution of mHealth Eco-System: A Step Towards Personalized Medicine

Saxena, Mohit; Saxena, Ankur

doi:10.1007/978-981-15-1286-5_30

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…mHealth is the term collectively used for the use of smart phone and other sensory devices for monitoring or improving medical care. 21 The concept of mHealth was first introduced in 2000 22 and was at that time defined as "mobile computing, medical sensor, and communications technologies for health-care". This concept has evolved to become the "4G Health", 23 defined as "the use of mobile devices equipped with wearable sensors in collecting health data and physiological signals, tele-consultation, delivery of health information to practitioners, patients, healthcare consumers and researchers, remote and real-time monitoring of vital signs such as heart rate and electrocardiogram, the direct provision of care as well as training and collaboration of health workers, etc.".…”

Section: Bias In Mhealthmentioning

confidence: 99%

“…mHealth is the term collectively used for the use of smart phone and other sensory devices for monitoring or improving medical care 21 . The concept of mHealth was first introduced in 2000 22 and was at that time defined as “mobile computing, medical sensor, and communications technologies for health‐care”.…”

Section: Bias In Mhealthmentioning

confidence: 99%

“…This concept has evolved to become the “4G Health”, 23 defined as “the use of mobile devices equipped with wearable sensors in collecting health data and physiological signals, tele‐consultation, delivery of health information to practitioners, patients, healthcare consumers and researchers, remote and real‐time monitoring of vital signs such as heart rate and electrocardiogram, the direct provision of care as well as training and collaboration of health workers, etc.” 24 . This 4G health will probably be soon replaced by “5G mHealth”, 24 even faster and more connected, with a lower cost, both from economic and ecological point of view, and made to create a kind of “mHealth ecosystem” 21 which combines the Internet of Things (IoT) and the use of artificial intelligence tools to analyze the big health data produced by this ecosystem. The promise of mHealth is thus to provide a real‐time monitoring of the patient (but also of the healthcare system), and to transform “the current reactive medicine” in a “proactive healthcare featured with diseases prevention” 24 .…”

Section: Bias In Mhealthmentioning

confidence: 99%

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For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth

Brault¹,

Saxena

2020

Evaluation Clinical Practice

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Rationale, aims and objectives: Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias.Methods: In this historical and conceptual article, we focus on two main problems: first, the data and the problem of its validity; second, the inference drawn from the data by AI, and the establishment of correlations through the use of algorithms. We use examples from the contemporary use of mobile health (mHealth), i.e. the practice of medicine and public health supported by mobile or wearable devices such as mobile phones or smart watches.Results: We show that the validity of the data and of the inferences drawn from these mHealth data are likely to be biased. As biases are insensitive to the size of the sample, even if the sample is the whole population, artificial intelligence and big data cannot avoid biases and even tend to increase them. Conclusions:The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.

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Section: Bias In Mhealthmentioning

confidence: 99%

Section: Bias In Mhealthmentioning

confidence: 99%

Section: Bias In Mhealthmentioning

confidence: 99%

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For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth

Brault¹,

Saxena

2020

Evaluation Clinical Practice

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“…Meanwhile, the usage of mobile health (mHealth) solutions has been broadly applied to support people's well-being [4][5][6]. mHealth applications have been used to support patients with cardiovascular diseases [7] with very promising results.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Citizens’ Well-Being in Areas with Natural Based Solutions Using Mobile Computing

Gallos

Menychtas

Panagopoulos³

et al. 2022

Studies in Health Technology and Informatics

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Urban planners, architects and civil engineers are integrating Nature-Based Solutions (NBS) to address contemporary environmental, social, health and economic challenges. Many studies claim that NBS are poised to improve citizens’ well-being in urban areas. NBS can also benefit Public Health, as they can contribute to optimising environmental parameters (such as urban heat island effects, floods, etc.), as well as to the reduction of diseases, as for example cardiovascular ones and the overall mortality rate. In addition, the usage of mobile health (mHealth) solutions has been broadly applied to support citizens’ well-being as they can offer monitoring of their physical and physiological status and promote a healthier lifestyle. The aim of this paper is to present the specifications, the design and the development of a mobile app for monitoring citizens’ well-being in areas where NBS have been applied. The users’ physical activity and vital signs are recorded by wearable devices and the users’ locations are recorded by the proposed mobile application. All collected data are transferred to the cloud platform where data management mechanisms aggregate data from different sources for combined analysis. The mobile application is currently available for Android and iOS devices and it is compatible with most smart devices and wearables. The “euPOLIS by BioAssist” application can be used as a health and other data collection tool to investigate citizen’s well-being improvement in areas with NBS.

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“…Meanwhile, the mobile health (mHealth) technologies contribution in people's wellbeing has been examined and found to be significant based on the international literature [8][9][10]. Several mHealth applications have been used to support patients with mental health or cardiovascular diseases [11][12][13] with very promising results.…”

Section: Introductionmentioning

confidence: 99%

Using mHealth Technologies to Promote Public Health and Well-Being in Urban Areas with Blue-Green Solutions

Gallos

Menychtas

Panagopoulos³

et al. 2022

Studies in Health Technology and Informatics

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European and International cities face crucial global geopolitical, economic, environmental, and other changes. All these intensify threats to and inequalities in citizens’ health. The implementation of Blue-Green Solutions in urban and rural areas have been broadly used to tackle the above challenges. The Mobile health (mHealth) technologies contribution in people’s well-being has found to be significant. In addition, several mHealth applications have been used to support patients with mental health or cardiovascular diseases with very promising results. The patients’ remote monitoring can be a valuable asset in chronic diseases management for patients suffering from diabetes, hypertension or arrhythmia, depression, asthma, allergies and others. The scope of this paper is to present the specifications, the design and the development of a mobile application which collects health-related and location data of users visiting areas with Blue-Green Solutions. The mobile application has been developed to record the citizens’ and patients’ physical activity and vital signs using wearable devices. The proposed application can also monitor patients physical, physiological, and emotional status as well as motivate them to engage in social and self-caring activities. Additional features include the analysis of the patients’ behavior to improve self-management. The “HEART by BioAsssist” application could be used as a health and other data collection tool as well as an “intelligent assistant” to monitor and promote patient’s physical activity.

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Evolution of mHealth Eco-System: A Step Towards Personalized Medicine

Cited by 13 publications

References 28 publications

For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth

For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth

Quantifying Citizens’ Well-Being in Areas with Natural Based Solutions Using Mobile Computing

Using mHealth Technologies to Promote Public Health and Well-Being in Urban Areas with Blue-Green Solutions

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