Computerized decision support for beneficial home-based exercise rehabilitation in patients with cardiovascular disease

Triantafyllidis, Andreas; Filos, Dimitrios; Buys, Roselien; Claes, Jomme; Cornelissen, Véronique; Kouidi, Evangelia; Chatzitofis, Anargyros; Zarpalas, Dimitris; Daras, Petros; Walsh, Deirdre; Woods, Catherine; Moran, Kieran; Maglaveras, Nicos; Chouvarda, Ioanna

doi:10.1016/j.cmpb.2018.04.030

Cited by 28 publications

(47 citation statements)

References 38 publications

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“…One study aimed at providing rehabilitation to patients provided recommendations in consultation with health care experts [75]. The last study aimed at rehabilitation provided real-time as well as weekly adaptation of exercises for the patients [52]. For the 8 studies on healthy lifestyle, their objectives included wellness services [32], exercise and diet recommendation [6,7,47,76-78], and a personalized coaching system [54], which was illustrated through 3 use cases, that is, neck coach, activity coach, and stress coach.…”

Section: Resultsmentioning

confidence: 99%

“…The interventions were presented or delivered to users in multiple ways, for example, through Web apps [7,66,79], mobile phone apps [2,3,74], Kinect devices [52,80], specific activity monitors [81], PDF report [35], text message (short message service, SMS) [9,68,82], printout [27,83,84], or telephone call [60]. In some cases the users were actively pushed by the system toward their PA goal by automatic delivery of interventions periodically [7,9,54].…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, 2 studies [48,93] also used various PA guidelines to recommend step goals. The last 3 studies [52,78,87] used PA guidelines for their specific types of users, that is, cancer patients and diabetes patients. Few studies among these [6,52,78,87] use knowledge-based system constructed using a BCT but provide recommendations based on clinical guidelines.…”

Section: Resultsmentioning

confidence: 99%

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User Models for Personalized Physical Activity Interventions: Scoping Review

Ghanvatkar¹,

Kankanhalli²,

Rajan³

2019

JMIR Mhealth Uhealth

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BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of suc...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

User Models for Personalized Physical Activity Interventions: Scoping Review

Ghanvatkar¹,

Kankanhalli²,

Rajan³

2019

JMIR Mhealth Uhealth

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“…Health-based applications on smartphones are classified into the following: general health and fitness-based applications, information on medicine-based applications, and applications for managing the healthcare. m-Health is the innovative application of upcoming mobile-based technologies in concurrence with wearable devices especially in the application of healthcare informatics in order to enhance the practices of healthcare [ 39 , 40 ]. m-Health has a scope of applying it to the mobile-based technologies.…”

Section: Mobile Healthmentioning

confidence: 99%

“…Due to the fast enhancement of the AI, it has been employed in various fields, such as the IoT [ 22 , 41 , 77 ], machine vision [ 78 ], driver assistance [ 79 , 80 ], and natural language processing [ 81 , 82 ]. AI has been put in application in various domains of healthcare [ 83 – 87 ] which includes cancer research [ 88 ], cardiology [ 89 ], diabetes [ 90 ], mental health [ 91 ], identification of prognosis [ 92 ], identification of Alzheimer's disease [ 93 ], identification of difference in the clinical groups [ 94 ], identification of cardiovascular disease [ 39 ], stroke-related studies [ 95 ], etc.…”

Section: Applications Of Artificial Intelligence In M-healthmentioning

confidence: 99%

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Khan

Alotaibi

2020

Journal of Healthcare Engineering

129

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Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

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