TY - JOUR
T1 - A Personal Health System for Self-Management of Congestive Heart Failure (HeartMan)
T2 - Development, Technical Evaluation, and Proof-of-Concept Randomized Controlled Trial
AU - Lugtrek, Mitja
AU - Bohanec, Marko
AU - Cavero Barca, Carlos
AU - Ciancarelli, Maria Costanza
AU - Clays, Els
AU - Dawodu, Amos Adeyemo
AU - Derboven, Jan
AU - De Smedt, Delphine
AU - Dovganl, Erik
AU - Lampe, Jure
AU - Marino, Flavia
AU - Mlakar, Miha
AU - Pioggia, Giovanni
AU - Puddu, Paolo Emilio
AU - Rodriguez, Juan Mario
AU - Schiariti, Michele
AU - Slapnicar, Gagper
AU - Slegers, Karin
AU - Tartarisco, Gennaro
AU - Valic, Jakob
AU - Vodopija, Aljoga
N1 - Funding Information:
The HeartMan project received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 689660. The project partners are Jožef Stefan Institute, Sapienza University, Ghent University, National Research Council, Atos Spain SA, SenLab, KU Leuven, Bittium Biosignals Ltd, and European Heart Network. The authors acknowledge financial support from the Slovenian Research Agency (research core funding no. P2-0209).
Publisher Copyright:
©Mitja Luštrek, Marko Bohanec, Carlos Cavero Barca, Maria Costanza Ciancarelli, Els Clays, Amos Adeyemo Dawodu, Jan Derboven, Delphine De Smedt, Erik Dovgan, Jure Lampe, Flavia Marino, Miha Mlakar, Giovanni Pioggia, Paolo Emilio Puddu, Juan Mario Rodríguez, Michele Schiariti, Gašper Slapničar, Karin Slegers, Gennaro Tartarisco, Jakob Valič, Aljoša Vodopija.
PY - 2021/3
Y1 - 2021/3
N2 - Background: Congestive heart failure (CHF) is a disease that requires complex management involving multiple medications, exercise, and lifestyle changes. It mainly affects older patients with depression and anxiety, who commonly find management difficult. Existing mobile apps supporting the self-management of CHF have limited features and are inadequately validated.Objective: The HeartMan project aims to develop a personal health system that would comprehensively address CHF self-management by using sensing devices and artificial intelligence methods. This paper presents the design of the system and reports on the accuracy of its patient-monitoring methods, overall effectiveness, and patient perceptions.Methods: A mobile app was developed as the core of the HeartMan system, and the app was connected to a custom wristband and cloud services. The system features machine learning methods for patient monitoring: continuous blood pressure (BP) estimation, physical activity monitoring, and psychological profile recognition. These methods feed a decision support system that provides recommendations on physical health and psychological support. The system was designed using a human-centered methodology involving the patients throughout development. It was evaluated in a proof-of-concept trial with 56 patients.Results: Fairly high accuracy of the patient-monitoring methods was observed. The mean absolute error of BP estimation was 9.0 mm Hg for systolic BP and 7.0 mm Hg for diastolic BP. The accuracy of psychological profile detection was 88.6%. The F-measure for physical activity recognition was 71%. The proof-of-concept clinical trial in 56 patients showed that the HeartMan system significantly improved self-care behavior (P=.02), whereas depression and anxiety rates were significantly reduced (PConclusions: The HeartMan project combined a range of advanced technologies with human-centered design to develop a complex system that was shown to help patients with CHF. More psychological than physical benefits were observed.
AB - Background: Congestive heart failure (CHF) is a disease that requires complex management involving multiple medications, exercise, and lifestyle changes. It mainly affects older patients with depression and anxiety, who commonly find management difficult. Existing mobile apps supporting the self-management of CHF have limited features and are inadequately validated.Objective: The HeartMan project aims to develop a personal health system that would comprehensively address CHF self-management by using sensing devices and artificial intelligence methods. This paper presents the design of the system and reports on the accuracy of its patient-monitoring methods, overall effectiveness, and patient perceptions.Methods: A mobile app was developed as the core of the HeartMan system, and the app was connected to a custom wristband and cloud services. The system features machine learning methods for patient monitoring: continuous blood pressure (BP) estimation, physical activity monitoring, and psychological profile recognition. These methods feed a decision support system that provides recommendations on physical health and psychological support. The system was designed using a human-centered methodology involving the patients throughout development. It was evaluated in a proof-of-concept trial with 56 patients.Results: Fairly high accuracy of the patient-monitoring methods was observed. The mean absolute error of BP estimation was 9.0 mm Hg for systolic BP and 7.0 mm Hg for diastolic BP. The accuracy of psychological profile detection was 88.6%. The F-measure for physical activity recognition was 71%. The proof-of-concept clinical trial in 56 patients showed that the HeartMan system significantly improved self-care behavior (P=.02), whereas depression and anxiety rates were significantly reduced (PConclusions: The HeartMan project combined a range of advanced technologies with human-centered design to develop a complex system that was shown to help patients with CHF. More psychological than physical benefits were observed.
KW - congestive heart failure
KW - personal health system
KW - mobile application
KW - mobile phone
KW - wearable electronic devices
KW - decision support techniques
KW - psychological support
KW - human centered design
U2 - 10.2196/24501
DO - 10.2196/24501
M3 - Article
VL - 9
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
SN - 2291-9694
IS - 3
M1 - e24501
ER -