Mitja Luštrek 1 , Marko Bohanec 2 , Carlos Cavero Barca 3 , Maria Costanza Ciancarelli 4 , Els Clays 5 , Amos Adeyemo Dawodu 4 , Jan Derboven 6 , Delphine De Smedt 5 , Erik Dovgan 1 , Jure Lampe 7 , Flavia Marino 8 , Miha Mlakar 1 , Giovanni Pioggia 8 , Paolo Emilio Puddu 4 , Juan Mario Rodríguez 3 , Michele Schiariti 4 , Gašper Slapničar 1 , Karin Slegers 9 , Gennaro Tartarisco 8 , Jakob Valič 1 , Aljoša Vodopija 1 Show Affiliations »
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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 (P<.001), as were perceived sexual problems (P=.01). According to the Unified Theory of Acceptance and Use of Technology questionnaire, a positive attitude toward HeartMan was seen among end users, resulting in increased awareness, self-monitoring, and empowerment. CONCLUSIONS: 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. TRIAL REGISTRATION: ClinicalTrials.gov NCT03497871; https://clinicaltrials.gov/ct2/history/NCT03497871. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12872-018-0921-2. ©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. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2021.
RCT Entities: Population
Interventions
Outcomes
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 (P<.001), as were perceived sexual problems (P=.01). According to the Unified Theory of Acceptance and Use of Technology questionnaire, a positive attitude toward HeartMan was seen among end users, resulting in increased awareness, self-monitoring, and empowerment. CONCLUSIONS: 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. TRIAL REGISTRATION: ClinicalTrials.gov NCT03497871; https://clinicaltrials.gov/ct2 /history/NCT03497871. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10 .1186/s12872-018-0921-2. ©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. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2021.
Entities: CellLine
Chemical
Disease
Gene
Species
Keywords:
congestive heart failure; decision support techniques; human centered design; mobile application; mobile phone; personal health system; psychological support; wearable electronic devices
Year: 2021
PMID： 33666562 DOI： 10.2196/24501
Source DB: PubMed Journal: JMIR Med Inform