An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation

Abstract

Aims Effective and efficient education and patient engagement are fundamental to improve health outcomes in heart failure (HF). The use of artificial intelligence (AI) to enable more effective delivery of education is becoming more widespread for a range of chronic conditions. We sought to determine whether an avatar-based HF-app could improve outcomes by enhancing HF knowledge and improving patient quality of life and self-care behaviour. Methods and results In a randomized controlled trial of patients admitted for acute decompensated HF (ADHF), patients at high risk (≥33%) for 30-day hospital readmission and/or death were randomized to usual care or training with the HF-app. From August 2019 up until December 2020, 200 patients admitted to the hospital for ADHF were enrolled in the Risk-HF study. Of the 72 at high-risk, 36 (25 men; median age 81.5 years; 9.5 years of education; 15 in NYHA Class III at discharge) were randomized into the intervention arm and were offered education involving an HF-app. Whilst 26 (72%) could not use the HF-app, younger patients [odds ratio (OR) 0.89, 95% confidence interval (CI) 0.82–0.97; P < 0.01] and those with a higher education level (OR 1.58, 95% CI 1.09–2.28; P = 0.03) were more likely to enrol. Of those enrolled, only 2 of 10 patients engaged and completed ≥70% of the program, and 6 of the remaining 8 who did not engage were readmitted. Conclusions Although AI-based education is promising in chronic conditions, our study provides a note of caution about the barriers to enrolment in critically ill, post-acute, and elderly patients.

mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review

“…The CVDs most commonly managed by mHealth apps in the literature are arrhythmias, heart failure, and coronary heart disease. Arrhythmia self-management is present in 34% of the reviewed studies [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], whereas heart failure self-management exhibited a frequency of 32% [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. In turn, coronary heart disease self-management is present in 18% [ 52 , 53 , 54 , 55 , 56 , 57 , 58 ].…”

“…mHealth apps for heart failure self-management include the Heart Failure app [ 27 ], HeartMan [ 28 ], Healthy Heart [ 29 ], Evident II [ 31 ], Abby [ 32 ], MOVIDA.eros [ 33 ], HeartKeeper [ 36 ], and SUPPORT-HF [ 37 ]. The majority of them focus on heart failure monitoring and treatment through issuing medical recommendations and medication management.…”

“…Zisis et al [27] Bohanec et al [28] Heiney et al [29] Koirala et al [30] Gonzalez-Sanchez et al [31] Barret et al [32] Silva et al [33] Foster [34] Sakakibara et al [35] De la Torre-Diez et al [36] K. Johnston et al [64] Heart failure Zisis et al [27] Bohanec et al [28] Heiney et al [29] Koirala et al [30] Gonzalez-Sanchez et al [31] Barret et al [32] Silva et al [33] Foster [34] Sakakibara et al [35] De la Torre-Diez et al [36] K. Rahimi et al [37] Bartlett et al [38] Arrhythmia Turchioe et al [39] Pierleoni et al [40] Reverberi et al [41] Fukuma et al [42] Bumgarner et al [43] Krivoshei et al [44] Guo et al [45] Evans et al [46] Halcox et al [47] Lowres et al [48] Hickey et al [49] McManus et al [50] Kakria et al [51] Coronary heart disease Brouwers et al [52] Zhang et al [53] Athilingam [54] Skobel et al [56] AM et al [57] Dale et al [58] Several Jiang et al [59] Baek et al…”

“…Additionally, 34.2% of these apps monitor patient parameters, such as physical activity, step count, weight control, blood pressure, heart rate, pulse wave, and body temperature [27][28][29]38,44,48,51,[56][57][58]60,64], while 21% allow for AF detection [41][42][43][44]46,47,49,50]. Finally, 21% allow for medication management [27,29,35,37,45,54,59,63,64], and 10.5% allow patients to access their electronic health records [28,45,60,64]. Most of the mHealth applications studied in this work have been demonstrated to be useful in the self-management of CVDs.…”

“…As for association rules and neural networks, they allow mHealth apps to create solutions for better decision making based on real data, thus improving CVD diagnosis, proposing customized treatment plans, reducing medical errors, increasing the effectiveness of CVD prevention measures, and promoting better CVD self-management. In regard to the IoT devices/sensors, this approach allows mHealth apps to retrieve real-time data on patient biometric variables, such as body temperature, heart rate, and blood pressure, through wearables, which in turn allow physicians to monitor patients remotely [27,28,[68][69][70]. Additionally, wearables provide apps with real-time data that facilitate risk factor tracking and prevent CVD events [71].…”