Heart failure is a serious condition and a major reason why older adults are admitted to the hospital. Even after going home, many patients struggle to get the follow-up care they need, leading to high rates of return to the hospital. While remote monitoring technology exists, it is often difficult to use, expensive, or hard for doctors to interpret. Recent research suggests that changes in a person's voice and speech can be early warning signs of worsening heart failure, but we need to test if this can work reliably in a home setting.This study tests a new, user-friendly platform called AVATAR-SC. This system uses a a computer-generated character to interact with patients. By analyzing short voice clips and simple health questionnaires provided by the patient, the system aims to make home monitoring easier and more effective. The study will involve 60 participants divided into two groups:half of the patients who have recently been discharged from the hospital following a heart failure episode; the other 30 patients who visit the hospital monthly for a specific heart failure treatment (Levosimendan). Participants will interact with the digital avatar twice a week. During these sessions, they will provide brief voice recordings and answer a few questions about how they feel. It is important to note that the AI in this study is not making medical decisions or changing the patient's current treatment; it is strictly being tested to see how well the technology functions. As a feasibility study, the primary goal is to evaluate the practical implementation of the technology rather than clinical outcomes. The research focuses on: Determining if patients can navigate the system easily and independently, Assessing how consistently participants engage with the scheduled sessions, Verifying the technical reliability of the platform in a home environment,identifying the strengths and challenges of the system to prepare for future, larger-scale trials. Benefits: Participants may feel more supported and more aware of their symptoms through regular check-ins. Risks: The risks are very low. They mostly involve getting used to new technology and ensuring that personal data is kept private and secure. Impact If successful, AVATAR-SC could lead to a new way for doctors to keep an eye on heart failure patients at home, catching problems early through the sound of their voice and preventing unnecessary hospital visits.
Heart Failure (HF) is a leading cause of hospitalization and mortality among the elderly, characterized by high readmission rates and a significant impact on the National Health Service. Despite guideline recommendations, only a minority of patients receive timely post-discharge follow-up, contributing to clinical deterioration and rising healthcare costs. Existing remote monitoring systems have shown mixed results, primarily due to poor patient adherence, data interpretation challenges, and high costs. While vocal and linguistic analysis supported by Artificial Intelligence (AI) algorithms has shown promise for the early identification of heart failure, its practical application in home monitoring has yet to be systematically evaluated. AVATAR-SC proposes an accessible, non-invasive platform based on advanced AI technologies. It integrates vocal biomarkers, clinical data, and Patient-Reported Outcome Measures (PROMs) to enhance home monitoring and support a more proactive management of heart failure patients. Study Population: 60 patients with Heart Failure (HF), including 30 patients discharged following hospitalization for acute heart failure and 30 patients receiving monthly Levosimendan therapy in a Day Hospital setting. Study Objectives: Although AI-supported vocal and linguistic analysis shows significant potential for the early detection of clinical deterioration in HF patients, there is a lack of data regarding feasibility, acceptability, adherence, integration into care pathways, and data quality within a real-world home monitoring context. A feasibility study is therefore necessary to:i) evaluate patients' ability to use AVATAR-SC independently;ii) estimate adherence to daily vocal data collection;iii) verify the technical performance of the platform;iv) define operational metrics (time, resources, completion rates);v) identify barriers and facilitators in preparation for a large-scale clinical trial.The study does not involve the use of AI systems as clinical tools, nor their application for the automated assessment of an individual patient's health status.Nature and extent of benefits and risks associated with study participation: Potential benefits include more regular symptom monitoring and increased support in disease management. Risks are minimal and primarily concern the use of technology and the processing of personal data. Intervention: Interactions via digital avatar ≥ 2 times/week, collection of short voice clips, and essential PROMs (Patient-Reported Outcome Measures). No alerts will be generated, and there will be no influence on the patient's clinical pathway.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
60
Patients discharged following hospitalization for acute heart failure will be enrolled in this study arm and will utilize AVATAR\_SC for periodic monitoring
Patients receiving monthly Levosimendan therapy at the ASST GOM Niguarda (Cardiology 2 - Heart Failure and Transplants) will be enrolled for longitudinal voice data collection. This approach captures data across all preclinical stages of the deterioration-compensation cycle, supported by periodic monitoring via the AVATAR-SC platform. Levosimendan will be prescribed as per clinical practice. .
ASST Grande Ospedale Metropolitano Niguarda
Milan, Ita, Italy
Feasability of the AVATAR-SC System in Heart Failure patients
≥80% of patients compleating 75% of scheduled interaction
Time frame: Through study completion, an average of 3 months per patients
Change in Perceived Quality of Life (QoL)
Outcome Measures. A clinical improvement defined as at least a 10% increase in perceived quality of life scores, as measured by standardized Patient-Reported Outcome Measures (KCCQ, EQ-5D).
Time frame: Through study completion, an average of 3 months per patients
Change in Vocal Biomarkers Score
Exploratory offline analysis to evaluate the statistical consistency between vocal patterns and clinical parameters. Quantitative metrics including accuracy, precision, recall, and F1-score will be used for descriptive purposes only. This analysis is purely exploratory, without the definition of clinical thresholds, individual classification, or operational use of the results
Time frame: Through study completion, an average of 3 months per patients
New York Heart Association (NYHA) Functional Class of Heart Failure
Clinical assessment performed by the physician to evaluate the patient's overall heart failure severity and functional limitations based on cardiac symptoms. The evaluation is performed using the New York Heart Association (NYHA) classification, which categorizes patients into one of four classes based on their limitations during physical activit (class I to IV) . Higher classes indicate greater severity of heart failure and worse functional status.
Time frame: Through study completion, an average of 3 months per patients
Change in Montreal Cognitive Assessment (MoCA) Score
The MoCA is a screening instrument for cognitive impairment. It assesses different cognitive domains: attention and concentration, executive functions, memory, language, visuoconstructional skills, conceptual thinking, calculations, and orientation. The total score is the sum of all item scores. The scale ranges from 0 to 30, where a score higher than 26 .
Time frame: Through study completion, an average of 3 months per patients ]
Change in Frontal Assessment Battery (FAB) Score
The FAB is a brief tool to assess executive functions. It consists of six subtests (similarities, lexical fluency, motor series, conflicting instructions, go-no-go, and grasping behavior). Total scores range from 0 to 18, where higher scores indicate better executive function.
Time frame: Through study completion, an average of 3 months per patients
Change in Patient Health Questionnaire-9 (PHQ-9) Score
The PHQ-9 is a self-report tool used to screen for and monitor the severity of depression. It consists of 9 items based on the DSM-IV criteria for depression. Each item is scored on a scale from 0 (not at all) to 3 (nearly every day). The total score ranges from 0 to 27, where higher scores indicate greater severity of depressive symptoms (e.g., 5-9: mild; 10-14: moderate; 15-19: moderately severe; 20-27: severe).
Time frame: Through study completion, an average of 3 months per patients
Change in Trail Making Test (TMT)
Psychological Well-being as Patient Health Questionnaire-9 (PHQ-9) for depressive symptoms and Generalized Anxiety Disorder-7 (GAD-7) for anxiety levels.
Time frame: Through study completion, an average of 3 months per patients
Change in Generalized Anxiety Disorder-7 (GAD-7) Score
The GAD-7 is a self-report scale for screening and severity measuring of generalized anxiety disorder. It consists of 7 items, each scored from 0 (not at all) to 3 (nearly every day). The total score ranges from 0 to 21, where higher scores indicate a higher level of anxiety (e.g., 5-9: mild; 10-14: moderate; 15-21: severe).The SDMT is used to assess psychomotor speed and attention. Patients are given a key matching symbols to numbers and must fill in the correct numbers for a series of symbols within 90 seconds. The score is the number of correct substitutions. Scores typically range from 0 to 110. A higher score indicates better cognitive processing speed and attention.
Time frame: Through study completion, an average of 3 months per patients
Change in Symbol Digit Modalities Test (SDMT) Score
The SDMT is used to assess psychomotor speed and attention. Patients are given a key matching symbols to numbers and must fill in the correct numbers for a series of symbols within 90 seconds. The score is the number of correct substitutions. Scores typically range from 0 to 110. A higher score indicates better cognitive processing speed and attention.
Time frame: Through study completion, an average of 3 months per patients
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