Personalized IoT-based Physical Activity Monitoring System for Heart Failure Patients
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-75
SponsorAbant Izzet Baysal University
About this trial
Current literature emphasizes the importance of increasing physical activity, ensuring its continuity, and reducing sedentary behaviors in patients with heart failure (HF). Many patients are referred to exercise-based rehabilitation programs following hospital discharge or an acute cardiac event. Although the benefits of these programs on cardiovascular health have been consistently demonstrated, adherence to recommended exercise regimens remains a major challenge. Previous studies indicate that through repeated and effective national health policies, large segments of society have adopted strategies to promote physical activity. However, despite the availability of various exercise and physical activity protocols, patients with HF remain prone to sedentary behaviors due to physical limitations, psychosocial factors, and lack of motivation.
Healthcare professionals play a critical role in promoting physical activity among HF patients, as encouraging participation in structured programs may improve health outcomes and reduce sedentary behaviors. Therefore, developing new and effective strategies to increase physical activity levels in this population is essential. Such strategies should focus on tailoring interventions to individual needs and health conditions, implementing long-term monitoring and support mechanisms to ensure continuity, and integrating technological innovations (e.g., smart wristbands, mobile applications) through user-friendly interfaces.
This study aims to improve physical activity levels and reduce sedentary behaviors among HF patients by designing a personalized, Internet of Things (IoT)-based physical activity monitoring system (IoT-HFActive). The central innovation of this system lies in its ability to generate personalized physical activity goals for the first time through automated mathematical algorithms that process real-time data collected from wearable devices.
During supervised exercise sessions, heart rate measurements obtained via smart wristbands will be used to calculate individual heart rate reserves (HRR). Based on these data, personalized activity goals will be established, including target heart rate zones, exercise intensity, and weekly activity duration. Subsequently, the server system will continuously monitor participants' daily physical activity levels and, through a specifically developed mobile application, provide real-time visualization of the results on participants' smartphones.
The system is designed with multiple functional components. Beyond setting personalized, patient-centered physical activity goals, it will also monitor adherence, deliver behavioral support techniques, and adapt targets over time. Participants will receive periodic individualized feedback, rewards such as virtual badges, progress visualizations, and video-supported motivational messages to reinforce engagement. Repeated time-series measurements of physical activity will allow dynamic recalibration of goals based on participants' performance.
In addition, participants will be able to track their personal progress, receive visual and video-based feedback, and observe how their activity behavior improves over time. These features are expected to strengthen motivation and adherence to exercise programs. Throughout the study, all procedures will be designed to align with participants' abilities and will be supported by user-friendly, intuitive interfaces to ensure accessibility and usability.
By combining personalized physical activity goals, real-time monitoring, and behaviorally informed feedback strategies, this study introduces an innovative, patient-centered IoT-based approach. The IoT-HFActive system is expected to address the long-standing challenge of exercise adherence in HF patients and to provide valuable evidence for the integration of technological innovations into cardiac rehabilitation services.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Diagnosis of HF confirmed by echocardiographic examination,
Heart failure characterized by reduced ejection fraction (HFrEF),
Individuals aged 18-75 years,
New York Heart Association (NYHA) functional class I, II, or III,
Disqualifiers
Presence of an ischemic lesion requiring revascularization on coronary angiography,
History of major cardiac surgery within the last three months,
Worsening dyspnea at rest and exercise intolerance,
Presence of arrhythmia problems such as ventricular tachyarrhythmia or atrial fibrillation,
Trial design
Design model
Parallel
Treatments tested in this trial
Device&behavioral
Other interventionThe IoT-HFActive intervention is a 12-month personalized program delivered via a mobile app, integrating patient-centered goal setting, real-time monitoring, and behavioral support. Intervention group participants will first attend two structured education sessions on exercise in HF and the use of the app and smart wristbands. They will then complete 12 supervised group-based sessions (3×50 min/week for 4 weeks) including warm-up, moderate aerobic exercise, and cool-down. Personalized activity goals will be calculated using heart rate reserve (HRR) via the Karvonen formula and exponential weighted moving average (EWMA) based on wristband data. Continuous remote monitoring will display target HR, achieved minutes, calories, and steps, and include daily confirmation prompts. Behavioral support features of the app will provide personalized feedback, motivational messages, visualizations, and monthly goal re-planning to promote adherence and reduce sedentary behavior.
Treatment groups
82 Participants
are divided into 2 treatment groupsGroup A: Intervention groupExperimental treatment 1 intervention
Group B: Control groupNo intervention 0 interventions
Trial outcomes
Primary outcomes
1
Physical activity adherence
Physical activity adherence will be evaluated using metric measurements, specifically the minutes participants reach HRtargetmin and HRtargetmax each month (intervention group only). In the first 3 months, adherence is calculated as \[(actual minutes/week) ÷ (target minutes/week) × 100\]; from month 4 onward, it is calculated as \[(actual minutes/week) ÷ (150 minutes/week target) × 100\], with 100% indicating full adherence. The central server automatically calculates and records adherence monthly as a percentage, and participants receive feedback on target achievement. Adherence is graded as: ≥80% adequate, ≥50% to \<80% partial, and \<50% low. These measurements will be used for subgroup analyses based on adherence levels.
Time frame
From the time of enrollment until the intervention ends at 12 months2
Physical activity level and sedentary behavior
It will be assessed using the Simple Physical Activity Questionnaire (SIMPAQ). The questionnaire, developed by Rosenbaum et al. (2020), evaluates physical activity and sedentary behaviors performed by participants over the past 7 days. It consists of five items covering time spent in bed, sedentary activities, walking, exercise, and incidental activities. The reported durations by participants should approximately total 24 hours. The duration of moderate-to-vigorous physical activity is calculated by summing the time spent walking and exercising. The questionnaire will be administered to all participants at three-month intervals.
Time frame
From enrollment to the end of the intervention at 12 monthsSecondary outcomes
1
Left ventricular function
Left ventricular functions will be assessed using comprehensive parameters of transthoracic echocardiography (TTE). TTE will be performed in the left lateral decubitus position at rest according to the American Society of Echocardiography criteria, using a Philips EPIQ 7 echocardiography system with a Philips X5-1 xMatrix 3.5 MHz transducer (Quiñones et al., 2002). Parameters include left ventricular ejection fraction (LVEF), left ventricular wall thickness, diastolic and systolic chamber dimensions, mitral and tricuspid annular E and A waves, tissue Doppler-derived e´ and a´ waves, E/A ratio, E/e´ ratio, TAPSE, and MAPSE. Mean values will be calculated, subgroup analyses will be conducted based on physical activity adherence, and between-group comparisons will be reported.
Time frame
From enrollment to the end of the intervention at 12 months2
Functional capacity
Functional capacity will be assessed using the Six-Minute Walk Test (6MWT). After at least 10 minutes of rest, participants will be instructed to walk at their own pace, as fast as possible, for six minutes along a 30-meter corridor marked at 1-meter intervals. They will be informed that they may slow down or stop to rest if they experience shortness of breath. Blood pressure will be measured at the beginning and end of the test, and oxygen saturation and heart rate will be recorded before, during, and after the test using a fingertip pulse oximeter. At the end of the test, the six-minute walking distance (6MWD) will be recorded in meters (ATS statement, 2002). Mean values will be calculated, subgroup analyses will be performed according to physical activity adherence, and between-group comparisons will be reported.
Time frame
From enrollment to the end of the intervention at 12 months3
Brain natriuretic peptide
For brain natriuretic peptide (BNP), venous blood samples (minimum 2 cc) will be collected into ethylenediaminetetraacetic acid (EDTA) tubes after an overnight fast, between 8:30 and 10:30 a.m. Samples will be analyzed using the "Architect BNP Test" kit with the fluorescence immunoassay method. This method is capable of measuring BNP levels in the range of 10-5000 pg/ml with high sensitivity and accuracy. Mean values will be calculated, subgroup analyses will be performed according to physical activity adherence, and between-group comparisons will be reported.
Time frame
From enrollment to the end of the intervention at 12 months4
Quality of Life measured by the Left Ventricular Dysfunction Scale
Quality of life will be assessed using the Left Ventricular Dysfunction Questionnaire (LVD-36). The LVD-36 is a self-report tool developed by O'Leary and Jones (2000) to measure the impact of left ventricular dysfunction on daily life and well-being, as well as the effect of the disease and treatment efficacy in patients with heart failure. The questionnaire consists of 36 items presented to individuals as true/false statements. Correct responses are summed, and the total number of correct responses is expressed as a percentage. Scores range from 0 to 100, with higher scores indicating lower quality of life. Mean values will be calculated, subgroup analyses will be conducted according to physical activity adherence, and between-group comparisons will be reported. In the scale development study, the Kuder-Richardson coefficient (internal consistency analysis) was found to be 0.95.
Time frame
From enrollment to the end of the intervention at 12 monthsLocations
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Source ClinicalTrials.gov
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