Personalized IoT-based Physical Activity Monitoring System for Heart Failure Patients

NCT07171372 | Not Yet Recruiting

• 1 other identifier: AIBU-HSF-SDEMIR-07
• Study Type: interventional
• Target: 82
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Heart Failure; Reduced Ejection Fraction Heart Failure

Keywords

heart failure; internet of things; physical activity; rehabilitation; sedentary behavior; telemedicine; wearable electronic devices

Outcome Measures

Primary Outcomes (2)

• 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.

  From the time of enrollment until the intervention ends at 12 months

• 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.

  From enrollment to the end of the intervention at 12 months

Secondary Outcomes (6)

• Left ventricular function

  From enrollment to the end of the intervention at 12 months

• Functional capacity

  From enrollment to the end of the intervention at 12 months

• Brain natriuretic peptide

  From enrollment to the end of the intervention at 12 months

• Quality of Life measured by the Left Ventricular Dysfunction Scale

  From enrollment to the end of the intervention at 12 months

• Motion analysis and energy expenditure parameters

  From enrollment to the end of the intervention at 12 months

Study Arms (2)

Intervention group

EXPERIMENTAL

Participants in the intervention group will receive the IoT-HFActive program, a 12-month personalized and automated mHealth intervention delivered via a mobile application. They will first attend two structured education sessions covering the importance of exercise in HF, active lifestyle recommendations, and training on the app and smart wristbands. Next, they will participate in 12 supervised, group-based aerobic exercise sessions (3×50 min/week for 4 weeks) with warm-up, moderate-intensity aerobic exercise, and cool-down. Individualized activity goals will then be established using HRR calculations via the Karvonen formula and EWMA analysis of wristband data. Real-time monitoring through the app will display personal targets, achieved minutes, calories, and steps, and will include daily confirmation prompts. Behavioral support features will provide personalized feedback, motivational messages, visualizations, and monthly re-planning of goals to strengthen adherence.

Other: Device&behavioral

Control group

NO INTERVENTION

In the control group, participants will receive routine care provided by the healthcare system, with no structured physical activity intervention or behavioral support. Follow-up assessments will be conducted in parallel with the intervention group.

Interventions

Device&behavioral OTHER

The 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.

Intervention group

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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,
• No evidence of ischemia on coronary angiography performed within the last three months,
• No physical limitations preventing exercise,
• Standardized Mini-Mental State Examination (MMSE) score ≥ 25,
• Ownership of a smartphone compatible with the mobile application to be used in the study.

You may not qualify if:

• 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,
• Uncontrolled diabetes (Hemoglobin A1C ≥ 7 mg/dl),
• Presence of chronic pulmonary disease or renal insufficiency,
• Symptomatic postural hypotension (≥20 mmHg systolic drop),
• Score ≥ 9 on the Edmonton Frail Scale (moderate to severe frailty),
• Morbid obesity (BMI \> 40 kg/m²),
• Neuropsychiatric disorders severely impairing cognitive functions such as dementia or Alzheimer's disease,
• Unwillingness to participate in the exercise program.

Sponsors & Collaborators

• Abant Izzet Baysal University: lead

MeSH Terms

Conditions

Heart Failure; Heart Failure, Systolic; Motor Activity; Sedentary Behavior

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Behavior

Study Officials

• Seyma Demir Erbas, PhD

  Abant Izzet Baysal University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Seyma Demir Erbas, PhD

CONTACT

05464715674; seymademir@ibu.edu.tr

Seyma Demir Erbas, PhD

CONTACT

+905464715674; seymademir@ibu.edu.tr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: September 6, 2025
• First Posted: September 12, 2025
• Study Start: December 13, 2025
• Primary Completion (Estimated): December 13, 2026
• Study Completion (Estimated): March 13, 2027
• Last Updated: September 12, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share