Cyber-Human Systems for Personalized Well-being and Health

NCT06490783 | Recruiting

• 1 other identifier: CNR-IRIB-PRO-2024-009
• Study Type: observational
• Target: 10
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of the present study is to evaluate the effectiveness of using multi-parameter monitoring devices in the elderly to improve their quality of life not already understood as the absence of disease but in a logic that is intrinsically linked to the body-mind relationship, which is increasingly significant as biological age advances. The study will be conducted on a sample of volunteer elderly subjects who will wear devices capable of constantly monitoring vital parameters such as heart rate, physical activity, sleep quality, stress levels and higher level activities, linked sensory and cognitive aspects ecologically integrated with the elderly person's living environment, in the sense of an evaluative and qualitative focus on relationships within the person's area of action/interaction, possibly supported and stimulated by individualized and easily usable activities. The signals interpreted and returned by the technology to the elderly person who uses it can also act as a reassuring self-assessment of even normal body states, sometimes experienced as threatening and anxiogenic, thus stressful. The collection and management of these data may serve as a reference to the recognition of distress signals and complex experiences (e.g., depressive) that normally have significant effects on mental health, understood as intrinsically linked to the health of the body.

Conditions

Geriatric Health Services; Health Services for the Elderly

Keywords

elderly; wellbeing; Artificial Intelligence; quality of life; AI

Outcome Measures

Primary Outcomes (6)

• Heart Rate(HR)

  The chest strap Polar H10 uses an electrocardiogram (ECG) sensor to detect the electrical activity of the heart and calculate heart rate in beats per minute (bpm).

  Through study completion, an average of 1 year

• Heart rate variability (HRV)

  The chest strap Polar H10 measures the time intervals between successive heart beats and calculates Heart Rate Variability (HRV) in milliseconds (ms).

  Through study completion, an average of 1 year

• Facial emotion recognition

  The smartphone's camera records facial expressions in order to evaluate and quantify human emotions. The Artificial Intelligence (AI) algorithms, recognizing the human face, identify important facial features and examine them to categorize different facial emotions. The emotions associated with these facial expressions are then extracted.

  Through study completion, an average of 1 year

• Steps taken

  This refers to the total number of times each subject take a step with either foot. It's a basic unit to measure overall activity level. Fitness trackers typically use steps to monitor movement throughout the day.

  Through study completion, an average of 1 year

• Distance traveled

  This indicates the actual physical length each subject covered during activity. It's usually measured in miles or kilometers. Distance traveled can be calculated based on the number of steps you take and your stride length (the distance between two consecutive footfalls with the same foot).

  Through study completion, an average of 1 year

• Calories burned

  This refers to the amount of energy each subject's body expends during activity. It's measured in calories (kcal). The number of calories burned depends on various factors like weight, height, activity intensity (e.g., walking vs running), and duration. Fitness trackers typically estimate calorie burn based on steps taken, distance traveled, and personal information.

  Through study completion, an average of 1 year

Study Arms (1)

healthy elderly

The sample consists of healthy elderly volunteers who will wear noninvasive wearable devices (smartwatch and heart rate monitor band) to monitor physiological parameters and emotional states related to anxiety and stress.

Other: Noninvasive wearable devices (smartwatch and heart rate monitor band)

Interventions

Noninvasive wearable devices (smartwatch and heart rate monitor band) OTHER

Each enrolled subject will be equipped with noninvasive wearable devices (smartwatch and heart rate monitor band) to monitor physiological parameters and emotional states related to anxiety and stress. Each subject will be required to wear the smartwatch on his or her wrist for the duration of the study, about 3-6 months; while the heart rate monitor band will be worn for about 10 minutes a day. At the same time, a mobile application, RESILIENT, will be developed and implemented to serve as the main interface for self-assessment data entry and for feedback and recommendations. The psycho-physical condition of each subject will be monitored by the app through customized and contextualized mental exercises based on daily activities. This approach will test the effectiveness of the proposed architecture in reducing unhealthy habits and promoting health and wellness recommendations

healthy elderly

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The elderly population plays a significant role in society, influencing social, economic, and cultural aspects. Intellectual stimulation and social interactions are crucial for keeping their minds active and preventing isolation, decline and depression. Physical activity can help to maintain strength and balance, preventing motor decline and reducing the risk of falls and injuries. Issues such as loss of intrinsic abilities, exposure to adversity, and functional deterioration can cause psychological distress. Common concerns among the elderly include health changes, loss of loved ones, income reduction, and a diminished sense of purpose after retirement. A proactive approach to health management and a strong support system can significantly enhance the overall well-being of the elderly.

You may qualify if:

• healthy male and female aged 60 years and older
• signing of informed consent

You may not qualify if:

• chronic diseases
• cardiovascular disease
• presence of dementia and/or depression
• presence of confirmed paranoid or psychotic symptoms

Sponsors & Collaborators

• Istituto per la Ricerca e l'Innovazione Biomedica: lead

Study Sites (1)

Institute for Biomedical Research and Innovation (IRIB)-National Reasearch Council (CNR), Messina 98164, Italy

Messina, 98164, Italy

RECRUITING

Related Publications (3)

• Abd-Alrazaq A, Alhuwail D, Schneider J, Toro CT, Ahmed A, Alzubaidi M, Alajlani M, Househ M. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. NPJ Digit Med. 2022 Jul 7;5(1):87. doi: 10.1038/s41746-022-00631-8.

  PMID: 35798934 BACKGROUND

• Jan et al.

  BACKGROUND

• Panicker et al.

  BACKGROUND

Study Officials

• Gennaro Tartarisco

  Istituto per la Ricerca e l'Innovazione Biomedica

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Gennaro Tartarisco

CONTACT

+393283377046; gennaro.tartarisco@irib.cnr.it

Maria Valeria Maiorana

CONTACT

mariavaleria.maiorana@irib.cnr.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Unit

Study Record Dates

• First Submitted: June 17, 2024
• First Posted: July 8, 2024
• Study Start: July 1, 2024
• Primary Completion: December 31, 2025
• Study Completion (Estimated): May 31, 2026
• Last Updated: May 1, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)