Clinical trial NCT06490783: Cyber-Human Systems for Personalized Well-being and Health
Official title: CybeR-human systEms for perSonalIzed mentaL and physIcal wEll-beiNg and Health
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Geriatric Health Services
Health Services for the Elderly
Keywords: elderly
wellbeing
Artificial Intelligence
quality of life
AI
Lead sponsor: Istituto per la Ricerca e l'Innovazione Biomedica
Sponsor class: OTHER
First posted: 2024-07-08
Enrollment: 10
Sex: ALL
Minimum age (years): 60
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: Heart Rate(HR)
timeFrame: Through study completion, an average of 1 year
measure: Heart rate variability (HRV)
timeFrame: Through study completion, an average of 1 year
measure: Facial emotion recognition
timeFrame: Through study completion, an average of 1 year
measure: Steps taken
timeFrame: Through study completion, an average of 1 year
measure: Distance traveled
timeFrame: Through study completion, an average of 1 year
measure: Calories burned
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Messina
country: Italy
facility: Institute for Biomedical Research and Innovation (IRIB)-National Reasearch Council (CNR), Messina 98164, Italy
Study officials:
affiliation: Istituto per la Ricerca e l'Innovazione Biomedica
name: Gennaro Tartarisco
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: Jan et al.
citation: Panicker et al.