The role of convergence of AI, wearable technologies, AR&VR, gamification and cognitive data analysis in digital interventions based on the LBD method for sustainable development of treatment, early prediction and prevention of Alzheimer's disease
Dashtinezhadasl, Parisa (2025)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025071023623
https://urn.fi/URN:NBN:fi:amk-2025071023623
Tiivistelmä
Artificial intelligence technologies such as deep learning algorithms, digital biomarkers, wearable devices, gamification and multimodal cognitive data analysis have been identified as the most effective tools in the prevention, early detection and rehabilitation of this disease.
This method is designed based on the findings of cognitive neuroscience, and is presented in a personalized way for each user, relying on six main parameters influencing the transition to Alzheimer's disease, including genetics, neurological, immune, metabolic, psychological and environmental.
Disease history, cognitive-psychological conditions, and even socio-cultural components are taken into account in adjusting the stimulation pattern and treatment protocol, which leads to increased effectiveness, patient acceptance, and sustainability of results. The method is provided by an integrated digital platform as well as training for occupational therapists.
According to WHO reports, late diagnosis of Alzheimer's disease is one of the serious challenges in Alzheimer's treatment. This is the main goal of this intervention, and the proposed method, using Bio-Psycho-Social and Life-Course models, provides a personalized protocol that takes into account genetic, cognitive, psychological characteristics, and even past medical history such as illness or addiction.
Advanced concepts such as quantum cognition and "neuron banks" for storing biological and cognitive data have also been introduced to provide a platform for future-oriented treatments.
This integrated framework paves the way for the development of a novel application that plays a key role in the sustainable management of Alzheimer's, from the prediction stage to continuous rehabilitation.
This method is designed based on the findings of cognitive neuroscience, and is presented in a personalized way for each user, relying on six main parameters influencing the transition to Alzheimer's disease, including genetics, neurological, immune, metabolic, psychological and environmental.
Disease history, cognitive-psychological conditions, and even socio-cultural components are taken into account in adjusting the stimulation pattern and treatment protocol, which leads to increased effectiveness, patient acceptance, and sustainability of results. The method is provided by an integrated digital platform as well as training for occupational therapists.
According to WHO reports, late diagnosis of Alzheimer's disease is one of the serious challenges in Alzheimer's treatment. This is the main goal of this intervention, and the proposed method, using Bio-Psycho-Social and Life-Course models, provides a personalized protocol that takes into account genetic, cognitive, psychological characteristics, and even past medical history such as illness or addiction.
Advanced concepts such as quantum cognition and "neuron banks" for storing biological and cognitive data have also been introduced to provide a platform for future-oriented treatments.
This integrated framework paves the way for the development of a novel application that plays a key role in the sustainable management of Alzheimer's, from the prediction stage to continuous rehabilitation.