Integrating AI in Postural Rehabilitation for Parkinson's Disease

NCT ID: NCT07010328

Last Updated: 2025-06-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

20 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-10-01

Study Completion Date

2028-12-31

Brief Summary

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Postural abnormalities are highly disabling complications of Parkinson's disease (PD). These include camptocormia, anterocollis, and Pisa Syndrome (PS). PS is characterized by a lateral trunk flexion (LTF) typically exceeding 10 degrees, often accompanied by axial rotation, asymmetric shoulder positioning, and poor awareness of the postural alteration. This condition worsens during upright activities and improves in a supine position. Patients with PD and PS are characterized by more pronounced motor asymmetry, a disorganized trunk muscle activity, back pain, balance issues, and reduced quality of life compared to PD patients without postural disorders.

Camptocormia, another disabling postural anomaly, involves an anterior trunk flexion that also improves when lying down. Both PS and camptocormia are challenging to treat, with limited and short-lasting benefits from current multidisciplinary approaches, including medication, physiotherapy, botulinum toxin injections, and transcranial direct current stimulation (tDCS).

Given the limitations of traditional rehabilitation strategies, there is a growing need for innovative and personalized approaches. In this context, advanced technologies such as artificial intelligence (AI) offer new possibilities for home-based treatment. This study aims to evaluate the feasibility of using a real-time visual feedback system powered by AI as a complementary intervention following inpatient neurorehabilitation for PD patients with trunk postural disorders (PS or camptocormia). A secondary objective is to assess whether an AI-guided, personalized exercise program can help maintain improvements in posture, mobility, and quality of life in the medium term.

By integrating quantitative and qualitative outcomes, this study seeks to fill a gap in the literature and explore the potential of AI-driven home rehabilitation to support long-term functional gains and foster greater independence and well-being in people with PD.

Detailed Description

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Postural abnormalities represent particularly disabling complications of Parkinson's disease (PD), significantly affecting patients' functional independence and quality of life. Among the most commonly observed postural deformities there are camptocormia, anterocollis, and Pisa Syndrome (PS). Pisa Syndrome is clinically characterized by a lateral trunk flexion (LTF) greater than 10 degrees, typically presenting with a uniform inclination from the sacrum to the C7 vertebra. It is often accompanied by axial rotation, leading to elevation and forward displacement of the contralateral shoulder. Additional clinical features include difficulty rotating the trunk away from the leaning side, worsening of symptoms during upright posture or walking, improvement in the supine position, and poor patient awareness of the postural deviation.

Patients with PD and PS exhibit greater motor symptom asymmetry, disorganized paraspinal muscle activation, back pain, balance and postural control issues, and reduced quality of life compared to PD patients without postural abnormalities. The current standard of care for these postural disorders typically includes a multidisciplinary approach involving pharmacological treatment, physical therapy, botulinum toxin injections, and transcranial direct current stimulation (tDCS). However, these interventions frequently yield only modest and short-lived improvements, highlighting the need for more sustainable and accessible treatment strategies.

Camptocormia is another severe postural abnormality seen in PD, defined as an involuntary forward flexion of the trunk that is mostly apparent during standing and walking, and tends to reduce when the patient is supine. Like PS, camptocormia is frequently associated with chronic back pain and is often resistant to dopaminergic medication, with many patients reporting no significant improvement in either "on" or "off" states.

The long-term management of these postural disorders remains a significant clinical challenge. Innovative, individualized rehabilitation strategies that can be seamlessly integrated into patients' daily routines are urgently needed. In recent years, technological advancements-particularly in the field of artificial intelligence (AI)-have opened up new avenues for delivering personalized home-based interventions.

This study investigates the feasibility and potential benefits of using a real-time, AI-based visual feedback system as a complementary treatment following hospital-based neurorehabilitation for PD patients with trunk postural disorders, specifically PS and camptocormia. The intervention uses the Kemtai platform, which analyzes movement through video-based AI to deliver customized exercise routines and real-time corrective feedback. This allows patients to engage in highly interactive and adaptive rehabilitation sessions from home, potentially improving adherence and functional outcomes.

The primary objective of the study is to assess the feasibility, safety, and usability of this AI-based system as a continuation of care after inpatient rehabilitation. The secondary objective is to evaluate whether this personalized exercise program can prolong, over the medium term, the gains in posture, mobility, and quality of life achieved during the structured hospital-based rehabilitation period.

By collecting both qualitative feedback (e.g., user satisfaction, perceived usefulness) and quantitative metrics (e.g., trunk inclination angles, mobility scores by means of cinemtaic analysis of movement), this study aims to contribute new evidence on the role of intelligent digital tools in managing postural abnormalities in PD. The overarching hypothesis is that a home-based, AI-monitored exercise program could offer a sustainable, effective, accessible, and patient-centered approach to maintaining functional gains and supporting greater autonomy in everyday life.

Conditions

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Parkinson Disease Physical Inactivity Physical Disability Pisa Syndrome

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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PD-Subjects

Subjects with Parkinson's Disease and postural disorders (Pisa syndrome or camptocormia).

Group Type EXPERIMENTAL

Neurorehabilitation

Intervention Type PROCEDURE

4-8 weeks of inhospital Neurorehabilitation

AI-based home rehabilitation for postural disorders

Intervention Type DEVICE

AI-based home rehabilitation for postural disorders by means of the Kemtai platform.

Interventions

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Neurorehabilitation

4-8 weeks of inhospital Neurorehabilitation

Intervention Type PROCEDURE

AI-based home rehabilitation for postural disorders

AI-based home rehabilitation for postural disorders by means of the Kemtai platform.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Age above 18 years
* Diagnosis of Parkinson's disease according to MDS criteria
* Hoehn and Yahr stage ≤ III
* Clinical diagnosis of camptocormia (presence of an anterior axial trunk flexion of at least 30°), or of Pisa Syndrome (presence of a lateral trunk flexion of at least 10°) at the time of enrollment (T0)
* Mini-Mental State Examination (MMSE) score \> 23

Exclusion Criteria

* Atypical parkinsonian syndromes
* History of spinal surgery
* Previous vertebral trauma
* Current or past spinal tumors or infections
* Idiopathic scoliosis
* Ankylosing spondylitis
* Spinal canal stenosis
* Other neurological conditions
* Severe dyskinesias
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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IRCCS National Neurological Institute "C. Mondino" Foundation

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Luca Martinis

Role: PRINCIPAL_INVESTIGATOR

IRCCS Mondino Foundation

Locations

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IRCCS Mondino Foundation

Pavullo nel Frignano, Pavia, Italy

Site Status RECRUITING

Countries

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Italy

Central Contacts

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Roberto De Icco

Role: CONTACT

+390382380425

Cinzia Fattore

Role: CONTACT

+390382380385

Facility Contacts

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Roberto De Icco

Role: primary

+390382380479

References

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Hoshiyama M, Kaneoke Y, Koike Y, Takahashi A, Watanabe S. Hypokinesia of associated movement in Parkinson's disease: a symptom in early stages of the disease. J Neurol. 1994 Aug;241(9):517-21. doi: 10.1007/BF00873512.

Reference Type BACKGROUND
PMID: 7798998 (View on PubMed)

Fasano A, Geroin C, Berardelli A, Bloem BR, Espay AJ, Hallett M, Lang AE, Tinazzi M. Diagnostic criteria for camptocormia in Parkinson's disease: A consensus-based proposal. Parkinsonism Relat Disord. 2018 Aug;53:53-57. doi: 10.1016/j.parkreldis.2018.04.033. Epub 2018 May 8.

Reference Type BACKGROUND
PMID: 29759930 (View on PubMed)

Doherty KM, van de Warrenburg BP, Peralta MC, Silveira-Moriyama L, Azulay JP, Gershanik OS, Bloem BR. Postural deformities in Parkinson's disease. Lancet Neurol. 2011 Jun;10(6):538-49. doi: 10.1016/S1474-4422(11)70067-9. Epub 2011 Apr 22.

Reference Type BACKGROUND
PMID: 21514890 (View on PubMed)

Other Identifiers

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PD_PISA_AI

Identifier Type: -

Identifier Source: org_study_id

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