This research aims to investigate the impact of Vertebral Subluxation, or dysfunction, on static and dynamic balance. The study will utilise machine-learning models and time series analysis to predict static and dynamic balance parameters based on subluxation severity and to analyse its evolution over time. Participants underwent chiropractic sessions, with static and dynamic balance parameters gauged using the Gait & Balance Mobile App. Key outcomes will determine whether chiropractic care influences balance and elucidate the relationship between subluxation and balance. The integration of Artificial Intelligence into chiropractic research aims to provide personalised interventions and deepen the understanding of patient care.

Grant Value: $25,947.50
Chief Investigator: Dr Imran Amjad – New Zealand College of Chiropractic
Co-Investigator: Dr Imran Khan Niazi – New Zealand College of Chiropractic
Status: Complete

Clinical Applications:
Chiropractic care improved balance measures such as gait symmetry and step speed, with the biggest gains in children. AI models predicted these outcomes accurately (R² up to 0.93), and subluxation measures added useful predictive value when biomechanical data were limited.

Researcher updates:

  • February 2026
    Data analysis, machine learning, and AI modelling have been completed, and the results have been finalised. A manuscript is being prepared for submission.
  • June 2025
    The data preprocessing and analysis using machine learning and AI models has been completed, and some initial results have been obtained. The models are now being refined further to produce final results.  Expected completion date is November 2025.