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Mobile health technologies offer new ways to assess mobility outside the clinic. However, data from unsupervised settings show significant differences compared to supervised assessments due to various influencing factors.

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Area of Science:

  • Biomedical Engineering
  • Clinical Mobility Assessment
  • Digital Health

Background:

  • Mobile health technologies (wearables, sensors) are increasingly used for quantifying mobility in daily life.
  • These unsupervised assessments complement traditional clinical evaluations by capturing real-world data and rare events.
  • Such data hold potential for clinical decision-making and as outcomes in clinical trials.

Purpose of the Study:

  • To highlight the emerging role of mobile health technologies in mobility assessment.
  • To discuss the disparities observed between unsupervised and supervised mobility assessments.
  • To identify factors influencing these disparities and guide clinical integration.

Main Methods:

  • Review of studies comparing unsupervised (daily living) and supervised (lab/hospital) mobility assessments.
  • Analysis of factors contributing to differences in mobility data.
  • Synthesis of implications for clinical practice and research.

Main Results:

  • Significant disparities exist between mobility parameters measured in unsupervised versus supervised settings.
  • These differences are influenced by psychological, physiological, cognitive, environmental, and technical factors.
  • Mobility types and patient diagnoses also impact the observed disparities.

Conclusions:

  • Mobile health technologies provide ecologically valid mobility data but differ from supervised assessments.
  • Understanding the multifaceted factors causing these disparities is crucial for successful clinical adoption.
  • Consideration of these factors is essential for reliable clinical decision-making and trial outcomes.