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Movement-Specific Reinvestment in Older People Explains Past Falls and Predicts Future Error-Prone Movements.

Lisa MusculusNoel KinradeSylvain LabordeMelina GleißertMiriam StreichBabett Helen Lobinger
Published in: International journal of environmental research and public health (2021)
The tendency to think about or consciously control automated movements (i.e., movement-specific reinvestment) is a crucial factor associated with falling in the elderly. We tested whether elderly people's movement-specific reinvestment depended on their past falling history and whether it can predict future error-prone movements. In a longitudinal pre-post design, we assessed n = 21 elderly people's (Mage = 84.38 years, SD = 5.68) falling history, movement-specific reinvestment (i.e., Movement-Specific Reinvestment Scale), and physical functioning (i.e., Short-Physical-Performance Battery). Following a baseline assessment, participants reported their movement behavior in a daily diary for 2 months, after which we assessed their movement-specific reinvestment and physical functioning again (longitudinal, pre-post design). Results revealed, first, that participants' movement self-consciousness score was fairly stable, while their conscious-motor-processing score was less stable. Second, conscious motor processing was higher in participants who had fallen as opposed to those who had not fallen in the past. Third, conscious motor processing predicted error-prone future movement behavior reported in the daily diary. For identifying individuals who are more prone to fall, caregivers, rehabilitation staff, or doctors could apply the Movement-Specific Reinvestment Scale to screen elderly people's psychomotor behavior. Based on conscious motor processing, monitoring cognitions could be tailored in theory-based, individual interventions involving both cognitive and motor training.
Keyphrases
  • physical activity
  • mental health
  • machine learning
  • deep learning
  • solid state