Individuals who rely on wheeled mobility have unique fall risk factors (e.g. seated postural control) but recommended fall risk screening tools are predominately designed for ambulatory individuals. Consequently, most non-ambulatory adults do not undergo comprehensive fall risk screening or receive targeted fall prevention strategies. PURPOSE: To examine the validity of smartphone-based postural control assessments in non-ambulatory adults. METHODS: Eleven participants (age: 35.4 ± 17.9) completed three clinical tests: The Trunk Control Test, Function in Sitting Test (FIST), and Tee-Shirt Test, as well as, four instrumented balance tasks in a standardized order: eyes open, eyes closed, functional reach, and functional stability boundary. During the balance tasks, participants held a smartphone and research-grade accelerometer to their chest. These devices measured root mean square (RMS) acceleration in the medial-lateral (ML), anterior-posterior, and vertical axes during all tasks. A median split of FIST scores differentiated participants with better and worse postural control. Spearman rank-order correlations between the two devices’ measurements were conducted, and receiver operating characteristic (ROC) and the area under the curves (AUC) were constructed to distinguish participants with better and worse postural control. RESULTS: Participant scores from the FIST differed between those with better and worse postural control (p = 0.020). There were significant moderate to strong correlations between measures derived from the smartphone and measures derived from the research-grade accelerometer during the balance tasks (ρ = 0.636-1.000; p < 0.01-0.035). The AUC for ROC plots were significant for RMS ML sway during the eyes open task and functional stability boundary (p=0.045 and 0.018, respectively). CONCLUSION: This pilot study illustrated that smartphone technology may be able to provide a valid assessment of seated postural control and have the ability to distinguish between those with better and worse postural control in the ML direction. Leveraging this form of technology could provide easily accessible and objective fall risk assessments for non-ambulatory adults.
Funded by: The National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR)