Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study

Abstract

Background Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD. Objective We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD. Methods Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system. Results High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F1,103=14.1, P<.001) and stride time variability (F1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores. Conclusions A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.

Gait and Balance Assessments using Smartphone Applications in Parkinson’s Disease: A Systematic Review

“…% Male: NA . Chronicity - Recurrent fallers: 8.1 (5.9 – 11.5) - Nonrecurrent fallers: 4.5 (1.5 – 6.8) - Recurrent fallers: 69.5 (65.5 – 76.5) - Nonrecurrent fallers: 69.5 (60 – 74) 59 Su et al 2021 [ 9 ] Cross-sectional . H & Y: NA .…”

“…Table 2 presents the details of the smartphone outcome measures, models of smartphone used, gait and balance outcome measures, measures of validity, reliability, sensitivity, and specificity of the smartphone measures, the main results, as well as the intended users of the smartphone, and locations of assessment. Only one of the included studies used a smartphone application to specifically predict future falls among individuals with PD [ 8 ], four studies used smartphone applications to specifically evaluate balance impairments [ 25 – 28 ], and 13 studies used smartphone applications to specifically assess gait including freezing of gait (FoG) [ 9 , 29 – 40 ]. In addition, 11 studies used smartphone applications to assess gait and balance simultaneously [ 41 – 51 ] and two studies used smartphone applications to assess gait, balance, and predict future falls among individuals with PD [ 52 , 53 ].…”

“…Clinicians . Laboratory based assessment Su et al 2021 [ 9 ] . Smartphone App based on accelerometer and gyroscope to assess gait .…”

“…Fiems et al [ 8 , 25 ] reported concurrent validity through significant correlations between an assessment performed with the Sway Balance™ smartphone application (balance and fall protocols) and IMUs placed at pelvic and thoracic regions (ρ = –0.61 to –0.92, p < 0.001). Su et al [ 9 ] found significant correlations between a customized smartphone application and mobility laboratory measures, including stride time and stride variability, and total UPDRS-III ( r = 0.98 – 0.99, p < 0.001 and β = 0.37 – 0.39, p < 0.001). Similarly, Borzì et al [ 41 ] reported significant correlations between a smartphone derived quality of movement (QoM) index, 6MWT, and gait and postural stability items of the UPDRS III ( r = 0.4 – 0.61, p < 0.0001).…”

“…However, the clinical test’s scoring criteria are often subjective, and it is recommended that trained healthcare professionals such as physical and occupational therapists administer the tests [ 8 ]. In addition, clinical outcomes are often insensitive to subtle dysfunctional changes and have poor reliability [ 9 ]. Within a laboratory-based setting, gait alterations and balance impairments have been accurately evaluated using motion capture, wearable sensor systems, or force plate [ 10 – 12 ].…”