Open AccessSimple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study
Author(s) -
Dongning Su,
Zhu Li,
Xin Jiang,
Fangzhao Zhang,
Wanting Yu,
Huizi Ma,
Chunxue Wang,
Zhan Wang,
Xuemei Wang,
Wanli Hu,
Brad Manor,
Tao Feng,
Junhong Zhou
Publication year - 2021
Publication title -
jmir mhealth and uhealth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.356
H-Index - 50
ISSN - 2291-5222
DOI - 10.2196/25451
Subject(s) - physical medicine and rehabilitation , gait , stride , rating scale , parkinson's disease , cadence , psychology , gold standard (test) , physical therapy , gait analysis , medicine , disease , developmental psychology , pathology
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 ( F 1,103 =14.1, P <.001) and stride time variability ( F 1,103 =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.