Quantitative Evaluation of Postural SmartVest’s Multisensory Feedback for Affordable Smartphone-Based Post-Stroke Motor Rehabilitation

Maria da Graca Campos Pimentel (), Amanda Polin Pereira, Olibario Jose Machado Neto, Larissa Cardoso Zimmermann and Valeria Meirelles Carril Elui
Additional contact information
Maria da Graca Campos Pimentel: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo (USP), São Carlos 13566-590, SP, Brazil
Amanda Polin Pereira: Programa de Pós-Graduação Interunidades em Bioengenharia, EESC-FMRP-IQSC, Universidade de São Paulo (USP), Ribeirão Preto 14048-900, SP, Brazil
Olibario Jose Machado Neto: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo (USP), São Carlos 13566-590, SP, Brazil
Larissa Cardoso Zimmermann: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo (USP), São Carlos 13566-590, SP, Brazil
Valeria Meirelles Carril Elui: Programa de Pós-Graduação Interunidades em Bioengenharia, EESC-FMRP-IQSC, Universidade de São Paulo (USP), Ribeirão Preto 14048-900, SP, Brazil

IJERPH, 2025, vol. 22, issue 7, 1-22

Abstract: Accessible tools for post-stroke motor rehabilitation are critically needed to promote recovery beyond clinical settings. This pilot study evaluated the impact of a posture correction intervention using the Postural SmartVest, a wearable device that delivers multisensory feedback via a smartphone app. Forty individuals with post-stroke hemiparesis participated in a single supervised session, during which each patient completed the same four-phase functional protocol: multidirectional walking, free walking toward a refrigerator, an upper-limb reaching and object-handling task, and walking back to the starting point. Under the supervision of their therapists, each patient performed the full protocol twice—first without feedback and then with feedback—which allowed within-subject comparisons across multiple metrics, including upright posture duration, number and frequency of posture-related events, and temporal distribution. Additional analyses explored associations with demographic and clinical variables and identified predictors through regression models. Wilcoxon signed-rank and Mann–Whitney U tests showed significant improvements with feedback, including an increase in upright posture time ( p < 0.001 ), an increase in the frequency of upright posture events ( p < 0.001 ), and a decrease in the total task time ( p = 0.038 ). No significant subgroup differences were found for age, sex, lateralization, or stroke chronicity. Regression models did not identify significant predictors of improvement.

Keywords: stroke rehabilitation; wearable technology; multisensory feedback; smartphone application; postural balance; mHealth; stabilization; digital intervention (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/22/7/1034/pdf (application/pdf)
https://www.mdpi.com/1660-4601/22/7/1034/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:22:y:2025:i:7:p:1034-:d:1689924

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().