The effect of the Lokomat® robotic-orthosis system on lower extremity rehabilitation in patients with stroke: a systematic review and meta-analysis

被引:7
作者
Wu, Lina [1 ]
Xu, Gui [1 ]
Wu, Qiaofeng [1 ]
机构
[1] Foresea Life Insurance Nanning Hosp, Dept Rehabil, Nanning, Guangxi, Peoples R China
来源
FRONTIERS IN NEUROLOGY | 2023年 / 14卷
关键词
Lokomat (R); stroke; lower extremity function; rehabilitation; meta-analysis; PEDRO SCALE; GAIT; SUBACUTE; PILOT; BALANCE; QUALITY;
D O I
10.3389/fneur.2023.1260652
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: The Lokomat (R) is a device utilized for gait training in post-stroke patients. Through a systematic review, the objective was to determine whether robot-assisted gait training with the Lokomat (R) is more effective in enhancing lower extremity rehabilitation in patients with stroke in comparison to conventional physical therapy (CPT).Methods: In this study, a systematic search was conducted in various databases, including CINAHL, MEDLINE, PubMed, Embase, Cochrane Library, Scopus, Web of Science, and Physiotherapy Evidence Database (PEDro), as well as bibliographies of previous meta-analyses, to identify all randomized controlled trials that investigated the use of Lokomat (R) devices in adult stroke patients. The study aimed to derive pooled estimates of standardized mean differences for six outcomes, namely, Fugl-Meyer Assessment lower-extremity subscale (FMA-LE), Berg Balance Scale (BBS), gait speed, functional ambulation category scale (FAC), timed up and go (TUG), and functional independence measure (FIM), through random effects meta-analyses.Results: The review analyzed 21 studies with a total of 709 participants and found that the use of Lokomat (R) in stroke patients resulted in favorable outcomes for the recovery of balance as measured by the BBS (mean difference = 2.71, 95% CI 1.39 to 4.03; p < 0.0001). However, the FAC showed that Lokomat (R) was less effective than the CPT group (mean difference = -0.28, 95% CI -0.45 to 0.11, P = 0.001). There were no significant differences in FMA-LE (mean difference = 1.27, 95% CI -0.88 to 3.42, P = 0.25), gait speed (mean difference = 0.02, 95% CI -0.03 to 0.07, P = 0.44), TUG (mean difference = -0.12, 95% CI -0.71 to 0.46, P = 0.68), or FIM (mean difference = 2.12, 95% CI -2.92 to 7.16, P = 0.41) between the Lokomat (R) and CPT groups for stroke patients.Conclusion: Our results indicate that, with the exception of more notable improvements in balance, robot-assisted gait training utilizing the Lokomat (R) was not superior to CPT based on the current literature. Considering its ability to reduce therapists' work intensity and burden, the way in which Lokomat (R) is applied should be strengthened, or future randomized controlled trial studies should use more sensitive assessment criteria.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Effect of physical exercise on fear of falling in patients with stroke: A systematic review and meta-analysis
    Chiu, Chi Yat
    Ng, Michael Yu-Hin
    Lam, Sum Chung
    Hui, Ka Yan
    Keung, Chun Ho
    Ouyang, Huixi
    Li, Xun
    Pang, Marco Yiu-Chung
    CLINICAL REHABILITATION, 2023, 37 (03) : 294 - 311
  • [22] Rhythmic Auditory Cueing in Motor Rehabilitation for Stroke Patients: Systematic Review and Meta-Analysis
    Yoo, Ga Eul
    Kim, Soo Ji
    JOURNAL OF MUSIC THERAPY, 2016, 53 (02) : 149 - 177
  • [23] The Effects of Transcranial Direct Current Stimulation on Balance and Gait in Stroke Patients: A Systematic Review and Meta-Analysis
    Dong, Ke
    Meng, Shifeng
    Guo, Ziqi
    Zhang, Rufang
    Xu, Panpan
    Yuan, Erfen
    Lian, Tao
    FRONTIERS IN NEUROLOGY, 2021, 12
  • [24] Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis
    Loro, Alberto
    Borg, Margherita Beatrice
    Battaglia, Marco
    Amico, Angelo Paolo
    Antenucci, Roberto
    Benanti, Paolo
    Bertoni, Michele
    Bissolotti, Luciano
    Boldrini, Paolo
    Bonaiuti, Donatella
    Bowman, Thomas
    Capecci, Marianna
    Castelli, Enrico
    Cavalli, Loredana
    Cinone, Nicoletta
    Cosenza, Lucia
    Di Censo, Rita
    Di Stefano, Giuseppina
    Draicchio, Francesco
    Falabella, Vincenzo
    Filippetti, Mirko
    Galeri, Silvia
    Gimigliano, Francesca
    Grigioni, Mauro
    Invernizzi, Marco
    Jonsdottir, Johanna
    Lentino, Carmelo
    Massai, Perla
    Mazzoleni, Stefano
    Mazzon, Stefano
    Molteni, Franco
    Morelli, Sandra
    Morone, Giovanni
    Nardone, Antonio
    Panzeri, Daniele
    Petrarca, Maurizio
    Posteraro, Federico
    Santamato, Andrea
    Scotti, Lorenza
    Senatore, Michele
    Spina, Stefania
    Taglione, Elisa
    Turchetti, Giuseppe
    Varalta, Valentina
    Picelli, Alessandro
    Baricich, Alessio
    BRAIN SCIENCES, 2023, 13 (01)
  • [25] Virtual reality for improving balance in patients after stroke: A systematic review and meta-analysis
    Li, Zhen
    Han, Xiu-Guo
    Sheng, Jing
    Ma, Shao-Jun
    CLINICAL REHABILITATION, 2016, 30 (05) : 432 - 440
  • [26] Robotic arm use for upper limb rehabilitation after stroke: A systematic review and meta-analysis
    Lee, Bih-O
    Saragih, Ita Daryanti
    Batubara, Sakti Oktaria
    KAOHSIUNG JOURNAL OF MEDICAL SCIENCES, 2023, 39 (05) : 435 - 445
  • [27] Synergistic Effect of Combined Mirror Therapy on Upper Extremity in Patients With Stroke: A Systematic Review and Meta-Analysis
    Luo, Zhonghua
    Zhou, Yuqing
    He, He
    Lin, Shanshan
    Zhu, Rui
    Liu, Zhen
    Liu, Jiemei
    Liu, Xiaoli
    Chen, Shuping
    Zou, Jihua
    Zeng, Qing
    FRONTIERS IN NEUROLOGY, 2020, 11
  • [28] Effectiveness of an ankle-foot orthosis on walking in patients with stroke: a systematic review and meta-analysis
    Choo, Yoo Jin
    Chang, Min Cheol
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [29] Exercise for depressive symptoms in stroke patients: a systematic review and meta-analysis
    Eng, Janice J.
    Reime, Birgit
    CLINICAL REHABILITATION, 2014, 28 (08) : 731 - 739
  • [30] The efficacy of virtual reality for upper limb rehabilitation in stroke patients: a systematic review and meta-analysis
    Soleimani, Mohsen
    Ghazisaeedi, Marjan
    Heydari, Soroush
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2024, 24 (01)