Reliability and validity of remote Life Space Assessment: LSA in persons with chronic stroke

 

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Abstract

Background StepWatch Activity Monitor (SAM) is used to measure the mobility of chronic hemiparetic patients and the Life Space Assessment (LSA) scale was developed to assess the displacement of hemiparetic patients in different contexts through self-reporting. Studies that apply the LSA remotely and correlate it with the number of steps measured by the SAM were not found.

Objective To evaluate the measurement properties of the LSA applied remotely and to evaluate the correlation between the LSA scale score and the number of steps measured by the SAM in post-stroke chronic hemiparetic patients.

Methods Nineteen patients participated in the study. The LSA scale was applied remotely and later, face to face. The SAM measured the steps taken by the participants over a period of three consecutive days. The correlation between the LSA and the SAM was performed using Pearson's correlation. The measurement properties calculated of remote LSA were the intraclass correlation coefficient (ICC), Cronbrach's alpha, standard error of measurement (SEM), and smallest real difference (SRD).

Results The reproducibility of the LSA scale between remote and face-to-face applications was considered excellent with ICC = 0.85 (IC 95% 0.62-0.94); SEM = 8.4; SRD = 23.2, and Cronbach's alpha = 0.85. The correlation between SAM and LSA was positive, considered moderate (r = 0.51) and significant (p = 0.025).

Conclusion The LSA is a reproducible measure for post-stroke chronic hemiparetic patients even if applied remotely and can be used as a remote measurement for mobility in a real-world environment for people with chronic hemiparesis after stroke.

Resumo

Antecedentes O StepWatch Activity Monitor (SAM) é utilizado para medir a mobilidade de pacientes hemiparéticos crônicos e a escala Life Space Assessment (LSA) avalia o deslocamento de pacientes hemiparéticos em diferentes contextos por meio de autorrelato. Não foram encontrados estudos que tenham aplicado a LSA remotamente nem que a correlacionam com o número de passos mensurados pelo SAM.

Objetivo Avaliar as propriedades de medida da LSA aplicada remotamente e avaliar a correlação entre o escore da escala LSA e o número de passos mensurados pelo SAM em pacientes com hemiparesia crônica pós-AVC.

Métodos Dezenove participantes responderam a LSA remotamente e, posteriormente, presencialmente. O SAM mediu os passos dados pelos participantes durante um período de três dias consecutivos. A correlação entre a LSA e o SAM foi realizada por meio da correlação de Pearson. As propriedades de medida calculadas da LSA aplicada remotamente foram o coeficiente de correlação intraclasse (ICC), alfa de Cronbrach, erro do padrão de medida (SEM) e menor diferença real (SRD).

Resultados A reprodutibilidade da escala LSA entre as aplicações remotas e presenciais foi considerada excelente com ICC = 0,85 (IC 95% 0,62-0,94); SEM = 8,4; SRD = 23,2 e alfa de Cronbrach = 0,85. A correlação entre SAM e a LSA foi positiva, considerada moderada (r = 0,51) e significativa (p= 0,025).

Conclusão A LSA é uma medida reprodutível para pacientes hemiparéticos crônicos pós-AVC mesmo se aplicada remotamente e pode ser usada como uma medida remota de mobilidade em ambiente real para pessoas com hemiparesia crônica após AVC.

Authors' Contributions

NKO: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, writing – original draft, writing – review e editing; LHCGS, GCR: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, writing – original draft; NDP: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, writing – review e editing.

Support

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Publication History

Received: 07 June 2023

Accepted: 28 November 2023

Article published online:
07 February 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

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• References

• 1 Diseases GBD, Injuries C. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396 (10258): 1204-1222
  CrossrefPubMedGoogle Scholar
• 2 Wesselhoff S, Hanke TA, Evans CC. Community mobility after stroke: a systematic review. Top Stroke Rehabil 2018; 25 (03) 224-238
  CrossrefPubMedGoogle Scholar
• 3 Morris JH, Oliver T, Kroll T, Joice S, Williams B. Physical activity participation in community dwelling stroke survivors: synergy and dissonance between motivation and capability. A qualitative study. Physiotherapy 2017; 103 (03) 311-321
  CrossrefPubMedGoogle Scholar
• 4 Simões MDSM, Garcia IF, Costa LDC, Lunardi AC. Life-Space Assessment questionnaire: Novel measurement properties for Brazilian community-dwelling older adults. Geriatr Gerontol Int 2018; 18 (05) 783-789
  CrossrefPubMedGoogle Scholar
• 5 Erler KS, Sullivan V, Mckinnon S, Inzana R. Social Support as a Predictor of Community Participation After Stroke. Front Neurol 2019; 10: 1013
  CrossrefPubMedGoogle Scholar
• 6 Chau JPC, Lo SHS, Choi KC, Butt L, Zhao J, Thompson DR. Participation self-efficacy plays a mediation role in the association between mobility and social participation among stroke survivors. Heart Lung 2021; 50 (06) 857-862
  CrossrefPubMedGoogle Scholar
• 7 Mazzà C, Alcock L, Aminian K. et al. Technical validation of real-world monitoring of gait: a multicentric observational study. BMJ Open 2021; 11 (12) e050785
  CrossrefPubMedGoogle Scholar
• 8 Gebruers N, Vanroy C, Truijen S, Engelborghs S, De Deyn PP. Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Arch Phys Med Rehabil 2010; 91 (02) 288-297
  CrossrefPubMedGoogle Scholar
• 9 Mudge S, Stott NS. Test–retest reliability of the StepWatch Activity Monitor outputs in individuals with chronic stroke. Clin Rehabil 2008; 22 (10-11): 871-877
  CrossrefPubMedGoogle Scholar
• 10 Danks KA, Pohlig R, Reisman DS. Combining Fast-Walking Training and a Step Activity Monitoring Program to Improve Daily Walking Activity After Stroke: A Preliminary Study. Arch Phys Med Rehabil 2016; 97 (9, Suppl) S185-S193
  CrossrefPubMedGoogle Scholar
• 11 French MA, Moore MF, Pohlig R, Reisman D. Self-efficacy mediates the relationship between balance/walking performance, activity, and participation after stroke. Top Stroke Rehabil 2016; 23 (02) 77-83
  CrossrefPubMedGoogle Scholar
• 12 Klassen TD, Semrau JA, Dukelow SP, Bayley MT, Hill MD, Eng JJ. Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation. Stroke 2017; 48 (09) 2614-2617
  CrossrefPubMedGoogle Scholar
• 13 Lynch EA, Jones TM, Simpson DB. et al; ACTIOnS Collaboration. Activity monitors for increasing physical activity in adult stroke survivors. Cochrane Database Syst Rev 2018; 7 (07) CD012543
  PubMedGoogle Scholar
• 14 Danks KA, Roos MA, McCoy D, Reisman DS. A step activity monitoring program improves real world walking activity post stroke. Disabil Rehabil 2014; 36 (26) 2233-2236
  CrossrefPubMedGoogle Scholar
• 15 Peel C, Sawyer Baker P, Roth DL, Brown CJ, Brodner EV, Allman RM. Assessing mobility in older adults: the UAB Study of Aging Life-Space Assessment. Phys Ther 2005; 85 (10) 1008-1119
  CrossrefPubMedGoogle Scholar
• 16 Estima AEMS, Dutra BMT, Martins JVP, Franzoi ACOB. Validação do Questionário “Life Space Assessment-LSA” em um grupo de pacientes hemiplégicos. Acta Fisiatr 2015; 22 (01) 1-4
  CrossrefGoogle Scholar
• 17 Pereira ND, Menezes IS, dos Anjos SM. Uso de três princípios de intervenção aumenta a efetividade da terapia por contensão induzida: estudo de caso. Rev Ter Ocup Univ Sao Paulo 2010; 21 (01) 33-40
  Google Scholar
• 18 Brucki SM, Nitrini R, Caramelli P, Bertolucci PH, Okamoto IH. [Suggestions for utilization of the mini-mental state examination in Brazil]. Arq Neuropsiquiatr 2003; 61 (3B): 777-781
  CrossrefPubMedGoogle Scholar
• 19 Curcio CL, Alvarado BE, Gomez F, Guerra R, Guralnik J, Zunzunegui MV. Life-Space Assessment scale to assess mobility: validation in Latin American older women and men. Aging Clin Exp Res 2013; 25 (05) 553-560
  CrossrefPubMedGoogle Scholar
• 20 Lee JY, Kwon S, Kim WS, Hahn SJ, Park J, Paik NJ. Feasibility, reliability, and validity of using accelerometers to measure physical activities of patients with stroke during inpatient rehabilitation. PLoS One 2018; 13 (12) e0209607
  CrossrefPubMedGoogle Scholar
• 21 Costa PHV, de Jesus TPD, Winstein C, Torriani-Pasin C, Polese JC. An investigation into the validity and reliability of mHealth devices for counting steps in chronic stroke survivors. Clin Rehabil 2020; 34 (03) 394-403
  CrossrefPubMedGoogle Scholar
• 22 Munro BH. Statistical methods for health care research. Lippincott Williams & Wilkins; 2005
  Google Scholar
• 23 Fleiss JL. Determining sample sizes needed to detect a difference between two proportions. Statistical Methods for Rates and Proportions; 2004: 64-85
  Google Scholar
• 24 Lexell JE, Downham DY. How to assess the reliability of measurements in rehabilitation. Am J Phys Med Rehabil 2005; 84 (09) 719-723
  CrossrefPubMedGoogle Scholar
• 25 Weir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 2005; 19 (01) 231-240
  CrossrefPubMedGoogle Scholar
• 26 Peretti A, Amenta F, Tayebati SK, Nittari G, Mahdi SS. Telerehabilitation: Review of the State-of-the-Art and Areas of Application. JMIR Rehabil Assist Technol 2017; 4 (02) e7
  CrossrefPubMedGoogle Scholar
• 27 da Costa T, Xavier Junior IJ, dos Santos JA. et al. Opportunity for telehealth in times of pandemics: an integrative review. Brazilian Journal of Development 2021; 7 (11) 106419-106432
  CrossrefGoogle Scholar
• 28 Chirra M, Marsili L, Wattley L. et al. Telemedicine in Neurological Disorders: Opportunities and Challenges. Telemed J E Health 2019; 25 (07) 541-550
  CrossrefPubMedGoogle Scholar
• 29 Amano S, Umeji A, Uchita A. et al. Reliability of remote evaluation for the Fugl-Meyer assessment and the action research arm test in hemiparetic patients after stroke. Top Stroke Rehabil 2018; 25 (06) 432-437
  PubMedGoogle Scholar
• 30 Grant CA, Wallace LM, Spurgeon PC, Tramontano C, Charalampous M. Construction and initial validation of the E-Work Life Scale to measure remote e-working. Employee Relat 2019; 41 (01) 16-33
  CrossrefGoogle Scholar
• 31 de Morais Fari CDC, Salmela LFT, de Araújo PA, Polese JC, Nascimento LR, Nadeau S. TUG-ABS Português-Brasil: instrumento para avaliação clínica da mobilidade de hemiparéticos pós-AVC. Revista Neurociências 2015; 23 (03) 357-367
  CrossrefGoogle Scholar