Physical activity participation in community dwelling stroke survivors: synergy and dissonance between motivation and capability. A qualitative study

Morris, Jacqui; Oliver, Tracey; Kroll, Thilo; Joice, Sara; Williams, Brian

doi:10.1016/j.physio.2016.05.001

Cited by 56 publications

(54 citation statements)

References 31 publications

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“…The motivation for automating the ratings is threefold: (1) to save time -evaluation with a model can take less than a few minutes, whereas performing the clinical test may take 30 minutes [2,13,15]; (2) to improve the precision of the evaluation, as the human eye may not detect small-scale movements that can be detected by accurate sensors [17]; and (3) to help post-stroke individuals to receive an evaluation of their movement quality when a clinician is not available to provide one, e.g., during home practice [4]. Using this system for home practice can also help patients who refrain from going to the clinic due to low availability and accessibility, lack of knowledge of opportunities, high costs of organized activity, inclement weather, or who feel uncomfortable exercising in public since they are concerned about how others might perceive them [45]. The models that have been developed thus far are useful in providing a clinical score.…”

Section: Discussionmentioning

confidence: 99%

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Kashi

Polak

Lerner

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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During the process of rehabilitation after stroke, it is important that patients know how well they perform their exercise, so they can improve their performance in future repetitions. Standard clinical rating conducted by human observation is the prevailing way today to monitor motor recovery of the patient. Therefore, patients cannot know whether they are performing a movement properly while exercising by themselves. Adhering to the exercise regime makes the rehabilitation process more effective and efficient, and thus a system that can give the patients feedback on their performance is of great value. Here, we build a machine-learning-based automated model that give patients accurate information on the compensatory (undesirable) movements that they make. To construct the model, we recorded movements from 30 stroke patients, who each performed 18 movements, used to identify the presence of six types of compensatory movements in stroke patients' movement trajectories. We used the random-forest algorithm for training this multi-label classification model. We achieved 85% average precision across the six movement compensations. This is the first study to automatically identify movement compensations based on stroke patients' data. This model can be adapted for use in in-clinic and at-home exercise programs for patients after stroke.

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Section: Discussionmentioning

confidence: 99%

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Kashi

Polak

Lerner

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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“…Patients need to get out of bed and sit or stand to avoid serious complications. In addition, more and more data show that activities need to be adapted to individual patient needs (Kristensen, Tistad, Koch, & Ytterberg, ; Morris, Oliver, Kroll, Joice, & Williams, ).…”

Section: Discussionmentioning

confidence: 99%

The physical environment and patients’ activities and care: A comparative case study at three newly built stroke units

Anåker

Koch

Sjöstrand

et al. 2018

Journal of Advanced Nursing

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“…Many studies have been concentrated on limitations in functional mobility skills such as transfer, walking, lifting, or carrying objects, and in ADLs such as feeding, dressing, bathing, grooming, toileting, and so on [2]. However, participation restrictions are problems that stroke survivors may experience while involving themselves in daily and functional activities as well as social interactions and leisure activities [20][21][22][23].…”

Section: Discussionmentioning

confidence: 99%

An investigation of factors influencing the participation of stroke survivors in social and leisure activities

Ahn

2018

PTRS

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Objective: Participation restrictions are serious problems that stroke survivors experience while reintegrating into family, work, community, and social situations after participating in rehabilitation programs. The purpose of this study was to explore the factors affecting participation in activities of daily living (ADL), as well as social and leisure activities of individuals with hemiparetic stroke. Design: Cross-sectional study. Methods: The study involved 96 participants who were diagnosed with a first stroke 6 months before the study (58 men, 38 women; 60.3±14.3 years). The Berg Balance Scale, Modified Barthel Index, Manual Function test, and Activity Card Sort were used to assess static and dynamic balance function, upper limb function, level of independence, and their level of participation within the community. A regression analysis was used to identify the influence of factors affecting participation in ADL, social and recreational activities. Results: The Activity Card Sort scores were significantly affected by the Manual Function test and Modified Barthel Index scores (p<0.05). Participation in leisure activities was affected by the level of independence. Participation in social activities was affected by the balance function and level of independence of the participants. Conclusions:The results of this study have shown that participation restrictions are affected by upper limb function, balance function, and the level of independence in individuals with hemiparetic stroke.

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Physical activity participation in community dwelling stroke survivors: synergy and dissonance between motivation and capability. A qualitative study

Abstract: . (2017). Physical activity participation in community dwelling stroke survivors: synergy and dissonance between motivation and capability. A qualitative study. Physiotherapy, 103(3), 311-321. https://doi

Cited by 56 publications

References 31 publications

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

The physical environment and patients’ activities and care: A comparative case study at three newly built stroke units

An investigation of factors influencing the participation of stroke survivors in social and leisure activities

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