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Study On Usage Intention To Lower Limb Rehabilitation Robots And Its Influencing Factors In Patients With Limb Dysfunction

Posted on:2024-06-15Degree:MasterType:Thesis
Country:ChinaCandidate:X Y BiFull Text:PDF
GTID:2544306917471714Subject:Nursing
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BackgroundWith the widespread penetration of Industry 4.0 in healthcare,lower limb rehabilitation robots have become effective strategies to meet rehabilitation nursing needs and deal with rehabilitation nursing challenges in the context of smart medicine,which brings significant changes to the future development of rehabilitation nursing.Yet,fatigue and discomfort during the human-robot interaction might directly influence the compliance and usage intention of patients with limb dysfunction,and ultimately affect their effectiveness,so it is particularly essential to explore patients’ usage intention to lower limb rehabilitation robots in depth.However,studies on the usage intention to lower limb rehabilitation robots in patients with limb dysfunction have been limited.ObjectiveUnder the guidance of the Almere model and Value Acceptance Model,this study aims to investigate usage intention to lower limb rehabilitation robots and its influencing factors in patients with limb dysfunction from four characteristics(user,robot,environment and human-robot interaction),and construct the influencing factors model of usage intention to lower limb rehabilitation robots in patients with physical dysfunction.It is expected to provide reference for promoting the high-quality development of nursing and addressing health challenges across the life cycle in the context of Industry 4.0.Methods1.A qualitative study of the usage intention to lower limb rehabilitation robots in patients with limb dysfunction: A descriptive phenomenological analysis was used,and rehabilitation hospitals that had implemented robotic services for lower limb rehabilitation were selected as the study sites.Patients with limb dysfunction who are using lower limb rehabilitation robots were recruited through purposive sampling methods,and semistructured interviews were conducted to gain insight into their usage intention to lower limb rehabilitation robots and to identify their facilitators and barriers.2.Development of questionnaire on usage intention to lower limb rehabilitation robots and its influencing factors in patients with limb dysfunction: Based on the literature review and qualitative study,integrating the Almere model and Value Acceptance Model,and combining the features of lower limb rehabilitation robots,the draft questionnaire was constructed by selecting tools from the relevant literature.Then,the final questionnaire was formed through the translation,back translation,cultural adaptation,pilot survey and formal survey.3.Validation of the influencing factor model of usage intention to lower limb rehabilitation robots in patients with limb dysfunction: A cross-sectional survey was conducted by the convenience sampling method among patients with limb dysfunction,and structural equation modeling was applied to explore the paths of four characteristics on patients’ usage intention to lower limb rehabilitation robots,including human,robot,environment,and human-robot interaction.Results1.A qualitative study of the usage intention to lower limb rehabilitation robots in patients with limb dysfunction: A total of twelve patients with limb dysfunction using the lower limb rehabilitation robot were interviewed in this study,and three themes and 10 subthemes were extracted.The first theme was repeated ambivalence and tangles(sub-themes:ambivalence under the interplay of curiosity and concern,tangles when weighing risks and benefits).The second theme was positive rehabilitation experiences to promote usage intention(sub-themes: recovering sensations of standing and walking,regaining physical function,awakening confidence and enthusiasm for rehabilitation,and support from multiple sources).The third one was multidimensional challenges that hindered usage intention(subthemes: programmed gait patterns,cumbersome and uncomfortable wearing process,lack of active control over the process,and unpredicted and unintended events).2.Development of questionnaire on usage intention to lower limb rehabilitation robots and its influencing factors in patients with limb dysfunction:The questionnaire on usage intention to lower limb rehabilitation robots and its influencing factors in patients with limb dysfunction was developed through the literature review,translation,back translation,cultural adaptation,pilot survey and formal survey.The results showed that the Cronbach’s alpha coefficients for each variable ranged from 0.883 to 0.964,the average variance extraction(AVE)exceeded 0.5,and the combined reliability exceeded 0.7.Also,the square root of AVE for each variable was greater than the correlation coefficient between that variable and the other ones,indicating its good reliability and validity.The questionnaire contained nine variables with a total of 33 items,including personal innovativeness,system quality,social influence,perceived ease of use,perceived usefulness,perceived value,perceived enjoyment,perceived risk and usage intention.A Likert 7-point scale was used,with higher scores indicating greater patient agreement with the variable.3.Validation of the influencing factor model of usage intention to lower limb rehabilitation robots in patients with limb dysfunction: The survey on 204 patients with limb dysfunction showed that:(1)patients with limb dysfunction scored(4.66±1.41)on usage intention to lower limb rehabilitation robots,with the highest to lowest scores on each variable being social influence,perceived value,perceived usefulness,system quality,perceived enjoyment,personal innovativeness,perceived ease of use,and perceived risk;(2)the difference in usage intention scores among patients of different ages,education,experience in using lower limb rehabilitation robots,and attention to robot-related news were statistically significant;(3)The results of the structural equation modeling and mediating effect analysis showed that the model fit indices all met the ideal criteria,and perceived ease of use,perceived value,perceived enjoyment and perceived risk could directly predict the usage intention to lower limb rehabilitation robots in patients with limb dysfunction,while personal innovativeness,system quality,social influence,and perceived usefulness only indirectly predicted the usage intention Conclusion1.Patients with physical dysfunction were relatively entangled in the use of lower limb rehabilitation robots.Positive rehabilitation training experiences could promote their usage intention,including recovering sensations of standing and walking,regaining physical function,awakening confidence and enthusiasm for rehabilitation,and support from multiple sources.However,multiple challenges might also hinder their usage intention,including programmed gait patterns,cumbersome and uncomfortable wearing process,lack of active control over the process,and unpredicted and unintended events.2.Based on the results of the literature review and qualitative study,this study developed a questionnaire on usage intention to lower limb rehabilitation robots and its influencing factors in patients with limb dysfunction.This questionnaire consisted of a total of 33 items with nine variables,including personal innovativeness,system quality,social influence,perceived ease of use,perceived usefulness,perceived value,perceived enjoyment,perceived risk and usage intention.3.In the current study,the influencing factor model of usage intention to lower limb rehabilitation robots in patients with limb dysfunction was constructed and validated.Their usage intention was influenced by various characteristics,among which perceived ease of use,perceived value,perceived enjoyment,and perceived risk could directly influence their usage intention,while personal innovativeness,system quality,social influence and perceived usefulness only indirectly influence their usage intention.
Keywords/Search Tags:patients with limb dysfunction, lower limb rehabilitation robot, usage intention, influencing factors, structural equation modeling
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