Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review
BackgroundThere is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Rehabilitation Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fresc.2022.855240/full |
| _version_ | 1797969481135292416 |
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| author | Vera C. Kaelin Vera C. Kaelin Mina Valizadeh Mina Valizadeh Zurisadai Salgado Julia G. Sim Dana Anaby Dana Anaby Andrew D. Boyd Andrew D. Boyd Andrew D. Boyd Natalie Parde Natalie Parde Mary A. Khetani Mary A. Khetani Mary A. Khetani Mary A. Khetani |
| author_facet | Vera C. Kaelin Vera C. Kaelin Mina Valizadeh Mina Valizadeh Zurisadai Salgado Julia G. Sim Dana Anaby Dana Anaby Andrew D. Boyd Andrew D. Boyd Andrew D. Boyd Natalie Parde Natalie Parde Mary A. Khetani Mary A. Khetani Mary A. Khetani Mary A. Khetani |
| author_sort | Vera C. Kaelin |
| collection | DOAJ |
| description | BackgroundThere is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed.ObjectiveTo understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI.MethodsWe conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2–3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs.ResultsTwenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. All assessment approaches captured behavioral involvement, and none captured emotional or cognitive involvement or attendance. Additionally, 24% (n = 5) of the assessment approaches captured participation-related constructs like activity competencies and 57% (n = 12) captured aspects not included in contemporary frameworks of participation.ConclusionsMain gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation. |
| first_indexed | 2024-04-11T03:01:51Z |
| format | Article |
| id | doaj.art-80474668bcc04e95bb98f65debad859e |
| institution | Directory Open Access Journal |
| issn | 2673-6861 |
| language | English |
| last_indexed | 2024-04-11T03:01:51Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Rehabilitation Sciences |
| spelling | doaj.art-80474668bcc04e95bb98f65debad859e2023-01-02T13:59:14ZengFrontiers Media S.A.Frontiers in Rehabilitation Sciences2673-68612022-04-01310.3389/fresc.2022.855240855240Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping ReviewVera C. Kaelin0Vera C. Kaelin1Mina Valizadeh2Mina Valizadeh3Zurisadai Salgado4Julia G. Sim5Dana Anaby6Dana Anaby7Andrew D. Boyd8Andrew D. Boyd9Andrew D. Boyd10Natalie Parde11Natalie Parde12Mary A. Khetani13Mary A. Khetani14Mary A. Khetani15Mary A. Khetani16Rehabilitation Sciences, University of Illinois at Chicago, Chicago, IL, United StatesChildren's Participation in Environment Research Lab, University of Illinois at Chicago, Chicago, IL, United StatesComputer Science, University of Illinois at Chicago, Chicago, IL, United StatesNatural Language Processing Laboratory, University of Illinois at Chicago, Chicago, IL, United StatesChildren's Participation in Environment Research Lab, University of Illinois at Chicago, Chicago, IL, United StatesChildren's Participation in Environment Research Lab, University of Illinois at Chicago, Chicago, IL, United StatesSchool of Physical and Occupational Therapy, McGill University, Montreal, QC, CanadaCanChild Centre for Childhood Disability Research, McMaster University, Hamilton, ON, CanadaRehabilitation Sciences, University of Illinois at Chicago, Chicago, IL, United StatesBiomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, United StatesPhysical Therapy, University of Illinois at Chicago, Chicago, IL, United StatesComputer Science, University of Illinois at Chicago, Chicago, IL, United StatesNatural Language Processing Laboratory, University of Illinois at Chicago, Chicago, IL, United StatesRehabilitation Sciences, University of Illinois at Chicago, Chicago, IL, United StatesChildren's Participation in Environment Research Lab, University of Illinois at Chicago, Chicago, IL, United StatesCanChild Centre for Childhood Disability Research, McMaster University, Hamilton, ON, CanadaOccupational Therapy, University of Illinois at Chicago, Chicago, IL, United StatesBackgroundThere is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed.ObjectiveTo understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI.MethodsWe conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2–3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs.ResultsTwenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. All assessment approaches captured behavioral involvement, and none captured emotional or cognitive involvement or attendance. Additionally, 24% (n = 5) of the assessment approaches captured participation-related constructs like activity competencies and 57% (n = 12) captured aspects not included in contemporary frameworks of participation.ConclusionsMain gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation.https://www.frontiersin.org/articles/10.3389/fresc.2022.855240/fullinvolvementengagementassessmentmeasurementnatural language processingmachine learning |
| spellingShingle | Vera C. Kaelin Vera C. Kaelin Mina Valizadeh Mina Valizadeh Zurisadai Salgado Julia G. Sim Dana Anaby Dana Anaby Andrew D. Boyd Andrew D. Boyd Andrew D. Boyd Natalie Parde Natalie Parde Mary A. Khetani Mary A. Khetani Mary A. Khetani Mary A. Khetani Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review Frontiers in Rehabilitation Sciences involvement engagement assessment measurement natural language processing machine learning |
| title | Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review |
| title_full | Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review |
| title_fullStr | Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review |
| title_full_unstemmed | Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review |
| title_short | Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review |
| title_sort | capturing and operationalizing participation in pediatric re habilitation research using artificial intelligence a scoping review |
| topic | involvement engagement assessment measurement natural language processing machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fresc.2022.855240/full |
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