Project Summary:
The project aims to reveal how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how a wearable-based performance monitoring system could be designed to better support the needs of the stakeholders. Semi-structured interviews were conducted with four stroke survivors in their chronic stage and 15 occupational therapists (OTs); all 19 participants completed the study. All interviews were audio-recorded, then transcribed and analyzed using the thematic approach. At first, the transcriptions were analyzed using open coding. Then, a coding scheme (i.e., axial coding) was built through affinity sessions. After iterative processes of open coding and discussion, the saturation of codes and agreement on the coding scheme were reached. The data project includes the resulting codes of both stroke survivors and OTs.
Wearable technologies have been considered as an effective, objective solution to monitor patients’ arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice. This study intended to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders.
Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and occupational therapists for using wearable sensors in clinical practice, (2) OTs’ envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice. These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.
Data Overview:
The data is based on the semi-structured interviews we conducted with four stroke survivors and 15 OTs. Stroke survivors were recruited within Massachusetts, USA, and interviewed from November 2019 until March 2020. OTs were recruited across USA and interviewed from July 2020 until September 2020. The documents provided include study probes, interview guides, and the thematic analysis results.
Data Analysis:
The collected semi-structured interview data was analyzed using thematic analysis. More specifically, all interviews were audio-recorded, then transcribed and analyzed using the thematic approach, using ATLAS.ti Cloud. The affinity sessions were performed to build a coding scheme (i.e., axial coding). After iterative processes of open coding and discussion, the saturation of codes and the agreement on the coding scheme were reached among all authors. The final coding scheme included three main codes and 11 sub-codes.
Data Organization:
The guideline for semi-structured interview with stroke survivors is in "Lee_interview_guideline-stroke_survivor.pdf". The study probe and the interview guideline for occupational therapists are in "Lee_interview_guideline-therapist.pdf". (A simplified and anonymized version of the therapist interview guidelines is included here, with all the authors-generated content intact, but without any decorative images or fictitious patient characters used in the study.) The themes and subthemes that were used to code interview data are in the file "Lee_Codebook.pdf". The coded interview excerpts of stroke survivors are in the file "Lee_codes_quotations-patients.tab". The coded interview excerpts of occupational therapists are in the file "Lee_codes_quotations-therapists.tab". All the tabular files were exported from ATLAS.ti Cloud.
Data Contributors:
Data collection, processing and analysis were performed by Dr. Hee-Tae Jung, Dr. Yoojung Kim, Dr. Sunghoon Ivan Lee, Dr. Eun Kyoung Choe, and PhD student Juhyeon Lee.
