EHEun-Kyoung Hong

Eun-Kyoung Hong

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


Eun-Kyoung Hong has a total of 6 co-authors. Here are the top co-authors by number of shared publications:

  • Akhila Veerubhotla

    Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, USA

    1 shared publication
  • Ann M. Spungen

    James J. Peters VA Medical Center

    1 shared publication
  • Dan Ding

    Nankai International Advanced Research Institute (Shenzhen Futian) Nankai University Shenzhen P. R. China

    1 shared publication
  • Steven Knezevic

    VA Rehabilitation Research & Development Service, National Center for the Medical Consequences of Spinal Cord Injury, James J. Peters VA Medical Center, Bronx, USA

    1 shared publication
  • Yousif Shwetar

    Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, USA

    1 shared publication
  • Zijian Huang

    Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, USA

    1 shared publication

Latest Publications

  • Predicting physical activity intensity using raw accelerometer signals in manual wheelchair users with spinal cord injury

    by Yousif Shwetar, Zijian Huang, Akhila Veerubhotla, Steven Knezevic, EunKyoung Hong, Ann M. Spungen, Dan Ding

    Study design Cross-sectional validation study. Objectives The performance of previously published physical activity (PA) intensity cutoff thresholds based on proprietary ActiGraph counts for manual wheelchair users (MWUs) with spinal cord injury (SCI) was initially evaluated using an out-of-sample dataset of 60 individuals with SCI. Two types of PA intensity classification models based on raw accelerometer signals were developed and evaluated. Setting Research institutions in Pittsburgh PA, Birmingham AL, and Bronx NY. Methods Data were collected from 60 MWUs with SCI who followed a structured activity protocol while wearing an ActiGraph activity monitor on their dominant wrist and portable metabolic cart which measured criterion PA intensity. Data was used to assess published models as well as develop and assess custom models using recall, specificity, precision, as well as normalized Mathew’s correlation coefficient (nMCC). Results All the models performed well for predicting sedentary vs non-sedentary activity, yielding an nMCC of 0.87–0.90. However, all models demonstrated inadequate performance for predicting moderate to vigorous PA (MVPA) with an nMCC of 0.76–0.82. Conclusions The mean absolute deviation (MAD) cutoff threshold yielded the best performance for predicting sedentary vs non-sedentary PA and may be used for tracking daily sedentary activity. None of the models displayed strong performance for MVPA vs non-MVPA. Future studies should investigate combining physiological measures with accelerometry to yield better prediction accuracies for MVPA.


  • Open Access

    Changing the Awareness and Acceptance of 3D Printing Technology in Occupational Therapy Education: Pilot Study

    by Eun-Kyoung Hong

    본 연구는 작업치료전공 수업시 3D 프린팅을 이용한 보조도구를 제작함에 따라 3D 프린팅 기술 수용에 대한 인식도 변화를 알아보고자 하였다. 연구 기간은 2020년 9월 8일부터 12월 15일까지이었고, 단일집단 사전-사후 설계를 사용하였다. 총 32명의 작업치료과 전공 1학년 학생들이 참여하였다. 2020학년도 2학기 교과목으로 개설된 ‘3D 프린팅 보조도구’ 교과목 수강 전과 후에 온라인설문조사를 통해 3D 프린팅 기술 수용에 대한 인식도를 조사하였다. 통계프로그램은 SPSS 20.0을 사용하여 분석하였다. 연구 결과, 3D 프린팅 기술 수용에 대한 인식도의 4개 범주 중 3개의 범주에서 수업 전에 비하여 후에 인식도가 긍정적으로 변화하였다. 그 3개의 범주는 ‘지각된 유용성’, ‘지각된 용이성’, ‘사용에 대한 의도’이었다. 통계학적 변화가 없는 범주는 ‘사용에 대한 태도’로 세부 문항인 ‘나는 작업치료사가 3D프린터를 사용하는 것이 좋은 생각이라고 본다’와 ‘미래의 작업치료사로서, 나는 3D프린터를 사용하는 것이 좋은 생각이라고 본다’에서도 통계적인 차이가 없었다. 연구의 결과는 작업치료와 융합된 치료 또는 기술을 적용하는데 기초 연구자료가 될 것이다.