본문 바로가기
KERD
논문KCI 등재

객관적 신체활동 좌식행동 측정 도구 ‘엑티그래프(actigraphy)’ 적용의 과제

Issues and Research Trends of Accelerometer-Based Physical Activity and Sedentary Behavior Measurement

이미영|조정환
한국체육측정평가학회 (Korean Society For Measurement And Evaluation In Physical Education And Sports Science)|2023.03|한국체육측정평가학회지 (The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science)|제25권 제1호|pp. 105-120|DOI 10.21797/ksme.2023.25.1.009

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

신체활동physical activitypopulationsedentary behaviorActigraphy인구집단좌식행동엑티그래프가속도계 알고리즘accelerometer algorithm

초록

국가수준의 객관적 신체활동량 측정에서 가속도계 기반의 엑티그래프(actigraphy)가 사용되고 있다. 확장된 인구 집단 적용을 위해서는 측정 장치의 기계적 구조와 가속도계 원시 신호 처리 알고리즘, 인구집단 적용 사례, 대상자 특성과 착용부위, 기기 모델간 및 스마트폰 앱과의 차이 등에 대한 이해가 요구되고 있다. 인구집단 신체활동 감시 체계에 엑티그래프 적용이 확산되고 있는 시점에서 최근 연구결과들이 주는 시사점은 다음과 같다. 첫째, 가속도계 기반의 엑티그래프 데이터 분석 과정에서 오픈소스 알고리즘 프로토콜 적용이 점차 일반화되고 있다. 연구 목적 외에도 국가수준의 가속도계 적용을 위한 정책적 준비과정의 일환으로 관련 연구네트워크의 조직과 지원이 필요하다. 둘째, 원시 가속 신호에서 얻어진 활동카운트로 컷 포인트를 결정하는 방법과 더불어 신호 분포와 분포 변화 특성을 반영하는 포괄적 분석 방법 적용이 요구된다. 셋째, 현재 사용되고 있는 가속도계는 기기 종류와 모델 그리고 사용되는 가속도 신호 범위에 따라 행동 추정의 정확도에 차이가 있는 만큼 국내 인구집단 적용을 위한 표준화된 프로토콜 개발이 필요하다.

Abstract

Accelerometer-based instrument has been widely employed to measure the amount of physical activity at the population level. For the application to the expanded population to measure physical activity objectively, it is criteria to consider important keys of utility criteria, such as understanding of the mechanical structure of the measuring device, the raw signal processing algorithm of the accelerometer, the characteristics of the population group, and the wearing part, and the difference between device models and smart phone apps. The implications of recent research results at a time when the application of actigraph is utilizing to the physical activity monitoring system of the population are as follows. First, the application of open source algorithm protocols is gradually becoming common in the process of accelerometer-based data analysis. In addition to the research purpose, the organization and support of related research networks are necessary as part of the policy decision-making process for the application of accelerometers at the national level. Second, it is required to apply a comprehensive analysis method that reflects the signal distribution and changes of data distribution along with determining more accurate cut point obtained from the raw acceleration signal. Third, it is necessary to develop a standardized protocol of the physical activity measurement employing the actigraph for the domestic population as the currently used accelerometers differ in the accuracy of physical activity behavior estimation depending on the type and model of the device and the range of acceleration signals.

참고문헌 (32)

  1. 이미영, "객관적 신체활동 검사도구의 타당도 증거와 검증 절차: 체계적 문헌분석", 한국체육측정평가학회지, 13(2), pp.17~37 (2011), 한국체육측정평가학회[DOI]
  2. 이효, "가속도계로 측정한 국민건강영양조사 신체활동 자료 처리방법과 활용", 한국체육측정평가학회지, 20(2), pp.83~94 (2018), 한국체육측정평가학회[DOI]
  3. Ahmadi, M. N., "Device-based measurement of physical activity in pre-schoolers:Comparison of machine learning and cut point methods", Plos one, 17(4), pp.e0266970~ (2022)[DOI]
  4. Airlie, J., "An investigation into the optimal wear time criteria necessary to reliably estimate physical activity and sedentary behaviour from ActiGraph wGT3X+ accelerometer data in older care home residents", BMC geriatrics, 22(1), pp.1~14 (2022)[DOI]
  5. Bammann, K., "Generation and validation of ActiGraph GT3X+ accelerometer cut-points for assessing physical activity intensity in older adults. The OUTDOOR ACTIVE validation study", PLoS One, 16(6), pp.e0252615~ (2021)[DOI]
  6. Cooper, A. R., "Objectively measured physical activity and sedentary time in youth : the International children’s accelerometry database(ICAD)", International journal of behavioral nutrition and physical activity, 12(1), pp.1~10 (2015)[DOI]
  7. Doherty, A., "Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study", PloS one, 12(2), pp.e0169649~ (2017)[DOI]
  8. Edwardson, C. L., "Accuracy of posture allocation algorithms for thigh-and waist-worn accelerometers", Medicine and Science in Sports and Exercise (2016)[DOI]
  9. Hibbing, P. R., "CRIB : A Novel Method for Device-Based Physical Behavior Analysis", Journal for the Measurement of Physical Behaviour, 5(4), pp.277~287 (2022)[DOI]
  10. Hicks, J. L., "Best practices for analyzing large-scale health data from wearables and smartphone apps", NPJ digital medicine, 2(1), pp.45~ (2019)[DOI]
  11. Kalisch, T., "Measuring sedentary behavior using waist-and thigh-worn accelerometers and inclinometersare the results comparable?", Therapeutic Advances in Musculoskeletal Disease, 14 (2022)[DOI]
  12. Kwan, R. Y. C., "A validation study of the use of smartphones and wrist-worn ActiGraphs to measure physical activity at different levels of intensity and step rates in older people", Gait & Posture, 82, pp.306~312 (2020)[DOI]
  13. Llorente-Cantarero, F. J., "Evaluation of Sedentary Behavior and Physical Activity Levels Using Different Accelerometry Protocols in Children from the GENOBOX Study", Sports Medicine-Open, 7(1), pp.1~13 (2021)[DOI]
  14. Loyen, A., "Sedentary time and physical activity surveillance through accelerometer pooling in four European countries", Sports Medicine, 47(7), pp.1421~1435 (2017)[DOI]
  15. "Objective Measurement of Sleep by Smartphone Application: Comparison with Actigraphy and Relation to Cognition, Mood, and Self-Reported Sleep. In BSU Honors Program Theses and Projects. Item 218" (2017)[URL]
  16. Migueles, J. H., "GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours(physical activity, sedentary behaviour and sleep)in epidemiological studies", British journal of sports medicine, 56(7), pp.376~384 (2022)[DOI]
  17. Migueles, J. H., "Equivalency of four research-grade movement sensors to assess movement behaviors and its implications for population surveillance", Scientific Reports, 12(1), pp.5525~ (2022)[DOI]
  18. Migueles, J. H., "GGIR : a research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data", Journal for the Measurement of Physical Behaviour, 2(3), pp.188~196 (2019)[DOI]
  19. Neishabouri, A., "Quantification of Acceleration as Activity Counts in ActiGraph Wearables", Sci Rep, 12, pp.11958~ (2022)
  20. Ngueleu, A. M., "Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults : a systematic review", Journal of neuroengineering and rehabilitation, 19(1), pp.1~18 (2022)[DOI]
  21. "Smartphone apps for tracking physical activity and sedentary behavior: A criterion validity review. Rev. bras. ativ. fís. saúde" (2022)[URL]
  22. Santos-Lozano, A., "Actigraph GT3X : validation and determination of physical activity intensity cut points", International journal of sports medicine, 34(11), pp.975~982 (2013)[DOI]
  23. Schwab, K. E., "Actigraphy to measure physical activity in the intensive care unit : a systematic review", Journal of intensive care medicine, 35(11), pp.1323~1331 (2020)[DOI]
  24. Steene-Johannessen, J., "Variations in accelerometry measured physical activity and sedentary time across Europe–harmonized analyses of 47, 497 children and adolescents", International Journal of Behavioral Nutrition and Physical Activity, 17(1), pp.1~14 (2020)[DOI]
  25. Steeves, J. A., "Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion", Medicine and science in sports and exercise, 47(5), pp.952~ (2015)[DOI]
  26. Straczkiewicz, M., "A systematic review of smartphone-based human activity recognition methods for health research", NPJ Digital Medicine, 4(1), pp.148~ (2021)[DOI]
  27. Ostendorf, D. M., "Objectively measured physical activity and sedentary behavior in successful weight loss maintainers", Obesity, 26(1), pp.53~60 (2018)[DOI]
  28. Topalidis, P., "Evaluation of a low-cost commercial actigraph and its potential use in detecting cultural variations in physical activity and sleep", Sensors, 21(11), pp.3774~ (2021)[DOI]
  29. Troiano, R. P., "Physical activity in the United States measured by accelerometer", Medicine & Science in Sports & Exercise, 40(1), pp.181~188 (2008)[DOI]
  30. Trost, S. G., "Population-level physical activity surveillance in young people : are accelerometer-based measures ready for prime time?", International Journal of Behavioral Nutrition and Physical Activity, 17(1), pp.1~4 (2020)[DOI]
  31. "Objective measurement of physical activity" (2023)[URL]
  32. Wu, X., "Impaired plantar sensitivity among the obese is associated with increased postural sway", Neuroscience letters, 583, pp.49~54 (2014)[DOI]

관련 논문AI추천

가속도계 기반 신체활동 자료수집 및 처리 방법: 문헌분석 업데이트권선희, 나충현, 신진이, 이미영한국체육측정평가학회 · 한국체육측정평가학회지2024 · 제26권 제1호객관적 신체활동 검사도구의 타당도 증거와 검증 절차: 체계적 문헌분석이미영한국체육측정평가학회 · 한국체육측정평가학회지2011 · 제13권 제2호보행수 기반 좌식시간 측정의 정확성과 활용 방향이효, 김재명한국체육측정평가학회 · 한국체육측정평가학회지2022 · 제24권 제4호신체활동 측정기반 좌식행동 검사도구의 타당도와 신뢰도 증거 : 체계적 문헌분석이미영한국체육측정평가학회 · 한국체육측정평가학회지2014 · 제16권 제3호가속도계로 측정된 한국 성인의 신체활동: 제6기 국민건강영양조사 가속도계 자료 분석이온, 김연수, 성호용, 임정준국민체육진흥공단 한국스포츠과학원 · 체육과학연구2020 · 제31권 제2호가속도계로 측정한 국민건강영양조사 신체활동 자료 처리방법과 활용이효, 김소연, 김윤정, 오경원, 이미영, 최지엽한국체육측정평가학회 · 한국체육측정평가학회지2018 · 제20권 제2호

이 간행물의 다른 논문 (8건)

대사증후군 질환별 비만 관련 위험 요인: 국민건강영양조사 (2016-2020) 자료 활용채진석, 신진이pp. 1-13여학생 체육수업 행복 질문지 개발 및 타당화 연구전현수, 이기봉pp. 15-25노인체육 환경의 형평성 및 효율성 평가오상은, 박재현, 안우진pp. 27-39체지방 평가를 위한 초음파와 피부두겹법 측정의 비교강성기pp. 41-50텍스트마이닝을 활용한 정부 생활체육 보도자료 분석권순보, 유진은pp. 51-63
원문 보기