เปรียบเทียบวิธี

	Accelerometer Cut-Point Calibration	Leisure Time-Use Sequence Analysis
สาขาวิชา	Sport Leisure Studies	Sport Leisure Studies
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	1998	2000
ผู้ริเริ่ม	Patty S. Freedson, Edward Melanson & John Sirard; Kelly R. Evenson et al.	Andrew Abbott & Angela Tsay (optimal matching in sociology); applied to time-use leisure sequences
ประเภท	Calibration regression / ROC model mapping accelerometer counts to activity intensity	Order-aware pipeline for clustering daily leisure activity sequences
แหล่งต้นตำรับ	Freedson, P. S., Melanson, E., & Sirard, J. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercise, 30(5), 777-781.	Abbott, A., & Tsay, A. (2000). Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect. Sociological Methods & Research, 29(1), 3-33.
ชื่อเรียกอื่น	Activity Count Calibration, Intensity Threshold Derivation, Accelerometer MET Calibration, Cut-Point Derivation	Leisure Day Sequence Analysis, Optimal Matching of Leisure Episodes, Activity Sequence Analysis, Time-Use Optimal Matching
ที่เกี่ยวข้อง	3	3
สรุป	Accelerometer cut-point calibration solves the central translation problem of objective physical-activity measurement: a wearable accelerometer outputs dimensionless 'counts,' but researchers and health guidelines speak in intensities — sedentary, light, moderate, vigorous. Calibration establishes the count thresholds that map the device's output onto those intensity categories. Patty Freedson, Edward Melanson, and John Sirard's 1998 study of the CSA (later ActiGraph) accelerometer set the template, regressing measured energy expenditure in METs on accelerometer counts during treadmill walking and running and solving the regression for the counts corresponding to moderate (3 METs) and vigorous (6 METs) activity. Later work, exemplified by Evenson and colleagues' 2008 calibration for children, increasingly used receiver-operating-characteristic (ROC) analysis to find the cut-point that best discriminates intensity categories. The result in both cases is a small set of count thresholds that turn raw accelerometer data into minutes of activity at each intensity.	Leisure time-use sequence analysis treats a person's day not as a bundle of activity totals but as an ordered sequence of states, and asks which whole-day patterns of leisure recur across a population. It imports optimal matching -- the alignment technique Andrew Abbott and Angela Tsay reviewed for sociology -- into the study of time-use diaries: each day becomes a string of categorical states (sport, active leisure, passive leisure, work, sleep, and so on) sampled at regular intervals, and the dissimilarity between any two days is the minimum cost of editing one sequence into the other. Clustering the resulting dissimilarity matrix yields a typology of leisure days -- the active morning, the evening screen-leisure pattern, the fragmented weekend -- that preserves the timing and ordering of activity that simple duration tallies discard.