Reference | Sample characteristics | Measurement | Protocol | Analytical strategy | Findings |
Tudor-Locke18 2005 | 25 men, 25 women; A convenience adult sample; 18–39 years (25.4±4.7 years for men, 23.6±3.4 years for women) | Steps: Yamax SW-200 pedometer, (Yamax, Tokyo); Indirect calorimetry: Physiodyne Instrument, Quogue, New York | 6 min exercise bouts at three treadmill speeds (4.8, 6.4 and 9.7 km/hour) | Actual METs were calculated for each speed; Linear regression was used to quantify the relationship between steps/min and METs; Regression equations generated were used to establish steps/min cut-point corresponding to moderate intensity |
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Marshall26 2009 | 39 men, 58 women; Community Latino adult sample; 32.1±10.6 years | Steps: Yamax SW-200 pedometer (Yamax, Tokyo); Indirect calorimetry: VacuMed | 6 min incremental walking bouts at 3.9, 4.8, 5.7 and 6.6 km/hour | Three analytic approaches: 1) multiple regression—step counts from each treadmill speed were used to develop a prediction equation for generating a cut-point associated with moderate intensity; 2) mixed modelling—random coefficients models was developed to take account of the data-dependence structure and 3) receiver operating characteristic (ROC) curves—optimal cut-point was examined using sensitivity and specificity |
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Beets45 2010 | 9 men, 11 women; Healthy adults; 20–40 years (26.4±4.6 years) | Steps: hand tally counter; Indirect calorimetry: K4, Cosmed, Italy | 6 min overground walking at 1.8, 2.7, 3.6, 4.5 and 5.4 km/hour | Actual METs were calculated for each speed; Random effects models were used to predict steps/min from METs and participant anthropometric measures; Regression equations generated were used to establish steps/min corresponding to 3 METs; Model estimates were used to predict steps/min corresponding to heights ranging from 5 ft. to 6 ft. 6 in. |
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Nielson1 2011 | 50 men, 50 women; A convenience sample of physically active adults; 23.3±3.9 years (24.2±4.0 for men and 22.4±3.5 for women) | Steps: hand tally counter; Indirect calorimetry: Trueman 2400 metabolic cart, Consentious Technologies, Sandy, Utah | 10 min treadmill walking bouts at cadences of 80, 90, 100, 110 and 120 steps/min | Energy expenditure at each stage was calculated by multiplying the average steady-state oxygen consumption by the appropriate caloric equivalent obtained from the measured steady-state non-protein respiratory exchange ratio value; Descriptive statistics were computed for the MET values |
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Rowe46 2011 | 37 men, 38 women; University employees and their families; 18–64 years (32.9±12.4 years) | Steps: hand tally counter; Indirect calorimetry | Three treadmill and overground walking trials at slow, medium, and fast walking speeds | Multiple regression analysis was used to develop a regression equation to predict overground VO2 from cadence and stride length indicators; Mixed model regression was used to develop an equation determining the cadence cut-point |
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Abel19 2011 | 9 men, 10 women; A convenience sample of physically active university students; 28.8±6.8 years (27.1±3.1 years for men and 30.3±8.9 years for women) | Steps: hand tally counter; Indirect calorimetry: TrueMax 2400, Sandy, Utah | 10 min treadmill walking trials at 3.2, 4.8 and 6.4 km/hour and running at 8.0, 9.7 and 11.3 km/hour | Linear and non-linear regression analyses were both used to develop prediction equations to determine cadence cut-points at various intensities |
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Wang20 2013 | 117 men, 109 women; Recreationally active community Chinese adults sample; 21.7±0.2 years | Steps: hand tally counter; Indirect calorimetry: Cortex MetaMax3B | Four 6 min bouts overground walking at 3.8, 4.8, 5.6 and 6.4 km/hour (50 m rectangular track) | ROC curves were used to determine optimal cadence cut-points |
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Rowe47 2013 | 25 currently inactive adults; 16–64 years (34±13 years) | Steps: hand tally counter; Indirect calorimetry: Cosmed, Italy and AEI Technologies, USA | A moderate intensity (4.3 km/hour) treadmill walking trial; Overground walking trial: a 10 min self-paced ‘brisk’ walk and moderate-paced (with metronome prompt) walk | Single-sample t-test, repeated measures t-test, Cohen’s d, Bland-Altman plots and one-way repeated measures analyses of variance were used to determine study outcomes |
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Rowe16 2014 | 17 unilateral transtibial amputees (TTAs); 52.2±12.9 years | Steps: hand tally counter; Indirect calorimetry: Servomex, Woburn, Massachusetts | Two 5 min walking trials around a speed corresponding to approximately 50% maximal age-predicted HR | Linear regression was used to develop prediction equations to determine intensity from cadence |
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Peacock17 2014 | 29 women; 60–87 years (71.3±12.4 years) | Steps: hand tally counter; Indirect calorimetry: Zoetermeer, The Netherlands | 4 min treadmill walking at self-selected slow, medium and fast speeds (order was counterbalanced) | A regression model (model 2 in the paper) was used to predict moderate-intensity cadence |
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Serrano14 2017 | 121 apparently healthy older adults, 49 men; 68.6±7.8 years; 60 for algorithm development (68.1±8.6 years) and 61 for algorithm validation (69.1±7.1 years) | Steps: step sensor+Garmin FR60 (Foot Pod, Garmin Rome, Italy); Indirect calorimetry: a portable metabolic cart | Visit 1—walking test on a treadmill to achieve maximal capacity (VO2peak) within 10–12 min; Visit 2—200 m flat surface walking test until achieving 40% of VO2reserve and 2 min walking at the targeted intensity | Linear regression was used to predict walking cadence at 40% VO2reserve from height, body weight, body mass index and cadence at self-selected walking speed |
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MET values presented for the Nielson et al (2011) study were calculated by dividing 150 from the recorded values of MET-minute (150 minutes) in the original article.45 Walking speeds were converted into kilometers perhour if other metrics were used in the original manuscript.
HRR, heart rate reserve; MET, metabolic equivalent; MVPA, moderate-to-vigorous intensity physical activity; NR, not reported.