Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure

Foster, Randal C.; Lanningham-Foster, Lorraine; Manohar, Chinmay; McCrady, Shelly K.; Nysse, Lana J.; Kaufman, Kenton R.; Padgett, Denny J.; Levine, James A.

doi:10.1016/j.ypmed.2005.07.006

Cited by 160 publications

(125 citation statements)

References 13 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…These monitors record an acceleration of the foot and then lock-out any additional "steps" until a period of time has passed equivalent to swing time of the subject's limb (based on subject height). SAMs have step count accuracy of better than 98 percent [39][40][41]. Repeated shaking of the foot is not counted as steps, since each acceleration occurs too often to be walking so the step-detection algorithm prevents recording.…”

Section: Methodsmentioning

confidence: 99%

How humans walk: Bout duration, steps per bout, and rest duration

Orendurff¹

2008

JRRD

266

222

View full text Add to dashboard Cite

Abstract-Much is known about human walking, but it is not known how walking is used during typical activities. Since improving walking ability is a key goal in many surgical, pharmacological, and physiotherapeutic interventions, understanding typical community mobility demands regarding the length of walking bouts, the number of sequential steps frequently performed, and the duration of common nonwalking (rest) behavior seems prudent. This study documents the gait of daily living in 10 nondisabled employed adults to define walking bout duration, sequential step counts, and length of rest periods over a 2-week period. Subjects wore a StepWatch™ Activity Monitor (OrthoCare Innovations; Mountlake Terrace, Washington) that counted steps in each 10-second time window. Custom code summed sequential steps, periods of walking behavior (bouts), and periods without steps (rest). Sixty percent of all walking bouts lasted just 30 seconds or less; a 2-minute walking bout was just 1 percent of total walking bouts. Forty percent of all walking bouts were less than 12 steps in a row, and 75 percent of all walking bouts were less than 40 steps in a row. Rest periods were predominantly very short, with half of all rests periods lasting 20 seconds or less. The community mobility demand for nondisabled employed adults appears to involve frequent short-duration walking behavior with low numbers of sequential steps strung together and many shortduration nonwalking (rest) behaviors.

show abstract

Section: Methodsmentioning

confidence: 99%

How humans walk: Bout duration, steps per bout, and rest duration

Orendurff¹

2008

JRRD

266

222

View full text Add to dashboard Cite

show abstract

“…Also with very high data acquisition rates, the internal buffers on the electronic systems need to be large (adding unnecessary cost). [48][49][50] Overall the rate of data gathering needs to be appropriate for the signal being traced. When walking briskly, a full stride (right and left heel strike) occurs about once per second which is 2 Hz.…”

Section: Choice Of Accelerometermentioning

confidence: 99%

Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes

Dadlani

Levine

McCrady-Spitzer

et al. 2015

J Diabetes Sci Technol

Self Cite

View full text Add to dashboard Cite

Type 1 diabetes (T1D) is characterized by complete or near complete absence of endogenous insulin secretion resulting in the need to deliver exogenous insulin in an individualized, precise manner.1 Current clinically approved technologies to replace insulin result in unacceptable risk for hypoglycemia, hyperglycemia, and glycemic variability (GV).2 Changes in plasma glucose concentration are more pronounced in T1D, occur rapidly and are related to food intake, physical activity and imprecise insulin delivery. The maturation of technologies such as continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII) or insulin pump, rapid acting insulin analogs and closed-loop control (CLC) algorithms that deliver insulin based on CGM data processing has resulted in CLC therapy becoming an area of active research with rapid therapeutic translation to clinical practice. 4 Current CLC studies show significant improvement in hypoglycemia and a bigger burden of hyperglycemia relative to time spent in hypoglycemic range. 4,5 Occurrence of hyper and hypoglycemia with current CLC, the burden associated with current CLC and limitations of eligible populations to use current technologies indicate the need for further innovation. Important variables in this context with potentially significant effect sizes are physical activity and meals. Significant efforts have been made to model the aforementioned parameters and understand their effect on glucose-insulin dynamics by incorporating/adapting them into CLC simulators. 6,7 Even though the day to day effect of physical activity on glucose status poses major challenges for T1D, limited research in this area has been conducted. 8 Since obesity and type 2 diabetes mellitus (T2D), 2 major disorders of whole body energy balance, continue to rise worldwide, most research to-date regarding physical activity capture device use has been pursued in these 2 disorders.9,10 T2D is characterized by hyperglycemia for a significant proportion of the day with the majority of such subjects having limited hypoglycemia AbstractPhysical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second hal...

show abstract

“…However, pedometers do not accurately quantify stride length in estimating the distance Fat-free mass þ Fat mass þ ACC RMS 0.848 2.8 566.5 *** traveled (stride length Â steps), as the length of each stride varies substantially according to the walking speed Schneider et al, 2003). In addition, Foster et al (2005) have suggested that the number of steps provides a reliable predictor of walking EE if the steps could be accurately counted. However, the limitation of accurate counting during slow-paced walking has been indicated, especially in a classical spring-levered pedometer Melanson et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Are pedometers adequate instruments for assessing energy expenditure?

2009

View full text Add to dashboard Cite

Objective: Assessing energy expenditure (EE) is important for the control of obesity. Daily step counts have become popular and constitute one practical technique for evaluating the physical activity (PA) in large population studies. However, information on the capacity of pedometers to track EE in free-living conditions remains scanty. Subjects and methods: The 24-h EE of 71 healthy adults was measured by indirect calorimetry in a large respiratory chamber. Two accelerometers were attached to the waist, one for counting the total daily steps (ACC STEP ) and another for measuring the anteroposterior whole body acceleration calculated as the root mean square of the acceleration signal at every second (ACC RMS ). Results: The ACC STEP was not associated with PA-related EE (PAEE) or 24-h EE. Body weight (BW) was the main determinant of both the values (explaining 30 and 75% of the variance, respectively). Approximately 8% (Po0.001) of the variance in PAEE was attributed to the ACC RMS after BW was accounted for, whereas the ACC STEP did not explain any additional variance. A multiple stepwise regression analysis revealed that BW, height and ACC RMS were highly significant determinants of 24-h EE and accounted for as much as 83% of the total variance. Conclusions: Recording the number of steps per day does not provide accurate information on EE, and at best is only a crude predictor of the general PA in terms of displacement. In contrast, accelerometry signals are considered to be a more meaningful factor in the assessment of EE rather than step counts under sedentary conditions.

show abstract

Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure

Cited by 160 publications

References 13 publications

How humans walk: Bout duration, steps per bout, and rest duration

How humans walk: Bout duration, steps per bout, and rest duration

Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes

Are pedometers adequate instruments for assessing energy expenditure?

Contact Info

Product

Resources

About