The DOI matches its Crossref record; 50 of the 55 references it cites were found in a registry or an index; 2 of the 55 sources its claims cite were checked for support.
Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges
Open accessAbstract
Background: The current guidelines for aerobic activity require that adults carry out ≥150 min/week of moderate-intensity physical activity, with a large body of epidemiological evidence showing this level of activity to decrease the incidence of many chronic diseases. Less is known about whether light-intensity activities also have such benefits, and whether sedentary behaviour is an independent predictor of increased risks of these chronic diseases, as imprecise assessments of these behaviours and cross-sectional study designs have limited knowledge to date. Methods: Recent technological advances in assessment methods have made the use of movement sensors, such as the accelerometer, feasible for use in longitudinal, large-scale epidemiological studies. Several such studies are collecting sensor-assessed, objective measures of physical activity with the aim of relating these to the development of clinical endpoints. This is a relatively new area of research; thus, in this article, we use the Women's Health Study (WHS) as a case study to illustrate the challenges related to data collection, data processing and analyses of the vast amount of data collected. Results: The WHS plans to collect 7 days of accelerometer-assessed physical activity and sedentary behaviour in ∼18 000 women aged ≥62 years. Several logistical challenges exist in collecting data; nonetheless, as of 31 August 2013, 11 590 women have already provided some data. In addition, the WHS experience on data reduction and data analyses can help inform other similar large-scale epidemiological studies. Conclusions: Important data on the health effects of light-intensity activity and sedentary behaviour will emerge from large-scale epidemiological studies collecting objective assessments of these behaviours.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This methods/commentary paper argues that accelerometers can objectively assess light-intensity physical activity and sedentary behavior in large longitudinal epidemiologic studies, addressing gaps left by self-report. It uses the Women’s Health Study (WHS) accelerometer ancillary study as a case study to illustrate logistical, data-reduction, and analytic challenges.
Key findings
- Current aerobic guidelines recommend ≥150 minutes/week of moderate-intensity activity, but they lack recommendations for light-intensity activity because few data are available, not because existing data show no benefit.
- In the WHS accelerometer study, as of 31 August 2013, 23,266 women were invited; 19,432 (84%) responded; 15,314 (79% of respondents) agreed to participate; and 11,590 had worn and returned devices. The study projects participation by ~18,000 (~63%) of the remaining 28,680 eligible women.
- An interim WHS dataset of 8,373 women (mean age 71 years; 20% obese; 4% current smokers) found 95% wore the device for ≥10 hours/day on ≥4 days. The low-frequency extension filter substantially overestimated steps: median ~5,000 steps/day with the normal filter vs ~13,000 steps/day with the LFE filter.
- Self-reported TV viewing and accelerometer-assessed sedentary time had a correlation of only 0.22, and the paper notes that some self-reported sedentary associations have not been replicated with accelerometry; longitudinal objective studies are needed.
Methods
- Design: A methods/commentary paper using the Women’s Health Study (WHS) as a case study to discuss issues and challenges in accelerometer use for large-scale epidemiologic research.
- Data/sample: The WHS was a completed randomized trial (1992–2004) of 39,876 healthy women ≥45 years; 33,681 (89% of those alive) consented to continue observational follow-up. The accelerometer ancillary study began in 2011, asked women to wear an ActiGraph GT3X+ on the hip during all waking hours for 7 days, and aimed to collect data in ~18,000 women aged ≥62 years.
- Main analysis: Descriptive reporting of WHS recruitment, compliance, data-reduction decisions, and preliminary accelerometer data, including comparison with a comparably aged US national sample; no formal disease-endpoint analysis is presented.
Limitations
- Stated in the paper: Hip-worn accelerometers primarily measure locomotor activity, miss upper-body movement, cannot account for carried weight, and do not measure posture, so they cannot distinguish sitting from standing still.
- Stated in the paper: There is no consensus on wear-time algorithms, cutpoints, or best practices for accelerometer data reduction and analysis; cutpoints are often laboratory-derived, may not apply to older populations, and energy-expenditure predictions can under- or overestimate.
- Evident from the text: The WHS preliminary data are interim and descriptive, with no hard clinical endpoints yet; findings come from a single cohort of older women, limiting generalizability, and the paper is not a systematic review or original prospective outcome study.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
36 Not checked yet · 15 Own interpretation · 6 Own result · 6 No basis given · 1 Partly supported · 1 Own method · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In several studies, investigators reported on the persistence of these associations even among persons active enough to meet physical activity recommendations.[13-20]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Sedentary Behaviour – An Independent Risk Factor?
What the sources say
- Not checked yetcites [13] source
- Not checked yetcites [14] source
- Not checked yetcites [15] source
- Supportedcites [16] source
The Discussion reports that the increased risk of type 2 diabetes associated with television watching was apparent at every level of physical activity in this cohort of Black women. The Results also report increasing diabetes risk with more television watching within each physical-activity category, including a category of at least three hours of vigorous activity per week; the paper discusses activity recommendations of substantially less than that amount. (LLM, conf=0.91)
- Not checked yetcites [17] source
- Not checked yetcites [18] source
- Partly supportedcites [19] sourceExperimental — accuracy not yet measured
The Results report that more sitting was associated with higher all-cause mortality regardless of physical activity level, and the Discussion says the association remained significant even among participants with the highest activity levels. The paper does not explicitly classify those participants as meeting physical activity recommendations, so that specific qualifier is not established by our copy. (LLM, conf=0.82)
- Not checked yetcites [20] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“as of 31 August 2013, 11,590 women have already provided some data.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Abstract > Results
plainn = 11,590- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“They complied well with instructions: 95% wore their device for ≥10 hours each day on ≥4 days (conventionally regarded as “valid” data[49]).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Some preliminary data
What the sources say
- Not checked yetcites [49] Tudor-Locke, 2012, A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and Nutrition Examination Survey, 2003-2006, Prev Chronic Dis
plainn = 8,373- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants do lose or fail to return their accelerometers; the rate of loss in the WHS is 2.1%.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Based on preliminary data, we have found that using logs only to record whether the device was “worn” or “not worn” for the day, but not to indicate times (minimizing participant burden and likelihood of missing data), and then applying the Choi algorithm best reflected actual wear-time as assessed by participant detailed logs.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data reduction
What the sources say
- Not checked yetcites [39] source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Preliminary data in the WHS indicates that the LFE filter substantially overestimates steps taken per day (median of ~5,000 steps per day using the normal filter; ~13,000 steps per day using the LFE filter); thus, we do not recommend this option for assessing steps.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data reduction
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, observational epidemiologic studies that rely on self-reports have tended to limit their assessments to activities of moderate- and vigorous-intensity only.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“It is unclear how well self-reported TV/screen time reflects total sedentary time.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Sedentary Behaviour – An Independent Risk Factor?
What the sources say
- Not checked yetcites [22] Clark, 2011, Relationship of television time with accelerometer-derived sedentary time: NHANES, Med Sci Sports Exerc source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Indeed, the US guidelines acknowledge that “we don’t understand enough about whether doing more baseline activity results in health benefits”.[8]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
What the sources say
- Not checked yetcites [8] U.S. Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. Secondary 2008 Physical Activity Guidelines for Americans. http://www.health.gov/paguidelines/
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“There do exist data from several studies that used accelerometers to objectively measure sedentary behavior, showing unfavourable associations with risk factors (e.g., the Australian AusDiab study,[24] the Proactiv UK trial,[25] the US National Health and Nutrition Examination Survey[26]).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Sedentary Behaviour – An Independent Risk Factor?
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 13
- It may state two separate claims as one. 27
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Several logistical challenges exist in collecting data; nonetheless as of 31 August 2013, 11,590 women have already provided some data.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Abstract > Results
plainn = 11,590- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Packets have been mailed to 14,073 women, of whom 11,590 have worn and returned their devices, 725 have requested participation at a later date (we assume some of these will eventually decline), and 1,087 have changed their mind about participation (the remaining 671 women were not due as of 31/8/13 to return their device).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
plainn = 14,073- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Some of the 725 WHS women who requested later participation will eventually decline.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As of 31 August 2013, 23,266 women have been invited; 19,432 (84%) have responded, of whom 15,314 (79% of respondents) have agreed to participate (16% declined and 5% were not eligible because they cannot walk outside without assistance).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
plainn = 23,266- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Typically, in the main study, we obtain ~80% response to the first mailing of study questionnaires, another ~10% to the second, and a further ~5% to the third, for a total response of ~95%.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Of those alive, 89% or 33,681 women consented, reporting on their health habits and medical history annually on questionnaires.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > The Women’s Health Study (WHS)
plainn = 33,681- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While this seems negligible, 2.1% of the ~20,000 whom we anticipate sending accelerometers translates to 420 units, at substantial cost.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
plaindirectn = 20,000- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Women were of mean age 71 years, 20% were obese, and 4% were current smokers.[30]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Some preliminary data
What the sources say
- Not checked yetcites [30] Shiroma EJ Freedson PS Trost SG . Patterns of accelerometer-assessed sedentary behavior in older women. JAMA; in press [published Online First].
plainn = 8,373- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Self-reports of physical activity are more reliable and valid for activities of moderate-to-vigorous intensity than light-intensity activities, which tend to be poorly reported.[9 10]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
What the sources say
- Not checked yetcites [9 10]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“A large body of epidemiologic literature, accumulated over more than 60 years, shows that individuals who are physically active have a better cardio-metabolic risk profile, lower rates of major non-communicable diseases, better physical and mental function, and live longer, compared with those inactive.[1-3]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Introduction
What the sources say
- Not checked yetcites [1] U.S. Department of Health & Human Services. 2008 Physical Activity Guidelines Advisory Committee report. Secondary 2008 Physical Activity Guidelines Advisory Committee report. http://www.health.gov/paguidelines/
- Not checked yetcites [2] source
- Not checked yetcites [3] World Health Organization. Global recommendations on physical activity for health. Geneva, Switzerland: WHO Press, 2010.
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Indeed, this high prevalence of insufficient activity has been estimated to cause as many deaths worldwide each year as smoking:[5]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Introduction
What the sources say
- Not checked yetcites [5] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“With regard to aerobic activities, the guidelines require that adults carry out at least 150 minutes per week of moderate-intensity physical activity (e.g., brisk walking, whether for leisure or commuting), or 75 minutes per week of vigorous-intensity activity (e.g., jogging), or some combination of activities from both intensities that expend an equivalent amount of energy.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
What the sources say
- Not checked yetcites [8] U.S. Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. Secondary 2008 Physical Activity Guidelines for Americans. http://www.health.gov/paguidelines/
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It is therefore unfortunate that approximately one-third of the world’s population does not get sufficient physical activity to meet current recommendations.[4]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Introduction
What the sources say
- Not checked yetcites [4] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As a consequence, current recommendations have no provision for light-intensity physical activity because few data are available, and not because existing data indicate no benefit.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
What the sources say
- Not checked yetcites [8] U.S. Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. Secondary 2008 Physical Activity Guidelines for Americans. http://www.health.gov/paguidelines/
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accelerometers do not inform on body posture; thus, they cannot distinguish between sitting and standing still.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Using Accelerometers in Large-Scale Epidemiologic Studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The expert panel did consider data from studies of physical activity in relation to cardio-metabolic risk factors (e.g., blood pressure, lipid parameters, measures of glucose and insulin processing); however, these data were considered supporting information.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
What the sources say
- Not checked yetcites [1] U.S. Department of Health & Human Services. 2008 Physical Activity Guidelines Advisory Committee report. Secondary 2008 Physical Activity Guidelines Advisory Committee report. http://www.health.gov/paguidelines/
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“in a nationally representative sample of adults from the Health Survey for England, cross-sectional analyses showed consistent deleterious associations of self-reported sitting time with a panel of cardio-metabolic risk factors; however, when accelerometer measures of sedentary behaviour were analysed, little relation was observed.[23]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Sedentary Behaviour – An Independent Risk Factor?
What the sources say
- Not checked yetcites [23] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The WHS is a completed randomized trial (1992-2004) testing aspirin and vitamin E for preventing cardiovascular disease and cancer among 39,876 healthy women, ≥45 years, living throughout the US.[32-34]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > The Women’s Health Study (WHS)
What the sources say
plainn = 39,876- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, these studies are predominantly cross-sectional in design; thus, one cannot ascertain the direction of association (i.e., does being sedentary result in poor health? Or, does being in poor health predispose one towards being sedentary?).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Sedentary Behaviour – An Independent Risk Factor?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Important data on the health effects of light-intensity activity and sedentary behaviour will emerge from large-scale epidemiologic studies collecting objective assessments of these behaviours.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Abstract > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“In recently published results, the intensive intervention clearly improved cardio-metabolic risk factors; however, there was no difference in rates of cardiovascular events between groups after a median follow-up of 9.6 years, when the trial was stopped for futility.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Sedentary Behaviour – An Independent Risk Factor?
What the sources say
- Not checked yetcites [27] source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One movement sensor capable of measuring light-intensity physical activity and sedentary behaviour objectively is the accelerometer.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Using Accelerometers in Large-Scale Epidemiologic Studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Recent technological advances in assessment methods have made the use of movement sensors, such as the accelerometer, feasible for use in longitudinal, large-scale epidemiologic studies.”
- Also stated in: § Using Accelerometers in Large-Scale Epidemiologic Studies · plain
“Thus, the accelerometer has now become feasible for use in large-scale studies, such as the examples shown in Table 1.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Abstract > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Using Accelerometers in Large-Scale Epidemiologic Studies · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They primarily measure locomotor activity when worn over the hip (typical placement position), and so miss upper body movement.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Using Accelerometers in Large-Scale Epidemiologic Studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This has occurred because such studies typically require thousands, and even hundreds of thousands, of subjects to possess sufficient statistical power for investigating associations of physical activity with the incidence of hard clinical endpoints (e.g., major non-communicable diseases), and self-report is a cost-effective method of assessment.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This panel emphasized data from studies that examined the relation between physical activity and the risks of developing hard clinical endpoints, such as all-cause mortality, cardiovascular disease, type 2 diabetes, and cancer, reviewing the data from individual studies in detail.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Current Physical Activity Guidelines – Why No Prescription for Light-Intensity Activity?
What the sources say
- Not checked yetcites [1] U.S. Department of Health & Human Services. 2008 Physical Activity Guidelines Advisory Committee report. Secondary 2008 Physical Activity Guidelines Advisory Committee report. http://www.health.gov/paguidelines/
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, the WHS experience on data reduction and data analyses can help inform other similar large-scale epidemiologic studies.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Abstract > Results
plainn = 11,590- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The Women’s Health Study is sufficiently similar to other large-scale epidemiologic studies that its data-reduction and analysis experience can inform them.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the ~30,000 total women, we expect 1320 (4.4%) to be non-eligible, and project participation by ~18,000 (~63%) of the remaining 28,680.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
hedgedn = 23,266- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, recent technological advances have made the use of movement sensors, such as the accelerometer, feasible for use in longitudinal, large-scale epidemiologic studies.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, we decided a priori to create an interim, closed dataset that comprised all women who returned their accelerometer by 31 March 2013 (n = 8,373; approximately half the total anticipated sample of ~18,000).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Some preliminary data
plainn = 8,373- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, there is no consensus on what the “correct” cutpoints might be, with several proposed in the literature based on calibration studies primarily carried out under laboratory settings and not in free-living conditions.[40 41]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data analyses
What the sources say
- Not checked yetcites [40 41]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Efforts are ongoing to better take advantage of the rich data, such as pattern recognition or machine learning data analytic approaches to identify specific activities carried out.[47 48]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data analyses
What the sources say
- Not checked yetcites [47 48]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recent work indicates a strong correlation between counts from the vertical axis and counts combined from all three axes using vector magnitude; thus, the vector magnitude data may not provide much additional information beyond vertical axis counts.[46]”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data analyses
What the sources say
- Not checked yetcites [46] source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Further, while best practices and standards have been proposed,[35-37] no consensus has been reached.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data reduction
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Automated algorithms are available (e.g., the commonly used Troiano[38] and Choi[39] algorithms) for wear-time determination; however, these were developed for protocols where participants received the device directly from investigators (thus, “mail noise” is not applicable).”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Challenges related to data reduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Imprecise assessments of these behaviours and cross-sectional study designs have limited our knowledge.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The Women’s Health Study is sufficiently similar to other large-scale epidemiologic studies that its data-reduction and analysis experience can inform them.
“Additionally, the WHS experience on data reduction and data analyses can help inform other similar large-scale epidemiologic studies.”
The paper relies on this without support.
In the WHS older women, walking is the most common physical activity.
“for example, in the WHS below that includes older women, walking is the most common physical activity.”
The authors state this as an assumption.
Some of the 725 WHS women who requested later participation will eventually decline.
“725 have requested participation at a later date (we assume some of these will eventually decline)”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Although there is no clear consensus about how to define light-intensity activities by accelerometry [37, 38]”
From Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study (2016) · cites this paper as [38]
In the citing paper
“The subjective measures likely underestimate the total amount of PA, 13 which may be attributed to the lack of consideration of light-intensity PA with an unstructured nature and dispersion during the day.14”
In the citing paper
“The relationship between light-intensity physical activity and health outcomes is largely unknown, partly due to the difficulty of its quantification as many methods to measure physical activity are not precise enough to capture such activities [38].”
From Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study (2016) · cites this paper as [38]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“48% (10,863/22,195) of invited participants returned a device with some data.”
§ Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiologic Studies: Issues and Challenges > A Case Study: Issues and Challenges > Logistical challenges
Reported 48 · recomputed 48.943456
Citations
Cites
The Physical Activity Guidelines for Americans
Piercy, K. L., Troiano, R. P., Ballard, R. M., Carlson, S. A., Fulton, J. E., Galuska, D. A., George, S. M., & Olson, R. D. (2018) · JAMA · doi:10.1001/jama.2018.14854 · Cited by 9,755
Lee, I.-M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., & Katzmarzyk, P. T. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61031-9 · Cited by 8,215
Self-citationPhysical Activity in the United States Measured by Accelerometer
TROIANO, R. P., BERRIGAN, D., DODD, K. W., MÂSSE, L. C., TILERT, T., & MCDOWELL, M. (2008) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31815a51b3 · Cited by 7,638 · cited 1×
“Figure 1 shows their physical activity levels compared with a comparably aged US national sample (data are presented using commonly used cut-points for de fi ning different intensities of physical activity 18 38 ).”
MethodGlobal physical activity levels: surveillance progress, pitfalls, and prospects
Hallal, P. C., Andersen, L. B., Bull, F. C., Guthold, R., Haskell, W., & Ekelund, U. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60646-1 · Cited by 5,711 · cited 1×
“4 Indeed, this high prevalence of insuf fi cient activity has been estimated to cause as many deaths worldwide each year as smoking 5 : although smoking increases the risk of dying for the individual more than does inactivity, the prevalence of smoking worldwide (about one-quarter) is less than that…”
BackgroundCardiovascular effects of intensive lifestyle intervention in type 2 diabetes
The Look AHEAD Research Group (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1212914 · Cited by 2,908
Conducting accelerometer-based activity assessments in field-based research
TROST, S. G., MCIVER, K. L., & PATE, R. R. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185657.86065.98 · Cited by 2,033
A Randomized Trial of Low-Dose Aspirin in the Primary Prevention of Cardiovascular Disease in Women
Ridker, P. M., Cook, N. R., Lee, I.-M., Gordon, D., Gaziano, J. M., Manson, J. E., Hennekens, C. H., & Buring, J. E. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa050613 · Cited by 1,945
Self-citationHu, F. B., Li, T. Y., Colditz, G. A., Willett, W. C., & Manson, J. E. (2003) · JAMA · doi:10.1001/jama.289.14.1785 · Cited by 1,892
Validation of Accelerometer Wear and Nonwear Time Classification Algorithm
CHOI, L., LIU, Z., MATTHEWS, C. E., & BUCHOWSKI, M. S. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181ed61a3 · Cited by 1,804
Wilmot, E. G., Edwardson, C. L., Achana, F. A., Davies, M. J., Gorely, T., Gray, L. J., Khunti, K., Yates, T., & Biddle, S. J. H. (2012) · Diabetologia · doi:10.1007/s00125-012-2677-z · Cited by 1,789 · cited 1×
“…of epidemiological literature over the past 5 – 10 years has described the associations between sedentary behaviour and increased risks of all-cause mortality and cardio-metabolic diseases, 11 with plausible biological mechanisms (eg, lipoprotein lipase regulation) proposed from animal studies.”
Background
Research works printed but not linked: 1
- World Health Organization. Global recommendations on physical activity for health. Geneva, Switzerland: WHO Press, 2010.
Cited by
Sorted by citations
Items 201–300 of 414
Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth
Mitchell, T. B., Borner, K. B., Finch, J. B., Kerr, J., & Carlson, J. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001266 · Cited by 15
“In addition, because of the evolving methodology for accelerometer data collection and processing (18,22,36), the current study also aimed to fill a gap in the literature by examining the validity of the inclinometer function of the hipworn accelerometer for measuring sit-to-stand transitions.”
MethodInvited Commentary: Meta-Physical Activity and the Search for the Truth
Arem, H. E., Keadle, S. K., & Matthews, C. E. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu472 · Cited by 14
“Accelerometers have become increasingly accessible for use in recent epidemiologic studies because the cost has gone down (15).”
BackgroundWalmsley, R., Chan, S., Smith-Byrne, K., Ramakrishnan, R., Woodward, M., Rahimi, K., Dwyer, T., Bennett, D., & Doherty, A. (2020) · doi:10.1101/2020.11.10.20227769 · Cited by 12
Vincent, A. C., Furman, H., Slepian, R. C., Ammann, K. R., Maria, C. D., Chien, J. H., Siu, K., & Slepian, M. J. (2022) · Applied Sciences · doi:10.3390/app12126173 · Cited by 13
Neale, C. D., Christensen, P. E., Dall, C. H., Ulrik, C. S., Godtfredsen, N. S., & Hansen, H. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192416804 · Cited by 13
“The outcome variables were measured around the clock for 5 days including weekends which we believe reflects a more valid expression of physical activity and sleep patterns [55].”
BackgroundAlansare, A. B., Paley, J. L., Quinn, T. D., & Gibbs, B. B. (2023) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002873 · Cited by 15
de Oliveira, J. J., Ribeiro, A. G. S. V., Silva, J. A. D. O., Barbosa, C. G. R., Silva, A. D. S. E., dos Santos, G. M., Verlengia, R., & Pertille, A. (2023) · Aging & Mental Health · doi:10.1080/13607863.2023.2248477 · Cited by 15
“However, this method has some biases and limitations, both in terms of validity (Shephard, 2003) and reporting the volume and intensity of physical activity accurately (Finger et al. 2015; Garber et al. 2011; Lee & Shiroma, 2014).”
Are We Overestimating Physical Activity Prevalence in Children?
Price, L., Wyatt, K. M., Lloyd, J., et al. (2018) · Journal of Physical Activity and Health · doi:10.1123/jpah.2018-0030 · Cited by 13
“26 As a result, some activities that have a metabolic cost equivalent to MVPA may have been misclassi fi ed, resulting in an underestimation of MVPA in the accelerometer data.”
Yano, S., Koohsari, M. J., Shibata, A., Ishii, K., Frehlich, L., McCormack, G. R., & Oka, K. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16173126 · Cited by 13
“, arm movements, standing, and sitting) [35].”
Effects of an “Active-Workstation” Cluster RCT on Daily Waking Physical Behaviors
ARGUELLO, D. J., Thorndike, A. N., Cloutier, G. J., Morton, A. L., Castaneda‐Sceppa, C., & John, D. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002594 · Cited by 14
“Sensor data were first processed to verify wake-wear intervals using a combination of the self-report logs and signal visualization (27).”
MethodRuiz-Zafra, A., Orantes-González, E., Noguera, M., Benghazi, K., & Heredia-Jimenez, J. (2015) · Sensors · doi:10.3390/s150818270 · Cited by 13
“However, since these proposals focus on the EE of a particular PA or are based on the use of a single accelerometer [8,21,22], the results obtained are fully conditional on the technology used or by the PA performed which means that in some cases EE has a higher margin of error.”
BackgroundClustering of Physical Activities for Quantified Self and mHealth Applications
Dobbins, C., & Rawassizadeh, R. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.213 · Cited by 13
“In other works, Lee and Shiroma [19] report the issues and challenges of using accelerometers to measure physical activity.”
BackgroundMonitoring Walking Devices For Calorie Balance In Patients With Medical Rehabilitation Needs
Kusuma, W. A., Sari, Z., Wibowo, H., et al. (2018) · International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) · doi:10.1109/eecsi.2018.8752761 · Cited by 12
Fukushima, N., Amagasa, S., Kikuchi, H., Takamiya, T., Odagiri, Y., Hayashi, T., Kitabayashi, M., & Inoue, S. (2019) · SANGYO EISEIGAKU ZASSHI · doi:10.1539/sangyoeisei.2019-013-b · Cited by 13
Estimation of Physical Activity Intensity in Spinal Cord Injury Using a Wrist-Worn ActiGraph Monitor
Veerubhotla, A. L., Hong, E., Knezevic, S., Spungen, A. M., & Ding, D. (2020) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2020.05.014 · Cited by 12
Machine learning aided classification of tremor in multiple sclerosis
Hossen, A., Anwar, A. R., Koirala, N., Ding, H., Budker, D., Wickenbrock, A., Heute, U., Deuschl, G., Groppa, S., & Muthuraman, M. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104152 · Cited by 12
“66 The use of the modulus accelerometer signal as in our study is advocated to overcome this limitation.”
Dalene, K. E., Anderssen, S. A., Ekelund, U., Thorén, A.-K. H., Hansen, B. H., & Kolle, E. (2016) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2016.08.011 · Cited by 12
Yonemoto, K., Honda, T., Kishimoto, H., Yoshida, D., Hata, J., Mukai, N., Shibata, M., Hirakawa, Y., Ninomiya, T., & Kumagai, S. (2019) · Journal of Physical Activity and Health · doi:10.1123/jpah.2017-0701 · Cited by 12
Sensing human activity to improve sedentary lifestyle
Boerema, S. (2018) · doi:10.3990/1.9789036546041 · Cited by 12
dos Santos, C. E. S., d’Orsi, E., & Rech, C. R. (2020) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2020.104238 · Cited by 10
Kilb, M., Dickhäuser, O., & Mata, J. (2022) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2022.2032074 · Cited by 13
Pereira, J. R., Sousa‐Sá, E., Zhang, Z., Cliff, D. P., & Santos, R. (2019) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2019.08.009 · Cited by 11
A Sensor Network Utilizing Consumer Wearables for Telerehabilitation of Post-Acute COVID-19 Patients
Bureš, M., Neumannová, K., Blažek, P., Klima, M., Schvach, H., Néma, J., Kopecký, M., Dygrýn, J., & Koblížek, V. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3188914 · Cited by 10
“The ActiGraph accelerometer is well validated [33] and has been extensively used for evaluating physical activity [34], [35].”
MethodZabatiero, J. G., Ng, L., Clayton, R., Middlemiss, S., Kang, K., Harrold, M., & Cavalheri, V. (2018) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2018.10.004 · Cited by 12
Calibration of the Active Australia questionnaire and application to a logistic regression model
Bassett, J. K., Swain, C. T. V., Hodge, A. M., Mahmood, S., Csizmadi, I., Owen, N. G., Dunstan, D. W., & Lynch, B. M. (2020) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2020.11.007 · Cited by 12
Estimating cut points: A simple method for new wearables
Hickey, A., Newham, J. J., Sławińska, M., Kwaśnicka, D., McDonald, S., Din, S. D., Sniehotta, F. F., Davis, P., & Godfrey, A. (2015) · Maturitas · doi:10.1016/j.maturitas.2015.10.003 · Cited by 11
Carter, S. J., Rogers, L. Q., Bowles, H. R., Norian, L. A., & Hunter, G. R. (2019) · European Journal of Applied Physiology · doi:10.1007/s00421-019-04227-1 · Cited by 10
Länsitie, M., Kangas, M., Jokelainen, J. J., Venojärvi, M., Vaaramo, E., Härkönen, P. K., Keinanen-Kiukaanniemi, S. M., & Korpelainen, R. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108937 · Cited by 11
Herbert, J., Matłosz, P., Martínez‐Rodríguez, A., Przednówek, K., Asif, M., & Wyszyńska, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19094999 · Cited by 11
“Currently, accelerometers are used in many studies on the level of PA [62].”
BackgroundTrost, S. G., & Tudor‐Locke, C. (2014) · British Journal of Sports Medicine · doi:10.1136/bjsports-2014-093865 · Cited by 10
Culverhouse, J., Hillsdon, M. M., Metcalf, B. S., Nunns, M., Lear, R., Brailey, G., & Pulsford, R. M. (2023) · BMJ Public Health · doi:10.1136/bmjph-2023-100000 · Cited by 10
“We could not include eight studies within meta-analyses, however, the proportion of these studies reporting positive associations between PA and PF was similar to those included in meta-analysis.”
24-H movement behaviours research in Chinese population: A scoping review
Huang, J., Memon, A. R., Bao, R., Fan, H., Wang, L., Liu, Y., Chen, S., & Li, C. (2024) · Journal of Exercise Science & Fitness · doi:10.1016/j.jesf.2024.07.005 · Cited by 11
“Several studies suggested that researchers in high-income countries were more likely to use device-based measures, 79,80 possibly due to greater investments in their research compared to those conducted in LMICs.”
ResultProtocol and Data Description: The Free-Living Activity Study for Health
Hibbing, P. R., Lamoureux, N. R., Matthews, C. E., & Welk, G. J. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2020-0052 · Cited by 9
Kingsnorth, A. P., Patience, M., Moltchanova, E. V., Esliger, D., Paine, N. J., & Hobbs, M. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2021-0005 · Cited by 9
CTASS: an intelligent framework for personalized travel behaviour advice to cardiac patients
Batool, T., Vanrompay, Y., Neven, A., Janssens, D., & Wets, G. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0847-7 · Cited by 9
Posture transition analysis with barometers: contribution to accelerometer-based algorithms
Rodríguez-Martín, D., Samà, A., Pérez‐López, C., Catalá, A., & Cabestany, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3759-8 · Cited by 9
“The common approach to use the signals provided by these inertial-based wearable devices consists in analysing them by means of machine learning techniques and biomechanical algorithms with the aim of determining the activity, posture transition, gait parameters and other mobility-related parameters [5, 8, 9].”
MethodRamos-Munell, J., Antczak, D., Álvarez‐Barbosa, F., Alfonso‐Rosa, R. M., Cruz, B. D. P., & del Pozo-Cruz, J. (2025) · Journal of Physical Activity and Health · doi:10.1123/jpah.2024-0640 · Cited by 7
Khan, K., Windt, A., Davis, J. C., Dawes, M. G., Liu-Ambrose, T. Y. L., Madden, K. M., Marra, C. A., Housden, L., Hoppmann, C. A., & Adams, D. J. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007441 · Cited by 8
Need for Digital Biomarkers in Musculoskeletal Trials
Goldhahn, J. (2017) · Digital Biomarkers · doi:10.1159/000479753 · Cited by 8
Energy expenditure associated with walking speed and angle of turn in children
Crossley, S. G. M., Mackintosh, K. A., Wilson, R. P., Lester, L., Griffiths, I. W., & McNarry, M. A. (2018) · European Journal of Applied Physiology · doi:10.1007/s00421-018-3981-1 · Cited by 7
Comparison of different software for processing physical activity measurements with accelerometry
Verhoog, S., Gubelmann, C., Bano, A., Muka, T., Franco, O. H., & Marques-Vidal, P. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-29872-7 · Cited by 9
Klamm, M. M., Duck, A. A., Welsch, M. A., Yan, Y., Torres, E. R., Wade, B., Stewart, M. W., Clayton, J., & Zhang, L. (2021) · Journal for Specialists in Pediatric Nursing · doi:10.1111/jspn.12364 · Cited by 9
Mediano, M. F. F., Mok, Y., Ballew, S. H., Gonzalez, F., Sotres‐Alvarez, D., Mossavar‐Rahmani, Y., Kaplan, R. C., Carlson, J., Alver, S., Daviglus, M. L., et al. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.100996 · Cited by 8
Hyde, E. T., LaCroix, A. Z., Evenson, K. R., Howard, A. G., Anuskiewicz, B., Di, C., Bellettiere, J., LaMonte, M. J., Manson, J. E., Buring, J. E., et al. (2023) · Cancer · doi:10.1002/cncr.34699 · Cited by 8
“However, self‐reported physical activity measures are prone to measurement error, which can be mitigated by using accelerometry.(7,8) Generally, the correlation coefficient for self‐reported and accelerometer‐measured physical activity is approximately 0.”
BackgroundSelf-citationHaddadj, R., Verdot, C., Salanave, B., Deschamps, V., & Vanhelst, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0316176 · Cited by 9
Greenwood-Hickman, M. A., Walker, R. L., Idu, A., Bellettiere, J., Wing, D., McCurry, S. M., Crane, P. K., Larson, E. B., Rosenberg, D. E., & LaCroix, A. Z. (2025) · BMC Public Health · doi:10.1186/s12889-025-22264-8 · Cited by 9
“Specifically, LPA, defined in these analyses as movement between 1.5 and 3.0 metabolic equivalents (METs) [34], comprises activities such as walking or moving slowly, lifting light objects, and light housework or gardening [51, 52].”
InfluentialPirnes, K. P., Kallio, J., Kankaanpää, A., HÄKKINEN, A. H., & Tammelin, T. (2020) · Scandinavian Journal of Pain · doi:10.1515/sjpain-2020-0038 · Cited by 7
“Accelerometers aremovement sensors that are feasible to be used in large scale studies and capable ofmeasuring time spent unmoved or at different PA intensities [26].”
BackgroundÖhlin, J., Toots, A., Almevall, A. D., Littbrand, H., Conradsson, M., Hörnsten, C., Werneke, U., Niklasson, J., Olofsson, B., Gustafson, Y., et al. (2021) · Gait & Posture · doi:10.1016/j.gaitpost.2021.11.019 · Cited by 10
Keadle, S. K., Martinez, J., Strath, S. J., Sirard, J. R., John, D., Intille, S. S., ARGUELLO, D. J., Amalbert‐Birriel, M. A., Barnett, R., Thapa-Chhetry, B., et al. (2023) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2022-0048 · Cited by 7
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Viegas, V. M. R., Pereira, J. M. D., Postolache, O. A., et al. (2018) · Sensors · doi:10.3390/s18020540 · Cited by 7
Zhang, P., Burns, R. D., Fu, Y., Godin, S., & Byun, W. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16203812 · Cited by 7
“Being able to monitor individual’s PA plays an important role on PA promotion and weight-management initiatives [2].”
BackgroundResearch on classification and recognition of badminton batting action based on machine learning
Sun, L. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179206 · Cited by 7
Oh, M., Jacobs, D. R., Gabriel, K. P., Bao, W., Pierce, G. L., Carr, L. J., Ding, J., & Whitaker, K. M. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002866 · Cited by 6
“It is difficult for study participants to accurately recall light activities because these activities are less salient, highly dispersed, and irregularly performed (46).”
BackgroundWalking Meeting Effects on Productivity and Mood Among White-Collar Workers
Kling, H. E., Moore, K. J., Brannan, D., & Caban-Martinez, A. J. (2020) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002098 · Cited by 7
“While accelerometers collect precise levels of objectively measured physical activity, it primarily measures locomotor activity when worn over the hip and does not classify upper body movement accurately.(22) As a result, upper body activity may be under-estimated in accounting for overall physical activity.”
BackgroundSchütz, N., Saner, H. E., Botros, A., Buluschek, P., Urwyler, P., Müri, R. M., & Nef, T. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.566595 · Cited by 6
Garza, J. L. B., Wu, Z. H., Singh, M., & Cherniack, M. G. (2021) · Annals of Work Exposures and Health · doi:10.1093/annweh/wxab065 · Cited by 6
Heron, L., Tully, M. A., Kee, F., & O’Neill, C. (2022) · The European Journal of Health Economics · doi:10.1007/s10198-022-01487-1 · Cited by 6
“[7, 8] In recent times, accelerometers have become a cheaper and more feasible method of measurement in large studies.”
BackgroundMethodMeasurement of Sedentary Behaviour in Population Studies
Ainsworth, B. E., Rivière, F., & Flórez-Pregonero, A. (2017) · Springer series on epidemiology and public health · doi:10.1007/978-3-319-61552-3_2 · Cited by 6
Machida, M., Takamiya, T., Amagasa, S., Kikuchi, H., Fukushima, N., Odagiri, Y., & Inoue, S. (2018) · Nippon Ronen Igakkai Zasshi Japanese Journal of Geriatrics · doi:10.3143/geriatrics.55.584 · Cited by 6
Stassen, G., Rudolf, K., Gernert, M., Thiel, A., & Schaller, A. (2021) · BMC Public Health · doi:10.1186/s12889-020-10113-9 · Cited by 8
Moore, R. P., Archer, K. R., & Choi, L. (2021) · Sensors · doi:10.3390/s21082726 · Cited by 6
“Due to advances in microelectronics, the proliferation of this technology has become particularly evident in healthcare research, where wearable accelerometers are often used for measuring the activity of both patients and participants in clinical trials [1].”
BackgroundChapron, K., Lapointe, P., Lessard, I., Darsmstadt-Belanger, H., Bouchard, K., Gagnon, C., Lavoie, M., Duchesne, É., & Gaboury, S. (2021) · IEEE Access · doi:10.1109/access.2021.3068877 · Cited by 6
Orlando, J. M., Smith, B. A., Hafer, J. F., et al. (2025) · Sensors · doi:10.3390/s25041261 · Cited by 5
“Other methods have been employed to evaluate activity in natural environments [47,48].”
Hyde, E. T., Evenson, K. R., Bandoli, G., Zou, J., Crespo, N. C., Parada, H., LaMonte, M. J., Howard, A. G., Nguyen, S., Skiba, M. B., et al. (2025) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkaf034 · Cited by 5
“14,15 The present study examined associations of accelerometer-derived measures of physical activity (light physical activity, moderate to vigorous physical activity, total physical activity, daily steps) and sedentary behavior (sitting time, sitting bout duration) with all-cause, cancer-related,…”
Self-citationShrestha, S., Turrell, G., Dale, M. J., & Carroll, S. J. (2025) · Obesity Reviews · doi:10.1111/obr.13944 · Cited by 6
A Million Steps: Developing a Health Promotion Program at the Workplace to Enhance Physical Activity
González‐Domínguez, M. E., Romero‐Sánchez, J. M., Camerino, A. A., Marchena-Aparicio, J. C., Flores-Muñoz, M., Infantes-Guzmán, I., León-Asuero, J. M., & Casals-Martín, F. (2017) · Workplace Health & Safety · doi:10.1177/2165079917705146 · Cited by 5
“It would also be desirable to improve the way physical activity was measured using accelerometers, which have been used in other studies (Lee & Shiroma, 2014).”
MethodHsu, Y., Liu, C.-C., Chang, Y.-J., Tsai, Y., Tsai, W.-C., & Fu, Y. (2020) · Journal of Science in Sport and Exercise · doi:10.1007/s42978-019-00047-z · Cited by 5
Tsunoda, K., Nagase, K., & 藤田, 久. (2023) · BMC Women s Health · doi:10.1186/s12905-023-02597-5 · Cited by 7
Stork, M., Santos, A., Locke, S., Little, J. P., & Jung, M. E. (2023) · International Journal of Behavioral Medicine · doi:10.1007/s12529-023-10224-8 · Cited by 6
Dawson, Z., Beaumont, A. J., & Carter, S. E. (2023) · Journal of Physical Activity and Health · doi:10.1123/jpah.2023-0195 · Cited by 9
la Cámara, M. Á. D., Ortíz, C., Granero-Melcón, B., Martínez-Portillo, A., Neira-León, M., & Galán, I. (2024) · BMC Public Health · doi:10.1186/s12889-024-20600-y · Cited by 8
“Moreover, objective methods are not without limitations [56], they present their own challenges, and they are difficult to implement in the context of large-scale epidemiological studies [57].”
Hammond, M. M., Zhang, Y., Pathiravasan, C. H., Lin, H., Sardana, M., Trinquart, L., Benjamin, E. J., Borrelli, B., Manders, E. S., Fusco, K., et al. (2022) · JMIR Cardio · doi:10.2196/32348 · Cited by 5
“Advances in technology permit the use of smaller, light-weight, relatively accessible accelerometers, and allow accelerometer usage in large epidemiological studies [23,24].”
BackgroundOutputs Available from Objective Monitors
Tudor‐Locke, C. (2016) · Springer series on epidemiology and public health · doi:10.1007/978-3-319-29577-0_3 · Cited by 5
Higgins, S., Higgins, L. Q., & Vallabhajosula, S. (2020) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002562 · Cited by 5
Comparing Accelerometer and Heart Rate Monitor in Interval Running, Interval Spinning and Zumba
Dyrstad, S. M. (2014) · IJASS(International Journal of Applied Sports Sciences) · doi:10.24985/ijass.2014.26.2.89 · Cited by 5
No Expiration Date on the Association Between Physical Activity and Mortality
Nicklas, B. J. (2018) · Journal of the American Geriatrics Society · doi:10.1111/jgs.15243 · Cited by 5
“The feasible use of accelerometers and inclinometers has made it possible to directly measure several important parameters of movement and sedentary behavior (sitting or lying) in real time in large-scale observational and interventional research.(3,4) The intensity (absolute, fixed, scaled to a person’s ability), fre…”
BackgroundCulverhouse, J., Hillsdon, M. M., Koster, A., Bosma, H., de Galan, B. E., Savelberg, H. H. C. M., & Pulsford, R. M. (2024) · European Review of Aging and Physical Activity · doi:10.1186/s11556-024-00343-w · Cited by 4
“In addition, the reliance on single thresholds of acceleration to define physical activity intensities can lead to misclassification, as this approach assumes that any physical activity above the threshold represents the same intensity for all individuals regardless of fitness [33].”
BackgroundProtocols for Data Collection, Management and Treatment
Tudor‐Locke, C. (2016) · Springer series on epidemiology and public health · doi:10.1007/978-3-319-29577-0_4 · Cited by 5
Combining distance and force measurements to monitor the usage of walker assistive devices
Pereira, J. M. D., Viegas, V. M. R., Postolache, O. A., et al. (2017) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2017.7969673 · Cited by 4
Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach
Verma, D., Bach, K., & Mork, P. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01081-2_28 · Cited by 5
“Over the last few years, researchers in public health domain have moved rapidly from using self-reported subjective activity data to objectively measured activity data with the use of body-worn sensors [4,18,20].”
BackgroundDallaway, A., Hattersley, J. G., Tallis, J. A., Renshaw, D. W., Griffen, C., & Duncan, M. J. (2021) · Journal of Aging and Physical Activity · doi:10.1123/japa.2020-0421 · Cited by 4
“In the current study, PA was measured using accelerometery and a recognised limitation of using accelerometers is their inability to detect non-ambulatory activities such as resistance exercise (I. M. Lee & Shiroma, 2014; Viljanen et al., 1991).”
Thompson, B., Meynadasy, M. A., Hajcak, G., & Brush, C. J. (2024) · Brain and Cognition · doi:10.1016/j.bandc.2024.106168 · Cited by 4
Use of Physical Activity Monitors in Rheumatic Populations
Pellegrini, C. A., Powell, S. M., Mook, N., DeVivo, K., & Ehrlich-Jones, L. (2018) · Current Rheumatology Reports · doi:10.1007/s11926-018-0786-5 · Cited by 3
Defining a valid day of accelerometer monitoring in adults with mental illness
Chapman, J. J., Brown, W. J., & Burton, N. W. (2015) · Mental health and physical activity · doi:10.1016/j.mhpa.2015.09.003 · Cited by 3
Maggio, A. B. R., Martin, X., Tabard-Fougère, A., Delhumeau, C., & Ceroni, D. (2018) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2018-000359 · Cited by 4
“Further, they cannot distinguish whether a person is carrying any weight (eg, walking carrying a heavy bag).(39) Accelerometers do not inform on body posture; thus, they cannot distinguish between sitting and standing still.”
BackgroundKikuchi, H., Fukushima, N., Amagasa, S., & Inoue, S. (2025) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003693 · Cited by 4
A theory-based model of cumulative activity
Phillips, K., Stanley, K. G., & Fuller, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-18982-3 · Cited by 3
Measurement of Sedentary Behaviour in Population Studies
Ainsworth, B. E., Rivière, F., & Flórez-Pregonero, A. (2023) · Springer series on epidemiology and public health · doi:10.1007/978-3-031-41881-5_3 · Cited by 3
Qual a intensidade das aulas de um programa de exercício físico para idosos?
Borges, L. J., Meurer, S. T., Borges, R. A., Gerage, A. M., & Benedetti, T. R. B. (2015) · Brazilian Journal of Kinanthropometry and Human Performance · doi:10.5007/1980-0037.2015v17n5p527 · Cited by 3
Target, N., Courivaud, C., Michel, P. A., Daoud, S., & Thomas, M. (2020) · BMC Nephrology · doi:10.1186/s12882-020-02127-7 · Cited by 3
“At the end of the inclusion visit (V0), participants (except in case of explicit refusal) will receive a connected 3-axis accelerometer (FITBIT®Flex2TM); 3-axis accelerometers have been shown to be accurate with a very low variability compared to other instruments [17] and can provide large amount of data on light-int…”
BackgroundSo, R., & Matsuo, T. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182312774 · Cited by 3
“ActiGraph, in particular, has been widely used to assess ST; however, its accuracy has been reported to be low when it measures light intensity PA, including sedentary behavior [9,31], and various cut-off points are also a limitation when considering the best approach to be used [5,10,32].”
MethodExploring the effect of sedentary behavior on increased adiposity in middle-aged adults
Macías, N., Monterrubio‐Flores, E., Salmerón, J., Meneses‐León, J., Flóres, Y. N., Jáuregui, A., Salvo, D., Villa, U., Olvera, A. G., & Gallegos-Carrillo, K. (2024) · BMC Public Health · doi:10.1186/s12889-024-19723-z · Cited by 3
“The limited adherence to accelerometer protocols hampers their application in large-scale studies [32].”
BackgroundNagovitsyn, R. S., Osipov, A. Y., Ratmanskaya, T. I., Loginov, D. V., & Prikhodov, D. S. (2021) · Winter Conferences of Sports Science · doi:10.14198/jhse.2021.16.proc3.03 · Cited by 3
Using Wearable Devices to Assess Physical Behavior in Epidemiologic Research
Freedson, P. S. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0029 · Cited by 3
“…opportunities and Freedson is Professor Emerita, Dept. of Kinesiology, University of Massachusetts, Amherst. challenges that result from collecting wearable sensor data in large studies (e.g., Keadle, Shiroma, Freedson, & Lee, 2014; Lee & Shiroma, 2014; Shiroma, Kamada, Smith, Harris, & Lee, 2015).”
Skeleton avatar technology as a way to measure physical activity in healthy older adults
Lincke, A., Fagerström, C., Ekstedt, M., et al. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100609 · Cited by 3
Liu, H., Li, Q., Li, Y., Wang, Y., Huang, Y., Bao, D., Liu, H., & Cui, Y. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.950074 · Cited by 3
Association of sedentary time with blood pressure in women of reproductive age
Spehar, S. M., Gibbs, B. B., Muldoon, M. F., & Catov, J. M. (2020) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2020.101219 · Cited by 5
Methods to Measure Physical Activity Behaviors in Health Education Research
Fitzhugh, E. C. (2015) · American Journal of Health Education · doi:10.1080/19325037.2014.978057 · Cited by 3
“Among these metrics the most commonly reported measure is minutes of moderateor vigorous-intensity physical activity (i.e., MVPA), either in bouts of 10 minutes or nonbouted minutes.11,12 These MVPA minutes then lend themselves to comparisons to the 2008 Physical Activity Guidelines for Americans.13 Through advances i…”
Method
Metadata
- Authors
- Lee, I.-M., & Shiroma, E. J.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Lee, I-Min
- Venue
- British Journal of Sports Medicine
- Publisher
- BMJ
- Published
- 2013-12-02
- Volume
- 48
- Issue
- 3
- Pages
- 197-201
- ISSN
- 0306-3674 (ISSN-L)
- 1473-0480
- PMC
- PMC3947179
- OpenAlex
- W2136019090
- Semantic Scholar
- 4f904384be8d865260d8f27dfd7a14dccefcee46
- Cited by
- 415
- References
- 55
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 29 · 2023: 27 · 2022: 31 · 2021: 62 · 2020: 41 · 2019: 32 · 2018: 39 · 2017: 46 · 2016: 33 · 2015: 26 · 2014: 16
- FWCI
- 17.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British Journal of Sports Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health (5r01hl043851-10)
- National Heart, Lung, and Blood Institute (R01HL080467, R01‐HL043851)
- National Cancer Institute (CA154647, R01CA154647, R01CA047988)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- accelerometer, physical activity measurement, sedentary behavior, light physical activity, Women's Health Study, data collection challenges, data processing and analysis, chronic disease risk, older women
- MeSH terms
- Data Collection, Female, Humans, Risk Factors, Exercise, Epidemiologic Studies, Women's Health, Practice Guidelines as Topic, Sedentary Behavior, Accelerometry
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