Guide to the Assessment of Physical Activity: Clinical and Research Applications: A Scientific Statement From the American Heart Association
Abstract
The deleterious health consequences of physical inactivity are vast, and they are of paramount clinical and research importance. Risk identification, benchmarks, efficacy, and evaluation of physical activity behavior change initiatives for clinicians and researchers all require a clear understanding of how to assess physical activity. In the present report, we have provided a clear rationale for the importance of assessing physical activity levels, and we have documented key concepts in understanding the different dimensions, domains, and terminology associated with physical activity measurement. The assessment methods presented allow for a greater understanding of the vast number of options available to clinicians and researchers when trying to assess physical activity levels in their patients or participants. The primary outcome desired is the main determining factor in the choice of physical activity assessment method. In combination with issues of feasibility/practicality, the availability of resources, and administration considerations, the desired outcome guides the choice of an appropriate assessment tool. The decision matrix, along with the accompanying tables, provides a mechanism for this selection that takes all of these factors into account. Clearly, the assessment method adopted and implemented will vary depending on circumstances, because there is no single best instrument appropriate for every situation. In summary, physical activity assessment should be considered a vital health measure that is tracked regularly over time. All other major modifiable cardiovascular risk factors (diabetes mellitus, hypertension, hypercholesterolemia, obesity, and smoking) are assessed routinely. Physical activity status should also be assessed regularly. Multiple physical activity assessment methods provide reasonably accurate outcome measures, with choices dependent on setting-specific resources and constraints. The present scientific statement provides a guide to allow professionals to make a goal-specific selection of a meaningful physical activity assessment method.
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A new 2-regression model for the Actical accelerometer
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The validity of two Omron pedometers during treadmill walking is speed dependent
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Validation of the Actical Activity Monitor in Middle-Aged and Older Adults
Hooker, S. P., Feeney, A., Hutto, B., Pfeiffer, K. A., McIver, K. L., Heil, D. P., Vena, J. E., LaMonte, M. J., & Blair, S. N. (2011) · Journal of Physical Activity and Health · doi:10.1123/jpah.8.3.372 · Cited by 66 · see citing works
Validation of the ActiGraph Two-Regression Model for Predicting Energy Expenditure
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Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association
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Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association
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Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association
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Heart disease and stroke statistics-2018 update: a report from the american heart association
Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., Chiuve, S. E., Cushman, M., Delling, F. N., Deo, R., et al. (2018) · Circulation · doi:10.1161/cir.0000000000000558 · Cited by 7,146 · see citing works
Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association
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Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association
Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Alonso, A., Beaton, A. Z., Bittencourt, M. S., Boehme, A. K., Buxton, A. E., Carson, A. P., Commodore-Mensah, Y., et al. (2022) · Circulation · doi:10.1161/cir.0000000000001052 · Cited by 5,987 · see citing works
Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association
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Physical Activity, Cognition, and Brain Outcomes: A Review of the 2018 Physical Activity Guidelines
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“For this study we did not use traditional cumbersome methods to collect sleep 34 and energy expenditure 35 groundtruth data, as we preferred to use proxy reference methods for free-living assessment at scale 35,36 .”
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A Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement
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Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review.
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Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification
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Diagnosis, pathophysiology, and management of exercise-induced arrhythmias
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Physical activity self-reports: past or future?
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Comparison of wrist-worn and hip-worn activity monitors under free living conditions
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Validation study of Polar V800 accelerometer
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Rockette-Wagner, B., Storti, K. L., Dabelea, D., Edelstein, S., Florez, H., Franks, P. W., Montez, M. G., Pomeroy, J., & Kriska, A. M. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.10.007 · Cited by 21 · see citing works
Hamer, M., Hackett, R. A., Bostock, S., Lazzarino, A. I., Carvalho, L. A., & Steptoe, A. (2014) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2014-000030 · Cited by 12 · see citing works
- Authors
- Strath, S. J., Kaminsky, L. A., Ainsworth, B. E., Ekelund, U., Freedson, P. S., Gary, R. A., Richardson, C. R., Smith, D. T., & Swartz, A. M.
- ORCID
- Venue
- Circulation
- Published
- 2013-11-12
- Volume
- 128
- Issue
- 20
- Pages
- 2259-2279
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2172173540
- Cited by
- 1,029
- References
- 238
- Citations by year
- 2026: 47 · 2025: 67 · 2024: 86 · 2023: 103 · 2022: 111 · 2021: 102 · 2020: 96 · 2019: 85 · 2018: 86 · 2017: 85 · 2016: 72 · 2015: 46 · 2014: 37 · 2013: 3
- FWCI
- 36.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (MC_UU_12015/3)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- physical activity measurement, physical inactivity, cardiovascular risk factors, exercise behavior change, decision matrix, physical activity monitoring, cardiovascular disease prevention
- MeSH terms
- American Heart Association, Cardiology, Cardiovascular Diseases, Humans, Life Style, Motor Activity, Risk Factors, United States, Risk Assessment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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