{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T18:14:15Z","timestamp":1778091255723,"version":"3.51.4"},"reference-count":49,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Estimating physical activity (PA) levels is a challenging and expensive task. An alternative could be the use of actigraphy devices to estimate PA. This has been previously done to a number of devices, including ActiGraph\u00ae GT3X\u2009+\u2009. In this study, we validated ActTrust\u00ae against the widely used GT3X+ and compared activity counts to metabolic equivalents (METs) derived from indirect calorimetry during treadmill walking and running. Fifty-six young adults (34 men, 22 women) participated in controlled effort exercises including light, moderate, vigorous, and very vigorous activity intensities. We developed a linear model to estimate energy expenditure (EE) from movement count of combinations of devices placed at hip or wrist. We then estimated cut-off points for each intensity range. Our results showed correlations between treadmill speed and both METs (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.95,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05) and movement counts from both GT3X+ and ActTrust devices placed either on the hip (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.94,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05;\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.93,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05) or on the wrist (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.88,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05;\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.88,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05), respectively. Our proposed model performed well with balanced accuracies above 0.77 for all intensity ranges and over 0.9 for light and moderate activity. This is the first study to model estimate and validate PA intensity thresholds on ActTrust\u00ae devices. Our findings support the use of ActTrust\u00ae devices as simple, cost-effective tool for 24-hour assessments of EE.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0348631","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:35:26Z","timestamp":1778088926000},"page":"e0348631","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["From movement to METs: A validation of ActTrust\u00ae for energy expenditure estimation and physical activity classification in young adults"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6738-6564","authenticated-orcid":true,"given":"Elias","family":"dos Santos Batista","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephania Ruth","family":"Basilio Silva Gomes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayrton Bruno","family":"de Morais Ferreira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0853-1319","authenticated-orcid":true,"given":"Lucas G. 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