Smartwatches can misjudge calories burned during exercise, with larger errors linked to higher body fat levels in participants.<\/p>\n
Smartwatches have become common tools for tracking exercise. Many people look at the calorie number after a workout to judge how much energy they used.<\/p>\n
That number can matter for weight management. <\/p>\n
A new analysis found that smartwatches can make large mistakes when estimating calories burned during exercise<\/a>, and those errors tended to grow as body fat percentage increased.<\/p>\n Watches estimate calorie burn<\/p>\n A smartwatch does not directly measure calories<\/a> burned. It uses heart rate, movement, personal details, and a company\u2019s calculation method to estimate energy use.<\/p>\n Most wrist devices measure heart rate with an optical sensor that shines light into the skin and detects changes linked to blood flow. <\/p>\n Motion sensors also help the watch judge movement.<\/p>\n Each company uses its own private formula. This can lead to different calorie totals for the same activity.<\/p>\n Four watches faced testing<\/p>\n Researchers at Florida International University (FIU<\/a>) tested 58 Hispanic adults between 18 and 50 years old. Their average age was 23, and the group included a range of body fat levels.<\/p>\n Each person completed one session on a recumbent bike, which supports the back while pedaling. <\/p>\n After five minutes of rest, they cycled for 10 minutes while switching between moderate and vigorous effort, followed by five minutes of recovery.<\/p>\n Participants wore an Apple Watch Series 8, Fitbit Sense 2, Samsung Galaxy Watch 5, and Garmin Forerunner 955. <\/p>\n The team compared their readings with a COSMED K5 metabolic system, which measures gases in each breath to estimate energy<\/a> use.<\/p>\n Calorie estimates often differed<\/p>\n The watches often disagreed with the laboratory measurement. <\/p>\n Median percentage errors were about 15% to 25%, while average errors were higher because some readings were much farther from the laboratory value.<\/p>\n Garmin<\/a> and Samsung<\/a> usually estimated more calories than the laboratory system measured. Apple also tended to overestimate, but its average difference was smaller.<\/p>\n Apple<\/a> averaged about 22 calories above the laboratory value. Garmin averaged about 69 calories higher, while Samsung averaged about 57 calories higher.<\/p>\n Fitbit readings varied widely<\/p>\n Fitbit produced several extreme readings. Seven results were more than 450% of the laboratory value, and one session produced no calorie estimate.<\/p>\n The team removed those clearly implausible readings from the main analysis. <\/p>\n After that, Fitbit\u2019s average difference was small, but including the extreme results raised its average difference to about 129 calories<\/a>.<\/p>\n Body fat affected accuracy<\/p>\n Body fat percentage means the share of body weight that comes from fat. Higher body fat percentage was linked with larger calorie errors across all four brands.<\/p>\n The pattern differed by device. Percentage errors increased more sharply for Fitbit and Garmin than for Apple, while the number of calories missed increased more sharply for Garmin and Samsung than for Apple.<\/p>\n These results show a relationship between body fat percentage and smartwatch<\/a> error. They do not prove that body fat itself directly causes the watches to become less accurate.<\/p>\n The team could not identify the exact reason because the companies do not make their calorie-estimating formulas public.<\/p>\n Skin tone showed no effect<\/p>\n The researchers checked whether skin tone changed how accurately smartwatches estimated calories.<\/p>\n They found no clear difference between skin types in this test. <\/p>\n However, only four people had type V skin (brown to dark brown skin), so the group was too small to be certain that skin tone has no effect.<\/p>\n Why the errors matter<\/p>\n Calorie estimates can influence how people think about exercise<\/a> and food intake. A repeated overestimate could make someone believe they used more energy than they actually did.<\/p>\n Lead author Jason Kostrna is an associate professor of kinesiology and exercise science at FIU.<\/p>\n \u201cYou can\u2019t treat the calories burned number it gives you as an accurate number, because it\u2019s not,\u201d Kostrna said.<\/p>\n \u201cYou could be hundreds of calories off each week and easily end up in a calorie surplus when you think you\u2019re in a calorie deficit.\u201d<\/p>\n Private formulas limit answers<\/p>\n The researchers do not yet know exactly why higher body fat percentage was linked with larger errors. Kostrna said the way companies build their formulas may be part of the problem.<\/p>\n \u201cWhen the companies are developing those algorithms, we don\u2019t know where the test data is coming from,\u201d Kostrna said.<\/p>\n \u201cThat\u2019s why I think it\u2019s beneficial for the companies to see this type of data, so they can refine their products over time.\u201d<\/p>\n The paper also points to possible problems with sensor signals and formulas that may not represent people with higher body fat well. <\/p>\n Because the formulas are private, the team could not test these explanations directly.<\/p>\n Limitations of the study<\/p>\n The experiment tested only cycling on a recumbent bike. The results may not apply in the same way to running, walking, strength training, or normal daily movement.<\/p>\n Each participant completed only one exercise session, so the researchers could not test whether a watch might adjust its estimates after repeated use. <\/p>\n The sample also included only Hispanic adults with Fitzpatrick skin types III to V.<\/p>\n The watches stayed in exercise mode during the rest periods, and participants wore several devices at the same time. These details may have affected calorie estimates, watch fit, or sensor contact.<\/p>\n The exact error levels should not be applied to every person or activity. More testing is needed across different populations and kinds of movement.<\/p>\n Trends may still help<\/p>\n The results do not mean smartwatches have no value. The paper says they may still be useful for following changes within the same person over time, although that needs more testing in everyday settings.<\/p>\n \u201cTrack your own trends over time. And if tracking helps, enjoy it,\u201d Kostrna said.<\/p>\n \u201cIf it becomes too much, don\u2019t fixate on it. Focus on how you\u2019re feeling and use data to get a little better over time.\u201d<\/p>\n A smartwatch calorie total is best understood as an estimate rather than an exact measurement. <\/p>\n The study is published in the journal PLOS One<\/a>.<\/p>\n \u2014\u2013<\/p>\n Like what you read? Subscribe to our newsletter<\/a> for engaging articles, exclusive content, and the latest updates.<\/p>\n Check us out on EarthSnap<\/a>, a free app brought to you by Eric Ralls<\/a> and Earth.com.<\/p>\n \u2014\u2013<\/p>\n","protected":false},"excerpt":{"rendered":"Smartwatches can misjudge calories burned during exercise, with larger errors linked to higher body fat levels in participants.…\n","protected":false},"author":2,"featured_media":671240,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[258],"tags":[18,282,19,17,82],"class_list":["post-671239","post","type-post","status-publish","format-standard","has-post-thumbnail","category-gadgets","tag-eire","tag-gadgets","tag-ie","tag-ireland","tag-technology"],"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/117209695885482525","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/671239","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=671239"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/671239\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/671240"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=671239"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=671239"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=671239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}