{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:05:19Z","timestamp":1789563919538,"version":"build-2803163510"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Physical activity is a key modifiable factor for preventing chronic disease, and digital health technologies are increasingly used to support physical activity. Prior population-based studies have typically examined single digital health behaviors, particularly wearable use, rather than combinations of behaviors that may cluster within individuals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 2024 Health Information National Trends Survey (HINTS 7) data from U.S. adults. Latent class analysis was applied to 17 binary indicators across five digital health domains among 5,804 respondents with complete coded digital health indicator data. Associations between profile membership and adherence to the Physical Activity Guidelines for Americans were examined using survey-weighted logistic regression among 4,930 respondents with complete physical activity outcome and covariate data. The primary outcome was concurrent adherence to both aerobic and muscle-strengthening guidelines; aerobic and muscle-strengthening adherence were examined separately as secondary outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Four profiles were identified: Broad Multidomain Engagement (32.0%), Wearable-Centered Engagement (11.8%), Portal-Centered Engagement (34.3%), and Limited Engagement (21.9%; reference), with high classification quality (entropy = 0.911). Relative to Limited Engagement, the Broad Multidomain Engagement (adjusted odds ratio [aOR] = 2.79, 95% CI: 1.81\u20134.30) and Wearable-Centered Engagement (aOR = 2.13, 95% CI: 1.42\u20133.19) profiles had higher odds of meeting both guidelines concurrently, whereas Portal-Centered Engagement membership was not significantly associated with concurrent adherence (aOR = 1.36, 95% CI: 0.97\u20131.89). In the primary complete-case models, only Broad Multidomain Engagement was associated with aerobic adherence (aOR = 1.64, 95% CI: 1.17\u20132.29), while all three digitally engaged profiles were associated with muscle-strengthening adherence (aOR range 1.58\u20132.58).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Digital health engagement among U.S. adults is organized into distinct multi-domain profiles that are differentially associated with physical activity guideline adherence. These findings support person-centered approaches to studying digital health behavior and may inform future profile-guided physical activity support, although longitudinal and intervention studies are needed before causal or adaptive-intervention claims can be made.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1901594","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T12:19:42Z","timestamp":1789561182000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Digital health engagement profiles and physical activity guideline adherence among adults in the United States: a latent class analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Heejun","family":"Kim","sequence":"first","affiliation":[{"name":"Health and Sport Analytics Laboratory, Department of Kinesiology and Sport, The University of Mississippi","place":["University, MS, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju-Pil","family":"Choe","sequence":"additional","affiliation":[{"name":"Health and Sport Analytics Laboratory, Department of Kinesiology and Sport, The University of Mississippi","place":["University, MS, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minsoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Health and Sport Analytics Laboratory, Department of Kinesiology and Sport, The University of Mississippi","place":["University, MS, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.2196\/11819","article-title":"Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis","volume":"7","author":"Brickwood","year":"2019","journal-title":"JMIR Mhealth. 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