{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T20:56:32Z","timestamp":1781384192006,"version":"3.54.1"},"reference-count":107,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"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. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The prevalence of mental illnesses among adolescents has increased over the past decade. Lifestyle factors, such as regular exercise or sleep behavior can play a significant role in the prevalence of mental health. Today, passive mobile sensing or smartwatches offer valuable insights into the relationships between device-derived vital parameters (e.g., steps, exercise, sleep, heart rate) and self-reported mental health indicators, such as mood. However, the link between vital parameters and self-reported mood in daily life among adolescents remains understudied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 53 adolescents participated in a two-week ambulatory assessment study. Participants were equipped with a wrist-worn Apple Watch and a research-grade thigh-worn accelerometer to continuously measure physical behavior in daily life. Concurrently, participants rated momentary mood up to six times a day on a self-developed mood barometer (i.e., a 10-point emoji scale) via a web-based application on a smartphone. We used multilevel modeling to analyze the within-subject effects of vital parameters on momentary mood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Step counts (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.023), standing time (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f&amp;lt;0.001), as well as exercise time (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.012) are positively associated with self-reported mood on a daily level. On a momentary level, step counts (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.006) before the assessment (60\u202fmin) were positively associated with momentary mood, whereas sedentary time (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f&amp;lt;0.001) and sedentary bouts (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.003) were negatively associated. Passively detected sleep duration was not associated with daily mood, whereas self-reported sleep quality (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.031) was positively associated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study indicates that vital parameters correlate with mood ratings on both momentary and daily levels in adolescents. Given the increasing prevalence of mental health disorders, there is an urgent need for practical, scalable solutions. Mobile health technologies designed to enhance mental health support for adolescents and younger adults show significant promise, making them a compelling focus for future research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2025.1623886","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T14:42:38Z","timestamp":1766587358000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Associations between wearables vital parameters and self-perceived mood\u2014an ecological momentary assessment study among healthy adolescents"],"prefix":"10.3389","volume":"16","author":[{"given":"Luisa","family":"Lutz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Polina","family":"Veltmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romain","family":"Meisterhans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Giurgiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e6","DOI":"10.2196\/cardio.8574","article-title":"Measuring moderate-intensity exercise with the apple watch: validation study","volume":"2","author":"Abt","year":"2018","journal-title":"JMIR Cardio"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"15409","DOI":"10.3390\/ijerph192215409","article-title":"Active school-based interventions to interrupt prolonged sitting improve daily physical activity: a systematic review and Meta-analysis","volume":"19","author":"Amor-Barbosa","year":"2022","journal-title":"IJERPH"},{"key":"ref3","author":"Anastasopoulou","year":"2012"},{"key":"ref4","year":"2023"},{"key":"ref5","year":""},{"key":"ref6","year":""},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1037\/met0000195","article-title":"Statistical power in two-level models: a tutorial based on Monte Carlo simulation","volume":"24","author":"Arend","year":"2019","journal-title":"Psychol. 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