{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:10:39Z","timestamp":1767705039262,"version":"build-2065373602"},"reference-count":46,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T00:00:00Z","timestamp":1609372800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["headachejournal.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Headache"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the feasibility in children of an intensive prospective data monitoring methodology for identifying precipitating conditions for migraine occurrence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Migraine headaches are a common pain condition in childhood and can become increasingly chronic and disabling with repeated episodes. Identifying conditions that forecast when a child's migraine is likely to occur may facilitate next\u2010generation adaptive treatments to prevent future migraine attacks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cohort study of a sample of 30 youth (ages 10\u201317) with migraine recruited through a pediatric headache clinic, smartphones supplemented with wearable biosensors were used over a period of 28\u00a0days to collect contextual data thought to be potentially relevant to headache occurrence. Self\u2010reported data on headache occurrence, lifestyle, and perceptions of the environment were collected in 4 epochs per day using custom real\u2010time reporting software. Data derived from the wearable biosensor included information on autonomic arousal and physical activity. Built\u2010in sensors on participants\u2019 own phones also were used to indicate location and to quantify the sensory environment (e.g., ambient noise and light levels). Data fidelity was monitored to evaluate feasibility of the methods, and participant acceptability was assessed via an end\u2010of\u2010study survey.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Self\u2010report data were obtained on a mean of 88.9% (24.9\/28) of assigned days (\n                      <jats:italic>SD<\/jats:italic>\n                      \u00a0=\u00a022.4%) and at a mean of 68.9% (77.2\/112) of assigned moments (\n                      <jats:italic>SD<\/jats:italic>\n                      \u00a0=\u00a024.5%). Data from the wearable biosensor were obtained for a mean of 18.7 hours per day worn (\n                      <jats:italic>SD<\/jats:italic>\n                      \u00a0=\u00a02.3 hours), with participants on average wearing the sensor on 20.3\u00a0days (\n                      <jats:italic>SD<\/jats:italic>\n                      \u00a0=\u00a09.9). Fidelity of obtaining objective data from phone sensors on the sensory environment and other environmental conditions was highly variable, with these data obtainable from 5 to 22\/30 (16.7%\u201373.3%) of participants\u2019 own phones. Most participants (63.3%\u2013100%) responded with at least \u201csomewhat agree\u201d to questions about acceptability of the study methods. However, 5 to 7\/30 (16.7%\u201323.3%) patients indicated difficulties with burden and remembering to wear the sensor. Almost all participants (29\/30, 96.7%) agreed that they would want information about when a migraine might occur.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A contemporary data sampling approach comprising ambulatory sensors and real\u2010time reporting appears to be acceptable to most youth with migraine in this study. Reliability of acquiring some data sources from participants\u2019 own phones, however, was suboptimal. Further refining these data sampling methods may enable a novel means of predicting and preventing recurrences of migraine episodes in youth.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/head.14028","type":"journal-article","created":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T07:12:19Z","timestamp":1609398739000},"page":"500-510","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Feasibility of using \u201cSMARTER\u201d methodology for monitoring precipitating conditions of pediatric migraine episodes"],"prefix":"10.1111","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8157-8901","authenticated-orcid":false,"given":"Mark A.","family":"Connelly","sequence":"first","affiliation":[{"name":"Division of Developmental and Behavioral Health Children\u2019s Mercy Kansas City Kansas City MO USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madeline 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