{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:11:22Z","timestamp":1780071082629,"version":"3.54.0"},"reference-count":26,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n Post\u2013COVID-19 fatigue affects millions worldwide; yet, evidence-based management strategies remain limited. Activity pacing, regulating activity to match available energy and minimize symptom exacerbation, may support symptom management, although optimal pacing approaches remain unclear.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to explore associations between activity pacing strategies delivered through a mobile app and daily fatigue levels in individuals with post\u2013COVID-19 fatigue.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this exploratory observational study, 19 adults with post\u2013COVID-19 fatigue used wearable devices (Fitbit Inspire 3) for objective activity monitoring and a mobile app (FatigueSense) to self-report daily symptoms (fatigue and energy levels) and optionally select activity pacing goals (light, balanced, or active) over a period of 3 months. We examined associations between pacing strategies and symptom outcomes by using mixed-effects linear models with random intercepts. Same-day outcomes (fatigue and energy reported on the day of goal selection) were analyzed controlling for age and sex. Next-day outcomes (fatigue and energy reported the day following goal selection) were analyzed controlling for age, sex, and prior-day symptoms.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Across 2182 observation days, participants self-selected pacing goals on 816 (37.4%) days, demonstrating symptom-responsive behavior with higher baseline fatigue on pacing days (mean score 1.74, SD 0.59 vs 1.58, SD 0.54 on nonpacing days on a 0-3 scale where 0=\u201cnone\u201d and 3=\u201csevere\u201d; P=.004). On pacing days (584\/816, 71.6% with complete data from 18 participants), active pacing was associated with reduced same-day fatigue (\u03b2=\u22120.34, 95% CI \u22120.49 to \u22120.19; P<.001) and increased same-day energy (\u03b2=8.8, 95% CI 4.7-12.9; P<.001) compared to light pacing. Balanced pacing also showed significant reductions in fatigue (\u03b2=\u22120.15; P=.008) and increases in energy (\u03b2=5.8; P<.001) compared to light pacing. Next-day effects were attenuated and nonsignificant (fatigue: \u03b2=\u22120.05, P=.53; energy: \u03b2=1.1, P=.61). Individual heterogeneity was substantial, with an intraclass correlation coefficient of 0.32, indicating that 32% of the variance was attributable to between-person differences. Among participants trying multiple strategies, 58.3% (7\/12) showed meaningful responses (\u22650.3-point fatigue reduction) to structured pacing strategies.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Structured activity pacing strategies (active and balanced) were associated with improved same-day symptom management in individuals with post\u2013COVID-19 fatigue. However, substantial confounding by indication (self-selection of pacing based on symptom state) and individual heterogeneity limit causal interpretation. These exploratory findings warrant testing in randomized controlled trials to establish efficacy and identify responder characteristics.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/91829","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:00:09Z","timestamp":1780070409000},"page":"e91829","source":"Crossref","is-referenced-by-count":0,"title":["Wearable- and Mobile App\u2013Based Activity Pacing and Fatigue Management in Post\u2013COVID-19 Condition: Exploratory Observational Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7627-4145","authenticated-orcid":false,"given":"Nana Yaw","family":"Aboagye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6916-1329","authenticated-orcid":false,"given":"Mark R","family":"Baker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-2911","authenticated-orcid":false,"given":"Kenneth","family":"Baker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1154-4751","authenticated-orcid":false,"given":"Silvia","family":"Del Din","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-022-00846-2"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101762"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-01909-w"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/medicina57050418"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/ptj\/pzac005"},{"key":"ref6","year":"2024","journal-title":"COVID-19 Rapid Guideline: Managing the Long-Term Effects of COVID-19"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3109\/09638288.2011.635746"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1080\/09638288.2022.2135776"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3928\/08910162-20080501-06"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2522\/ptj.20090047"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.molmed.2021.06.002"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s13365-021-01002-x"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(22)00260-7"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0224-8"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0260-4"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2196\/10527"},{"key":"ref17","unstructured":"FatigueSense2026-05-09https:\/\/fatiguesense.com"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511790942","author":"Gelman, A","year":"2006","journal-title":"Data Analysis Using Regression and Multilevel\/Hierarchical Models"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1002\/9781119513469","author":"Fitzmaurice, GM","year":"2011","journal-title":"Applied Longitudinal Analysis"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1111\/j.2041-210x.2012.00261.x"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1249\/01.mss.0000181680.35503.ce"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1037\/\/0003-066x.57.9.705"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1037\/\/0033-295x.84.2.191"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1177\/003591576505800503"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1186\/1479-5868-8-79"},{"issue":"1","key":"ref26","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1097\/00001648-199001000-00010","volume":"1","author":"Rothman, KJ","year":"1990","journal-title":"Epidemiology"}],"container-title":["JMIR Formative Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/formative.jmir.org\/2026\/1\/e91829","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:00:11Z","timestamp":1780070411000},"score":1,"resource":{"primary":{"URL":"https:\/\/formative.jmir.org\/2026\/1\/e91829"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":26,"URL":"https:\/\/doi.org\/10.2196\/91829","relation":{"has-preprint":[{"id-type":"doi","id":"10.2196\/preprints.91829","asserted-by":"object"}]},"ISSN":["2561-326X"],"issn-type":[{"value":"2561-326X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]}}}