HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression

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cris.lastimport.scopus2026-04-08T03:10:56Z
dc.abstract.enIntroduction: Mobile health (mHealth) technologies have become increasingly popular for monitoring mental health symptoms and lifestyle behaviours, and are largely reported to be feasible and acceptable to users. However, to date, the efficacy of such technologies to improve perinatal mental health outcomes has been mixed. Within the perinatal context, much of this work has been done in the context of postpartum depression, stemming from electronic health records as well as cohort studies. There is, however, a dearth of studies focusing on depression in pregnancy, and machine learning-based clinical decision support systems remain underexplored. The HappyMums application has been developed to meet this need, and its use across Europe will be tested in this study. Methods and analysis: A total of 1000 pregnant people currently suffering from, or at risk of, antenatal depression will be recruited across six countries. All participants will be between 13 and 28 weeks’ gestation and will be given access to the new purposefully developed HappyMums mobile application, to use from enrolment until 2 months postpartum. The application leverages passively collected data from smartphone sensors relating to physical activity and behaviour, as well as requiring active engagement from the user to complete mental health questionnaires and ‘game-like’ activities. Digital data types will be combined with traditional mental health measurement methods, such as standardised questionnaires and interviews, to develop novel predictive models capable of identifying mental health trajectories in women at risk of developing antenatal depression and to test the app’s utility for use as personalised risk prediction and depression identification tool. The primary outcome of this study is to determine what proportion of users will continue to use the mobile application and engage with its tasks and activities at least weekly, while secondary exploratory outcomes include assessing usability of the app and testing the predictive ability of a novel machine learning-based model. These outcomes will, for the first time, be assessed by integrating active as well as passive data. Ethics and dissemination: Ethical approval has been granted by local research ethics committees in each recruiting centre. At King’s College London (leading the clinical study), the study was reviewed by the East of England—Essex Research Ethics Committee and granted favourable opinion (REC reference 24/EE/0129). All other sites collecting participant data have the study approved for local delivery. Findings relating to the primary and secondary outcomes will be submitted for publication in open access, peer-reviewed journals, as well as presentations at conferences as symposia or posters. Findings will be made available to a non-specialist audience through open access digital mental health magazines and promotion on social media.
dc.affiliationWydział Psychologii w Sopocie
dc.affiliationInstytut Psychologii
dc.affiliationWydział Psychologii w Warszawie
dc.contributor.authorPriestley, Kristi
dc.contributor.authorLaijawala, Riddhi
dc.contributor.authorHazelgrove, Katie
dc.contributor.authorBind, Rebecca
dc.contributor.authorRebecchini, Lavinia
dc.contributor.authorMariani, Nicole
dc.contributor.authorAlford, Sorcha
dc.contributor.authorKirkpatrick, Madeline
dc.contributor.authorMancino, Francesca
dc.contributor.authorKim, Seungyoung
dc.contributor.authorPushpakanthan, Suvasthiga
dc.contributor.authorBiaggi, Alessandra
dc.contributor.authorCavaliere, Libera
dc.contributor.authorDi Benedetto, Maria Grazia
dc.contributor.authorMatijaš, Marijana
dc.contributor.authorŽutić, Maja
dc.contributor.authorBrekalo, Maja
dc.contributor.authorNakić Radoš, Sandra
dc.contributor.authorŻukowska, Katarzyna
dc.contributor.authorBraniecka, Anna
dc.contributor.authorJackowska, Marta
dc.contributor.authorBessi, Margherita
dc.contributor.authorAgnoletto, Elena
dc.contributor.authorMelloni, Elisa Maria Teresa
dc.contributor.authorBenedetti, Francesco
dc.contributor.authorBulgheroni, Maria
dc.contributor.authorLa Gamba, Margherita
dc.contributor.authorMartín Isla, Carlos
dc.contributor.authorIzquierdo Morcillo, Cristian
dc.contributor.authorLekadir, Karim
dc.contributor.authorSalo, Verna
dc.contributor.authorSeikku, Tiina
dc.contributor.authorRäikkönen, Katri
dc.contributor.authorGodara, Malvika
dc.contributor.authorSchneider-Schmid, Ulrike Maria
dc.contributor.authorEntringer, Sonja
dc.contributor.authorBuß, Claudia
dc.contributor.authorde Barra, Deirdre
dc.contributor.authorWoods, Anthony
dc.contributor.authorDazzan, Paola
dc.contributor.authorCattaneo, Annamaria
dc.contributor.authorPariante, Carmine
dc.date.access2026-02-04
dc.date.accessioned2026-02-06T12:31:54Z
dc.date.available2026-02-06T12:31:54Z
dc.date.created2026-01-15
dc.date.issued2026-02-04
dc.description.abstract<jats:sec> <jats:title>Introduction</jats:title> <jats:p>Mobile health (mHealth) technologies have become increasingly popular for monitoring mental health symptoms and lifestyle behaviours, and are largely reported to be feasible and acceptable to users. However, to date, the efficacy of such technologies to improve perinatal mental health outcomes has been mixed. Within the perinatal context, much of this work has been done in the context of postpartum depression, stemming from electronic health records as well as cohort studies. There is, however, a dearth of studies focusing on depression in pregnancy, and machine learning-based clinical decision support systems remain underexplored. The HappyMums application has been developed to meet this need, and its use across Europe will be tested in this study.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods and analysis</jats:title> <jats:p>A total of 1000 pregnant people currently suffering from, or at risk of, antenatal depression will be recruited across six countries. All participants will be between 13 and 28 weeks’ gestation and will be given access to the new purposefully developed HappyMums mobile application, to use from enrolment until 2 months postpartum. The application leverages passively collected data from smartphone sensors relating to physical activity and behaviour, as well as requiring active engagement from the user to complete mental health questionnaires and ‘game-like’ activities. Digital data types will be combined with traditional mental health measurement methods, such as standardised questionnaires and interviews, to develop novel predictive models capable of identifying mental health trajectories in women at risk of developing antenatal depression and to test the app’s utility for use as personalised risk prediction and depression identification tool. The primary outcome of this study is to determine what proportion of users will continue to use the mobile application and engage with its tasks and activities at least weekly, while secondary exploratory outcomes include assessing usability of the app and testing the predictive ability of a novel machine learning-based model. These outcomes will, for the first time, be assessed by integrating active as well as passive data.</jats:p> </jats:sec> <jats:sec> <jats:title>Ethics and dissemination</jats:title> <jats:p>Ethical approval has been granted by local research ethics committees in each recruiting centre. At King’s College London (leading the clinical study), the study was reviewed by the East of England—Essex Research Ethics Committee and granted favourable opinion (REC reference 24/EE/0129). All other sites collecting participant data have the study approved for local delivery. Findings relating to the primary and secondary outcomes will be submitted for publication in open access, peer-reviewed journals, as well as presentations at conferences as symposia or posters. Findings will be made available to a non-specialist audience through open access digital mental health magazines and promotion on social media.</jats:p> </jats:sec> <jats:sec> <jats:title>Trial registration number</jats:title> <jats:p> <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="clintrialgov" xlink:href="NCT06578845">NCT06578845</jats:ext-link> . </jats:p> </jats:sec>
dc.description.accesstimeat_publication
dc.description.grantnumber101057390
dc.description.granttitleEuropean Union’s Horizon Europe research and innovation programme
dc.description.issue2
dc.description.physical1-10
dc.description.sdgGoodHealthAndWellBeing
dc.description.sdgIndustryInnovationAndInfrastructure
dc.description.sdgReducedInequalities
dc.description.versionfinal_published
dc.description.volume16
dc.identifier.doi10.1136/bmjopen-2025-106978
dc.identifier.eissn2044-6055
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/2173
dc.identifier.weblinkhttps://bmjopen.bmj.com/content/16/2/e106978
dc.languageen
dc.pbn.affiliationpsychologia
dc.rightsCC-BY
dc.rights.questionYes_rights
dc.share.articleOPEN_JOURNAL
dc.swps.sciencecloudsend
dc.titleHappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression
dc.title.journalBMJ Open
dc.typeJournalArticle
dspace.entity.typeArticle