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doi:10.22028/D291-36349
Titel: | Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment |
VerfasserIn: | Hart, Alexander Reis, Dorota Prestele, Elisabeth Jacobson, Nicholas C. |
Sprache: | Englisch |
Titel: | Journal of Medical Internet Research |
Bandnummer: | 24 |
Heft: | 4 |
Verlag/Plattform: | JMIR Publications |
Erscheinungsjahr: | 2022 |
Freie Schlagwörter: | digital biomarkers machine learning ecological momentary assessment smartphone sensors internal states paradata accelerometer gyroscope mood mobile phone |
DDC-Sachgruppe: | 150 Psychologie |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
Abstract: | Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R 2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R 2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference. |
DOI der Erstveröffentlichung: | 10.2196/34015 |
URL der Erstveröffentlichung: | https://www.jmir.org/2022/4/e34015/ |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-363492 hdl:20.500.11880/33013 http://dx.doi.org/10.22028/D291-36349 |
ISSN: | 1438-8871 |
Datum des Eintrags: | 2-Jun-2022 |
Fakultät: | HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft |
Fachrichtung: | HW - Psychologie |
Professur: | HW - Keiner Professur zugeordnet |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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PDF.pdf | 311,64 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons