Detecting momentary reward and affect with real-time passive digital sensor data DOI Creative Commons
Samir Akre, Zachary D. Cohen,

Amelia Welborn

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 23, 2024

Abstract This study explores the capability of passive digital sensor data from smartphones and smartwatches to predict self-reported ecological momentary assessments (EMA) affect, motivation, interest, pleasure in activities an unseen test sample. Using 245 depressed participants with high-to-low anhedonia (195 train, 50 test) generating 23,812 EMA sessions, we evaluated whether behaviors physiological factors could detect subjective states. For 11 15 questions asked, machine learning models exceeded random chance fully-held-out sample, suggesting detectable signals between measures Dependent on type, optimal aggregation periods ranged minutes 3 hours, generally at least two hours being required. Subgroup analyses revealed variations model performance by demographics, depression severity, severity. These findings demonstrate potential for sensing help monitor aspects mental health a large scale.

Language: Английский

Digital phenotyping for mental health based on data analytics: A systematic literature review DOI
Wesllei Felipe Heckler, Luan Paris Feijó, Juliano Varella de Carvalho

et al.

Artificial Intelligence in Medicine, Journal Year: 2025, Volume and Issue: 163, P. 103094 - 103094

Published: March 1, 2025

Language: Английский

Citations

0
Day-to-day Social Interactions Online and Offline: The Interplay Between Interaction Mode, Interaction Quality, and Momentary Well-being DOI
Timon Elmer, Aurelio Fernández, Jeffrey A. Hall

et al.

Communication Research, Journal Year: 2025, Volume and Issue: unknown

Published: May 31, 2025

Digital social interactions differ in many ways from face-to-face interactions. This study examines four preregistered hypotheses on the within-person interplay between interaction mode (i.e., digital vs. interactions), quality, and momentary well-being. Young adults Spain ( N 1 = 216) Netherlands 2 22)—provided 5,116 1,386 Ecological Momentary Assessments (EMA), respectively. In Spanish sample, there were no differences quality interactions, whereas Dutch of higher quality. Interaction was positively associated with well-being both samples. after but not sample. did mediate relationship well-being; instead, it moderated Although consistently well-being, only partially explains why ones.

Language: Английский

Citations

0
A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures DOI Open Access
Anna M. Langener,

Björn S. Siepe,

Mahmoud Medhat Elsherif

et al.

Published: March 19, 2024

Passive smartphone measures hold significant potential and are increasingly employed in psychological biomedical research to capture an individual's behavior. These involve the near-continuous unobtrusive collection of data from smartphones without requiring active input participants. For example, GPS sensors used determine (social) context a person, accelerometers measure movement. However, utilizing passive presents methodological challenges during analysis. Researchers must make multiple decisions when working with such measures, which can result different conclusions. Unfortunately, transparency these decision-making processes is often lacking. The implementation open science practices only beginning emerge digital phenotyping studies varies widely across studies. Well-intentioned researchers may fail report on some due variety choices that be made. To address this issue enhance reproducibility studies, we propose adoption preregistration as way forward. Although there have been attempts preregister template for registering currently missing. This could problematic high level complexity requires well-structured template. Therefore, our objective was develop easy use understandable researchers. Additionally, explain provide resources assist making informed regarding collection, cleaning, Overall, aim researchers' explicit, transparency, elevate standards measures.

Language: Английский

Citations

1
A template and tutorial for preregistering studies using passive smartphone measures DOI Creative Commons
Anna M. Langener,

Björn S. Siepe,

Mahmoud Medhat Elsherif

et al.

Behavior Research Methods, Journal Year: 2024, Volume and Issue: 56(8), P. 8289 - 8307

Published: Aug. 7, 2024

Abstract Passive smartphone measures hold significant potential and are increasingly employed in psychological biomedical research to capture an individual's behavior. These involve the near-continuous unobtrusive collection of data from smartphones without requiring active input participants. For example, GPS sensors used determine (social) context a person, accelerometers measure movement. However, utilizing passive presents methodological challenges during analysis. Researchers must make multiple decisions when working with such measures, which can result different conclusions. Unfortunately, transparency these decision-making processes is often lacking. The implementation open science practices only beginning emerge digital phenotyping studies varies widely across studies. Well-intentioned researchers may fail report on some due variety choices that be made. To address this issue enhance reproducibility studies, we propose adoption preregistration as way forward. Although there have been attempts preregister template for registering currently missing. This could problematic high level complexity requires well-structured template. Therefore, our objective was develop easy use understandable researchers. Additionally, explain provide resources assist making informed regarding collection, cleaning, Overall, aim researchers' explicit, transparency, elevate standards measures.

Language: Английский

Citations

0
From Data Streams to Mental Health Predictions: Improving the use of Passive Measures from Digital Devices DOI Creative Commons

Anna Langener

Published: Aug. 13, 2024

Poor mental health is a global concern, with the World Health Organization reporting that one in eight people suffer from disorder. Identification and treatment are hampered by limited access to care inadequate insurance coverage. Digital technologies, such as smartphones, offer promising tools for improving through continuous monitoring timely intervention. These devices can collect rich data on various factors, social context behavior, active (e.g., questionnaires) passive GPS tracking) methods. Researchers often aim use this passively collected predict outcomes. Despite its potential, collection still evolving, current predictive accuracy remains low moderate. The overall goal of thesis therefore optimize measures digital predicting outcomes.The first part focuses Results show combining methods outperforms alone, but performance Advanced machine learning models also only moderate success variability depressive symptoms. second transparency reproducibility studies using measures. It highlights key challenges researchers face provides guidance working measures, example proposing preregistration template. Preregistration involves publicly outlining study plan before research begins, which increase prevent bias.

Language: Английский

Citations

0
Navigating the Intersection of Technology and Depression Precision Medicine DOI

M Burcu Irmak-Yazicioglu,

Ayla Arslan

Advances in experimental medicine and biology, Journal Year: 2024, Volume and Issue: unknown, P. 401 - 426

Published: Jan. 1, 2024

Language: Английский

Citations

0
Detecting momentary reward and affect with real-time passive digital sensor data DOI Creative Commons
Samir Akre, Zachary D. Cohen,

Amelia Welborn

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 23, 2024

Abstract This study explores the capability of passive digital sensor data from smartphones and smartwatches to predict self-reported ecological momentary assessments (EMA) affect, motivation, interest, pleasure in activities an unseen test sample. Using 245 depressed participants with high-to-low anhedonia (195 train, 50 test) generating 23,812 EMA sessions, we evaluated whether behaviors physiological factors could detect subjective states. For 11 15 questions asked, machine learning models exceeded random chance fully-held-out sample, suggesting detectable signals between measures Dependent on type, optimal aggregation periods ranged minutes 3 hours, generally at least two hours being required. Subgroup analyses revealed variations model performance by demographics, depression severity, severity. These findings demonstrate potential for sensing help monitor aspects mental health a large scale.

Language: Английский

Citations

0