Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression

Pratap, Abhishek; Homiar, Ava; Waninger, Luke; Herd, Calvin; Suver, Christine; Volponi, Joshua; Anguera, Joaquin A.; Areán, Patricia A

doi:10.1038/s41597-022-01633-7

Cited by 6 publications

(8 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PHQ-2 was administered daily and passive data was constantly collected for the study duration of 12 weeks. For further information, we refer to Pratap et al [24].…”

Section: Methodsmentioning

confidence: 99%

Predicting dimensions of depression from smartphone data

Holstein,

Akre,

Leenings

et al. 2024

Preprint

View full text Add to dashboard Cite

Depressive disorders are highly prevalent but demand nuanced personalized treatment that traditional approaches in psychiatry cannot address. This gap has prompted a surge of interest in leveraging digital technology, particularly smartphones, for remote monitoring to enhance outpatient care. This study utilizes the BRIGHTEN dataset to construct interpretable prediction models for overall depression severity, measured by PHQ-9, and various depression dimensions using a factor modelling approach.Our factor model unveils a three-factor solution encompassing mood, somatic, and concentration/psychomotor-related factors. Machine learning models effectively predict both the PHQ-9 scores and individual factors, with feature importance methods analyses underscoring the influence of the PHQ-2 scale and communication-related features. These findings are corroborated by models trained on data subsets.Through nested multi-level models, we identify between-subject effects for the PHQ-2 and select communication-related features, along with within-subject effects for these features. In summary, this study underscores the robust predictive capacity of ecological momentary assessments and highlights features of potential relevance for future investigations, such as communication-related features. We advocate for future studies to assess the cost-effectiveness and intervention potential of these models.

show abstract

“…PHQ-2 was administered daily and passive data was constantly collected for the study duration of 12 weeks. For further information, we refer to Pratap et al [24].…”

Section: Methodsmentioning

confidence: 99%

Predicting dimensions of depression from smartphone data

Holstein,

Akre,

Leenings

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Engagement has also been a challenge across all digital measures in mental health to date [95], but when technology is created with patient feedback [96] and data are used to advance patient care, engagement can actually be quite high [97]. Overcoming these challenges and continuing research on digital measures in mental health, especially longitudinal studies, holds the potential to help understand mechanisms, individual-level behaviors, and finally move the field toward prevention [98,99]. engagement.…”

Section: "From Efficacy To Effectiveness" Use Casesmentioning

confidence: 99%

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Clay,

Peerenboom,

Connors

et al. 2023

Digit Biomark

View full text Add to dashboard Cite

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled “Reverse Engineering of Digital Measures,” was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

show abstract

“…The advent of electronic methods of collecting information, e.g., smartphone sensors or wearable devices, means that behavioral measures can now be obtained as individuals go about their daily lives. Over the last ten years there has been considerable progress in using these digital behavioral phenotypes to infer mood and depression 7–15 . Yet, most digital mental health studies suffer from one or more of the following limitations 16–18 .…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, sufferers are unlikely to volunteer that they are depressed because of the reduced social contact associated with low mood and because of the stigma attached to admitting to being depressed. Developing new ways to quickly and accurately diagnose MDD or monitor depressive symptoms in real time would substantially alleviate the burden of this common and debilitating condition.The advent of electronic methods of collecting information, e.g., smartphone sensors or wearable devices, means that behavioral measures can now be obtained as individuals go about their daily lives.Over the last ten years there has been considerable progress in using these digital behavioral phenotypes to infer mood and depression [7][8][9][10][11][12][13][14][15] . Yet, most digital mental health studies suffer from one or more of the following limitations [16][17][18] .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Personalized Mood Prediction from Patterns of Behavior Collected with Smartphones

Balliu

Douglas

Shenhav

et al. 2022

Preprint

View full text Add to dashboard Cite

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer mood and diagnose major depressive disorder. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a novel combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2≥ 80%). We show that the passive behavioral phenotypes enhance the prediction of future mood over and above a baseline model which predicts future mood based on past depression severity alone for 52% of individuals in our cohort. In conclusion, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

show abstract

Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression

Cited by 6 publications

References 61 publications

Predicting dimensions of depression from smartphone data

Predicting dimensions of depression from smartphone data

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Personalized Mood Prediction from Patterns of Behavior Collected with Smartphones

Contact Info

Product

Resources

About