Daniel A Adler
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Explore the profile of Daniel A Adler including associated specialties, affiliations and a list of published articles.
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11
Citations
103
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Recent Articles
1.
Adler D, Yang Y, Viranda T, Xu X, Mohr D, VAN Meter A, et al.
Proc ACM Interact Mob Wearable Ubiquitous Technol
. 2024 Dec;
8(4).
PMID: 39639863
Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent...
2.
Adler D, Stamatis C, Meyerhoff J, Mohr D, Wang F, Aranovich G, et al.
Res Sq
. 2024 May;
PMID: 38746448
AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms...
3.
Adler D, Stamatis C, Meyerhoff J, Mohr D, Wang F, Aranovich G, et al.
Npj Ment Health Res
. 2024 Apr;
3(1):17.
PMID: 38649446
AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression...
4.
Nghiem J, Adler D, Estrin D, Livesey C, Choudhury T
JMIR Form Res
. 2023 Aug;
7:e47380.
PMID: 37561561
Background: Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little...
5.
Adler D, Tseng E, Moon K, Young J, Kane J, Moss E, et al.
Proc ACM Hum Comput Interact
. 2023 Jan;
6(CSCW2).
PMID: 36714170
Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal...
6.
Adler D, Wang F, Mohr D, Choudhury T
PLoS One
. 2022 Apr;
17(4):e0266516.
PMID: 35476787
Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from...
7.
Adler D, Tseng V, Qi G, Scarpa J, Sen S, Choudhury T
Proc ACM Interact Mob Wearable Ubiquitous Technol
. 2022 Apr;
5(2).
PMID: 35445162
Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility...
8.
Militello L, Sobolev M, Okeke F, Adler D, Nahum-Shani I
JMIR Form Res
. 2022 Mar;
6(3):e30606.
PMID: 35311675
Background: Given the interrelated health of children and parents, strategies to promote stress regulation are critically important in the family context. However, the uptake of preventive mental health is limited...
9.
Adler D, Wang F, Mohr D, Estrin D, Livesey C, Choudhury T
BJPsych Open
. 2022 Mar;
8(2):e58.
PMID: 35236540
Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and...
10.
Adler D, Ben-Zeev D, Tseng V, Kane J, Brian R, Campbell A, et al.
JMIR Mhealth Uhealth
. 2020 Sep;
8(8):e19962.
PMID: 32865506
Background: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect...