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  1. Article ; Online: Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research.

    Langholm, Carsten / Kowatsch, Tobias / Bucci, Sandra / Cipriani, Andrea / Torous, John

    Digital biomarkers

    2023  Volume 7, Issue 1, Page(s) 104–114

    Abstract: The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. ...

    Abstract The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following:
    Language English
    Publishing date 2023-08-25
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2504-110X
    ISSN (online) 2504-110X
    DOI 10.1159/000530698
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Monitoring sleep using smartphone data in a population of college students.

    Langholm, Carsten / Byun, Andrew Jin Soo / Mullington, Janet / Torous, John

    Npj mental health research

    2023  Volume 2, Issue 1, Page(s) 3

    Abstract: Sleep is fundamental to all health, especially mental health. Monitoring sleep is thus critical to delivering effective healthcare. However, measuring sleep in a scalable way remains a clinical challenge because wearable sleep-monitoring devices are not ... ...

    Abstract Sleep is fundamental to all health, especially mental health. Monitoring sleep is thus critical to delivering effective healthcare. However, measuring sleep in a scalable way remains a clinical challenge because wearable sleep-monitoring devices are not affordable or accessible to the majority of the population. However, as consumer devices like smartphones become increasingly powerful and accessible in the United States, monitoring sleep using smartphone patterns offers a feasible and scalable alternative to wearable devices. In this study, we analyze the sleep behavior of 67 college students with elevated levels of stress over 28 days. While using the open-source mindLAMP smartphone app to complete daily and weekly sleep and mental health surveys, these participants also passively collected phone sensor data. We used these passive sensor data streams to estimate sleep duration. These sensor-based sleep duration estimates, when averaged for each participant, were correlated with self-reported sleep duration (r = 0.83). We later constructed a simple predictive model using both sensor-based sleep duration estimates and surveys as predictor variables. This model demonstrated the ability to predict survey-reported Pittsburgh Sleep Quality Index (PSQI) scores within 1 point. Overall, our results suggest that smartphone-derived sleep duration estimates offer practical results for estimating sleep duration and can also serve useful functions in the process of digital phenotyping.
    Language English
    Publishing date 2023-03-17
    Publishing country England
    Document type Journal Article
    ISSN 2731-4251
    ISSN (online) 2731-4251
    DOI 10.1038/s44184-023-00023-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Classifying and clustering mood disorder patients using smartphone data from a feasibility study.

    Langholm, Carsten / Breitinger, Scott / Gray, Lucy / Goes, Fernando / Walker, Alex / Xiong, Ashley / Stopel, Cindy / Zandi, Peter / Frye, Mark A / Torous, John

    NPJ digital medicine

    2023  Volume 6, Issue 1, Page(s) 238

    Abstract: Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this ... ...

    Abstract Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.
    Language English
    Publishing date 2023-12-21
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-023-00977-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study.

    Breitinger, Scott / Gardea-Resendez, Manuel / Langholm, Carsten / Xiong, Ashley / Laivell, Joseph / Stoppel, Cynthia / Harper, Laura / Volety, Rama / Walker, Alex / D'Mello, Ryan / Byun, Andrew Jin Soo / Zandi, Peter / Goes, Fernando S / Frye, Mark / Torous, John

    Journal of medical Internet research

    2023  Volume 25, Page(s) e47006

    Abstract: Background: In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological ... ...

    Abstract Background: In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams.
    Objective: This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study.
    Methods: We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders.
    Results: We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions.
    Conclusions: Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.
    MeSH term(s) Humans ; Female ; Adult ; Male ; Mood Disorders/diagnosis ; Feasibility Studies ; Pilot Projects ; Longitudinal Studies ; Bipolar Disorder/diagnosis
    Language English
    Publishing date 2023-12-29
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1438-8871
    ISSN (online) 1438-8871
    ISSN 1438-8871
    DOI 10.2196/47006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

    Wannan, Cassandra M J / Nelson, Barnaby / Addington, Jean / Allott, Kelly / Anticevic, Alan / Arango, Celso / Baker, Justin T / Bearden, Carrie E / Billah, Tashrif / Bouix, Sylvain / Broome, Matthew R / Buccilli, Kate / Cadenhead, Kristin S / Calkins, Monica E / Cannon, Tyrone D / Cecci, Guillermo / Chen, Eric Yu Hai / Cho, Kang Ik K / Choi, Jimmy /
    Clark, Scott R / Coleman, Michael J / Conus, Philippe / Corcoran, Cheryl M / Cornblatt, Barbara A / Diaz-Caneja, Covadonga M / Dwyer, Dominic / Ebdrup, Bjørn H / Ellman, Lauren M / Fusar-Poli, Paolo / Galindo, Liliana / Gaspar, Pablo A / Gerber, Carla / Glenthøj, Louise Birkedal / Glynn, Robert / Harms, Michael P / Horton, Leslie E / Kahn, René S / Kambeitz, Joseph / Kambeitz-Ilankovic, Lana / Kane, John M / Kapur, Tina / Keshavan, Matcheri S / Kim, Sung-Wan / Koutsouleris, Nikolaos / Kubicki, Marek / Kwon, Jun Soo / Langbein, Kerstin / Lewandowski, Kathryn E / Light, Gregory A / Mamah, Daniel / Marcy, Patricia J / Mathalon, Daniel H / McGorry, Patrick D / Mittal, Vijay A / Nordentoft, Merete / Nunez, Angela / Pasternak, Ofer / Pearlson, Godfrey D / Perez, Jesus / Perkins, Diana O / Powers, Albert R / Roalf, David R / Sabb, Fred W / Schiffman, Jason / Shah, Jai L / Smesny, Stefan / Spark, Jessica / Stone, William S / Strauss, Gregory P / Tamayo, Zailyn / Torous, John / Upthegrove, Rachel / Vangel, Mark / Verma, Swapna / Wang, Jijun / Rossum, Inge Winter-van / Wolf, Daniel H / Wolff, Phillip / Wood, Stephen J / Yung, Alison R / Agurto, Carla / Alvarez-Jimenez, Mario / Amminger, Paul / Armando, Marco / Asgari-Targhi, Ameneh / Cahill, John / Carrión, Ricardo E / Castro, Eduardo / Cetin-Karayumak, Suheyla / Mallar Chakravarty, M / Cho, Youngsun T / Cotter, David / D'Alfonso, Simon / Ennis, Michaela / Fadnavis, Shreyas / Fonteneau, Clara / Gao, Caroline / Gupta, Tina / Gur, Raquel E / Gur, Ruben C / Hamilton, Holly K / Hoftman, Gil D / Jacobs, Grace R / Jarcho, Johanna / Ji, Jie Lisa / Kohler, Christian G / Lalousis, Paris Alexandros / Lavoie, Suzie / Lepage, Martin / Liebenthal, Einat / Mervis, Josh / Murty, Vishnu / Nicholas, Spero C / Ning, Lipeng / Penzel, Nora / Poldrack, Russell / Polosecki, Pablo / Pratt, Danielle N / Rabin, Rachel / Rahimi Eichi, Habiballah / Rathi, Yogesh / Reichenberg, Avraham / Reinen, Jenna / Rogers, Jack / Ruiz-Yu, Bernalyn / Scott, Isabelle / Seitz-Holland, Johanna / Srihari, Vinod H / Srivastava, Agrima / Thompson, Andrew / Turetsky, Bruce I / Walsh, Barbara C / Whitford, Thomas / Wigman, Johanna T W / Yao, Beier / Yuen, Hok Pan / Ahmed, Uzair / Byun, Andrew Jin Soo / Chung, Yoonho / Do, Kim / Hendricks, Larry / Huynh, Kevin / Jeffries, Clark / Lane, Erlend / Langholm, Carsten / Lin, Eric / Mantua, Valentina / Santorelli, Gennarina / Ruparel, Kosha / Zoupou, Eirini / Adasme, Tatiana / Addamo, Lauren / Adery, Laura / Ali, Munaza / Auther, Andrea / Aversa, Samantha / Baek, Seon-Hwa / Bates, Kelly / Bathery, Alyssa / Bayer, Johanna M M / Beedham, Rebecca / Bilgrami, Zarina / Birch, Sonia / Bonoldi, Ilaria / Borders, Owen / Borgatti, Renato / Brown, Lisa / Bruna, Alejandro / Carrington, Holly / Castillo-Passi, Rolando I / Chen, Justine / Cheng, Nicholas / Ching, Ann Ee / Clifford, Chloe / Colton, Beau-Luke / Contreras, Pamela / Corral, Sebastián / Damiani, Stefano / Done, Monica / Estradé, Andrés / Etuka, Brandon Asika / Formica, Melanie / Furlan, Rachel / Geljic, Mia / Germano, Carmela / Getachew, Ruth / Goncalves, Mathias / Haidar, Anastasia / Hartmann, Jessica / Jo, Anna / John, Omar / Kerins, Sarah / Kerr, Melissa / Kesselring, Irena / Kim, Honey / Kim, Nicholas / Kinney, Kyle / Krcmar, Marija / Kotler, Elana / Lafanechere, Melanie / Lee, Clarice / Llerena, Joshua / Markiewicz, Christopher / Matnejl, Priya / Maturana, Alejandro / Mavambu, Aissata / Mayol-Troncoso, Rocío / McDonnell, Amelia / McGowan, Alessia / McLaughlin, Danielle / McIlhenny, Rebecca / McQueen, Brittany / Mebrahtu, Yohannes / Mensi, Martina / Hui, Christy Lai Ming / Suen, Yi Nam / Wong, Stephanie Ming Yin / Morrell, Neal / Omar, Mariam / Partridge, Alice / Phassouliotis, Christina / Pichiecchio, Anna / Politi, Pierluigi / Porter, Christian / Provenzani, Umberto / Prunier, Nicholas / Raj, Jasmine / Ray, Susan / Rayner, Victoria / Reyes, Manuel / Reynolds, Kate / Rush, Sage / Salinas, Cesar / Shetty, Jashmina / Snowball, Callum / Tod, Sophie / Turra-Fariña, Gabriel / Valle, Daniela / Veale, Simone / Whitson, Sarah / Wickham, Alana / Youn, Sarah / Zamorano, Francisco / Zavaglia, Elissa / Zinberg, Jamie / Woods, Scott W / Shenton, Martha E

    Schizophrenia bulletin

    2024  Volume 50, Issue 3, Page(s) 496–512

    Abstract: This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of ... ...

    Abstract This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
    MeSH term(s) Humans ; Psychotic Disorders ; Schizophrenia ; Prospective Studies ; Adult ; Prodromal Symptoms ; Young Adult ; International Cooperation ; Adolescent ; Research Design/standards ; Male ; Female
    Language English
    Publishing date 2024-03-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 439173-1
    ISSN 1745-1701 ; 0586-7614
    ISSN (online) 1745-1701
    ISSN 0586-7614
    DOI 10.1093/schbul/sbae011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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