Literature DB >> 30123324

Smartphone-Based Passive Assessment of Mobility in Depression: Challenges and Opportunities.

Brenna N Renn1, Abhishek Pratap2,3, David C Atkins1, Sean D Mooney2, Patricia A Areán1.   

Abstract

Advances in technology have ushered in exciting potential for smartphone sensors to inform mental health care. This commentary addresses the practical challenges of collecting smartphone-based physical activity data. Using data (N = 353) from a large scale, fully remote randomized clinical trial for depression, we discuss findings and limitations associated with using passively collected mobility data to make inferences about depressive symptom severity. We highlight a range of issues in associating mobility data with mental health symptoms, including a high degree of variability, data featurization, granularity, and sparsity. Given the considerable efforts toward leveraging technology in mental health care, it is important to consider these challenges to optimize assessment and guide best practices. Clinical Trials.gov identifier: NCT01808976.

Entities:  

Keywords:  depression; mobile health (mHealth); mobility; passive data collection; physical activity; smartphone

Year:  2018        PMID: 30123324      PMCID: PMC6095666          DOI: 10.1016/j.mhpa.2018.04.003

Source DB:  PubMed          Journal:  Ment Health Phys Act        ISSN: 1878-0199


  12 in total

1.  Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.

Authors:  Dror Ben-Zeev; Emily A Scherer; Rui Wang; Haiyi Xie; Andrew T Campbell
Journal:  Psychiatr Rehabil J       Date:  2015-04-06

2.  Needed Innovation in Digital Health and Smartphone Applications for Mental Health: Transparency and Trust.

Authors:  John Torous; Laura Weiss Roberts
Journal:  JAMA Psychiatry       Date:  2017-05-01       Impact factor: 21.596

3.  The PHQ-9: validity of a brief depression severity measure.

Authors:  K Kroenke; R L Spitzer; J B Williams
Journal:  J Gen Intern Med       Date:  2001-09       Impact factor: 5.128

4.  Psychiatric history and subthreshold symptoms as predictors of the occurrence of depressive or anxiety disorder within 2 years.

Authors:  Julie Karsten; Catharina A Hartman; Johannes H Smit; Frans G Zitman; Aartjan T F Beekman; Pim Cuijpers; A J Willem van der Does; Johan Ormel; Willem A Nolen; Brenda W J H Penninx
Journal:  Br J Psychiatry       Date:  2011-03       Impact factor: 9.319

5.  Associations of objectively-assessed physical activity and sedentary time with depression: NHANES (2005-2006).

Authors:  Jeff K Vallance; Elisabeth A H Winkler; Paul A Gardiner; Genevieve N Healy; Brigid M Lynch; Neville Owen
Journal:  Prev Med       Date:  2011-07-23       Impact factor: 4.018

Review 6.  Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

Authors:  David C Mohr; Mi Zhang; Stephen M Schueller
Journal:  Annu Rev Clin Psychol       Date:  2017-03-17       Impact factor: 18.561

Review 7.  Recognition of depression by non-psychiatric physicians--a systematic literature review and meta-analysis.

Authors:  Monica Cepoiu; Jane McCusker; Martin G Cole; Maida Sewitch; Eric Belzile; Antonio Ciampi
Journal:  J Gen Intern Med       Date:  2007-10-26       Impact factor: 5.128

8.  The Patient Health Questionnaire-2: validity of a two-item depression screener.

Authors:  Kurt Kroenke; Robert L Spitzer; Janet B W Williams
Journal:  Med Care       Date:  2003-11       Impact factor: 2.983

9.  Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study.

Authors:  Sohrab Saeb; Mi Zhang; Christopher J Karr; Stephen M Schueller; Marya E Corden; Konrad P Kording; David C Mohr
Journal:  J Med Internet Res       Date:  2015-07-15       Impact factor: 5.428

10.  The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial.

Authors:  Patricia A Arean; Kevin A Hallgren; Joshua T Jordan; Adam Gazzaley; David C Atkins; Patrick J Heagerty; Joaquin A Anguera
Journal:  J Med Internet Res       Date:  2016-12-20       Impact factor: 5.428

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  8 in total

1.  Exploratory Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior.

Authors:  Runze Yan; Whitney R Ringwald; Julio Vega Hernandez; Madeline Kehl; Sang Won Bae; Anind K Dey; Carissa Low; Aidan G C Wright; Afsaneh Doryab
Journal:  Future Gener Comput Syst       Date:  2022-02-24       Impact factor: 7.187

2.  MindKind: A mixed-methods protocol for the feasibility of global digital mental health studies in young people.

Authors: 
Journal:  Wellcome Open Res       Date:  2022-05-12

3.  What Can Mobile Sensing and Assessment Strategies Capture About Human Subjectivity?

Authors:  Bruno Biagianti
Journal:  Front Digit Health       Date:  2022-04-15

Review 4.  Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review.

Authors:  Jussi Seppälä; Ilaria De Vita; Maria Bulgheroni; Timo Jämsä; Jouko Miettunen; Matti Isohanni; Katya Rubinstein; Yoram Feldman; Eva Grasa; Iluminada Corripio; Jesus Berdun; Enrico D'Amico
Journal:  JMIR Ment Health       Date:  2019-02-20

5.  Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability.

Authors:  Joy He-Yueya; Benjamin Buck; Andrew Campbell; Tanzeem Choudhury; John M Kane; Dror Ben-Zeev; Tim Althoff
Journal:  NPJ Schizophr       Date:  2020-11-23

6.  Using digital health tools for the Remote Assessment of Treatment Prognosis in Depression (RAPID): a study protocol for a feasibility study.

Authors:  Valeria de Angel; Serena Lewis; Sara Munir; Faith Matcham; Richard Dobson; Matthew Hotopf
Journal:  BMJ Open       Date:  2022-05-06       Impact factor: 3.006

Review 7.  Decision Models and Technology Can Help Psychiatry Develop Biomarkers.

Authors:  Daniel S Barron; Justin T Baker; Kristin S Budde; Danilo Bzdok; Simon B Eickhoff; Karl J Friston; Peter T Fox; Paul Geha; Stephen Heisig; Avram Holmes; Jukka-Pekka Onnela; Albert Powers; David Silbersweig; John H Krystal
Journal:  Front Psychiatry       Date:  2021-09-09       Impact factor: 4.157

8.  Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study.

Authors:  Mariko Makhmutova; Raghu Kainkaryam; Marta Ferreira; Jae Min; Martin Jaggi; Ieuan Clay
Journal:  JMIR Mhealth Uhealth       Date:  2022-03-25       Impact factor: 4.947

  8 in total

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