Evidence map›Paper›PMID 35930336›Full record

ArticleJMIR research protocols2022

Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study.

Alexander S Young, Abigail Choi, Shay Cannedy, Lauren Hoffmann, Lionel Levine, Li-Jung Liang, Melissa Medich, Rebecca Oberman, Tanya T Olmos-Ochoa

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR research protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05023252 (Passive Mobile Self-Tracking of Mental Health by Veterans With Serious Mental Illness), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05023252 nacompletednot on this map

Passive Mobile Self-Tracking of Mental Health by Veterans With Serious Mental Illness

TypeinterventionalSponsorVA Office of Research and DevelopmentRan2021 to 2024Enrolled87ConditionsSchizophrenia, Bipolar Disorder, Schizoaffective Disorder, Post-traumatic Stress DisorderArmsmobile application
3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Alexander S YoungSemel Institute for Neuroscience & Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-9367-9213
Abigail ChoiSemel Institute for Neuroscience & Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-7692-0709
Shay CannedyVeterans Integrated Service Network-22 Mental Illness Research, Education and Clinical Center, Greater Los Angeles Veterans Healthcare System, Department of Veterans Affairs, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-3163-4615
Lauren HoffmannVeterans Integrated Service Network-22 Mental Illness Research, Education and Clinical Center, Greater Los Angeles Veterans Healthcare System, Department of Veterans Affairs, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-6895-3727
Lionel LevineHenry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-6926-7438
Li-Jung LiangDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-2080-4359
Melissa MedichVeterans Integrated Service Network-22 Mental Illness Research, Education and Clinical Center, Greater Los Angeles Veterans Healthcare System, Department of Veterans Affairs, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-4541-0858
Rebecca ObermanCenter for the Study of Healthcare Innovation, Implementation & Policy, Greater Los Angeles Veterans Healthcare Center, Department of Veterans Affairs, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-5277-7493
Tanya T Olmos-OchoaCenter for the Study of Healthcare Innovation, Implementation & Policy, Greater Los Angeles Veterans Healthcare Center, Department of Veterans Affairs, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-8532-5933
University of California, Los Angeles · USCenter for the Study of Healthcare Provider Behavior · US

Funding

HSRD VA I01 HX003129
6 · The paper itself

Abstract

backgroundSerious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms.

objectiveThe aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms.

methodsA mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms.

resultsThe study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021.

conclusionsFindings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes.

trial registrationClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39010.

Indexed as

assessmentbehaviorhealth informaticsmental healthmobile healthpassive sensingpredictself-trackingsensorserious mental illness

Identifiers

PMID35930336
PMCPMC9391975
OpenAlexW4281655592

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.