患有严重精神疾病的退伍军人对心理健康的被动移动自我跟踪：一项以用户为中心的设计和前瞻性队列研究方案

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

作者信息

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

机构信息

Semel Institute for Neuroscience & Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States.

Veterans 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.

出版信息

JMIR Res Protoc. 2022 Aug 5;11(8):e39010. doi: 10.2196/39010.

DOI:10.2196/39010

PMID:35930336

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9391975/

Abstract

BACKGROUND

Serious 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.

OBJECTIVE

The 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.

METHODS

A 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.

RESULTS

The 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.

CONCLUSIONS

Findings 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 REGISTRATION

ClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39010.

摘要

背景

严重精神疾病（SMI）很常见，会导致残疾，且治疗具有挑战性，需要多年的监测和治疗调整。压力或药物依从性降低会导致症状和行为迅速恶化。疾病加重和复发通常在临床医生几乎没有实时察觉的情况下发生，留给调整治疗的机会有限。先前的研究表明，被动式移动传感可能对患有严重精神疾病的个体有益，可帮助他们监测心理健康状况和行为，并快速检测心理健康恶化情况以便及时评估和干预。然而，关于其可行性、可接受性以及被动数据能在多大程度上预测行为或症状变化的研究太少。

目的

本研究旨在探讨被动式移动传感用于跟踪严重精神疾病患者治疗期间行为和症状的可行性、可接受性及安全性，同时开发利用被动数据预测行为和症状变化的分析方法。

方法

一款移动应用程序监测并传输被动式移动传感器和手机使用数据，用于跟踪严重精神疾病患者的活动、社交能力和睡眠情况。该研究包括以用户为中心的设计阶段和移动传感阶段。在设计阶段，焦点小组、访谈和可用性测试为应用程序的进一步开发提供依据。在移动传感阶段，参与者进行为期9个月的每周评估，期间进行被动式移动传感。在3个月和9个月时进行访谈，了解对被动式移动传感的看法以及应用程序使用的便捷性。在移动传感阶段前后对临床医生进行访谈，研究应用程序在临床护理中的实用性和可行性。构建、训练和选择预测分析模型，并利用机器学习方法。模型使用传感器和手机使用数据预测行为变化和症状。