Depression Clinical Trial
Official title:
Examining the Feasibility of a Mobile Mental Health Application (Rose) for Augmentation of In-person Psychotherapy
There is an urgent need to better understand and manage the mental health burden among
working adults. Up to 40% of persons with serious mental illnesses do not receive care or
stop intervention programs due to negative stigma. Additionally, nearly 50 percent of
respondents diagnosed with an anxiety disorder said it interfered with their relationships
with coworkers. Close to $6000 USD is lost per working person each year due to depression and
its effects.
Rose (Recognition of Speech and Emotion) is a mental health-focused application that utilizes
artificial intelligence to identify symptoms and provides targeted, tailored in-person
therapy solutions for its users. The RoSE application provides solutions to supplement
ongoing in-person psychotherapy. This includes journaling and daily assessments that provide
curated content and feedback. The current feasibility study aims to recruit 45 established
outpatients with at least mild depressive and/or anxiety symptoms who will be consented and
enrolled in a five to ten-week study. There are two study arms: (1) the intervention arm and
(2) a waitlist control arm. During the course of the study, the participants in the
intervention arm will use the RoSE application daily. They will receive either weekly
in-person psychotherapy with their established psychotherapist for a total of four sessions
over four weeks or biweekly in-person psychotherapy with their established psychotherapist
for a total of four sessions over eight weeks. The participants in the waitlist arm will
serve as controls unless there is attrition from the intervention group at which time
waitlist participants will be offered a spot in the intervention arm. The primary objectives
of the study are (1) To assess the usability of the RoSE application and (2) To evaluate the
short-term impact on mood and anxiety of using the RoSE application to augment in-person
psychotherapy. The secondary objectives are (1) To examine the usage and utility of an
in-application journaling function and (2) To examine the usage and utility of in-application
curated insights.
Problem description Mental illness among working adults is of huge detriment to society on a
large scale. Not only does mental illness lead to a lower quality of life and negative
outcomes for the individual, but it places a burden on the economy, resulting in lost
productivity, lost workdays and reduced economic growth. According to the National Institute
of Mental Health an estimated 17.3 million adults in the United States had at least one major
depressive episode in 2017. This number represented 7.1% of all U.S. adults. Furthermore,
according to ADAA anxiety is one of the most common mental illnesses in America with around
40 million adults in the US having anxiety disorders every year. In addition to the millions
lost on medical costs from mental illness, studies show an added societal burden. On average
depressive symptoms can lead to close to 27 lost workdays per year and 18 days of lost
productivity.
While mental illness negatively affects adults in significant ways, primary care doctors
identify less than one-third of all depression cases and up to 40% of persons with serious
mental illnesses do not receive care or stop intervention programs due to negative stigma.
With only one in four adults disclosing their anxiety disorder in time, a much smaller
proportion are ever diagnosed and receive treatment for major depression. Lastly, but perhaps
most importantly, according to the Center for Workplace Mental Health, 80 percent of adults
treated for mental health problems report improvements in their overall satisfaction and
productivity.
Study model This study uses is a mental health-focused application that utilizes artificial
intelligence to identify symptoms and provides targeted, tailored in-person therapy solutions
for its users. The application is meant to augment in-person psychotherapy utilizing online
journaling and daily patient assessments to provide curated content and feedback. In brief,
the application utilizes machine learning and artificial intelligence to select content
(e.g., self-help articles) and provide feedback (e.g., weekly summary score of mood and
anxiety) based on user input (e.g., journal entries, daily mood rating)
Study Design The proposed study is a 5-week feasibility study to assess the usability and
potential short-term benefits of the mental health platform in a population of adults with
established mental health care.
Potential participants will be informed of the study using study brochures provided to their
established psychotherapists. All written materials are submitted for approval. Interested
patients will be directed to a secure online survey, which will screen them for eligibility.
Patients that meet all inclusion criteria and no exclusion criteria will be offered entrance
into the study. Of note, no medical records of the patients will be accessed as part of the
study.
After the initial screening survey, 45 eligible patients will complete the consent process.
The consented participants will be randomized to one of the two study arms: (1) the
intervention arm or (2) the waitlist control arm in a 2:1 ratio. The intervention arm is
described in detail below. The participants in the waitlist control arm will serve as
controls for the study. They will complete the pre-pilot assessment and the post-pilot
assessment. It will be made clear that the participants can continue to seek a standard of
care services. During their time on the waitlist, participants may reach out to study
personnel if they need assistance with their psychiatric care. We aim to have 22 participants
complete the intervention arm and in the setting of participant attrition from the
intervention arm, participants in the waitlist control arm will be offered entrance to the
intervention arm. If recruited off the waitlist, the participant will complete consent, this
time selecting "Intervention" as the study arm and will be entered into the five-week
intervention study.
Participants in the intervention arm will be sent an individualized redemption code to
download the mobile app application on to their personal mobile phones. The participants
enter individual login details. During the first week of the study, each participant will be
prompted by the application to complete key surveys and assessments at predetermined time
intervals. The mental health platform generates a summary health metric on-demand that is a
combination of responses entered from the in-app assessments.
A summary health metric will be pushed in-app after the first week of use. Details for
scheduling the first in-person therapy session will be provided at the same time as the
summary health metric. Participants will be informed that the general health metrics will be
shared with the therapist to help the therapist better plan the sessions. The therapy session
will be scheduled in a private room on-campus. The therapists will offer standard-of-care
psychotherapy (e.g., supportive psychotherapy, cognitive-behavioral therapy). The
participants use the app for a total of 5 weeks and receive weekly in-person therapy for a
total of 4 weeks (one-week application only lead-in, four weeks application plus in-person
psychotherapy).
Two functions of the application that will be independently examined for usage and utility
are its journaling function and curated insights. Participants will be asked to use the
journaling function daily (more details below). The curated insights are selected by the
application based on participant responses to the daily mood and anxiety surveys, as well as
built-in sentiment analysis of the journaling.
At the end of 5 weeks, all participants in both study arms will complete the post-pilot
assessment using the same assessments as pre-pilot.
Participants who agree to be contacted post-pilot study will be given the option at 3, 6, and
12 months to take the same online assessments as at pre- and post-pilot. This evaluation will
be used to test the potential long-term impact of the app. In addition, post-pilot focus
groups may be completed. There would be one focus group with therapists and separate focus
groups with participants. These optional assessments will be pending study results, funding,
and resources and would be submitted in the future as a change in research to the current
Institutional Review Board (IRB).
Details of Study Assessments Scales / Questionnaires Mood / Anxiety Meter:
The application will send timed notifications three times a day (morning, afternoon and
evening) for the user to pick current mood and anxiety on a Likert scale General Anxiety
Disorder-7 (GAD-7) anxiety scale. The app will prompt the user to fill in this assessment
every 2 weeks. The GAD-7 parallels the 7 diagnostic symptom criteria that define DSM-IV GAD.
The format and temporal framework of the items also correspond to the Diagnostic and
Statistical Manual of Mental Disorders (DSM-IV) criteria and will facilitate the follow-up
review of symptoms and diagnostic processes. At only 7 items, the GAD-7 is substantially
shorter than most anxiety screening measures. This instrument has also demonstrated
acceptability among non-psychiatric patients and among busy primary care providers and is a
valid and reliable screening tool for anxiety.
Patient Health Questionnaire-9 (PHQ-9) depression scale. The app will prompt the user to fill
in this assessment every 2 weeks. The PHQ-9 parallels the 9 diagnostic symptom criteria that
define DSM-IV. The format and temporal framework of the items also correspond to the DSM-IV
criteria and will facilitate the follow-up review of symptoms and diagnostic processes. At
only 9 items, the PHQ-9 is substantially shorter than most depression screening measures.
This instrument has also demonstrated acceptability among non-psychiatric patients and among
busy primary care providers and is a valid and reliable screening tool for major depression.
Mobile Application Rating Scale (MARS): The MARS is a well-established framework for
classifying and assessing the objective and subjective quality of apps, as well as the app's
perceived impact. It is designed to score apps on the criteria of engagement, functionality,
aesthetics, and information quality, as well as app subjective quality. Each MARS item used a
5-point scale (1-Inadequate, 2-Poor, 3-Acceptable, 4-Good, 5-Excellent). The MARS is
calculated as mean scores of the engagement, functionality, aesthetics, and information
quality objective sub-scales, and an overall mean app quality total score.
Other Online Journaling: The online journaling feature allows the user to enter his/her
feelings and thoughts, like in the case of a diary. The user is asked daily "How has your day
been? (please provide at least four sentences to describe how you are doing and feeling
today)". Every two weeks the user is asked "How have you been the last two weeks? (please
provide at least four sentences describing your emotional well-being over the last two
weeks)". This second prompt is meant to allow comparison with the PHQ-9 and GAD-7, both of
which ask about a two-week time frame's neural network is built and calibrated to provide an
emotional context through pre-labeled datasets that tag words, phrases, and sentences to a
specific emotion (e.g., fear, sadness, anxiety). The neural network model then analyzes the
input from the journaling features and assigns it an emotion weighting. User input is then
analyzed and the output indicates what condition participants are likely currently
experiencing based on participant's entry.
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