Using mHealth to Optimize Pharmacotherapy Regimens

Study Overview
=================
Brief Summary
-----------------
This project will use a smartphone technology to improve medication prescribing for individuals with FEP. We will collect real-time symptom and functioning data via smartphones to provide prescribers and other clinical team members with clinically relevant and time-sensitive information that will inform and promote shared decision making (SDM) and personalized interventions. The result will be a time-sensitive, data-driven, collaborative process to optimize medication regimens in order to maximize benefits, minimize harms, and promote adherence.

Detailed Description
-----------------
Comprehensive early treatment of individuals experiencing schizophrenia has the potential to alter the course of illness and improve long-term outcomes. Psychotropic medications are a critical component of early treatment strategies. First-episode psychosis (FEP) is a critical time to optimize prescribing but evidence suggests that prescribing for this population is suboptimal. A contributing factor to these difficulties is thought to be the lack of accurate information about the effects of medications on symptoms, their side effects, as well as their behavioral, cognitive, and emotional correlates. At medication management appointments, prescribers typically rely on patients' recollection of how they were doing over periods of weeks. Such retrospective assessments are problematic as they are vulnerable to the influence of memory difficulties, cognitive biases including recency effects and frequency illusions, and reframing. Recent advances in smartphone technologies (mobile Health; mHealth) may help to overcome many of the limitations of retrospective assessments. This pilot study will be a collaboration with OnTrackNY, an innovative coordinated specialty care (CSC) program for individuals aged 16-30 who are experiencing FEP. OnTrackNY originated as part of the NIMH Recovery After an Initial Schizophrenia Episode (RAISE) Implementation and Evaluatoin Study. The initial phase of this project will use input from stakeholders including patients, front-line providers, clinical leaders, and members of the research team to adapt and refine the mHealth intervention to improve feasibility and clinical utility. A pilot study at 3 OnTrackNY sites will then examine its feasibility and effectiveness by comparing the management and outcomes of 60 patients randomly assigned to the mHealth application or usual care.

Official Title
-----------------
Using mHealth to Optimize Pharmacotherapy Regimens

Conditions
-----------------
Schizophrenia, First-Episode Psychosis

Intervention / Treatment
-----------------
* Other: mHealth Intervention


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Male or female OnTrackNY participants, ages 16 to 30, of any race/ethnicity Able and willing to give informed consent and participate in the intervention Participating in OnTrackNY for less than 1 year because this group may be more in need of medication adjustments and will be less likely to graduate from the program before 6 months of participation. Exclusion Criteria: Suicidal at baseline with C-SSRS score 4 or 5. PANSS baseline score of 5 (moderately severe) or greater on the Conceptual Disorganization item (P2)

Ages Eligible for Study
-----------------
Minimum Age: 16 Years
Maximum Age: 30 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Prospective cohort with comparison group
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: mHealth intervention group<br>participants in the mHealth Group will download the app on to their smartphone and complete ecological momentary assessments on 3 consecutive weekdays every 2 weeks for 6 months. | Other: mHealth Intervention<br>* Participants will download the app onto their smartphone and complete ecological momentary assessements<br>|
| No Intervention: Treatment as Usual group<br>participants will undergo their routine treatment. | |

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| change of Treatment Satisfaction Questionnaire for Medication (TQSM) from baseline to 6 months | The TSQM assesses client satisfaction with medication. It comprises 14 items across four domains focusing on effectiveness (3 items), side effects (5 items), convenience (3 items), and global satisfaction (3 items) of the medication over the previous 2-3 weeks, or since the patient's last use. With the exception of item 4 (presence of side effects; yes or no), all items have five or seven responses, scored from one (least satisfied) to five or seven (most satisfied).The 7-item scales had a non-neutral midpoint, such that there were more positive response options than negative response options, to allow for precise information to be obtained at the upper end of the score distribution. Item scores are summed to give four domain scores, which are in turn transformed to a scale of 0-100. Item 4 was not included for scoring. If an item score is missing and half of the items in the domain are complete, domain scores may be imputed from the person-specific mean score of completed items. | baseline to 6 months | 
| change of Client Satisfaction Questionnaire (CSQ-8) from baseline to 6 months | 8-item easily scored and administered measurement that is designed to measure client satisfaction with services. The overall score is produced by summing all item responses. Scores range from 8 to 32, with higher values indicating higher satisfaction. | baseline to 6 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
mHealth, Schizophrenia, EMA