Testing the Value of Smartphone Assessments of People With Mood Disorders

2018-02-18 18:36:20 | BioPortfolio


The purpose of this study is two-fold:

1. To identify the best smartphone data features (based on keyboard, sensor, voice/speech data) that correlate with mood, anxiety, and cognitive assessments in patients with Major Depressive Disorder (MDD) and Bipolar Depression (BD).

2. To identify the best smartphone data features (based on keyboard, sensor, voice/speech at a) that predict relapse and remission in MDD or BD.


Progress in psychiatry will require better measurement. Currently, few clinicians use standard rating instruments for either diagnosis or outcomes. As a result, clinicians often fail to detect when a patient is not improving and fail to adjust treatments accordingly. Yet even with the use of standard rating instruments, there are serious shortcomings because they are used episodically, usually in an office environment, and they rely solely on subjective reports. Given the importance of objective measures of mood, anxiety, and cognition, it is critical for clinicians and patients to have better assessments if we are to achieve better mental healthcare.

The advent of smartphones provides a potential solution for the lack of objective measurement. The wide use of phones gives us the opportunity to collect passive, objective, continuous data relevant to mood, anxiety, and cognition. While several studies have demonstrated the feasibility of digital phenotyping psychiatric patients, the clinical value of this approach has yet to be demonstrated.

Stated simply the question is: can the data from a smartphone be used in clinical practice to improve care? Specifically, will sensor data, keyboard behavior, or voice and speech metrics yield features that can be validated with clinical assessments? Can these features identify relapse and remission even before clinical change is apparent with traditional ratings? Will this information alter care and improve outcomes? This pilot investigation is designed to answer the questions about validation and prediction by studying mood changes in patients treated with ketamine, a rapid-acting antidepressant. The final question about care and outcomes will be addressed separately.

Thus, the current study is a 6 month open, exploratory study that follows longitudinally patients with MDD or BD who are receiving outpatient treatment at the Kadima Clinic. The Kadima Clinic is a private outpatient mental health service providing a range of treatments for people with mood disorders. All patients will be receiving ketamine treatment in the Kadima Clinic; however, this is a study of smartphone features as digital biomarkers, not a study of ketamine efficacy or safety. While the ketamine treatment will not be the focus of this study, its rapid efficacy yields an ideal opportunity for testing and tuning the Mindstrong digital phenotyping app. There will not be randomization, use of placebo, or administration of blinded medication. Patients will be selected based on their willingness to participate in clinical assessments and their use of a smartphone.

The design includes screening and assessment during the week prior to treatment and follow-up at regular intervals for at least 3 weeks and no more than 6 months following treatment. Assessments will be completed by the patient, by a trained clinical rater, and by a significant other.

After completing the enrollment consent, the Mindstrong application (app) is installed in each participant's smartphone. The app is resident on the participant's mobile phone for 6 months. The participant can at any time choose to uninstall the app. The Mindstrong app unobtrusively monitors the participant's use of the mobile phone. The data captured by the app is transmitted over secure channel to a secure data storage site on Amazon Web Services (AWS) cloud infrastructure. All data captured by the app is encrypted.

Study Design


Major Depressive Disorder


Health by Mindstrong


Kadima Neuropsychiatry Institute
La Jolla
United States





Results (where available)

View Results


Published on BioPortfolio: 2018-02-18T18:36:20-0500

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