Your future mental health doc will be a smart phone


You've likely seen the ads and features talking about the latest fitness app or the newest calorie-counting dieter's app for the iPhone, Android, or other smart phones. What about mental health? Is there an app for that?

Well, as it turns out, there is. There are several, in fact. Few are clinically proven to be useful, however, but several are coming that will be.

Last week, the Consumer Electronics Show (CES) debuted several helpful medical technologies that included some showcases of apps-in-testing for mental health. The 2013 mHealth Summit late last year also featured many apps in development to assist psychiatrists and psychologists in assessing a patient's progress and well being.

Most of the apps that have potential are those which both help monitor and record the patient's daily routines in and around mental health and help alleviate much of the paper and busywork that often takes up so much of a clinician's time when they could be talking with the patient or furthering their treatment. These two apps cover both of these and add a lot of innovation to what can be done with data like this.


Dror Ben-Zeev, PhD at Dartmouth University's Center for Technology and Behavioral Health is honing an app called FOCUS as a schizophrenia management tool. This app uses techniques such as sleep modeling and predictive sensory data to assess the likelihood of a relapse.

The app uses the mobile phone's data, such as device lock time, ambient lighting, and ambient audio to predict sleep times to within forty minutes, which can give a truer assessment of the patient's actual reported sleep times, which can vary due to memory lapses and the loss of time awareness associated with early sleep stages. This is latent to the phone, requiring no special action on the user's part to activate the phone's sleep time guesswork.

Further, the app attempts to predict schizophrenia relapses based on sensor data such as sedentary times, changed sleep patterns, and increased isolation. This can be used to create a "lapse signature" unique to the patient, helping with clinical treatment and intervention.

Ben-Zeev's latest project, CrossCheck, works to involve both the patient and the treatment team in this data collection and assessment. During a predicted or apparent relapse, the app can send text messages to the patient and care team to prompt for action on a predetermined plan.


This is an academic project headed by Professor David Mohr at the Center for Behavioral Intervention Technologies at Northwestern University. This is a "context-awareness" app that combines data the patient inputs in survey form with physical data from the phone, such as location, Bluetooth connection state, rotation/movement, velocity, and gravity. Over time, it can predict the patient's mood based on these physical factors and the time of day - for instance, a patient may be in a better mood in the morning, while riding the train to work, than in the afternoon, when arriving home from work.

The app requires no sensors or additional equipment, thus making it far less likely that the patient will ignore it or "cheat" to avoid the extra work.

The early phases of this app's development saw the release of Purple Robot, an open-source Android sensor framework that has been used in several other research projects - some medical, many not. Mohr's group is also planning work on retrospective assessments to include delayed surveys so that patients can record data when it's most convenient, even if the timing does not match the app's daily routine.


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