John Torous1,2, Patrick Staples3, Linda Slaters4, Jared Adams5, Luis Sandoval2, J P Onnela3, Matcheri Keshavan2. 1. Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA. 2. Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA. 3. Division of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA. 4. Schizophrenia and Related Disorders Alliance of America, Houston, TX. 5. Self Care Catalyst, San Francisco, CA.
Abstract
INTRODUCTION: Despite growing interest in smartphone apps for schizophrenia, little is known about how these apps are utilized in the real world. Understanding how app users are engaging with these tools outside of the confines of traditional clinical studies offers an important information on who is most likely to use apps and what type of data they are willing to share. METHODS: The Schizophrenia and Related Disorders Alliance of America, in partnership with Self Care Catalyst, has created a smartphone app for schizophrenia that is free and publically available on both Apple iTunes and Google Android Play stores. We analyzed user engagement data from this app across its medication tracking, mood tracking, and symptom tracking features from August 16th 2015 to January 1st 2017 using the R programming language. We included all registered app users in our analysis with reported ages less than 100. RESULTS: We analyzed a total of 43,451 mood, medication and symptom entries from 622 registered users, and excluded a single patient with a reported age of 114. Seventy one percent of the 622 users tried the mood-tracking feature at least once, 49% the symptom tracking feature, and 36% the medication-tracking feature. The mean number of uses of the mood feature was two, the symptom feature 10, and the medication feature 14. However, a small subset of users were very engaged with the app and the top 10 users for each feature accounted for 35% or greater of all entries for that feature. We find that user engagement follows a power law distribution for each feature, and this fit was largely invariant when stratifying for age or gender. DISCUSSION: Engagement with this app for schizophrenia was overall low, but similar to prior naturalistic studies for mental health app use in other diseases. The low rate of engagement in naturalistic settings, compared to higher rates of use in clinical studies, suggests the importance of clinical involvement as one factor in driving engagement for mental health apps. Power law relationships suggest strongly skewed user engagement, with a small subset of users accounting for the majority of substantial engagements. There is a need for further research on app engagement in schizophrenia.
INTRODUCTION: Despite growing interest in smartphone apps for schizophrenia, little is known about how these apps are utilized in the real world. Understanding how app users are engaging with these tools outside of the confines of traditional clinical studies offers an important information on who is most likely to use apps and what type of data they are willing to share. METHODS: The Schizophrenia and Related Disorders Alliance of America, in partnership with Self Care Catalyst, has created a smartphone app for schizophrenia that is free and publically available on both Apple iTunes and Google Android Play stores. We analyzed user engagement data from this app across its medication tracking, mood tracking, and symptom tracking features from August 16th 2015 to January 1st 2017 using the R programming language. We included all registered app users in our analysis with reported ages less than 100. RESULTS: We analyzed a total of 43,451 mood, medication and symptom entries from 622 registered users, and excluded a single patient with a reported age of 114. Seventy one percent of the 622 users tried the mood-tracking feature at least once, 49% the symptom tracking feature, and 36% the medication-tracking feature. The mean number of uses of the mood feature was two, the symptom feature 10, and the medication feature 14. However, a small subset of users were very engaged with the app and the top 10 users for each feature accounted for 35% or greater of all entries for that feature. We find that user engagement follows a power law distribution for each feature, and this fit was largely invariant when stratifying for age or gender. DISCUSSION: Engagement with this app for schizophrenia was overall low, but similar to prior naturalistic studies for mental health app use in other diseases. The low rate of engagement in naturalistic settings, compared to higher rates of use in clinical studies, suggests the importance of clinical involvement as one factor in driving engagement for mental health apps. Power law relationships suggest strongly skewed user engagement, with a small subset of users accounting for the majority of substantial engagements. There is a need for further research on app engagement in schizophrenia.
Authors: Michael L Birnbaum; Asra F Rizvi; Keren Faber; Jean Addington; Christoph U Correll; Carla Gerber; Adrienne C Lahti; Rachel L Loewy; Daniel H Mathalon; Leigh Anne Nelson; Aristotle N Voineskos; Elaine F Walker; Elise Ward; John M Kane Journal: Psychiatr Serv Date: 2018-09-26 Impact factor: 3.084
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