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Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility and advantages of utilizing smartphone-based assessments to capture both cognitive functioning and digital phenotyping data and compare these results to gold standard measures. We conducted a secondary analysis of data from 76 individuals with schizophrenia, who were recruited across three sites (one in Boston, two in India) was conducted. The open-source mindLAMP smartphone app captured digital phenotyping data and Trails A/B assessments of attention / memory for up to 12 months. The smartphone-cognitive tasks exhibited potential for normal distribution and these scores showed small but significant correlations with the results from the Brief Assessment of Cognition in Schizophrenia, especially the digital span and symbol coding tasks (r2 = 0.21). A small but significant correlation (r2 = 0.29) between smartphone-derived cognitive scores and health-related behaviors such as sleep duration patterns was observed. Smartphone-based cognitive assessments show promise as cross-cultural tools that can capture relevant data on momentary states among individuals with schizophrenia. Cognitive results related to sleep suggest functional applications to digital phenotyping data, and the potential of this multimodal data approach in research.
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BACKGROUND: Identifying biomarkers of response to transcranial magnetic stimulation (TMS) in treatment-resistant depression is a priority for personalizing care. Clinical and neurobiological determinants of treatment response to TMS, while promising, have limited scalability. Therefore, evaluating novel, technologically driven, and potentially scalable biomarkers, such as digital phenotyping, is necessary. OBJECTIVE: This study aimed to examine the potential of smartphone-based digital phenotyping and its feasibility as a predictive biomarker of treatment response to TMS in depression. METHODS: We assessed the feasibility of digital phenotyping by examining the adherence and retention rates. We used smartphone data from passive sensors as well as active symptom surveys to determine treatment response in a naturalistic course of TMS treatment for treatment-resistant depression. We applied a scikit-learn logistic regression model (l1 ratio=0.5; 2-fold cross-validation) using both active and passive data. We analyzed related variance metrics throughout the entire treatment duration and on a weekly basis to predict responders and nonresponders to TMS, defined as ≥50% reduction in clinician-rated symptom severity from baseline. RESULTS: The adherence rate was 89.47%, and the retention rate was 73%. The area under the curve for correct classification of TMS response ranged from 0.59 (passive data alone) to 0.911 (both passive and active data) for data collected throughout the treatment course. Importantly, a model using the average of all features (passive and active) for the first week had an area under the curve of 0.7375 in predicting responder status at the end of the treatment. CONCLUSIONS: The results of our study suggest that it is feasible to use digital phenotyping data to assess response to TMS in depression. Early changes in digital phenotyping biomarkers, such as predicting response from the first week of data, as shown in our results, may also help guide the treatment course.
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Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.
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Objective: To examine feasibility and acceptability of smartphone mental health app use for symptom, cognitive, and digital phenotyping monitoring among people with schizophrenia in India and the United States. Methods: Participants in Boston, USA and Bhopal and Bangalore, India used a smartphone app to monitor symptoms, play cognitive games, access relaxation and psychoeducation resources and for one month, with an initial clinical and cognitive assessment and a one-month follow-up clinical assessment. Engagement with the app was compared between study sites, by clinical symptom severity and by cognitive functioning. Digital phenotyping data collection was also compared between three sites. Results: By Kruskal-Wallis rank-sum test, we found no difference between app activities completed or digital phenotyping data collected across the three study sites. App use also did not correlate to clinical or cognitive assessment scores. When using the app for symptom monitoring, preliminary findings suggest app-based assessment correlate with standard cognitive and clinical assessments. Conclusions: Smartphone app for symptom monitoring and digital phenotyping for individuals with schizophrenia appears feasible and acceptable in a global context. Clinical utility of this app for real-time assessments is promising, but further research is necessary to determine the long-term efficacy and generalizability for serious mental illness.
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BACKGROUND: Despite significant advancements in healthcare technology, digital health solutions - especially those for serious mental illnesses - continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India. METHODS: Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality. RESULTS: Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement. CONCLUSION: People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary - not substitutive - of therapeutic care from a clinician.