Patrick Staples

Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder

Background Accurate reporting of patient symptoms is critical for diagnosis and therapeutic monitoring in psychiatry. Smartphones offer an accessible, low-cost means to collect patient symptoms in real time and aid in care. Objective To investigate adherence among psychiatric outpatients diagnosed with major depressive disorder in utilizing their personal smartphones to run a custom app to monitor Patient Health Questionnaire-9 (PHQ-9) depression symptoms, as well as to examine the correlation of these scores to traditionally administered (paper-and-pencil) PHQ-9 scores. Methods A total of 13 patients with major depressive disorder, referred by their clinicians, received standard outpatient treatment and, in addition, utilized their personal smartphones to run the study app to monitor their symptoms. Subjects downloaded and used the Mindful Moods app on their personal smartphone to complete up to three survey sessions per day, during which a randomized subset of PHQ-9 symptoms of major depressive disorder were assessed on a Likert scale. The study lasted 29 or 30 days without additional follow-up. Outcome measures included adherence, measured by the percentage of completed survey sessions, and estimates of daily PHQ-9 scores collected from the smartphone app, as well as from the traditionally administered PHQ-9. Results Overall adherence was 77.78% (903/1161) and varied with time of day. PHQ-9 estimates collected from the app strongly correlated (r=.84) with traditionally administered PHQ-9 scores, but app-collected scores were 3.02 (SD 2.25) points higher on average. More subjects reported suicidal ideation using the app than they did on the traditionally administered PHQ-9. Conclusions Patients with major depressive disorder are able to utilize an app on their personal smartphones to self-assess their symptoms of major depressive disorder with high levels of adherence. These app-collected results correlate with the traditionally administered PHQ-9. Scores recorded from the app may potentially be more sensitive and better able to capture suicidality than the traditional PHQ-9.

Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry

Smartphones are now ubiquitous and can be harnessed to offer psychiatry a wealth of real-time data regarding patient behavior, self-reported symptoms, and even physiology. The data collected from smartphones meet the three criteria of big data: velocity, volume, and variety. Although these data have tremendous potential, transforming them into clinically valid and useful information requires using new tools and methods as a part of assessment in psychiatry. In this paper, we introduce and explore numerous analytical methods and tools from the computational and statistical sciences that appear readily applicable to psychiatric data collected using smartphones. By matching smartphone data with appropriate statistical methods, psychiatry can better realize the potential of mobile mental health and empower both patients and providers with novel clinical tools.

Incorporating Contact Network Structure in Cluster Randomized Trials.

Whenever possible, the efficacy of a new treatment is investigated by randomly assigning some individuals to a treatment and others to control, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, clusters of individuals are assigned to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.e. the probability of correctly detecting a real treatment effect. We investigate the relationships among power, within-cluster structure, cross-contamination via between-cluster mixing, and infectivity by simulating an infectious process on a collection of clusters. We demonstrate that compared to simulation-based methods, current formula-based power calculations may be conservative for low levels of between-cluster mixing, but failing to account for moderate or high amounts can result in severely underpowered studies. Power also depends on within-cluster network structure for certain kinds of infectious spreading. Infections that spread opportunistically through highly connected individuals have unpredictable infectious breakouts, making it harder to distinguish between random variation and real treatment effects. Our approach can be used before conducting a trial to assess power using network information, and we demonstrate how empirical data can inform the extent of between-cluster mixing.

Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers

Background: While clinical evidence for the efficacy of brain training remains in question, numerous smartphone applications (apps) already offer brain training directly to consumers. Little is known about why consumers choose to download these apps, how they use them, and what benefits they perceive. Given the high rates of smartphone ownership in those with internet access and the younger demographics, we chose to approach this question first with a general population survey that would capture primarily this demographic. Method: We conducted an online internet-based survey of the US population via mTurk regarding their use, experience, and perceptions of brain training apps. There were no exclusion criteria to partake although internet access was required. Respondents were paid 20 cents for completing each survey. The survey was offered for a 2-week period in September 2015. Results: 3125 individuals completed the survey and over half of these were under age 30. Responses did not significantly vary by gender. The brain training app most frequently used was Lumosity. Belief that a brain-training app could help with thinking was strongly correlated with belief it could also help with attention, memory, and even mood. Beliefs of those who had never used brain-training apps were similar to those who had used them. Respondents felt that data security and lack of endorsement from a clinician were the two least important barriers to use. Discussion: Results suggest a high level of interest in brain training apps among the US public, especially those in younger demographics. The stability of positive perception of these apps among app-naïve and app-exposed participants suggests an important role of user expectations in influencing use and experience of these apps. The low concern about data security and lack of clinician endorsement suggest apps are not being utilized in clinical settings. However, the public’s interest in the effectiveness of apps suggests a common theme with the scientific community’s concerns about direct to consumer brain training programs.

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia

Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia. We find that measures of empirical coverage of collected accelerometer and GPS data, as well as survey timing and survey completion metrics, are significantly associated with future survey scores for a variety of symptom domains. We also find evidence that specific measures of data quality are indicative of domain-specific future survey outcomes. These results suggest that for smartphone-based digital phenotyping, metadata is not independent of patient-reported survey scores, and is therefore potentially useful in predicting future clinical outcomes. This work raises important questions and considerations for future studies; we explore and discuss some of these implications.Digital phenotyping: assessing data quality in schizophreniaA pilot study shows that smartphone-collected data from patients with schizophrenia could be used to infer their mental-health status. Using smartphones as scientific data gathering tools holds great promise for understanding some of the behavioral features of psychiatric disorders and could provide an early indication of worsening symptoms. However, few studies have assessed the quality of the collected data, and thus the accuracy of clinical outcome prediction. Patrick Staples at the Harvard T. H. Chan School of Public Health in Boston, MA, and colleagues examined the relationship between data quality and future symptom-related survey responses in 16 patients with schizophrenia. They found that smartphone sensor data as well as phone-use metrics related to the completion of symptom-related surveys were significantly associated with survey results, highlighting the clinical relevance of this approach.

Relapse prediction in schizophrenia through digital phenotyping: a pilot study

Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of those discharged may relapse within 1 year even with appropriate treatment. Passively collected smartphone behavioral data present a scalable and at present underutilized opportunity to monitor patients in order to identify possible warning signs of relapse. Seventeen patients with schizophrenia in active treatment at a state mental health clinic in Boston used the Beiwe app on their personal smartphone for up to 3 months. By testing for changes in mobility patterns and social behavior over time as measured through smartphone use, we were able to identify statistically significant anomalies in patient behavior in the days prior to relapse. We found that the rate of behavioral anomalies detected in the 2 weeks prior to relapse was 71% higher than the rate of anomalies during other time periods. Our findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic. Real-time detection of behavioral anomalies could signal the need for an intervention before an escalation of symptoms and relapse occur, therefore reducing patient suffering and reducing the cost of care.

A comparison of passive and active estimates of sleep in a cohort with schizophrenia

Sleep abnormalities are considered an important feature of schizophrenia, yet convenient and reliable sleep monitoring remains a challenge. Smartphones offer a novel solution to capture both self-reported and objective measures of sleep in schizophrenia. In this three-month observational study, 17 subjects with a diagnosis of schizophrenia currently in treatment downloaded Beiwe, a platform for digital phenotyping, on their personal Apple or Android smartphones. Subjects were given tri-weekly ecological momentary assessments (EMAs) on their own smartphones, and passive data including accelerometer, GPS, screen use, and anonymized call and text message logs was continuously collected. We compare the in-clinic assessment of sleep quality, assessed with the Pittsburgh Sleep Questionnaire Inventory (PSQI), to EMAs, as well as sleep estimates based on passively collected accelerometer data. EMAs and passive data classified 85% (11/13) of subjects as exhibiting high or low sleep quality compared to the in-clinic assessments among subjects who completed at least one in-person PSQI. Phone-based accelerometer data used to infer sleep duration was moderately correlated with subject self-assessment of sleep duration (r = 0.69, 95% CI 0.23–0.90). Active and passive phone data predicts concurrent PSQI scores for all subjects with mean average error of 0.75 and future PSQI scores with a mean average error of 1.9, with scores ranging from 0–14. These results suggest sleep monitoring via personal smartphones is feasible for subjects with schizophrenia in a scalable and affordable manner.Patient monitoring: Smartphones can track schizophrenia-related sleep abnormalitiesSmartphones may one-day offer accessible, clinically-useful insights into schizophrenia patients’ sleep quality. Despite the clinical relevance of sleep to disease severity, monitoring technologies still evade convenience and reliability. In search of a preferential method, a group of Harvard University researchers led by Patrick Staples investigated the validity of data collected via patients’ own mobile phones. The team, with a cohort of 17 schizophrenia patients, compared the quality of data produced by smartphone sensors and smartphone-delivered questionnaires to that of an in-clinic evaluation. The results significantly showed that smartphone monitoring could generate information that approached the accuracy of in-clinic assessments. The team noted some areas for improvement; however, this study provides convincing justifications for further research into this non-invasive, low-cost, scalable method to monitor the sleep quality of schizophrenic patients.Sleep abnormalities are considered an important feature of schizophrenia, yet convenient and reliable sleep monitoring remains a challenge. Smartphones offer a novel solution to capture both self-reported and objective measures of sleep in schizophrenia. In this three-month observational study, 17 subjects with a diagnosis of schizophrenia currently in treatment downloaded Beiwe, a platform for digital phenotyping, on their personal Apple or Android smartphones. Subjects were given tri-weekly ecological momentary assessments (EMAs) on their own smartphones, and passive data including accelerometer, GPS, screen use, and anonymized call and text message logs was continuously collected. We compare the in-clinic assessment of sleep quality, assessed with the Pittsburgh Sleep Questionnaire Inventory (PSQI), to EMAs, as well as sleep estimates based on passively collected accelerometer data. EMAs and passive data classified 85% (11/13) of subjects as exhibiting high or low sleep quality compared to the in-clinic assessments among subjects who completed at least one in-person PSQI. Phone-based accelerometer data used to infer sleep duration was moderately correlated with subject self-assessment of sleep duration (r = 0.69, 95% CI 0.23–0.90). Active and passive phone data predicts concurrent PSQI scores for all subjects with mean average error of 0.75 and future PSQI scores with a mean average error of 1.9, with scores ranging from 0–14. These results suggest sleep monitoring via personal smartphones is feasible for subjects with schizophrenia in a scalable and affordable manner.

Characterizing Smartphone Engagement for Schizophrenia: Results of a Naturalist Mobile Health Study

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.

Corrigendum: Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers.

[This corrects the article on p. 180 in vol. 10, PMID: 27148026.].

A crossroad for validating digital tools in schizophrenia and mental health

Schizophrenia remains one of the most devastating chronic illnesses, impacting nearly 1.5% of the global population and creating an economic burden of up to 1.65% gross domestic product. It is not surprising that digital tools for schizophrenia, often smartphone-based software in the form of apps, have received so much recent attention and enthusiasm. Digital phenotyping holds tremendous potential in elucidating the complex heterogeneity of what we call schizophrenia and would therefore advance research. On a clinical front, smartphone data may become increasingly valuable in monitoring course and treatment response given the fact that these patients frequently have difficulties in adherence with clinical visits, and are often poor historians. However, the power of this paradigm is currently fueled more by the increasing ubiquity of technology than breakthroughs in clinical science. The accessibility and affordability of digital care derives from increasing global ownership of smartphones: it is estimated that six billion smartphones will be in circulation worldwide by 2020. As devices and sensors become ever cheaper and more sophisticated, the ability to capture a plethora of relevant data and deliver a myriad of content via network connectivity will continue to fuel the potential of digital approaches in mental health. As validation and reproducibility lags behind enthusiasm and availability, the potential clinical impact of these digital tools is at a crossroads. How might this new approach advance clinical care? Affordable and accurate diagnostics from smartphones paired with ondemand or automatically deployed interventions enables unprecedented access to mental health services. Apps today are designed to perform a wide range of healthcare tasks ranging from telehealth to medication tracking. In addition, new platforms are currently being developed to measure novel behavioral and physiological markers using passive long-term smartphone data, enabling objective measurement without burden for patients and healthcare providers. Clinically relevant and passively collected smartphone data comprises a wide range of sensors, including accelerometer data to estimate activity, anonymized call/text log information to estimate sociability, and screen touch data to estimate cognition. Passive measurement might also be able to distinguish disease subtypes to help better classify psychotic illnesses, similar to recent research using genetic, physiological, and cognitive markers. The considerable potential impact of these tools is paralleled by substantial new challenges. Formidable analytical complexities accompany all data-driven approaches, including clinical inference using passive smartphone data. Missing data, the highdimensional and temporally dense nature of the collected data, habits of smartphone use, quality of user experience with the app, and the quality of the software implementation may all act as confounders to underlying clinical disease state. Estimates of clinical accuracy and efficacy of these devices remains broadly understudied. Daunting implementation challenges also accompany this smartphone-based work: simple questions such as which patients are comfortable with smartphone monitoring, how long should it be used for, how information should be shared with patients and psychiatrists all remain largely unknown. Ethical questions also remain with respect to appropriate storage, access, and usage protocols for this highly personal data. Ignoring these challenges and questions would be both a scientific mistake and also a missed opportunity for clinical care. Recall the humorous 2009 case report of the dead North Atlantic Salmon, who was asked to detect emotions in photos during a fMRI task, resulting in the “finding” of correlated neural activity— due to failing to control for multiple comparisons. Online analyses of digital phenotyping data performed on say, a daily basis, are faced with the same challenge of correcting for multiple comparisons. The more recent discovery of widespread statistical software issues in thousands of fMRI research protocols underscores how simple mistakes can be amplified with digital tools. The promise of fMRI is owed to its high-resolution detail, which is directly tied to data complexity and the peril of inappropriate statistical inference. Digital phenotyping data, which is in situ, multi-sensor, partially observed, and longitudinal, brings even more complexity to bear, and deserves proportionate circumspection. To meet these challenges and to hone this approach into clinically useful tools, the development of research platforms must be accompanied with empirical research on the properties of the data it collects, called metadata. A simple example of metadata is the time it may take you respond to a smartphone query, instead of the response to the query itself. Studying metadata is useful for two reasons. First, understanding the limitations and biases of our tools used to draw clinical inferences will improve their specificity and clarify where they can be beneficially used. Second, metadata itself might offer novel insights about patient behavior and especially cognition that is not available using traditional metrics or evaluations. Recent research from our group suggests that properties of smartphone data may be more complex than often portrayed in the popular press, which holds relevance for clinical use. We show a correlation in the outcomes between metadata such as accelerometer coverage, GPS coverage, and survey completion timings, and future responses to questions about mood, anxiety, and psychotic symptoms. This might be evidence that these measures might help predict disease progression. We also find that some of these measures differ by operating system (iOS vs. Android), potentially indicating confounding by operating system or other variables, such as socioeconomic status. In addition to understanding metadata, the development of digital phenotyping tools will benefit from other considerations. Data standards and extensive testing may take significant forethought and precious resources, but such care typically helps the success of deployment in the long run. Without standards in collecting, processing, and reporting for digital phenotyping data, the end result might be a continuation of the pilot studies we currently see, which are expensive and their results are often left unreplicated. This pales in comparison to the value of highthroughput, well-coordinated, multi-site research efforts seen in