Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

BACKGROUND

Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI).

OBJECTIVE

An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI.

METHODS

The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group.

RESULTS

Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI.

CONCLUSIONS

The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies

Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.

The Digital Navigator: Standardizing Human Technology Support in App-Integrated Clinical Care

Background: Mental health apps offer scalable care, yet clinical adoption is hindered by low user engagement and integration challenges into clinic workflows. Human support staff called digital navigators, trained in mental health technology, could enhance care access and patient adherence and remove workflow burdens from clinicians. While the potential of this role is clear, training staff to become digital navigators and assessing their impact are primary challenges. Methods: We present a detailed manual/framework for implementation of the Digital Navigator within a short-term, cognitive-behavioral therapy-focused hybrid clinic. We analyze patient engagement, satisfaction, and digital phenotyping data quality outcomes. Data from 83 patients, for the period spanning September 2022 to September 2023, included Digital Navigator satisfaction, correlated with demographics, mindLAMP app satisfaction, engagement, and passive data quality. Additionally, average passive data across 33 clinic patients from November 2023 to January 2024 were assessed for missingness. Results: Digital Navigator satisfaction averaged 18.8/20. Satisfaction was not influenced by sex, race, gender, or education. Average passive data quality across 33 clinic patients was 0.82 at the time this article was written. Digital Navigator satisfaction scores had significant positive correlation with both clinic app engagement and perception of that app. Conclusions: Results demonstrate preliminary support and patient endorsement for the Digital Navigator role and positive outcomes around digital engagement and digital phenotyping data quality. Through sharing training resources and standardizing the role, we aim to enable clinicians and researchers to adapt and utilize the Digital Navigator for their own needs.

Improving the design of patient-generated health data visualizations: design considerations from a Fitbit sleep study

Interactive data visualization can be a viable way to discover patterns in patient-generated health data and enable health behavior changes. However, very few studies have investigated the design and usability of such data visualization. The present study aimed to (1) explore user experiences with sleep data visualizations in the Fitbit app, and (2) focus on end users' perspectives to identify areas of improvement and potential solutions. The study recruited eighteen pre-medicine college students, who wore Fitbit watches for a two-week sleep data collection period and participated in an exit semi-structured interview to share their experience. A focus group was conducted subsequently to ideate potential solutions. The qualitative analysis identified six pain points (PPs) from the interview data using affinity mapping. Four design solutions were proposed by the focus group to address these PPs and illustrated by a set of mock-ups. The study findings informed four design considerations: (1) usability, (2) transparency and explainability, (3) understandability and actionability, and (4) individualized benchmarking. Further research is needed to examine the design guidelines and best practices of sleep data visualization, to create well-designed visualizations for the general population that enables health behavior changes.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Implementing Technologies to Enhance Coordinated Specialty Care Framework: Implementation Outcomes From a Development and Usability Study

BACKGROUND

Coordinated specialty care (CSC) has demonstrated efficacy in improving outcomes in individuals at clinical high risk for psychosis and individuals with first-episode psychosis. Given the limitations of scalability and staffing needs, the augmentation of services using digital mental health interventions (DMHIs) may be explored to help support CSC service delivery.

OBJECTIVE

In this study, we aimed to understand the methods to implement and support technology in routine CSC and offered insights from a quality improvement study assessing the implementation outcomes of DMHIs in CSC.

METHODS

Patients and clinicians including psychiatrists, therapists, and supported education and employment specialists from a clinical-high-risk-for-psychosis clinic (Center for Early Detection Assessment and Response to Risk [CEDAR]) and a first-episode-psychosis clinic (Advancing Services for Psychosis Integration and Recovery [ASPIRE]) participated in a quality improvement project exploring the feasibility of DMHIs following the Access, Alignment, Connection, Care, and Scalability framework to implement mindLAMP, a flexible and evidenced-based DMHI. Digital navigators were used at each site to assist clinicians and patients in implementing mindLAMP. To explore the differences in implementation outcomes associated with the app format, a menu-style format was delivered at CEDAR, and a modular approach was used at ASPIRE. Qualitative baseline and follow-up data were collected to assess the specific implementation outcomes.

RESULTS

In total, 5 patients (ASPIRE: n=3, 60%; CEDAR: n=2, 40%) were included: 3 (60%) White individuals, 2 (40%) male and 2 (40%) female patients, and 1 (20%) transgender man, with a mean age of 19.6 (SD 2.05) years. Implementation outcome data revealed that patients and clinicians demonstrated high accessibility, acceptability, interest, and belief in the sustainability of DMHIs. Clinicians and patients presented a wide range of interest in unique use cases of DMHI in CSC and expressed variable feasibility and appropriateness associated with nuanced barriers and needs. In addition, the results suggest that adoption, penetration, feasibility, and appropriateness outcomes were moderate and might continue to be explored and targeted.

CONCLUSIONS

Implementation outcomes from this project suggest the need for a patient- and clinician-centered approach that is guided by digital navigators and provides versatility, autonomy, and structure. Leveraging these insights has the potential to build on growing research regarding the need for versatility, autonomy, digital navigator support, and structured applications. We anticipate that by continuing to research and improve implementation barriers impeding the adoption and penetration of DMHIs in CSC, accessibility and uptake of DMHIs will improve, therefore connecting patients to the demonstrated benefits of technology-augmented care.

CardinalKit: open-source standards-based, interoperable mobile development platform to help translate the promise of digital health

Smartphone devices capable of monitoring users' health, physiology, activity, and environment revolutionize care delivery, medical research, and remote patient monitoring. Such devices, laden with clinical-grade sensors and cloud connectivity, allow clinicians, researchers, and patients to monitor health longitudinally, passively, and persistently, shifting the paradigm of care and research from low-resolution, intermittent, and discrete to one of persistent, continuous, and high resolution. The collection, transmission, and storage of sensitive health data using mobile devices presents unique challenges that serve as significant barriers to entry for care providers and researchers alike. Compliance with standards like HIPAA and GDPR requires unique skills and practices. These requirements make off-the-shelf technologies insufficient for use in the digital health space. As a result, budget, timeline, talent, and resource constraints are the largest barriers to new digital technologies. The CardinalKit platform is an open-source project addressing these challenges by focusing on reducing these barriers and accelerating the innovation, adoption, and use of digital health technologies. CardinalKit provides a mobile template application and web dashboard to enable an interoperable foundation for developing digital health applications. We demonstrate the applicability of CardinalKit to a wide variety of digital health applications across 18 innovative digital health prototypes.

Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study

BACKGROUND

Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used.

OBJECTIVE

We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD.

METHODS

Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes.

RESULTS

Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use.

CONCLUSIONS

Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

A guiding framework for creating a comprehensive strategy for mHealth data sharing, privacy, and governance in low- and middle-income countries (LMICs)

With the numerous advances and broad applications of mobile health (mHealth), establishing concrete data sharing, privacy, and governance strategies at national (or regional) levels is essential to protect individual privacy and data usage. This article applies the recent Health Data Governance Principles to provide a guiding framework for low- and middle-income countries (LMICs) to create a comprehensive mHealth data governance strategy. We provide three objectives: (1) establish data rights and ownership to promote equitable benefits from health data, (2) protect people through building trust and addressing patients' concerns, and (3) promote health value by enhancing health systems and services. We also recommend actions for realizing each objective to guide LMICs based on their unique mHealth data ecosystems. These objectives require adopting a regulatory framework for data rights and protection, building trust for data sharing, and enhancing interoperability to use new datasets in advancing healthcare services and innovation.

Clinician perspectives on how digital phenotyping can inform client treatment

This qualitative study explores mental health clinician perspectives on how information extracted from client interactions with digital devices such as smartphones and the Internet (their digital footprint data) can inform client treatment. The process of learning about an individual's behaviours and psychology from their digital footprint, what has been termed 'digital phenotyping', has emerged in recent years as a field of research with potential to offer insights of clinical value that could be used to predict/detect mental ill-health and inform treatment. This research agenda has largely consisted of quantitative studies exploring statistical associations between smartphone data and psychometric outcomes among relatively small participant cohorts. We on the other hand focus on how the data gathered from smartphones and other digital sources could be converted to pieces of meaningful information that clinicians could directly access and interpret to augment their practice and inform their treatment of clients. Through a reflexive thematic analysis of interviews involving clinical psychologists, this study presents ideas and a framework for understanding how digital phenotyping can inform, augment, and innovate client treatment. In total, five themes concerning the ethics, praxis, and value of digital phenotyping for client treatment are generated.

Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study

BACKGROUND

The experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection.

OBJECTIVE

In this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities.

METHODS

To create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app's sampling reliability and perceived user experience. As m-Path is already widely used, the ease of use of the ESM system was not investigated.

RESULTS

Data from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of sampling frequency in the pilot study was satisfactory for most sensors, based on the absolute number of collected observations. However, the relative coverage rate-the ratio between the actual and expected number of measurements-was below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants' perceived user experience.

CONCLUSIONS

To better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM.

Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies

BACKGROUND

Digital phenotyping methods present a scalable tool to realise the potential of personalised medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements.

OBJECTIVE

To assess the impact of population, clinical, research and technological factors on the digital phenotyping data quality as measured by rates of missing digital phenotyping data.

METHODS

This study analyses retrospective cohorts of mindLAMP smartphone application digital phenotyping studies run at Beth Israel Deaconess Medical Center between May 2019 and March 2022 involving 1178 participants (studies of college students, people with schizophrenia and people with depression/anxiety). With this large combined data set, we report on the impact of sampling frequency, active engagement with the application, phone type (Android vs Apple), gender and study protocol features on missingness/data quality.

FINDINGS

Missingness from sensors in digital phenotyping is related to active user engagement with the application. After 3 days of no engagement, there was a 19% decrease in average data coverage for both Global Positioning System and accelerometer. Data sets with high degrees of missingness can generate incorrect behavioural features that may lead to faulty clinical interpretations.

CONCLUSIONS

Digital phenotyping data quality requires ongoing technical and protocol efforts to minimise missingness. Adding run-in periods, education with hands-on support and tools to easily monitor data coverage are all productive strategies studies can use today.

CLINICAL IMPLICATIONS

While it is feasible to capture digital phenotyping data from diverse populations, clinicians should consider the degree of missingness in the data before using them for clinical decision-making.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Background

Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary

In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Key Messages

In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research

The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.

Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study

Background

Smartphone apps that capture surveys and sensors are increasingly being leveraged to collect data on clinical conditions. In mental health, this data could be used to personalize psychiatric support offered by apps so that they are more effective and engaging. Yet today, few mental health apps offer this type of support, often because of challenges associated with accurately predicting users’ actual future mental health.

Objective

In this protocol, we present a study design to explore engagement with mental health apps in college students, using the Technology Acceptance Model as a theoretical framework, and assess the accuracy of predicting mental health changes using digital phenotyping data.

Methods

There are two main goals of this study. First, we present a logistic regression model fit on data from a prior study on college students and prospectively test this model on a new student cohort to assess its accuracy. Second, we will provide users with data-driven activity suggestions every 4 days to determine whether this type of personalization will increase engagement or attitudes toward the app compared to those receiving no personalized recommendations.

Results

The study was completed in the spring of 2022, and the manuscript is currently in review at JMIR Publications.

Conclusions

This is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement.

International Registered Report Identifier (IRRID)

PRR1-10.2196/37954

Usable Data Visualization for Digital Biomarkers: An Analysis of Usability, Data Sharing, and Clinician Contact

Background

While digital phenotyping smartphone apps can collect vast amounts of information on participants, less is known about how these data can be shared back. Data visualization is critical to ensuring applications of digital signals and biomarkers are more informed, ethical, and impactful. But little is known about how sharing of these data, especially at different levels from raw data through proposed biomarkers, impacts patients' perceptions.

Methods

We compared five different graphs generated from data created by the open source mindLAMP app that reflected different ways to share data, from raw data through digital biomarkers and correlation matrices. All graphs were shown to 28 participants, and the graphs' usability was measured via the System Usability Scale (SUS). Additionally, participants were asked about their comfort sharing different kinds of data, administered the Digital Working Alliance Inventory (D-WAI), and asked if they would want to use these visualizations with care providers.

Results

Of the five graphs shown to participants, the graph visualizing change in survey responses over the course of a week received the highest usability score, with the graph showing multiple metrics changing over a week receiving the lowest usability score. Participants were significantly more likely to be willing to share Global Positioning System data after viewing the graphs, and 25 of 28 participants agreed that they would like to use these graphs to communicate with their clinician.

Discussion/Conclusions

Data visualizations can help participants and patients understand digital biomarkers and increase trust in how they are created. As digital biomarkers become more complex, simple visualizations may fail to capture their multiple dimensions, and new interactive data visualizations may be necessary to help realize their full value.

Digital phenotyping correlations in larger mental health samples: analysis and replication

BACKGROUND

Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.

AIMS

To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.

METHOD

Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.

RESULTS

Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.

CONCLUSIONS

Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.