Global Collaboration Around Digital Mental Health: The LAMP Consortium

Bilden, Rebecca; Torous, John

doi:10.1007/s41347-022-00240-y

Cited by 14 publications

(10 citation statements)

References 23 publications

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“…We offer details of our recruitment process and procedures in this paper that outlines details of our recruitment, screening, and data coverage procedures [ 12 ]. The mindLAMP app remains open-source software currently deployed at over 50 clinical sites worldwide, and our algorithms are also publicly accessible via GitHub [ 27 ]. This enables others to validate and expand upon our work transparently.…”

Section: Discussionmentioning

confidence: 99%

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

Currey¹,

Torous²

2022

JMIR Res Protoc

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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

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Section: Discussionmentioning

confidence: 99%

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

Currey¹,

Torous²

2022

JMIR Res Protoc

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“…Participants for this study were recruited from a larger study investigating engagement with the mindLAMP app [ 9 , 10 ]. mindLAMP is a smartphone-based app, which both collects many commonly used digital biomarkers, such as GPS, accelerometer, or step count, and can be used to remotely administer surveys and common cognitive games [ 19 ], in a research or clinical setting [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…While the potential of digital biomarkers is already well known [ 9 , 10 , 11 , 12 , 13 ], challenges to their uptake include growing concerns around data sharing and perceived lack of clinical value. Indeed, several studies confirm that usage rates of these mental health apps drop to less than 5% within 10 days [ 14 , 15 ], and apps that collect data without visualizing it for users are often found to be unengaging [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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

Scheuer¹,

Torous²

2022

Digit Biomark

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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.

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“…The LAMP consortium currently consists of over 50 sites around the world, encouraging result sharing and comparisons across different populations. 9 In addition to hand-crafted features, autoencoders could be considered for automatic feature extraction, although they require large sample sizes with less missingness to produce meaningful results. Moreover, future work should seek to train individualised models, as it may be that passive data is useful on an individual, but not population level.…”

Section: Discussionmentioning

confidence: 99%

“…Data were collected with the open-source mindLAMP app (this can be downloaded at https://docs.lamp.digital), developed by the Digital Psychiatry Lab at Beth Israel Deaconess Medical Center (BIDMC) and used by clinical and research teams around the world. [7][8][9] mindLAMP is a smartphone app for iOS and Android that provides surveys, mindfulness audio and cognitive games to users. Additionally, mindLAMP can collect sensor data such as GPS, accelerometer and screen state from a participant's smartphone.…”

Section: Data-setmentioning

confidence: 99%

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

Currey

Torous

2022

BJPsych open

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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.

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Global Collaboration Around Digital Mental Health: The LAMP Consortium

Cited by 14 publications

References 23 publications

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

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

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

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

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