Advancing the use of sensor-based digital health technologies (sDHTs) for mental health research and clinical practice
Creators: name: Parish, Amber
name: Trust, The Wellcome
Publisher: name: F1000 Research Limited
Year: 2025

description: The Digital Health Measurement Collaborative Community (DATAcc) by the Digital Medicine Society (DiMe), in collaboration with the UCLA Depression Grand Challenge, and supported by an Academic Advisory Committee and Wellcome (see Acknowledgements), present here recommendations for advancing the use of sensor-based digital health technologies (sDHTs) for mental health research and clinical practice.<br>
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This work builds on the results of the Digital Sensing Workshop held from February 28 through March 2, 2023, at UCLA. More than 50 leading mental health and computer science researchers, industry experts, advocates, and funders from six countries came together across five working groups to discuss a shared vision and common goals for incorporating sDHTs in mental health research and care. Additionally, we share insights from two expert workshops run by Wellcome in July 2024 on the ethical and social considerations of using sDHTs and sensor data in mental health research. Contributors came from Colombia, India, Kenya, South Africa, Uganda, the UK, and the US and brought clinical, commercial, community, lived experience, research, and technical perspectives.<br>
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To delve deeper into the topics identified in these workshops, we applied a mixed-methods approach to:<br>
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1 Identify the most promising behavioral and physiological aspects of health relevant to anxiety, depression, and psychosis that can be effectively and acceptably measured using sDHTs and outline the current state of sDHTs to capture these aspects of health;<br>
2 Explore the characteristics of fit-for-purpose sDHTs, along with potential development, usage, and implementation barriers;<br>
3 Develop concrete and actionable recommendations on how to advance the use of sDHTs for mental health.<br>
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We conducted research in two phases. The first phase included qualitative in-depth interviews with clinicians, researchers, individuals with lived experience, and care partners across low, middle, and high-income countries (LMIC and HIC, respectively). A narrative literature review established a comprehensive summary of the evidence available about relevant aspects of depression, anxiety, and psychosis and the use of sDHTs to capture them.<br>
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The second phase took learnings from the workshops, interviews, and literature review to conduct a modified Delphi process with a panel of experts. The panel included clinicians, researchers, care partners, and individuals with lived experience across LMIC and HIC countries and identified high-priority aspects of mental health, sDHT characteristics, barriers to adoption, and consideration to drive adoption of sDHTs in mental health research and clinical practice. Figure 1 showcases the diversity in the participants' demographics, expertise, and background in all research phases.<br>
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Candidate aspects of mental health for digital measurement<br>
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We identified the following behavioral and physiological aspects of health that are impacted by mental health and can be captured with sDHTs:<br>
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● Aspects of sleep and social behavior have the strongest scientific evidence and validation in mental health. Measures of sleep are commonly captured using sensors embedded in wristband wearables or via smartphone sensors. More advanced sDHTs include sleep pads, radar-based contactless sensors, and home-use electroencephalography or even polysomnography solutions. Social behaviors can be inferred from common smartphone features, including GPS, Bluetooth, WiFi, and microphones, as well as app usage patterns (e.g., texts, calls, social media).<br>
● Physical activity and stress responses, such as those indicated by an increase in heart rate, are also well-supported by research. Heart rate can be measured via photoplethysmography (PPG) and electrocardiography (ECG) sensors embedded, often embedded in wrist or chest patch form factors. Additionally, physical activity can be quantified from GPS, actigraphy, and indoor positioning systems.<br>
● Speech and language-derived measures are promising emerging indicators of mental health conditions but require substantial research to validate and generalize findings and algorithms. Microphones are the primary sensor used to capture speech and language.<br>
● Stress-related breathing changes can be captured with chest impedance measurements or wearable stethoscopes, and gastrointestinal symptoms such as gastric motility can be captured with electrogastrography, while weight changes and body composition can be captured with weight scales with whole body impedance sensors. The application of these technologies in mental health is still in its early stages.<br>
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Technology characteristics of fit-for-purpose sDHTs<br>
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We identified technology characteristics that are important when considering an sDHT for mental health. Some apply across therapeutic areas, some are more specific to mental health conditions. This is by design, as most sDHTs that are available today were not specifically developed for mental health.<br>
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● The most important general technology characteristics for sDHTs are ease of use, reliable performance, strong data privacy and security, and long battery life. Interoperability is considered very important in both clinical practice and research.<br>
● Condition-specific technology characteristics include: in depression, offline functionality allows sDHT usage without an active internet connection and minimal user interaction requirements address low motivation during episodes; in psychosis, discreet, non-obtrusive designs help prevent paranoia; and in anxiety, well-chosen alerts to avoid worsening symptoms or exacerbating clinical anxiety.<br>
● General considerations and research questions guiding research and development of sDHTs for mental health conditions include ensuring their effectiveness, safety, validating their use in diverse populations, and focusing on scalability, sustainability, and ethical practices with clear descriptions of risks, benefits, and data policies.<br>
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Considerations for implementation in clinical research and practice<br>
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We asked the panel of experts to think about what is needed to successfully implement sDHTs for mental health application from the lens of their personal experience.<br>
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● Key considerations for sDHT implementation in clinical research include balancing user engagement with technology utility, offering participation incentives, managing the burden of active assessments versus passive data collection, and ensuring training for participants and research staff. Trust-building through clear communication of study goals, risks and benefits, data use, and feedback is crucial.<br>
● For clinical practice, implementation priorities include long-term usability, clinician-oriented feedback, an unobtrusive device design to reduce stigma, and improved access and affordability through introduction of subsidies, lowering production and usage costs, or extending insurance coverage.<br>
● Sensor, hardware, and algorithm components of sDHTs are currently not likely to have been developed for a specific mental health condition. It is critical that clinical validation is conducted in the appropriate population(s) to ensure that behavioral and physiological traits specific to mental health conditions are reliably captured. The development of sensors and algorithms that generate a measure that is specific to a mental health condition (e.g., an anxiety level score) will generate actionable insights that are more specific to that condition and will require less interpretation by and training of clinicians.<br>
● Capturing contextual information is essential across many therapeutic areas, such as accounting for environmental changes, emotional stress, comorbidities, or medications that may impact the measures. Given the significant influence of surroundings on mental health, this becomes especially critical for products designed to address mental health conditions.<br>
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Barriers to adoption and use, and mitigation strategies<br>
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We found that several barriers impede the widespread adoption of sDHTs in the mental health field. Understanding these challenges and implementing targeted solutions will be important to support broad adoption and use.<br>
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● The largest barriers to adopting sDHTs in mental health research and care include high costs and limited access, data privacy concerns, poor technological literacy, and a lack of adaptation to the specific needs of clinical populations. Additional barriers include signal misattribution and insufficient user-centered design for the intended population.<br>
● Mitigating cost and access challenges requires financial subsidies, insurance coverage, infrastructure development, and device donations.<br>
● Building trust involves demonstrating trustworthy practices, such as transparent communication about data use and scientific evidence, compliance with privacy regulations, and co-designing solutions with end-users and lived experience experts.<br>
● To ensure high usability, user engagement, and sDHT effectiveness, training for users and clinicians, culturally tailored implementations, and thorough testing with diverse populations are essential.<br>
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Actionable recommendations to improve adoption of sDHTs for mental health<br>
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Our report identifies the following recommendations to guide future innovation and development, funding decisions, and research focus areas:

 Advance clinical research and practical applications for validated sensor-based measures: Measures of sleep, physical activity, stress, and social behaviors have the strongest evidence for research and clinical utility in mental health, with broad expert and patient support, and should be prime targets for clinical research and practice. Emerging measures derived from speech and language require further validation, while research into the relationship between mental health conditions and gastrointestinal symptoms, breathing rate, and body temperature is in its infancy.
 Integrate and innovate sensors, validate and refine algorithms: Today, non-mental health specific sDHTs with well-tested sensors are readily available in a variety of form factors and can be implemented into clinical research and practice for mental health conditions. Researchers, developers and funders should assess the available validation evidence carefully in the intended population, factoring in the maturity of the sDHT and all relevant available evidence. Algorithms should be validated and refined for mental health indications, ensuring they reliably generate insights into aspects of health in the intended populations. Beyond well established sensors, research and development should focus on novel sensor modalities, e.g., those that capture subtle behavioral cues, or non-invasively measure biochemical components in sweat or other bodily fluids.
 Incorporate qualitative data into AI models alongside sensor data: Building sDHT datasets in populations of high interest will be important to develop more effective and specific AI-based research, diagnostic and prognostic tools for mental health. AI models must address typical considerations such as biases in the data and model interpretability. More specifically for mental health, AI models may also encode culturally relevant practices, such as storytelling and meditation techniques that could be leveraged to develop more personalized therapies. AI agent therapists could provide on-demand empathic support, including in low resource settings, and in closed-loop sDHT feedback systems.
 Develop infrastructure, standards and norms for measuring mental health: Standardization and interoperability of sDHT data and systems will be critical to integrating these technologies into clinical practice. Efforts should focus on establishing core data elements, creating interoperable platforms, and fostering collaboration through public-private partnerships and centralized hubs for research and innovation. Ontologies for specific mental health conditions should be developed, which will be an important enabler to harmonize research data collection efforts and ensure compatibility with major health information exchange formats. We discuss core data elements for sDHTs in this report.
 Improve access, equity, and inclusion: Overcoming barriers like the cost of products and inequitable access requires financial support mechanisms, inclusive design, and communication campaigns. Providing accessibility features and multilingual interfaces, and conducting community engagement should build trust and encourage widespread adoption of sDHTs in mental health, among clinicians and people with mental health conditions alike.