Recovery capital: a concept analysis.

Digital phenotyping is an innovative health monitoring method that involves collecting and analyzing longitudinal digital trace data to characterize individuals’ behavioral, psychological, and physiological states in real-world contexts []. This approach aims to provide a detailed and timely understanding of individual health as it naturally unfolds in everyday life. Smartphones are particularly well-suited for digital phenotyping due to their ubiquity, portability, and rich array of built-in sensors that enable real-time, continuous capture of diverse, high-frequency data streams. Complementary to electronic health records or web browsing histories, smartphone-based traces provide fine-grained insights into individual-specific dynamics across time and space, including mobility patterns, interaction behaviors, and media exposure. Prior studies have shown the predictive potential of these smartphone-based signals; for instance, using physical activity data and insulin injection logs to forecast hypoglycemic events in individuals with diabetes [,], and using screen activity to detect early signs of mental health crises []. As demonstrated in these studies, smartphone-based digital phenotyping holds significant promise as a foundation for delivering timely, adaptive medical and behavioral interventions that support individuals’ health. Identifying and tracking the evolution of individuals’ health phenotypes requires sustained collection of continuous, real-time data from multimodal sensors and concurrent analysis of heterogeneous data streams. This poses significant engineering challenges due to the limited processing power, memory, and storage of smartphones [-]. These hardware constraints generally preclude the use of large-scale multiple-input or multiple-output models or extensive modality-specific preprocessing that would otherwise support interoperability across diverse data modalities. Consequently, most existing digital phenotyping systems adopt a sequential, cloud-centric processing model, in which data are collected on-device, processed in a linear task order, and periodically offloaded to external servers for retrospective analysis. This “traditional” model—defined here as sequential on-device ingestion with delayed cloud-based processing—constitutes the dominant architectural baseline in widely used open-source mobile sensing platforms, including Beiwe and AWARE, where raw sensor streams are written locally and batch-uploaded for offline feature extraction [,]. This sequential, cloud-centric pipeline has two key bottlenecks: a throughput bottleneck, where sequential processing limits data handled per unit time, and overall performance is constrained by the slowest task [,], and a bandwidth bottleneck, where data transmission capacity delays off-device processing [,]. When mobile resources (ie, CPU and memory) are strained by these bottlenecks, a third constraint, the power wall, emerges, wherein thermal and energy limitations prevent processors from scaling performance [-]. Hitting this power wall results in further performance degradation, heightened data loss, and an increased risk of OS app suspension. Ultimately, the constraints persistently threaten to interrupt ongoing data collection and introduce significant blind spots into longitudinal health records. In short, the promise of smartphone-based digital phenotyping and delivery of timely, adaptive medical and behavioral interventions that support individuals’ health cannot be realized if systems continue to rely on the bottleneck-prone sequential, cloud-centric architecture on which the current systems are built. Emerging applications—including just-in-time adaptive interventions, relapse prevention, acute stress detection, and context-aware behavioral coaching—require new architectures that can generate accurate and timely digital phenotypes. It is increasingly critical, for both patient safety and treatment efficacy, that we develop new architectures that are immune to network latency, data loss, and service interruptions. To address the engineering challenges noted above, mobile systems researchers are increasingly exploring parallel processing [,,] and edge computing architectures [,,]. While these approaches have been successfully applied in domains such as consumer on-device AI, robotics, and social media personalization [-], where high data throughput and real-time responsiveness are essential, they have not been integrated into mHealth (mobile health) tools. We propose here how these approaches might be successfully leveraged into a new architecture to support continuous, real-time digital phenotyping and delivery of timely, context-sensitive interventions that better help individuals achieve their health goals.