{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T22:42:48Z","timestamp":1786142568536,"version":"build-2736575974"},"reference-count":68,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Digital phenotyping\u2014the use of continuous data streams from digital devices such as smartphones to assess behavioral, psychological, and physiological states\u2014holds transformative potential for health monitoring and personalized care. However, real-time analysis of large multimodal data often exceeds mobile devices\u2019 computational resources, leading most platforms to rely on sequential processing and cloud-based computation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We propose the Stanford Screenomics platform as a software reference architecture that uses a modular design to integrate parallel processing and edge computing, enabling scalable, real-time digital phenotyping on smartphones.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Two prototype apps were developed: one following the parallel, on-device architecture (Stanford Screenomics platform) and another based on a traditional sequential, cloud-based design (traditional). Both processed identical multimodal data streams at the same intensity; only the location and sequence of computation differed. In two 48-hour experiments, performances were compared across four load profiles: low (\u224810 MB\/min), medium (\u224830 MB\/min), heavy (\u224840 MB\/min), and very heavy (\u224860 MB\/min). In the first experiment, offline resource performance was assessed under continuous simulated smartphone use. Virtual users completed six tasks in a fixed five-minute sequence: watching YouTube (Google LLC), reading eBooks, browsing TikTok (ByteDance Ltd), web surfing, listening to Spotify, and scrolling Instagram Reels (Meta). Minute-by-minute measurements of CPU usage (%), RAM usage (MB), battery drain (%\/h), and data loss (%) were collected. Descriptive statistics (mean\u00b1SD) summarized performance, and independent\n                      <jats:italic>t<\/jats:italic>\n                      tests compared architectures. Data loss trajectories were analyzed to determine whether growth was linear or exponential under increasing load. In the second experiment, end-to-end phenotyping latency was evaluated over stable Wi-Fi. Five key-stage timestamps per trial tracked local writes, preprocessing, memory parsing, phenotype analysis, and intervention delivery. Total phenotype update time per trial was the primary outcome, and latency differences between architectures were analyzed using linear mixed-effects models, with IQRs reported to capture variability across load conditions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Stanford Screenomics platform consistently demonstrated lower CPU usage (3.9%\u201014.6% vs 10.5%\u201026.9%) and RAM usage (97\u2010132 \u202fMB vs 101\u2010155 \u202fMB) than the traditional, with reduced battery drain (0.9%\u20102.1%\/h vs 1.4%\u20103.2%\/h). Data fidelity was higher in the Stanford Screenomics, with shallow linear data loss (0.4%\u20101.5%\/h) compared to exponential growth in the traditional (2%\u20107.1%\/h), achieving up to 9.4\u00d7 greater data retention under very heavy load. The Stanford Screenomics completed phenotype updates in 0.90\u202fseconds under low load and 9.32\u202fseconds under very heavy load, compared to 30.1\u2010398.1 \u202fseconds for traditional, representing 34\u201043\u00d7faster processing with substantially narrower variability (IQR 0.3\u20106\u202fs vs 11\u202f s-5 \u202fmin).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results demonstrate that the Stanford Screenomics platform architecture enables real-time, on-device digital phenotyping with high fidelity and low latency. This validated prototype architecture establishes a resilient foundation for the next generation of scalable, reliable, and context-aware deployment of real-world mobile health interventions on mobile devices.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/87320","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:45:09Z","timestamp":1783698309000},"page":"e87320-e87320","source":"Crossref","is-referenced-by-count":0,"title":["Software Reference Architecture for Real-Time Mobile Digital Phenotyping: Evaluation of System Designs"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0818-3692","authenticated-orcid":false,"given":"Ian","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2367-0774","authenticated-orcid":false,"given":"Thomas N","family":"Robinson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1546-8397","authenticated-orcid":false,"given":"Byron B","family":"Reeves","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8804-7804","authenticated-orcid":false,"given":"Nick","family":"Haber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1671-5257","authenticated-orcid":false,"given":"Nil\u00e0m","family":"Ram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0166-1","article-title":"Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety","volume":"2","author":"Huckvale","journal-title":"NPJ Digit Med"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1177\/19322968231186401","article-title":"Association of smartphone-based activity tracking and nocturnal hypoglycemia in people with type 1 diabetes","volume":"19","author":"Gardner","journal-title":"J Diabetes Sci Technol"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00985-7","article-title":"Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic\/Latino adults with or at risk of type 2 diabetes","volume":"7","author":"Pai","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01740-w","article-title":"Smartphone screen time and suicide risk in daily life captured through high-resolution screenshot data","volume":"8","author":"Jacobucci","journal-title":"NPJ Digit Med"},{"key":"R5","doi-asserted-by":"crossref","unstructured":"Ali S Khusro S Khan A Khan H . 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