Smartphone Passive Digital Phenotyping in Frontotemporal Lobar Degeneration
- 1University of California, San Francisco
Abstract
IntroductionFrontotemporal lobar degeneration (FTLD) is a devastating disease that commonly results in early onset dementia, yet its rarity and heterogeneity limit large-scale research and clinical trials. Remote monitoring via low-burden digital health tools may overcome these barriers. We evaluated clinical utility of passive smartphone monitoring in FTLD using a multi-domain mobile assessment platform.
MethodsParticipants were 567 adults (53% clinically normal, 21% prodromal FTLD, 26% symptomatic FTLD) who completed an in-person study visit and downloaded the ALLFTD Mobile App on their personal smartphones. The app delivered unsupervised cognitive tests and passively collected continuous data on battery percentage (proxy for smartphone use) and step count. Longitudinal follow-up included smartphone monitoring and annual in-person study visits. Primary analyses examined associations between passive smartphone features and markers of disease severity at baseline and longitudinally, and tested whether passive features added incremental value beyond app-based cognitive testing.
ResultsPassive smartphone features were feasible to collect and showed excellent reliability with <2 weeks of monitoring (ICCs>0.9). Features capturing smartphone use and movement demonstrated sensitivity to gold-standard clinical measures of disease severity both at baseline and longitudinally. A classification model combining passive features, the app-based cognitive composite score, and demographics detected longitudinal functional decline (AUC=0.89); passive features alone (AUC=0.84) performed comparably to a cognitive screener (AUC=0.85).
DiscussionPassive smartphone monitoring is a valid, zero-burden measure of disease severity and progression in FTLD that adds modest incremental value beyond app-based cognitive testing. Findings support its use as a scalable digital endpoint in longitudinal FTLD research.
Related
- Passive Smartphone-Based Digital Phenotyping for the Identification of PTSD Symptoms: Initial Validation of a Digital Behavioral Marker
- Association Between Postmortem Pathologic Burden and the Rate of Clinical Progression in Patients with Frontotemporal Lobar Degeneration
- Plasma immune signatures associate with neurodegeneration in frontotemporal lobar degeneration
- Myo-inositol as a spectroscopic marker of neuroinflammation in frontotemporal lobar degeneration
- Classical machine learning versus transformers for predicting daily psychological distress from passive smartphone sensing and self-report: a prospective cohort study
BibTeX
@misc{paolillo2026smartphone,
title = {{Smartphone Passive Digital Phenotyping in Frontotemporal Lobar Degeneration}},
author = {E. W. Paolillo and S. Dhanam and J. C. Taylor and M. Sanderson-Cimino and R. Fregly and R. Saloner and K. B. Casaletto and J. H. Kramer and B. L. Miller and W. W. Seeley and M. L. Gorno-Tempini and P. A. Ljubenkov and J. C. Rojas and S. Lee and V. Sturm and B. Appleby and E. Bayram and D. Clark and C. M. Considine and R. R. Darby and G. S. Day and A. De Vito and M. Eldaief and J. A. Fields and N. Ghoshal and E. D. Huey and D. J. Irwin and K. Kantarci and J. Y. Kwan and I. R. Mackenzie and J. C. Masdeu and C. U. Onyike and A. Pantelyat and B. Pascual and T. Popli and K. Rascovsky and N. Rezaii and S. W. Scholz and A. Snyder and M. C. Tartaglia and B. J. Traynor and B. Wong},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.26363439},
url = {https://www.medrxiv.org/content/10.64898/2026.09.18.26363439v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Smartphone Passive Digital Phenotyping in Frontotemporal Lobar Degeneration
AU - E. W. Paolillo
AU - S. Dhanam
AU - J. C. Taylor
AU - M. Sanderson-Cimino
AU - R. Fregly
AU - R. Saloner
AU - K. B. Casaletto
AU - J. H. Kramer
AU - B. L. Miller
AU - W. W. Seeley
AU - M. L. Gorno-Tempini
AU - P. A. Ljubenkov
AU - J. C. Rojas
AU - S. Lee
AU - V. Sturm
AU - B. Appleby
AU - E. Bayram
AU - D. Clark
AU - C. M. Considine
AU - R. R. Darby
AU - G. S. Day
AU - A. De Vito
AU - M. Eldaief
AU - J. A. Fields
AU - N. Ghoshal
AU - E. D. Huey
AU - D. J. Irwin
AU - K. Kantarci
AU - J. Y. Kwan
AU - I. R. Mackenzie
AU - J. C. Masdeu
AU - C. U. Onyike
AU - A. Pantelyat
AU - B. Pascual
AU - T. Popli
AU - K. Rascovsky
AU - N. Rezaii
AU - S. W. Scholz
AU - A. Snyder
AU - M. C. Tartaglia
AU - B. J. Traynor
AU - B. Wong
PY - 2026
DA - 2026/09/18
PB - medRxiv
DO - 10.64898/2026.09.18.26363439
UR - https://www.medrxiv.org/content/10.64898/2026.09.18.26363439v1
AB - IntroductionFrontotemporal lobar degeneration (FTLD) is a devastating disease that commonly results in early onset dementia, yet its rarity and heterogeneity limit large-scale research and clinical trials. Remote monitoring via low-burden digital health tools may overcome these barriers. We evaluated clinical utility of passive smartphone monitoring in FTLD using a multi-domain mobile assessment platform. MethodsParticipants were 567 adults (53% clinically normal, 21% prodromal FTLD, 26% symptomatic FTLD) who completed an in-person study visit and downloaded the ALLFTD Mobile App on their personal smartphones. The app delivered unsupervised cognitive tests and passively collected continuous data on battery percentage (proxy for smartphone use) and step count. Longitudinal follow-up included smartphone monitoring and annual in-person study visits. Primary analyses examined associations between passive smartphone features and markers of disease severity at baseline and longitudinally, and tested whether passive features added incremental value beyond app-based cognitive testing. ResultsPassive smartphone features were feasible to collect and showed excellent reliability with <2 weeks of monitoring (ICCs>0.9). Features capturing smartphone use and movement demonstrated sensitivity to gold-standard clinical measures of disease severity both at baseline and longitudinally. A classification model combining passive features, the app-based cognitive composite score, and demographics detected longitudinal functional decline (AUC=0.89); passive features alone (AUC=0.84) performed comparably to a cognitive screener (AUC=0.85). DiscussionPassive smartphone monitoring is a valid, zero-burden measure of disease severity and progression in FTLD that adds modest incremental value beyond app-based cognitive testing. Findings support its use as a scalable digital endpoint in longitudinal FTLD research.
ER -