Resources

Discoveries over the years have emphasized that research using the Life Analytics Monitoring Platform has the ability to detect meaningful change better than traditional clinical or research methods. Listed here is a selected list of relevant ORCATECH publications.

  1. Wu, Chao-Yi et al. “Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study.” JMIR formative research vol. 7 e45693. 10 Aug. 2023, doi:10.2196/45693
  2. Hantke, Nathan C et al. “Correlating continuously captured home-based digital biomarkers of daily function with postmortem neurodegenerative neuropathology.” PloS one vol. 18,6 e0286812. 8 Jun. 2023, doi:10.1371/journal.pone.0286812
  3. Reynolds, Christina et al. “Association Between Mild Cognitive Impairment and Seasonal Rest-Activity Patterns of Older Adults.” Frontiers in digital health vol. 4 809370. 23 Feb. 2022, doi:10.3389/fdgth.2022.809370
  4. Wu, Chao-Yi et al. “Reproducibility and replicability of high-frequency, in-home digital biomarkers in reducing sample sizes for clinical trials.” Alzheimer’s & dementia (New York, N. Y.) vol. 7,1 e12220. 31 Dec. 2021, doi:10.1002/trc2.12220
  5. Bernstein, John P K et al. “Passively-Measured Routine Home Computer Activity and Application Use Can Detect Mild Cognitive Impairment and Correlate with Important Cognitive Functions in Older Adulthood.” Journal of Alzheimer’s disease: JAD vol. 81,3 (2021): 1053-1064. doi:10.3233/JAD-210049
  6. Dorociak, Katherine E et al. “Subtle Changes in Medication-taking Are Associated With Incident Mild Cognitive Impairment.” Alzheimer disease and associated disorders vol. 35,3 (2021): 237-243. doi:10.1097/WAD.0000000000000439
  7. Beattie, Zachary et al. “The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community.” Digital biomarkers vol. 4,Suppl 1 100-118. 26 Nov. 2020, doi:10.1159/000512208
  8. Piau, Antoine et al. “When Will My Patient Fall? Sensor-Based In-Home Walking Speed Identifies Future Falls in Older Adults.” The journals of gerontology. Series A, Biological sciences and medical sciences vol. 75,5 (2020): 968-973. doi:10.1093/gerona/glz128
  9. Thomas, Neil William Douglas et al. “An Ecologically Valid, Longitudinal, and Unbiased Assessment of Treatment Efficacy in Alzheimer Disease (the EVALUATE-AD Trial): Proof-of-Concept Study.” JMIR research protocols vol. 9,5 e17603. 27 May. 2020, doi:10.2196/17603
  10. Kaye, Jeffrey et al. “Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data.” Journal of visualized experiments : JoVE ,137 56942. 27 Jul. 2018, doi:10.3791/56942
  11. Seelye, Adriana et al. “Weekly observations of online survey metadata obtained through home computer use allow for detection of changes in everyday cognition before transition to mild cognitive impairment.” Alzheimer’s & dementia : the journal of the Alzheimer’s Association vol. 14,2 (2018): 187-194. doi:10.1016/j.jalz.2017.07.756
  12. Dodge, Hiroko H et al. “Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials.” PloS one vol. 10,9 e0138095. 17 Sep. 2015, doi:10.1371/journal.pone.0138095
  13. Lyons, Bayard E et al. “Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy.” Frontiers in aging neuroscience vol. 7 102. 10 Jun. 2015, doi:10.3389/fnagi.2015.00102
  14. Seelye, Adriana et al. “Computer mouse movement patterns: A potential marker of mild cognitive impairment.” Alzheimer’s & dementia (Amsterdam, Netherlands) vol. 1,4 (2015): 472-480. doi:10.1016/j.dadm.2015.09.006
  15. Kaye, Jeffrey et al. “Unobtrusive measurement of daily computer use to detect mild cognitive impairment.” Alzheimer’s & dementia : the journal of the Alzheimer’s Association vol. 10,1 (2014): 10-7. doi:10.1016/j.jalz.2013.01.011
  16. Dodge, H H et al. “In-home walking speeds and variability trajectories associated with mild cognitive impairment.” Neurology vol. 78,24 (2012): 1946-52. doi:10.1212/WNL.0b013e318259e1de
  17. Kaye, Jeffrey A et al. “Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging.” The journals of gerontology. Series B, Psychological sciences and social sciences vol. 66 Suppl 1,Suppl 1 (2011): i180-90. doi:10.1093/geronb/gbq095