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Ross MK, Demos AP, Zulueta J, Piscitello A, Langenecker SA, McInnis M, Ajilore O, Nelson PC, Ryan KA, Leow A. Naturalistic smartphone keyboard typing reflects processing speed and executive function. Brain Behav 2021; 11:e2363. [PMID: 34612605 PMCID: PMC8613429 DOI: 10.1002/brb3.2363] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/11/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE The increase in smartphone usage has enabled the possibility of more accessible ways to conduct neuropsychological evaluations. The objective of this study was to determine the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests. METHODS Using a custom-built keyboard, naturalistic keypress dynamics were unobtrusively recorded in individuals with bipolar disorder (n = 11) and nonbipolar controls (n = 8) on an Android smartphone. Keypresses were matched to digital trail making tests part B (dTMT-B) administered daily in two periods and weekly mood assessments. Following comparison of dTMT-Bs to the pencil-and-paper equivalent, longitudinal mixed-effects models were used to analyze daily dTMT-B performance as a function of typing and mood. RESULTS Comparison of the first dTMT-B to paper TMT-B showed adequate reliability (intraclass correlations = 0.74). In our model, we observed that participants who typed slower took longer to complete dTMT-B (b = 0.189, p < .001). This trend was also seen in individual fluctuations in typing speed and dTMT-B performance (b = 0.032, p = .004). Moreover, participants who were more depressed completed the dTMT-B slower than less depressed participants (b = 0.189, p < .001). A practice effect was observed for the dTMT-Bs. CONCLUSION Typing speed in combination with depression scores has the potential to infer aspects of cognition (visual attention, processing speed, and task switching) in people's natural environment to complement formal in-person neuropsychological assessments that commonly include the trail making test.
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Affiliation(s)
- Mindy K Ross
- University of Illinois at Chicago, Chicago, Illinois, USA
| | | | - John Zulueta
- University of Illinois at Chicago, Chicago, Illinois, USA
| | | | | | | | | | - Peter C Nelson
- University of Illinois at Chicago, Chicago, Illinois, USA
| | - Kelly A Ryan
- University of Michigan, Ann Arbor, Michigan, USA
| | - Alex Leow
- University of Illinois at Chicago, Chicago, Illinois, USA
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Vesel C, Rashidisabet H, Zulueta J, Stange JP, Duffecy J, Hussain F, Piscitello A, Bark J, Langenecker SA, Young S, Mounts E, Omberg L, Nelson PC, Moore RC, Koziol D, Bourne K, Bennett CC, Ajilore O, Demos AP, Leow A. Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study. J Am Med Inform Assoc 2021; 27:1007-1018. [PMID: 32467973 DOI: 10.1093/jamia/ocaa057] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/16/2020] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. MATERIALS AND METHODS BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. RESULTS We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. CONCLUSIONS Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.
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Affiliation(s)
- Claudia Vesel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Homa Rashidisabet
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - John Zulueta
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jonathan P Stange
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jennifer Duffecy
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Andrea Piscitello
- Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA
| | - John Bark
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA
| | | | | | | | | | - Peter C Nelson
- Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Raeanne C Moore
- Department of Psychiatry, University of California, San Diego, San Diego, California, USA
| | - Dave Koziol
- Arbormoon Software, Inc, Ann Arbor, Michigan, USA
| | - Keith Bourne
- Arbormoon Software, Inc, Ann Arbor, Michigan, USA
| | - Casey C Bennett
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, USA.,School of Intelligence, Hanyang University, Seoul, Korea
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Alexander P Demos
- Department of Psychology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Alex Leow
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA
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Zulueta J, Demos AP, Vesel C, Ross M, Piscitello A, Hussain F, Langenecker SA, McInnis M, Nelson P, Ryan K, Leow A, Ajilore O. The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age. Front Psychiatry 2021; 12:739022. [PMID: 35002792 PMCID: PMC8727438 DOI: 10.3389/fpsyt.2021.739022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/19/2021] [Indexed: 11/19/2022] Open
Abstract
Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology. Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ. Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = -5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037). Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.
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Affiliation(s)
- John Zulueta
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | | | - Claudia Vesel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Mindy Ross
- Graduate College, University of Illinois at Chicago, Chicago, IL, United States
| | - Andrea Piscitello
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Scott A Langenecker
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Peter Nelson
- College of Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Kelly Ryan
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
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Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker SA, McInnis M, Ajilore O, Nelson PC, Ryan K, Leow A. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. J Med Internet Res 2018; 20:e241. [PMID: 30030209 PMCID: PMC6076371 DOI: 10.2196/jmir.9775] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 05/13/2018] [Accepted: 05/29/2018] [Indexed: 12/15/2022] Open
Abstract
Background Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. Objective The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. Methods Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912). Results A statistically significant mixed-effects regression model for the prediction of HDRS-17 item scores was created: conditional R2=.63, P=.01. A mixed-effects regression model for YMRS scores showed the variance accounted for by random effect was zero, and so an ordinary least squares linear regression model was created: R2=.34, P=.001. Multiple significant variables were demonstrated for each measure. Conclusions Mood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances.
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Affiliation(s)
- John Zulueta
- University of Illinois at Chicago, Chicago, IL, United States
| | | | - Mladen Rasic
- University of Illinois at Chicago, Chicago, IL, United States
| | - Rebecca Easter
- University of Illinois at Chicago, Chicago, IL, United States
| | - Pallavi Babu
- University of Michigan, Ann Arbor, MI, United States
| | | | | | - Olusola Ajilore
- University of Illinois at Chicago, Chicago, IL, United States
| | - Peter C Nelson
- University of Illinois at Chicago, Chicago, IL, United States
| | - Kelly Ryan
- University of Michigan, Ann Arbor, MI, United States
| | - Alex Leow
- University of Illinois at Chicago, Chicago, IL, United States
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Stange JP, Zulueta J, Langenecker SA, Ryan KA, Piscitello A, Duffecy J, McInnis MG, Nelson P, Ajilore O, Leow A. Let your fingers do the talking: Passive typing instability predicts future mood outcomes. Bipolar Disord 2018; 20. [PMID: 29516666 PMCID: PMC5940490 DOI: 10.1111/bdi.12637] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
| | - John Zulueta
- University of Illinois at Chicago, Chicago, IL, USA
| | | | - Kelly A. Ryan
- University of Michigan Medical Center, Ann Arbor, MI, USA
| | | | | | | | - Pete Nelson
- University of Illinois at Chicago, Chicago, IL, USA
| | | | - Alex Leow
- University of Illinois at Chicago, Chicago, IL, USA
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Wichrowska E, Piscitello A. [Study of the cutaneous thermometry on various areas in the elderly patient]. Folia Clin Int (Barc) 1976; 26:422-31. [PMID: 1001567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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