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Pyper E, McKeown S, Hartmann-Boyce J, Powell J. Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review. J Med Internet Res 2023; 25:e46992. [PMID: 37819698 PMCID: PMC10600647 DOI: 10.2196/46992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Digital health technologies (DHTs) play an ever-expanding role in health care management and delivery. Beyond their use as interventions, DHTs also serve as a vehicle for real-world data collection to characterize patients, their care journeys, and their responses to other clinical interventions. There is a need to comprehensively map the evidence-across all conditions and technology types-on DHT measurement of patient outcomes in the real world. OBJECTIVE We aimed to investigate the use of DHTs to measure real-world clinical outcomes using patient-generated data. METHODS We conducted this systematic scoping review in accordance with the Joanna Briggs Institute methodology. Detailed eligibility criteria documented in a preregistered protocol informed a search strategy for the following databases: MEDLINE (Ovid), CINAHL, Cochrane (CENTRAL), Embase, PsycINFO, ClinicalTrials.gov, and the EU Clinical Trials Register. We considered studies published between 2000 and 2022 wherein digital health data were collected, passively or actively, from patients with any specified health condition outside of clinical visits. Categories for key concepts, such as DHT type and analytical applications, were established where needed. Following screening and full-text review, data were extracted and analyzed using predefined fields, and findings were reported in accordance with established guidelines. RESULTS The search strategy identified 11,015 publications, with 7308 records after duplicates and reviews were removed. After screening and full-text review, 510 studies were included for extraction. These studies encompassed 169 different conditions in over 20 therapeutic areas and 44 countries. The DHTs used for mental health and addictions research (111/510, 21.8%) were the most prevalent. The most common type of DHT, mobile apps, was observed in approximately half of the studies (250/510, 49%). Most studies used only 1 DHT (346/510, 67.8%); however, the majority of technologies used were able to collect more than 1 type of data, with the most common being physiological data (189/510, 37.1%), clinical symptoms data (188/510, 36.9%), and behavioral data (171/510, 33.5%). Overall, there has been real growth in the depth and breadth of evidence, number of DHT types, and use of artificial intelligence and advanced analytics over time. CONCLUSIONS This scoping review offers a comprehensive view of the variety of types of technology, data, collection methods, analytical approaches, and therapeutic applications within this growing body of evidence. To unlock the full potential of DHT for measuring health outcomes and capturing digital biomarkers, there is a need for more rigorous research that goes beyond technology validation to demonstrate whether robust real-world data can be reliably captured from patients in their daily life and whether its capture improves patient outcomes. This study provides a valuable repository of DHT studies to inform subsequent research by health care providers, policy makers, and the life sciences industry. TRIAL REGISTRATION Open Science Framework 5TMKY; https://osf.io/5tmky/.
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Affiliation(s)
- Evelyn Pyper
- Department for Continuing Education, University of Oxford, Oxford, United Kingdom
| | - Sarah McKeown
- Department for Continuing Education, University of Oxford, Oxford, United Kingdom
| | - Jamie Hartmann-Boyce
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Department of Health Promotion and Policy, University of Massachusetts Amherst, Amherst, MA, United States
| | - John Powell
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Braund TA, O'Dea B, Bal D, Maston K, Larsen M, Werner-Seidler A, Tillman G, Christensen H. Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study. JMIR Ment Health 2023; 10:e44986. [PMID: 37184904 DOI: 10.2196/44986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/24/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. OBJECTIVE In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. METHODS A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. RESULTS Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. CONCLUSIONS Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. TRIAL REGISTRATION Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664&isReview=true.
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Affiliation(s)
- Taylor A Braund
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Bridianne O'Dea
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Debopriyo Bal
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Kate Maston
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Mark Larsen
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Aliza Werner-Seidler
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Gabriel Tillman
- Institute of Health and Wellbeing, Federation University, Ballarat, Australia
| | - Helen Christensen
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
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Ross MK, Tulabandhula T, Bennett CC, Baek E, Kim D, Hussain F, Demos AP, Ning E, Langenecker SA, Ajilore O, Leow AD. A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity. SENSORS (BASEL, SWITZERLAND) 2023; 23:1585. [PMID: 36772625 PMCID: PMC9920816 DOI: 10.3390/s23031585] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/11/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.
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Affiliation(s)
- Mindy K. Ross
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Theja Tulabandhula
- Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Casey C. Bennett
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
- Department of Computing, DePaul University, Chicago, IL 60604, USA
| | - EuGene Baek
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
| | - Dohyeon Kim
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Alexander P. Demos
- Department of Psychology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Emma Ning
- Department of Psychology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | | | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60612, USA
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Ceolini E, Brunner I, Bunschoten J, Majoie MH, Thijs RD, Ghosh A. A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease. iScience 2022; 25:104792. [PMID: 36039359 PMCID: PMC9418593 DOI: 10.1016/j.isci.2022.104792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/19/2022] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
Smartphones offer unique opportunities to trace the convoluted behavioral patterns accompanying healthy aging. Here we captured smartphone touchscreen interactions from a healthy population (N = 684, ∼309 million interactions) spanning 16 to 86 years of age and trained a decision tree regression model to estimate chronological age based on the interactions. The interactions were clustered according to their next interval dynamics to quantify diverse smartphone behaviors. The regression model well-estimated the chronological age in health (mean absolute error = 6 years, R2 = 0.8). We next deployed this model on a population of stroke survivors (N = 41) to find larger prediction errors such that the estimated age was advanced by 6 years. A similar pattern was observed in people with epilepsy (N = 51), with prediction errors advanced by 10 years. The smartphone behavioral model trained in health can be used to study altered aging in neurological diseases.
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Affiliation(s)
- Enea Ceolini
- Cognitive Psychology Unit, Institute of Psychology, Leiden University, Wassenaarseweg 52, Leiden 2333, the Netherlands
| | - Iris Brunner
- IRIS Brunner, Hammel Neurocenter and University Research Clinic, Aarhus University, Aarhus, Denmark
| | - Johanna Bunschoten
- Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Marian H.J.M. Majoie
- Department of Neurology, Academic Centre for Epileptology, Epilepsy Centre Kempenhaeghe & Maastricht University Medical Centre, Maastricht, the Netherlands
- MHeNS, School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Maastricht, the Netherlands
- School of Health Professions Education, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Roland D. Thijs
- Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- UCL Queen Square Institute of Neurology, London, UK
| | - Arko Ghosh
- Cognitive Psychology Unit, Institute of Psychology, Leiden University, Wassenaarseweg 52, Leiden 2333, the Netherlands
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Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:7690. [PMID: 35546606 PMCID: PMC9095860 DOI: 10.1038/s41598-022-11865-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Nayeefa Chowdhury
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz Quebrada, 1499-002, Lisbon, Portugal
| | - K Ray Chaudhuri
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
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