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Rykov YG, Ng KP, Patterson MD, Gangwar BA, Kandiah N. Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning. Comput Biol Med 2024; 180:108959. [PMID: 39089109 DOI: 10.1016/j.compbiomed.2024.108959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/04/2024] [Accepted: 07/26/2024] [Indexed: 08/03/2024]
Abstract
Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled "Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment".
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Affiliation(s)
- Yuri G Rykov
- Neuroglee Therapeutics, 2 Venture Dr, #08-18, Singapore, 608526
| | - Kok Pin Ng
- Department of Neurology, National Neuroscience Institute, 11 Jln Tan Tock Seng, 308433, Singapore; Duke-NUS Medical School, 8 College Rd, 169857, Singapore
| | | | - Bikram A Gangwar
- Neuroglee Therapeutics, 2 Venture Dr, #08-18, Singapore, 608526.
| | - Nagaendran Kandiah
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Level 18 308232, Singapore
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2
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Corponi F, Li BM, Anmella G, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie SM, Whalley HC, Hidalgo-Mazzei D, Vergari A. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Transl Psychiatry 2024; 14:161. [PMID: 38531865 DOI: 10.1038/s41398-024-02876-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/09/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.
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Affiliation(s)
- Filippo Corponi
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Bryan M Li
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Gerard Anmella
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Ariadna Mas
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Isabella Pacchiarotti
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Marc Valentí
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Iria Grande
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Antoni Benabarre
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Marina Garriga
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Antonio Vergari
- School of Informatics, University of Edinburgh, Edinburgh, UK
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Powell D. Walk, talk, think, see and feel: harnessing the power of digital biomarkers in healthcare. NPJ Digit Med 2024; 7:45. [PMID: 38396034 PMCID: PMC10891042 DOI: 10.1038/s41746-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/18/2024] [Indexed: 02/25/2024] Open
Affiliation(s)
- Dylan Powell
- Faculty of Health Sciences & Sport, University of Stirling, Stirling, UK.
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Tsai CH, Christian M, Kuo YY, Lu CC, Lai F, Huang WL. Sleep, physical activity and panic attacks: A two-year prospective cohort study using smartwatches, deep learning and an explainable artificial intelligence model. Sleep Med 2024; 114:55-63. [PMID: 38154150 DOI: 10.1016/j.sleep.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/16/2023] [Accepted: 12/17/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Sleep and physical activity suggestions for panic disorder (PD) are critical but less surveyed. This two-year prospective cohort study aims to predict panic attacks (PA), state anxiety (SA), trait anxiety (TA) and panic disorder severity (PDS) in the upcoming week. METHODS We enrolled 114 PD patients from one general hospital. Data were collected using the DSM-5, the MINI, clinical app questionnaires (BDI, BAI, PDSS-SR, STAI) and wearable devices recording daily sleep, physical activity and heart rate from 16 June 2020 to 10 June 2022. Our teams applied RNN, LSTM, GRU deep learning and SHAP explainable methods to analyse the data. RESULTS The 7-day prediction accuracies for PA, SA, TA, and PDS were 92.8 %, 83.6 %, 87.2 %, and 75.6 % from the LSTM model. Using the SHAP explainable model, higher initial BDI or BAI score and comorbidities with depressive disorder, generalized anxiety disorder or agoraphobia predict a higher chance of PA. However, PA decreased under the following conditions: daily average heart rate, 72-87 bpm; maximum heart rate, 100-145 bpm; resting heart rate, 55-60 bpm; daily climbing of more than nine floors; total sleep duration between 6 h 23 min and 10 h 50 min; deep sleep, >50 min; and awake duration, <53 min. LIMITATIONS Moderate sample size and self-report questionnaires were the limitations. CONCLUSIONS Deep learning predicts recurrent PA and various anxiety domains with 75.6-92.8 % accuracy. Recurrent PA decreases under adequate daily sleep and physical activity.
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Affiliation(s)
- Chan-Hen Tsai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan; Department of Psychiatry, En Chu Kong Hospital, New Taipei City, Taiwan
| | - Mesakh Christian
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei City, Taiwan
| | - Ying-Ying Kuo
- Department of Psychiatry, En Chu Kong Hospital, New Taipei City, Taiwan
| | - Chen Chun Lu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei City, Taiwan
| | - Wei-Lieh Huang
- Department of Psychiatry, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan; Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan; Cerebellar Research Centre, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan.
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5
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Kasputytė G, Jenciūtė G, Šakinis N, Bunevičienė I, Korobeinikova E, Vaitiekus D, Inčiūra A, Jaruševičius L, Bunevičius R, Krikštolaitis R, Krilavičius T, Juozaitytė E, Bunevičius A. Smartphone sensors for evaluating COVID-19 fear in patients with cancer: a prospective study. Front Public Health 2024; 11:1308003. [PMID: 38249398 PMCID: PMC10797074 DOI: 10.3389/fpubh.2023.1308003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 12/04/2023] [Indexed: 01/23/2024] Open
Abstract
Objective This study aimed to analyze the association between the behavior of cancer patients, measured using passively and continuously generated data streams from smartphone sensors (as in digital phenotyping), and perceived fear of COVID-19 and COVID-19 vaccination status. Methods A total of 202 patients with different cancer types and undergoing various treatments completed the COVID-19 Fears Questionnaire for Chronic Medical Conditions, and their vaccination status was evaluated. Patients' behaviors were monitored using a smartphone application that passively and continuously captures high-resolution data from personal smartphone sensors. In all, 107 patients were monitored for at least 2 weeks. The study was conducted between August 2022 and August 2023. Distributions of clinical and demographical parameters between fully vaccinated, partially vaccinated, and unvaccinated patients were compared using the Chi-squared test. The fear of COVID-19 among the groups was compared using the Mann-Whitney and the Kruskal-Wallis criteria. Trajectories of passively generated data were compared as a function of fear of COVID-19 and COVID-19 vaccination status using local polynomial regression. Results In total, 202 patients were included in the study. Most patients were fully (71%) or partially (13%) vaccinated and 16% of the patients were unvaccinated for COVID-19. Fully vaccinated or unvaccinated patients reported greater fear of COVID-19 than partially vaccinated patients. Fear of COVID-19 was higher in patients being treated with biological therapy. Patients who reported a higher fear of COVID-19 spent more time at home, visited places at shorter distances from home, and visited fewer places of interest (POI). Fully or partially vaccinated patients visited more POI than unvaccinated patients. Local polynomial regression using passively generated smartphone sensor data showed that, although at the beginning of the study, all patients had a similar number of POI, after 1 week, partially vaccinated patients had an increased number of POI, which later remained, on average, around four POI per day. Meanwhile, fully vaccinated or unvaccinated patients had a similar trend of POI and it did not exceed three visits per day during the entire treatment period. Conclusion The COVID-19 pandemic continues to have an impact on the behavior of cancer patients even after the termination of the global pandemic. A higher perceived fear of COVID-19 was associated with less movement, more time spent at home, less time spent outside of home, and a lower number of visited places. Unvaccinated patients visited fewer places and were moving less overall during a 14-week follow-up as compared to vaccinated patients.
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Affiliation(s)
| | - Gabrielė Jenciūtė
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Nerijus Šakinis
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Inesa Bunevičienė
- Faculty of Political Science and Diplomacy, Vytautas Magnus University, Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Domas Vaitiekus
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arturas Inčiūra
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | | | | | | | - Tomas Krilavičius
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Adomas Bunevičius
- Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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Abd-Alrazaq A, AlSaad R, Harfouche M, Aziz S, Ahmed A, Damseh R, Sheikh J. Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis. J Med Internet Res 2023; 25:e48754. [PMID: 37938883 PMCID: PMC10666012 DOI: 10.2196/48754] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. OBJECTIVE This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. METHODS Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. RESULTS Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. CONCLUSIONS Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. TRIAL REGISTRATION PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
| | - Manale Harfouche
- Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
- World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
| | - Rafat Damseh
- Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar
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Nghiem J, Adler DA, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Form Res 2023; 7:e47380. [PMID: 37561561 PMCID: PMC10450536 DOI: 10.2196/47380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.
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Affiliation(s)
- Jodie Nghiem
- Medical College, Weill Cornell Medicine, New York, NY, United States
| | - Daniel A Adler
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Deborah Estrin
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Cecilia Livesey
- Optum Labs, UnitedHealth Group, Minnetonka, MN, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Tanzeem Choudhury
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
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Friligkou E, Koller D, Pathak GA, Miller EJ, Lampert R, Stein MB, Polimanti R. Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.02.23293170. [PMID: 37577704 PMCID: PMC10418572 DOI: 10.1101/2023.08.02.23293170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Background Anxiety disorders are associated with decreased heart rate variability (HRV), but the underlying mechanisms remain elusive. Methods We selected individuals with whole-genome sequencing, Fitbit, and electronic health record data (N=920; 61,333 data points) from the All of Us Research Program. Anxiety PRS were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts-UK Biobank, FinnGen, and the Million Veterans Program (N Total =364,550). The standard deviation of average RR intervals (SDANN) was calculated using five-minute average RR intervals over full 24-hour heart rate measurements. Antidepressant exposure was defined as an active antidepressant prescription at the time of the HRV measurement in the EHR. The associations of daily SDANN measurements with the anxiety PRS, antidepressant classes, and antidepressant substances were tested. Participants with lifetime diagnoses of cardiovascular disorders, diabetes mellitus, and major depression were excluded in sensitivity analyses. One-sample Mendelian randomization (MR) was employed to assess potential causal effect of anxiety on SDANN. Results Anxiety PRS was independently associated with reduced SDANN (beta=-0.08; p=0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta=-0.12, p=0.002) and bupropion (beta=-0.071, p=0.01), tricyclic antidepressants (beta=-0.177, p=0.0008), selective serotonin reuptake inhibitors (beta=-0.069; p=0.0008) and serotonin and norepinephrine reuptake inhibitors (beta=-0.16; p=2×10 -6 ) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta=-2.22, p=0.03). Conclusions Anxiety and antidepressants are independently associated with decreased HRV, and anxiety appears to exert a causal effect on HRV. Our observational findings provide novel insights into the impact of anxiety on HRV.
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Rigatti M, Chapman B, Chai PR, Smelson D, Babu K, Carreiro S. Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder. COGENT MENTAL HEALTH 2023; 2:2240375. [PMID: 37546179 PMCID: PMC10399596 DOI: 10.1080/28324765.2023.2240375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/17/2023] [Indexed: 08/08/2023]
Abstract
Opioid use disorder (OUD) is one of the most pressing public health problems of the past decade, with over eighty thousand overdose related deaths in 2021 alone. Digital technologies to measure and respond to disease states encompass both on- and off-body sensors. Such devices can be used to detect and monitor end-user physiologic or behavioral measurements (i.e. digital biomarkers) that correlate with events of interest, health, or pathology. Recent work has demonstrated the potential of digital biomarkers to be used as a tools in the prevention, risk mitigation, and treatment of opioid use disorder (OUD). Multiple physiologic adaptations occur over the course of opioid use, and represent potential targets for digital biomarker based monitoring strategies. This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use. Technologies to detect opioid administration, withdrawal, hyperalgesia and overdose will be reviewed. Driven by empirically derived algorithms, these technologies have important implications for supporting the safe prescribing of opioids, reducing harm in active opioid users, and supporting those in recovery from OUD.
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Affiliation(s)
- Marc Rigatti
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Brittany Chapman
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Peter R Chai
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - David Smelson
- Department of Psychiatry, UMass Chan Medical School, Worcester, MA, USA
| | - Kavita Babu
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Stephanie Carreiro
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
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Frank AC, Li R, Peterson BS, Narayanan SS. Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review. JMIR Ment Health 2023; 10:e45572. [PMID: 37463010 PMCID: PMC10394606 DOI: 10.2196/45572] [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: 01/15/2023] [Revised: 05/27/2023] [Accepted: 06/13/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies. OBJECTIVE Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD. METHODS In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: ("OCD" OR "obsessive" OR "obsessive-compulsive") AND ("smartphone" OR "phone" OR "wearable" OR "sensing" OR "biofeedback" OR "neurofeedback" OR "neuro feedback" OR "digital" OR "phenotyping" OR "mobile" OR "heart rate variability" OR "actigraphy" OR "actimetry" OR "biosignals" OR "biomarker" OR "signals" OR "mobile health"). RESULTS We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention. CONCLUSIONS Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers.
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Affiliation(s)
- Adam C Frank
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Ruibei Li
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bradley S Peterson
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Division of Child and Adolescent Psychiatry, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Shrikanth S Narayanan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
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González-Pérez A, Matey-Sanz M, Granell C, Diaz-Sanahuja L, Bretón-López J, Casteleyn S. AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health. J Biomed Inform 2023; 141:104359. [PMID: 37044134 DOI: 10.1016/j.jbi.2023.104359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/10/2023] [Accepted: 04/05/2023] [Indexed: 04/14/2023]
Abstract
In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.
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Affiliation(s)
- Alberto González-Pérez
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| | - Miguel Matey-Sanz
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| | - Carlos Granell
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| | - Laura Diaz-Sanahuja
- Department of Basic Psychology, Clinical and Psychobiology, Universitat Jaume I, Castellon, 12071, Spain.
| | - Juana Bretón-López
- Department of Basic Psychology, Clinical and Psychobiology, Universitat Jaume I, Castellon, 12071, Spain; CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, 28029, Spain.
| | - Sven Casteleyn
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
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