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Simon L, Terhorst Y, Cohrdes C, Pryss R, Steinmetz L, Elhai JD, Baumeister H. The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior. Sleep Med X 2024; 7:100114. [PMID: 38765885 PMCID: PMC11099321 DOI: 10.1016/j.sleepx.2024.100114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/07/2023] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
Introduction Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the correlations between smartphone usage features (SUF) and insomnia symptoms and their predictive value for detecting insomnia symptoms. Methods In an observational study of a German convenience sample, the Insomnia Severity Index (ISI) and smartphone usage data (e.g., time the screen was active, longest time the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlation analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to training, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neighbor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores ≥15. Results 752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SUF and insomnia symptoms were found. In the ML models, sensitivity was low, ranging from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes were the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in the testing subsample indicated a low discrimination capacity. Conclusions Given the small magnitude of the correlations and low discrimination capacity of the ML models, SUFs, as measured in this study, do not appear to be sufficient for detecting insomnia symptoms. Further research is necessary to explore whether examining intra-individual variations and subpopulations or employing alternative smartphone sensors yields more promising outcomes.
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Affiliation(s)
- Laura Simon
- Institute of Psychology and Education, Department of Clinical Psychology and Psychotherapy, Ulm University, Lise-Meitner-Str. 16, Ulm, Germany
| | - Yannik Terhorst
- Institute of Psychology and Education, Department of Clinical Psychology and Psychotherapy, Ulm University, Lise-Meitner-Str. 16, Ulm, Germany
| | - Caroline Cohrdes
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute Berlin, Nordufer 20, Berlin, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-University of Würzburg, Sanderring 2, Würzburg, Germany
| | - Lisa Steinmetz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, Medical Center, University of Freiburg, Hauptstraße 5, Freiburg, Germany
| | - Jon D. Elhai
- Department of Psychology, University of Toledo, 2801 West Bancroft Street, Toledo, OH, USA
- Department of Psychiatry, University of Toledo, 3000 Arlington Avenue, Toledo, OH, USA
| | - Harald Baumeister
- Institute of Psychology and Education, Department of Clinical Psychology and Psychotherapy, Ulm University, Lise-Meitner-Str. 16, Ulm, Germany
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Ahmed NN, Reagu S, Alkhoori S, Cherchali A, Purushottamahanti P, Siddiqui U. Improving Mental Health Outcomes in Patients with Major Depressive Disorder in the Gulf States: A Review of the Role of Electronic Enablers in Monitoring Residual Symptoms. J Multidiscip Healthc 2024; 17:3341-3354. [PMID: 39010931 PMCID: PMC11247372 DOI: 10.2147/jmdh.s475078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/27/2024] [Indexed: 07/17/2024] Open
Abstract
Up to 75% of individuals with major depressive disorder (MDD) may have residual symptoms such as amotivation or anhedonia, which prevent full functional recovery and are associated with relapse. Globally and in the Gulf region, primary care physicians (PCPs) have an important role in alleviating stigma and in identifying and monitoring the residual symptoms of depression, as PCPs are the preliminary interface between patients and specialists in the collaborative care model. Therefore, mental healthcare upskilling programmes for PCPs are needed, as are basic instruments to evaluate residual symptoms swiftly and accurately in primary care. Currently, few if any electronic enablers have been designed to specifically monitor residual symptoms in patients with MDD. The objectives of this review are to highlight how accurate evaluation of residual symptoms with an easy-to-use electronic enabler in primary care may improve functional recovery and overall mental health outcomes, and how such an enabler may guide pharmacotherapy selection and positively impact the patient journey. Here, we show the potential advantages of electronic enablers in primary care, which include the possibility for a deeper "dive" into the patient journey and facilitation of treatment optimisation. At the policy and practice levels, electronic enablers endorsed by government agencies and local psychiatric associations may receive greater PCP attention and backing, improve patient involvement in shared clinical decision-making, and help to reduce the general stigma around mental health disorders. In the Gulf region, an easy-to-use electronic enabler in primary care, incorporating aspects of the Hamilton Depression Rating Scale to monitor amotivation, and aspects of the Montgomery-Åsberg Depression Rating Scale to monitor anhedonia, could markedly improve the patient journey from residual symptoms through to full functional recovery in individuals with MDD.
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Affiliation(s)
- Nahida Nayaz Ahmed
- SEHA Mental Health & Wellbeing Services, College of Medicine and Health Sciences of the United Arab Emirates University, Abu Dhabi, United Arab Emirates
| | - Shuja Reagu
- Weill Cornell Medicine, Doha, Qatar; Hamad Medical Corporation, Doha, Qatar
| | - Samia Alkhoori
- Rashid Hospital, Dubai Health, Dubai, United Arab Emirates
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Zierer C, Behrendt C, Lepach-Engelhardt AC. Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering. J Affect Disord 2024; 356:438-449. [PMID: 38583596 DOI: 10.1016/j.jad.2024.03.163] [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: 09/04/2023] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease. METHOD This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included. RESULTS The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods. LIMITATIONS Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022. CONCLUSION While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically.
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Affiliation(s)
- Carolin Zierer
- Department of Psychology, PFH Private University of Applied Sciences, Göttingen, Lower Saxony, Germany
| | - Corinna Behrendt
- Department of Psychology, PFH Private University of Applied Sciences, Göttingen, Lower Saxony, Germany.
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Rottstädt F, Becker E, Wilz G, Croy I, Baumeister H, Terhorst Y. Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial. Front Digit Health 2024; 6:1335776. [PMID: 38698889 PMCID: PMC11063245 DOI: 10.3389/fdgth.2024.1335776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 04/02/2024] [Indexed: 05/05/2024] Open
Abstract
Objective Smart sensing has the potential to make psychotherapeutic treatments more effective. It involves the passive analysis and collection of data generated by digital devices. However, acceptance of smart sensing among psychotherapy patients remains unclear. Based on the unified theory of acceptance and use of technology (UTAUT), this study investigated (1) the acceptance toward smart sensing in a sample of psychotherapy patients (2) the effectiveness of an acceptance facilitating intervention (AFI) and (3) the determinants of acceptance. Methods Patients (N = 116) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a control video. An online questionnaire was used to assess acceptance of smart sensing, performance expectancy, effort expectancy, facilitating conditions and social influence. The intervention effects of the AFI on acceptance were investigated. The determinants of acceptance were analyzed with structural equation modeling (SEM). Results The IG showed a moderate level of acceptance (M = 3.16, SD = 0.97), while the CG showed a low level (M = 2.76, SD = 1.0). The increase in acceptance showed a moderate effect in the intervention group (p < .05, d = 0.4). For the IG, performance expectancy (M = 3.92, SD = 0.7), effort expectancy (M = 3.90, SD = 0.98) as well as facilitating conditions (M = 3.91, SD = 0.93) achieved high levels. Performance expectancy (γ = 0.63, p < .001) and effort expectancy (γ = 0.36, p < .001) were identified as the core determinants of acceptance explaining 71.1% of its variance. The fit indices supported the model's validity (CFI = .95, TLI = .93, RMSEA = .08). Discussion The low acceptance in the CG suggests that enhancing the acceptance should be considered, potentially increasing the use and adherence to the technology. The current AFI was effective in doing so and is thus a promising approach. The IG also showed significantly higher performance expectancy and social influence and, in general, a strong expression of the UTAUT factors. The results support the applicability of the UTAUT in the context of smart sensing in a clinical sample, as the included predictors were able to explain a great amount of the variance of acceptance.
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Affiliation(s)
- Fabian Rottstädt
- Department of Clinical Psychology, Friedrich Schiller University of Jena, Jena, Germany
- DZPG (German Center for Mental Health), Partner Site Halle-Jena-Magdeburg, Jena, Germany
| | - Eduard Becker
- Department of Clinical Psychology, Friedrich Schiller University of Jena, Jena, Germany
| | - Gabriele Wilz
- Department of Clinical-Psychological Intervention, Friedrich Schiller University of Jena, Jena, Germany
| | - Ilona Croy
- Department of Clinical Psychology, Friedrich Schiller University of Jena, Jena, Germany
- DZPG (German Center for Mental Health), Partner Site Halle-Jena-Magdeburg, Jena, Germany
| | - Harald Baumeister
- Department of Clinical Psychology and Psychotherapy, University Ulm, Ulm, Germany
- DZPG (German Center for Mental Health), Partner Site Mannheim-Ulm-Heidelberg, Ulm, Germany
| | - Yannik Terhorst
- Department of Clinical Psychology and Psychotherapy, University Ulm, Ulm, Germany
- DZPG (German Center for Mental Health), Partner Site Mannheim-Ulm-Heidelberg, Ulm, Germany
- Department of Psychological Methods and Assessment, Ludwigs-Maximilian University Munich, Munich, Germany
- DZPG (German Center for Mental Health), Partner Site München, Munich, Germany
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van Wegen EEH, van Balkom TD, Hirsch MA, Rutten S, van den Heuvel OA. Non-Pharmacological Interventions for Depression and Anxiety in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2024:JPD230228. [PMID: 38607762 DOI: 10.3233/jpd-230228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Affiliation(s)
- Erwin E H van Wegen
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Ageing & Vitality, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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| | - Tim D van Balkom
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention, Neurodegeneration, Amsterdam, The Netherlands
| | - Mark A Hirsch
- Carolinas Medical Center, Atrium Health Carolinas Rehabilitation, Department of Physical Medicine and Rehabilitation, Charlotte, NC, USA
- Wake Forest School of Medicine, Department of Orthopedic Surgery and Rehabilitation, Winston-Salem, NC, USA
| | - Sonja Rutten
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention, Neurodegeneration, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention, Neurodegeneration, Amsterdam, The Netherlands
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Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [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: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
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Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
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Abd-Alrazaq A, Alajlani M, Ahmad R, AlSaad R, Aziz S, Ahmed A, Alsahli M, Damseh R, Sheikh J. The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e52622. [PMID: 38294846 PMCID: PMC10867751 DOI: 10.2196/52622] [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: 09/10/2023] [Revised: 10/24/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Students usually encounter stress throughout their academic path. Ongoing stressors may lead to chronic stress, adversely affecting their physical and mental well-being. Thus, early detection and monitoring of stress among students are crucial. Wearable artificial intelligence (AI) has emerged as a valuable tool for this purpose. It offers an objective, noninvasive, nonobtrusive, automated approach to continuously monitor biomarkers in real time, thereby addressing the limitations of traditional approaches such as self-reported questionnaires. OBJECTIVE This systematic review and meta-analysis aim to assess the performance of wearable AI in detecting and predicting stress among students. METHODS Search sources in this review included 7 electronic databases (MEDLINE, Embase, PsycINFO, ACM Digital Library, Scopus, IEEE Xplore, and Google Scholar). We also checked the reference lists of the included studies and checked studies that cited the included studies. The search was conducted on June 12, 2023. This review included research articles centered on the creation or application of AI algorithms for the detection or prediction of stress among students using data from wearable devices. In total, 2 independent reviewers performed study selection, data extraction, and risk-of-bias assessment. The Quality Assessment of Diagnostic Accuracy Studies-Revised tool was adapted and used to examine the risk of bias in the included studies. Evidence synthesis was conducted using narrative and statistical techniques. RESULTS This review included 5.8% (19/327) of the studies retrieved from the search sources. A meta-analysis of 37 accuracy estimates derived from 32% (6/19) of the studies revealed a pooled mean accuracy of 0.856 (95% CI 0.70-0.93). Subgroup analyses demonstrated that the accuracy of wearable AI was moderated by the number of stress classes (P=.02), type of wearable device (P=.049), location of the wearable device (P=.02), data set size (P=.009), and ground truth (P=.001). The average estimates of sensitivity, specificity, and F1-score were 0.755 (SD 0.181), 0.744 (SD 0.147), and 0.759 (SD 0.139), respectively. CONCLUSIONS Wearable AI shows promise in detecting student stress but currently has suboptimal performance. The results of the subgroup analyses should be carefully interpreted given that many of these findings may be due to other confounding factors rather than the underlying grouping characteristics. Thus, wearable AI should be used alongside other assessments (eg, clinical questionnaires) until further evidence is available. Future research should explore the ability of wearable AI to differentiate types of stress, distinguish stress from other mental health issues, predict future occurrences of stress, consider factors such as the placement of the wearable device and the methods used to assess the ground truth, and report detailed results to facilitate the conduct of meta-analyses. TRIAL REGISTRATION PROSPERO CRD42023435051; http://tinyurl.com/3fzb5rnp.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Mohannad Alajlani
- Institute of Digital Healthcare, WMG, University of Warwick, Warwick, United Kingdom
| | - Reham Ahmad
- Institute of Digital Healthcare, WMG, University of Warwick, Warwick, United Kingdom
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Mohammed Alsahli
- Health Informatics Department, College of Health Science, Saudi Electronic University, Riyadh, Saudi Arabia
| | - 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, Qatar Foundation, Doha, Qatar
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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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