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Zafar F, Fakhare Alam L, Vivas RR, Wang J, Whei SJ, Mehmood S, Sadeghzadegan A, Lakkimsetti M, Nazir Z. The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review. Cureus 2024; 16:e56472. [PMID: 38638735 PMCID: PMC11025697 DOI: 10.7759/cureus.56472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.
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Affiliation(s)
- Fabeha Zafar
- Internal Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK
| | | | - Rafael R Vivas
- Nutrition, Food and Exercise Sciences, Florida State University College of Human Sciences, Tallahassee, USA
| | - Jada Wang
- Medicine, St. George's University, Brooklyn, USA
| | - See Jia Whei
- Internal Medicine, Sriwijaya University, Palembang, IDN
| | | | | | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhuo E, Wang Z, Chen X, Zou J, Fang Y, Zhuo J, Li Y, Zhang J, Gong Z. Wearable Smart Fabric Based on Hybrid E-Fiber Sensor for Real-Time Finger Motion Detection. Polymers (Basel) 2023; 15:2934. [PMID: 37447578 DOI: 10.3390/polym15132934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023] Open
Abstract
Wearable electronic sensors have attracted considerable interest in hand motion monitoring because of their small size, flexibility, and biocompatibility. However, the range of motion and sensitivity of many sensors are inadequate for complex and precise finger motion capture. Here, organic and inorganic materials were incorporated to fabricate a hybrid electronic sensor and optimized and woven into fabric for hand motion detection. The sensor was made from flexible porous polydimethylsiloxane (PDMS) filled with multiwalled carbon nanotubes (MWCNTs). The weight ratios of MWCNTs and geometric characteristics were optimized to improve the hybrid electronic sensor, which showed a high elongation at the breaking point (i.e., more than 100%) and a good sensitivity of 1.44. The strain-related deformation of the PDMS/MWCNT composite network resulted in a variation in the sensor resistance; thus, the strain level that corresponds to different finger motions is be calculated. Finally, the fabricated and optimized electronic sensor in filiform structure with a 6% MWCNT ratio was integrated with smart fabric to create a finger sleeve for real-time motion capture. In conclusion, a novel hybrid E-fiber sensor based on PDMS and MWCNTs was successfully fabricated in the current study with an optimal M/P ratio and structure, and textile techniques were adopted as new packaging approaches for such soft electronic sensors to create smart fabric for wearable and precise detection with highly enhanced sensing performance. The successful results in the current study demonstrate the great potential of such hybrid soft sensors in smart wearable healthcare management, including motion detection.
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Affiliation(s)
- Erhan Zhuo
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Ziwen Wang
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Xiaochen Chen
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Junhao Zou
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Yuan Fang
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Jiekai Zhuo
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Yicheng Li
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Jun Zhang
- Laboratory for Artificial Intelligence in Design, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Zidan Gong
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
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Lukenga MP, Billonnet L, Gaugue J, Denis J. Exploring female students' perceptions of the use of digital technologies in managing academic stress. Front Psychol 2023; 14:1199038. [PMID: 37333588 PMCID: PMC10274148 DOI: 10.3389/fpsyg.2023.1199038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 04/02/2023] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Objective The purpose of this research is to explore the perceptions of female students regarding the implementation of digital technologies for academic stress management. We aim to determine if the contribution of these technologies could offer to female students a better management of the stress related to their studies and thus, a better deployment of strategies to cope with academic difficulties. Method A qualitative study using the focus group methodology was conducted. Our inductive and exploratory approach allowed us to focus on the experience and perception of eleven female students from the University of Mons. The cohort was divided into two groups according to their score on the Perceived Stress Scale-10. Results The data collected was analyzed using the thematic analysis of which allowed us to identify fourteen sub-themes divided into three axes: coping strategies used to manage academic stress, students' needs to improve their management of academic stress, and the implementation of technology for managing academic stress. Conclusion Our results show that the issues present in the academic context lead students to use various coping strategies, some of which are harmful to their physical and mental health. The implementation of digital technologies and biofeedback seems to be an approach that could help students adopt more functional coping strategies and alleviate their daily difficulties in managing academic stress.
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Affiliation(s)
| | | | - Justine Gaugue
- Department of Clinical Psychology, University of Mons, Mons, Belgium
| | - Jennifer Denis
- Department of Clinical Psychology, University of Mons, Mons, Belgium
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Kim H, Song J, Kim S, Lee S, Park Y, Lee S, Lee S, Kim J. Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review. Biosensors (Basel) 2023; 13:bios13040470. [PMID: 37185545 PMCID: PMC10136450 DOI: 10.3390/bios13040470] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/06/2023] [Accepted: 04/09/2023] [Indexed: 05/17/2023]
Abstract
Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities. As a result, for more accurate stress monitoring, validations requiring two or more stress-related biomarkers are demanded. In this review, the combinations of various types of sensors (hereafter referred to as multiplexed sensor systems) that can be applied to monitor stress are discussed, referring to physical and chemical biomarkers. Multiplexed sensor systems are classified as multiplexed physical sensors, multiplexed physical-chemical sensors, and multiplexed chemical sensors, with the effect of measuring multiple biomarkers and the ability to measure stress being the most important. The working principles of multiplexed sensor systems are subdivided, with advantages in measuring multiple biomarkers. Furthermore, stress-related chemical biomarkers are still limited to cortisol; however, we believe that by developing multiplexed sensor systems, it will be possible to explore new stress-related chemical biomarkers by confirming their correlations to cortisol. As a result, the potential for further development of multiplexed sensor systems, such as the development of wearable electronics for mental health management, is highlighted in this review.
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Affiliation(s)
- Heena Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jaeyoon Song
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Sehyeon Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Suyoung Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Yejin Park
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Seungjun Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Seunghee Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jinsik Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
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Robinson T, Condell J, Ramsey E, Leavey G. Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices. Int J Environ Res Public Health 2023; 20:ijerph20032636. [PMID: 36768002 PMCID: PMC9916237 DOI: 10.3390/ijerph20032636] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/21/2023] [Accepted: 01/28/2023] [Indexed: 05/05/2023]
Abstract
RATIONALE Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders. AIMS This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system. METHODOLOGY a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018-2022, focusing primarily on mental health management using technology. RESULTS We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.
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Affiliation(s)
- Tony Robinson
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK
- Correspondence:
| | - Joan Condell
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business and Enterprise, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK
| | - Gerard Leavey
- The Bamford Centre for Mental Health and Wellbeing, School of Psychology, Ulster University, Coleraine Campus, Cromore Rd., Coleraine BT52 1SA, UK
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Yao X, Zhou Y, Wang Y, Li Z. Cross-disciplinary training of nursing informatics and nursing engineering at the postgraduate level: A feasibility analysis based on the qualitative method. Nurse Educ Today 2023; 121:105708. [PMID: 36634504 DOI: 10.1016/j.nedt.2023.105708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/06/2022] [Accepted: 01/01/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND The trend of interdisciplinary education is becoming increasingly prominent. Nursing informatics and nursing engineering have received much attention and development at different levels of nursing education in many Western countries. Meanwhile, in China, the cultivation of interdisciplinary nursing talents has either not been initiated or has only entered an initial stage. OBJECTIVES This study aims to explore experts' opinions from nursing, informatics and engineering on the feasibility of interdisciplinary education at graduate master's level in nursing through interview. DESIGN This was a descriptive qualitative study. SETTING Interviews were conducted online or face to face. PARTICIPANTS Experts in the fields of nursing, informatics, and engineering who met the study qualifications were enrolled. METHODS This study used a purposive sampling method and collected data via semi-structured interviews. A total of 14 experts were involved based on data saturation, which eight were interviewed face-to-face and six were interviewed online. A content analysis method was used to summarize and analyze the attitudes, opinions, and suggestions of experts. RESULTS A total of 579 min of interviews with 66,387 words were transcribed and analyzed after 30-50 min time range of each interview, and 4 themes were established. A consensus was obtained on the necessity and importance of interdisciplinary education. Policy guidance, financial support, and mutual recognition were the prerequisites for the cultivation. Moreover, feasibility of interdisciplinary education depends on multi-cooperation, including society, university, and hospital. Finally, a linkage mechanism among relevant stakeholders was required. CONCLUSION The necessity and feasibility of such integrated training was concluded. Learning from the experience of relevant countries, China should launch an interdisciplinary training model suitable for its national condition.
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Affiliation(s)
- Xiuyu Yao
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100144, China.
| | - Ying Zhou
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100144, China
| | - Yidan Wang
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100144, China
| | - Zheng Li
- School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100144, China.
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Gomes N, Pato M, Lourenço AR, Datia N. A Survey on Wearable Sensors for Mental Health Monitoring. Sensors (Basel) 2023; 23:1330. [PMID: 36772370 PMCID: PMC9919280 DOI: 10.3390/s23031330] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/20/2023] [Accepted: 01/21/2023] [Indexed: 06/18/2023]
Abstract
Mental illness, whether it is medically diagnosed or undiagnosed, affects a large proportion of the population. It is one of the causes of extensive disability, and f not properly treated, it can lead to severe emotional, behavioral, and physical health problems. In most mental health research studies, the focus is on treatment, but fewer resources are focused on technical solutions to mental health issues. The present paper carried out a systematic review of available literature using PRISMA guidelines to address various monitoring solutions in mental health through the use of wearable sensors. Wearable sensors can offer several advantages over traditional methods of mental health assessment, including convenience, cost-effectiveness, and the ability to capture data in real-world settings. Their ability to collect data related to anxiety and stress levels, as well as panic attacks, is discussed. The available sensors on the market are described, as well as their success in providing data that can be correlated with the aforementioned health issues. The current wearable landscape is quite dynamic, and the current offerings have enough quality to deliver meaningful data targeted for machine learning algorithms. The results indicate that mental health monitoring is feasible.
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Affiliation(s)
- Nuno Gomes
- ISEL, Lisbon School of Engineering, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
| | - Matilde Pato
- ISEL, Lisbon School of Engineering, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
- LASIGE & IBEB, FCUL, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
- FIT-ISEL, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
| | - André Ribeiro Lourenço
- ISEL, Lisbon School of Engineering, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
- CardioID Technologies Lda., Rua Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
| | - Nuno Datia
- ISEL, Lisbon School of Engineering, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
- FIT-ISEL, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
- NOVA LINCS, NOVA School of Science and Technology, 2829-516 Caparica, Portugal
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Abd-Alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. J Med Internet Res 2023; 25:e42672. [PMID: 36656625 PMCID: PMC9896355 DOI: 10.2196/42672] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/18/2022] [Accepted: 12/11/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. OBJECTIVE This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. METHODS We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. RESULTS Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. CONCLUSIONS Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Kerstin Denecke
- Institute for Medical Informatics, Bern University of Applied Science, Bern, Switzerland
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Faisal Farooq
- Qatar Computing Research Institute, Hamad bin Khalifa University, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Abstract
With the continuous development of the times, social competition is becoming increasingly fierce, people are facing enormous pressure and mental health problems have become common. Long-term and persistent mental health problems can lead to severe mental disorders and even death in individuals. The real-time and accurate prediction of individual mental health has become an effective method to prevent the occurrence of mental health disorders. In recent years, smart wearable devices have been widely used for monitoring mental health and have played an important role. This paper provides a comprehensive review of the application fields, application mechanisms, common signals, common techniques and results of smart wearable devices for the detection of mental health problems, aiming to achieve more efficient and accurate prediction for individual mental health, and to achieve early identification, early prevention and early intervention to provide a reference for improving the level of individual mental health.
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Affiliation(s)
- Nannan Long
- Department of Nursing, The Third Xiangya Hospital, Central South University, Changsha 410013, China
- Xiangya Nursing School, Central South University, Changsha 410031, China
| | - Yongxiang Lei
- Department of Mechanical Engineering, Politecnico di Milano, Milan 10056, Italy
| | - Lianhua Peng
- Xiangya Nursing School, Central South University, Changsha 410031, China
- Affiliated Hospital of Jinggangshan University, Jianggangshan 343100, China
| | - Ping Xu
- ZiBo Hospital of Traditional Chinese and Western Medicine, Zibo 255020, China
| | - Ping Mao
- Department of Nursing, The Third Xiangya Hospital, Central South University, Changsha 410013, China
- Hunan Key Laboratory of Nursing, Changsha 410013, China
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