Overview Of The Use Of Wearable Artificial Intelligence And Smart Devices In The Treatment Of Anxiety And Antidressant

Abstract

Depression is a complex brain illness that is manifested by the interplay of multiple pathogenic processes, including aberrant functioning of the sympathetic nervous system, brain monoamine neurotransmitters, inflammation, and endocrine systems. The clinical management of depression has been made easier by digital health technology, which is expected to continue growing as it is incorporated into patient care. The prevalence of anxiety, despair, and chronic stress is rising, and so is the manufacturing of smart gadgets that enable people to track many aspects of their health. Wearable technology has a lot of potential to help with anxiety and depression-related mental health issues. This study presents a current review of the advancements and applications of wearable technology in depression research.

Keywords

Introduction

Depression is an internal illness and generally diagnosed as neuropsychiatric complaint with symptoms that significantly affects the person’s studies, actions and passions, similar as depressed mood, loss of interest, disturbed sleep, tone- injury or suicidal studies, and has come a major global psychiatric problem. There are about 322 million people with depression encyclopaedically, with the frequence rate of, and the frequence rate of depression in China is about 4.2.  Depression affects the quality- of- life of cases and families, and public health care costs have also been monstrously impacted by depression. Unfortunately, there are not largely effective and lowly toxic medicines for depression treatment[1]. The intricate brain illness known as depression is manifested by the interplay of multiple pathogenic processes, including aberrant functioning of the sympathetic nervous system, brain monoamine neurotransmitters, inflammation, and endocrine systems. Numerous studies have shown the involvement of monoamine neurotransmitters, including dopamine, serotonin, and noradrenaline, in the ethology and therapy of various disorders. The pathophysiology of depression has been thought to be primarily caused by a lack of monoamine in the brain, and several antidepressants work by raising the level of monoamine at the brain's synapses[1]. Depression continues to be the most common cause of disability globally, with a poor prognosis and a mostly chronic duration. Early diagnosis, easy access to care, and improved trial approach, have been connected to better prognosis and treatment outcomes. Digital technology tracking of behaviour and mood has great promise for therapeutic care and for advancing depression research. Smartphone and wearable sensors give continuous data on behaviours that are essential to psychiatric assessment, such as sociability, sleep/wake cycles, cognition, activity, and movement. These sensors work by passively monitoring motion, heart rate, and other physiological characteristics[2]. Anxiety and depression are two "common mental illnesses" that are prevalent around the world. Different cultures have different lifetime prevalence rates for depression; in some, like the US, it can reach 20%. In addition to their substantial economic costs to society, anxiety and depression also have a major personal cost in terms of years lost to illness. Disability-adjusted life years for disorders of the heart and blood vessels are equivalent to those for mental illness. At 32.4% worldwide, the percentage of years lived with disability due to mental health issues is noteworthy. Depression is not the sole significant risk factor for suicide[3]. Generating features—the smallest created building elements intended to explain the behaviours of interest—is the first stage in producing relevant clinical information from data received from digital sensors. These low-level characteristics are frequently combined to define high-level behavioural markers, also referred to as symptoms. GPS data, for instance, is a sensor that may be used to translate low-level features such as "location type." High-level behaviours, such as "increased time at home location," can be obtained from location data and may be linked to symptoms such as social disengagement or low energy.[2] Artificial intelligence (AI) and wearable technology have advanced rapidly in recent years, providing many benefits for managing the treatment of psychiatric disorders, such as depression and anxiety, and for individualizing diagnoses in the healthcare and clinical settings. Electronic devices that can be worn on the body (such as ECG electrodes), in the body (such as implanted smart patches), and in close proximity to the body (such as smartwatches, smart glasses, and smart wristbands) are all considered wearable technology. The purpose of wearable technology is to deliver a steady stream of medical data for the diagnosis and management of illnesses. To do this, physiological data like temperature, blood pressure, blood oxygen level, respiratory rate, physical activity, and the electrical activity of the skin, heart, and brain are continuously recorded. Wearable technology allows for the real-time collection of numerous characteristics that can be used to measure anxiety and depression symptoms in order to diagnose and track people who suffer from these conditions[4]. One of the most important advances in mental health has been the development of artificial intelligence. It is crucial to comprehend the existing instruments utilized for this goal before correctly analysing their application in the screening, diagnosis, and therapy of depression and anxiety. When treating anxiety and depression, a precise diagnosis is essential. This introduction gives an overview of the diagnostic techniques now used in clinical practice to identify and evaluate these illnesses, giving readers an understanding of how doctors diagnose patients. By identifying those who need assistance, depression screening can help improve the overall clinical condition and well-being of the screened population. Tools for short screening questionnaires can be used with little staff[5].

Types Of Medical Devices:

1. Smart Devices:

Important details about a person's health can be found in their vital signs, electroencephalogram (EEG), electrocardiogram (ECG), skin temperature, and skin conductance response (electrodermal activity). The problem lies in figuring out how to make this data more widely accessible outside of the clinical setting while still utilizing wearable, semi-validated technology that is well-tolerated by users and has regulatory approval for the intended use. The development of wearable technology and widely accessible smart gadgets for health monitoring has surged in the last ten years. Smart textiles, pedometers, wearable EEG systems, smart watches with photoplethysmography, and several more gadgets that may non-invasively assess a variety of health-related parameters are among the many smart wearables that are currently being developed[6]. Nevertheless, stress and mental disease fall under a different paradigm and are frequently challenging to observe objectively. This is why there has been significant interest in the viability of developing technology that can detect mental states. Additionally, studies conducted over the past 10 years have shown that people find it uncomfortable and unsettling to wear large, intrusive equipment for the purpose of evaluating their health state. In today's world, cell phones are considered essential items to have on one's person and are frequently utilized as tools for tracking and identifying symptoms of depression, anxiety, and stress[6].  In response to contextual signals derived from individual behaviour, the brain, or environmental signals, wearable devices that can detect or stimulate the body and brain—such as ambulatory EEG or tDCS—are likely to make it possible to create closed-loop systems that can influence brain circuits in real-time. Autonomous dosage systems, like to the insulin pump but for psychiatric medications and neurostimulators therapies, may progressively become possible as remote sensor networks increasingly record the information indicating the effectiveness or inefficacy of these treatments[7].This systematic review specifically seeks to respond to the following queries:

1. What kinds of smart gadgets are used to identify or track stress, anxiety, and depression?
2. What physiological or other processes are used by smart devices to identify stress, anxiety, and depression?
3. Which of these technological products have been produced and put on the market?

2. Wearable Artificial Intelligence:

It is possible to wear electronic devices on the body, such as wearables with different sensors and technology. Giving consumers access to real-time information about their surroundings, activities, or health is the main objective of these wearables[5]. A group of technologies known as artificial intelligence (AI) enable computers and other machines to mimic human intelligence[8]. There is little emphasis on AI research in the area of prenatal mental health, despite technological advancements and the issue receiving a lot of attention in digital health. As far as we are aware, there hasn't been any literature reviewed on artificial intelligence (AI) methods and applications for perinatal mental health issues, despite this being a crucial field for the public health and healthcare industries. Our goal in this research is to present a thorough analysis of AI-based decision support systems and their uses in the field of prenatal mental health. We aim to investigate the AI technologies that have been investigated, their accomplishments, and the ways in which they supplement conventional methods in various tasks related to perinatal mental health[8]. In addition, we hope to point out technical difficulties and facts that are relevant to this particular area of health and offer recommendations for future study paths. Through the synthesis of current research, we hope to draw attention to the advancement and use of AI techniques in this area and promote ongoing AI approach optimization through cooperative efforts for research-driven translation in the clinical setting and enhanced patient outcomes[8]. Numerous parameters that are gathered by wearable technology can be used to evaluate depressive symptoms. Artificial Intelligence (AI) has been applied to wearable technology in response to the need for automated, objective, effective, and real-time methods to identify or forecast depression. This has led to the introduction of what we call "Artificial Intelligence Wearable." Wearable AI is the term for wearable technology combined with AI to evaluate a lot of wearable data and give tailored criticism. The promise of wearable AI is to give a prompt, precise diagnosis and forecast of depression[9]. A growing number of people are experiencing mental health issues, such as anxiety, depressive disorders, and other related disorders, which is negatively impacting people's health. Over the past few years, there has been a significant increase in the frequency of articles on mental health and artificial intelligence (AI). Deep learning (DL) techniques are essential for managing complex medical data sets and creating robust data sets that can be shared throughout institutions. Recent artificial intelligence research offered encouraging paths for using deep learning and other technologies to advance our knowledge of mental health diagnosis and treatment[10]. The advancement of machine learning supports a data-driven strategy whereby a general-purpose program automatically picks up knowledge from data without the requirement for prior knowledge. Deep learning-based techniques in the field of machine learning have significantly enhanced the state of psychotherapy today. These techniques are used in a variety of applications, including the ability to identify objects in pictures, recognize speech, translate between languages, comprehend the genetic causes of diseases, and forecast health status using electronic health records. With modern technology enabling robotic care and online therapies for dementia and autism[10]. Applications of AI in mental health can provide light on novel therapeutic modalities[4], [10] Smart watches and wristbands are examples of wearable technology, which has led to the growth of wearable gadgets as significant technologies. Though wearables are still in their infancy, they influence our choices and actions in a manner similar to how smartphone apps have affected mental health. Wearable technology has improved to a degree of monitoring previously only possible in medical settings with the integration of sensors. Although wearable technology has long been used in clinical settings for health interventions with encouraging results, the simplicity and accessibility of consumer-level devices with such integrated sensors—whether in clothing or other accessories—could further democratize their use and potentially help users who struggle with depression and anxiety[3] In 32% of the studies, a smart band and in 29% of the studies, a smartwatch was used. In 15% of the research, the actigraphy brand is the most widely utilized commercial brand. In just 7% of the research, smart glasses were used. In just 3% of the studies, smart clips, necklaces, and belts were used. A smart ring, a human performance electrodes device, a skin conductance biofeedback device, and a wearable near-infrared spectroscopy (NIRS) device are among the other unusual items that were only mentioned once[3]. Wearable technology is still not widely used for depression diagnosis and treatment, despite the market for wearables expanding quickly. Consequently, the goal of this study was to present a current review of the advancements and applications of wearable technology in depression research. Here, we've gathered unique research on wearable technology for depression diagnosis and therapy, highlighting the latest advancements and trends in the field[11].

MATERIALS AND METHODS:

1. Search sources:

The PubMed and Web of Science databases were searched for literature in order to locate studies on the application of wearable technology in depression research. Only anything published before December 31, 2020 was included in the search. (wearable, actigraph, actiwatch, actimetry, smartwatch, wrist-worn, "fitness tracker," "inertial sensor," or "digital outcome measure") AND (major depressive illness, or MDD) OR bipolar, unipolar, OR "affective disorder," OR "mood disorder." This specified the precise search query. This search yielded only article titles as results[11]. Utilizing the PsycINFO, PubMed, and Web of Science databases, a systematic review was carried out. Following a literature study, search terms were created and entered on February 9th, 2020 for mobile devices, mHealth, and depression. For a list of precise search phrases, see Appendix A. Only studies published in 2008 and later were included, in accordance with earlier systematic reviews of mobile treatments (Donker et al., 2013; Dubad et al., 2018), as this was the year when the first mobile applications were made accessible for download by the general public. After finishing the electronic searches and eliminating duplicates, the first author reviewed the titles and abstracts to ensure they met the inclusion and exclusion criteria[12].  The following bibliographic databases were searched for this study: Google Scholar, IEEE Xplore, PsycINFO, MEDLINE, EMBASE, ACM Digital Library, and PsycINFO.  The period of bibliographic collecting was July 10–12, 2021. Only the first 100 results were scanned because Google Scholar usually returns several hundred articles ordered by relevance to the search topic. In order to find further articles that might be pertinent to the review, the reference lists of the research and reviews that were included were also checked.

Additionally, Google Scholar's "cited by" function (forward reference list checking) was used to find pertinent papers that mentioned the included research[3].

2. Search term:

The mobile device (e.g., app) utilized for the study intervention. The interventions were then classified as follows: "ecological momentary assessment" if it only asked users to complete quick assessments of mood or other constructs; "hybrid" if it combined structured and unstructured components; "structured" if it used locked, sequential modules; "unstructured" if it used tools that could be accessed at any time; and so on[12]. Artificial Intelligence (AI) is the theory and development of computer systems that can carry out tasks that often require human intelligence, like speech recognition, visual perception, and decision-making. Artificial intelligence that can recognize patterns, learn from data, and make judgments with little to no human input is known as machine learning. Support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering are examples of machine learning approaches[10].

Future Scope:

Digital health technology is changing the way mental health care is delivered quickly. It has the potential to improve mental health care quality and accessibility, which will help with the clinical management of depression[13]. The accuracy of stress detection is increased by adjunctive EEG; future research must evaluate the suitability of dual devices for chronic stress monitoring over an extended period of time. EDA was regarded as a helpful metric for stress detection, and according to one author, it's the best wearable because it's easy to set up and simple to use. Nevertheless, another author noted that motion artifact could cause some inconsistency in the findings of EDA measurement using wearable technology. Wearable EEG and accelerometers are being used to detect depression; the former can detect depression alone, while physical activity in a machine learning model can accurately detect depression. However, detecting depression with wearable sensors is still a challenge[6]. Wearable technology offers the benefit of continuous and objective patient monitoring, which makes it ideal for use in research involving depression patients[11]. The path from algorithms to AI apps for diagnosing anxiety and depression is still in its early stages. When AI moves from theoretical promises to real-world applications, it truly becomes valuable. Enhancing patient care through the use of AI tools in clinical settings has enormous potential. Imagine routine check-ups with AI-powered screening tools that provide preliminary assessments and identify possible situations for more investigation. By leveraging AI, telehealth services have the potential to overcome constraints related to accessibility and geography by providing individualized therapies and real-time emotional support. AI might also analyse enormous volumes of clinical data to spot trends and provide guidance for creating more specialized and successful treatment regimens[5]. Wearable technology may be utilized to create cutting-edge medical procedures or diagnostic techniques. In addition to the parameters mentioned above, sensors can measure other factors. For instance, speech patterns and voice analyses via sensors can be used to evaluate the degree of depression and the effectiveness of treatment. Furthermore, wearable technology can record human movements utilizing sensors such as electromyography signals, bending sensors, and inertial sensors. With research indicating that depressed individuals may have motor irregularities and unpredictability in their gait, it is anticipated that future studies will employ wearable technology to examine the ways in which depression affects movement[11].

RESULTS AND DISCUSSION:

Three wearables used electrodermal activity (EDA) or galvanic skin response (GSR) to evaluate stress using the integumentary system's physiology, while one device used skin conductance and heart rate (HR) to measure stress. Two wearables that assessed EEG signals, one that measured HR and podometry, one that used a podometer to measure activity, and one that used functional near-infrared spectroscopy (fNIRS) to record brain oxy-hemodynamic responses were all used to diagnose depression. A wearable that could measure GSR, skin temperature, ECG, and breathing rate in order to identify stress in soldiers was known as a multiparametric garment. Last but not least, two relatively new wearables assessed respiratory rate to identify stress, however only one study provided sufficient data[6]. The most popular wearable technology employed in the research was smart bands, which were worn on the wrist. This has also been mentioned in earlier reviews. This can be explained by the fact that wearable technology worn on the wrist is more fashionable, more recognizable to most people, and less intrusive and distracting. Such capabilities are essential for people to accept and use wearable technologies, according to Hunkin et al[14]. Although it has certain potential drawbacks, AI technology has enormous potential to revolutionize the treatment of mental illness[10]. Anxiety is a frequent mental health problem, especially in Australia where it is becoming more prevalent. It can be defined as an unpleasant, emotional reaction that is out of proportion to a specific stressor, event even when there isn't one. The reaction can or won't be protracted, leading to tension and physical symptoms[6].

CONCLUSIONS:

The clinical management of depression has been made easier by digital health technology, which is expected to continue growing as it is incorporated into patient care. Globally, the prevalence of anxiety, despair, and chronic stress is rising, and so is the manufacturing of smart gadgets that enable people to track many aspects of their health. The wearables that the aforementioned search phrases turned up revealed that the most helpful physiological indicator for stress and anxiety identification is heart rate variability (HRV). The development of wearable medical technology is progressing quickly. Numerous parameters that are objectively gathered in real-time via wearable technology can be used to quantify depressive symptoms. In general, AI tools like deep learning apps can help cure mental illnesses whenever it's convenient for the patient. In order to negotiate the finest research, more investigation into the realm of artificial intelligence and psychological therapies is necessary, as well as addressing the wider ethical and societal issues surrounding these technologies. Wearable artificial intelligence has a lot of potential to help with anxiety and depression-related mental health issues. People can employ wearable AI for pre-screening assessments of sadness and anxiety. To statistically summarize the findings of research on the functionality and efficacy of wearable AI, more reviews are required. Further research is required to fully understand how wearable technology is used and how people react emotionally and behaviourally to the automatic feedback that these gadgets provide.

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