Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

Ahmed, Arfan; Aziz, Sarah; Abd‐Alrazaq, Alaa; Farooq, Faisal; Sheikh, Javaid I.

doi:10.2196/36010

Cited by 26 publications

(8 citation statements)

References 54 publications

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“…Therefore, the need for a review that focuses on AI-paired wearable devices for anxiety and depression has never been higher. The quality of this review should as high as that of a previous review conducted on AI-paired wearable devices for diabetes [16]. The current review aims to explore the features of wearable AI used for anxiety and depression to both help customers make educated selections and help the research community advance in this field by identifying gaps and examining future prospects.…”

Section: Research Problem and Aimmentioning

confidence: 99%

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Abd‐Alrazaq¹,

AlSaad²,

Aziz³

et al. 2023

J Med Internet Res

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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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Section: Research Problem and Aimmentioning

confidence: 99%

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Abd‐Alrazaq¹,

AlSaad²,

Aziz³

et al. 2023

J Med Internet Res

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“…Real-time and continuous monitoring of these biomarkers must be supported by computational frameworks to automate the process of stress detection, thereby overcoming the aforementioned limitations of the current tools. Wearable artificial intelligence (AI) is a promising solution that has been used to address the issue of assessing stress [29][30][31]. Wearable AI is an advanced technology that depends on AI techniques to analyze a large amount of data (eg, HR, HRV, EDA, activity level, and skin temperature) collected by sensors in wearable devices to provide personalized feedback.…”

Section: Changes In Biomarkers (Eg Heart Rate [Hr] Hr Variability [Hr...mentioning

confidence: 99%

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis

Abd-alrazaq,

Alajlani,

Ahmad

et al. 2024

J Med Internet Res

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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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“…However, a paradigm shift towards patient-centered care now textitasizes individuals taking an active health management role. For instance, wearable devices enable continuous, noninvasive monitoring of vital signs and biometrics to improve outcomes and quality of life [52], [53]. By providing realtime data, wearables facilitate early detection of conditions like heart disease, diabetes, and sleep disorders, allowing 1,881…”

Section: ) Most Impactful Articlesmentioning

confidence: 99%