2020
DOI: 10.1016/j.jad.2019.11.156
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Mobile and wearable technology for monitoring depressive symptoms in children and adolescents: A scoping review

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Cited by 76 publications
(64 citation statements)
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“…Recently, researchers have proposed the use of mobile health (mHealth) technologies, such as wearable and smartphone devices, to passively index mental health in everyday life (Torous et al, 2016. Despite many review articles delineating the potential promise of these devices (Miller, 2012;Onnela & Rauch, 2016;Torous et al, 2016;Torous & Roberts, 2017) and some studies emerging on smartphone sensor data and mental health (Ben-Zeev et al, 2015;Jacobson & Chung, 2020;Pratap et al, 2019;Saeb et al, 2016), there is currently a dearth of research examining 1) how passively collected wearable biobehavioral features (e.g., heart rate, step count, sleep duration and their variability) are associated with mental health functioning, 2) with even fewer research studies conducted during adolescence (Sequeira et al, 2020), and 3) virtually no research conducted with traditional hypothesis testing as opposed to computational psychiatry approaches (i.e., machine learning) that are prevalent in the field. Note that while machine learning approaches are valuable and may increase predictive validity, traditional hypothesis testing is also needed to provide an explanation of associations among mHealth variables of interest and mental health.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, researchers have proposed the use of mobile health (mHealth) technologies, such as wearable and smartphone devices, to passively index mental health in everyday life (Torous et al, 2016. Despite many review articles delineating the potential promise of these devices (Miller, 2012;Onnela & Rauch, 2016;Torous et al, 2016;Torous & Roberts, 2017) and some studies emerging on smartphone sensor data and mental health (Ben-Zeev et al, 2015;Jacobson & Chung, 2020;Pratap et al, 2019;Saeb et al, 2016), there is currently a dearth of research examining 1) how passively collected wearable biobehavioral features (e.g., heart rate, step count, sleep duration and their variability) are associated with mental health functioning, 2) with even fewer research studies conducted during adolescence (Sequeira et al, 2020), and 3) virtually no research conducted with traditional hypothesis testing as opposed to computational psychiatry approaches (i.e., machine learning) that are prevalent in the field. Note that while machine learning approaches are valuable and may increase predictive validity, traditional hypothesis testing is also needed to provide an explanation of associations among mHealth variables of interest and mental health.…”
Section: Introductionmentioning
confidence: 99%
“…Wearable technology has the potential to feedback accurate data about a child’s health, or adherence to the intervention, without placing a burden on the parent or child to actively monitor behaviours. Wearable technology has been developed for a variety of paediatric conditions, including ADHD (Tavakoulnia et al, 2019), mood disorders (Sequeira et al, 2020), juvenile idiopathic arthritis (Heale et al, 2018), cerebral palsy (Bialocerkowski, 2011), asthma (Siering et al, 2019), and epilepsy (Bruno et al, 2018). Child-centred design principles could be incorporated, such as rewards and gamification (Brigden et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, wearable technology can also be used to monitor mood. Sequeira et al [35] have demonstrated the feasibility of wearable tools in the prediction of depressive symptoms in children and adolescents. Health monitoring of pregnant women can be divided into 2 aspects [36].…”
Section: Health and Safety Monitoringmentioning
confidence: 99%
“…Through sensor technology, wearable health devices can collect all kinds of user information, such as health information, geographical location, and living habits [34]. The various formats, large scale, and numerous mobile links of these data may increase the risk of leakage and tampering [8,35,122,123].…”
Section: Security and Privacy Concernsmentioning
confidence: 99%