Perceived acceptability of wearable devices for the treatment of mental health problems

Hunkin, Hugh; King, Daniel L.; Zajac, Ian

doi:10.1002/jclp.22934

Cited by 32 publications

(23 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although psychologists sometimes voice negative views about telepsychology, our field has known for a long time that many potential patients have negative views about traditional psychological treatment. Ironically, telepsychological approaches such as wearable devices may actually be more appealing to people holding negative views toward psychological treatment and who have less experience in help‐seeking (Hunkin, King, & Zajac, 2020). Other extremely promising web‐based approaches that have the potential to reach individuals not entering traditional treatment, or as a supplement to traditional treatment, are showcased in this special issue, including Acceptance and Commitment therapy for college students (Viskovich & Pakenham, 2020), an avatar‐based intervention for military family caregivers (Wilcox, 2020), and a self‐management positive psychology intervention (Görges, Oehler, von Hirschhausen, Hegerl, & Rummel‐Kluge, 2020).…”

Section: Telepsychology Special Issuementioning

confidence: 99%

Rapid telepsychology deployment during the COVID‐19 pandemic: A special issue commentary and lessons from primary care psychology training

et al. 2020

View full text Add to dashboard Cite

Objective: This article positions the special issue on telepsychology amidst the COVID-19 pandemic, which has dramatically accelerated the adoption and dissemination of telepsychology.Method: The article makes general observations about the themes emerging in the special issue with considerations for application, training, theory-driven research, and policy. It then presents as a case example the rapid deployment during the pandemic of telepsychology doctoral training and services at the Virginia Commonwealth University (VCU) Primary Care Psychology Collaborative.Results: Facilitators to VCU telepsychology deployment included trainee and supervisor resources, strong telepsychology training, and prior experience. Barriers to overcome included limited clinic capacity, scheduling, technology, and accessibility and diversity issues. Lessons learned involved presenting clinical issues, supervision, and working with children and adolescents.Conclusions: Telepsychology is crucial for psychological service provision, during the COVID-19 pandemic more than ever, and that is unlikely to change as psychologists and patients increasingly continue to appreciate its value. K E Y W O R D S COVID-19, integrated care, primary care psychology, telehealth, telemedicine, telepsychology, training 1 | INTRODUCTION Our commentary to this Journal of Clinical Psychology special issue on "Telepsychology: Research, Training, Practice,and Policy" has several purposes. We begin by making general observations about some of the themes emerging in the special issue with considerations for application, training, theory-driven research, and policy. We then position telepsychology amidst the backdrop of the COVID-19 pandemic, which arguably is a historical event that has had the largest impact on the provision of telepsychology services since the invention of the webcam. The article will present as a case example the rapid deployment during the pandemic of telepsychology doctoral training and services at the Virginia Commonwealth University (VCU) Primary Care Psychology Collaborative, which is funded by two grants from the Health Resources and Services Administration (HRSA) and at any given time trains over 30 psychology doctoral students across a dozen or so primary care psychology training sites. The article will focus on the changes being made at VCU to adapt from in-person services and training to telepsychology, successes, and supports in that adaptation, barriers encountered, and lessons learned for telepsychology training and service provision. | TELEPSYCHOLOGY SPECIAL ISSUEMore than any other theme, the articles in this special issue underscore the critical nature of strong telepsychology training as a growth opportunity for the field. Pierce, Perrin, and McDonald (2020) found in their path model that the strongest predictors of psychologists' use of telepsychology were subjective norms, perceived ease of use, and perceived usefulness of telepsychology. Put simply, psychologists who do not use telepsychology do not feel that others are usi...

show abstract

Section: Telepsychology Special Issuementioning

confidence: 99%

Rapid telepsychology deployment during the COVID‐19 pandemic: A special issue commentary and lessons from primary care psychology training

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Interestingly, no interventions designed for use on other devices, such as wearable technologies, have been identified. This is despite research suggesting that they are acceptable for treating mental health issues among those who do not typically engage with mental health services [ 72 ]. However, this is still a relatively new area, and wearable devices designed to treat or help manage mental health difficulties are predicted to increase over the next few years [ 73 ].…”

Section: Discussionmentioning

confidence: 99%

mHealth Interventions for Self-Harm: Scoping Review

Cliffe¹,

Tingley²,

Greenhalgh³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Self-harm is a growing issue with increasing prevalence rates; however, individuals who self-harm do not often receive treatment. Mobile health (mHealth) interventions are a possible solution to some of the barriers that individuals face when seeking support, and they have also been found to be effective in improving mental health. Thus far, reviews of mHealth interventions for self-harm have been limited by study type. Therefore, we determined that a broader scoping review will provide a more exhaustive understanding of mHealth interventions for self-harm. Objective This scoping review aims to identify mHealth interventions for self-harm within the literature, understand the types and features of interventions that have been developed and evaluated, highlight research findings around mHealth interventions for self-harm, and determine what outcomes are typically used to assess the efficacy of interventions. Methods A search was conducted using Embase, PubMed, PsycINFO, PsycEXTRA, Web of Science, and the Cochrane Library. Studies were included if they described an mHealth intervention designed to have a direct (ie, if the intervention was designed for self-harm or for people who self-harm) or indirect (ie, if self-harm was measured as an outcome) treatment effect and if the paper was available in English. There were no exclusion criteria based on the study design. Results A total of 36 papers were included in the review, and most of them were randomized controlled trials published within the last 4 years. The interventions were mostly smartphone apps and calling or texting services, with 62% (21/34) having underlying therapeutic models to inform the intervention content. They were generally shown to be promising and appealing, but only 5 were widely available for use. Outcomes focused on a reduction of self-harm and suicidality, mood, and the users’ experiences of the intervention. Samples were typically nondiverse, and there was limited variety in the study designs and in the measurements of self-harm recovery. Conclusions Promising and appealing mHealth interventions have been developed but are not widely available. Research could benefit from greater diversity as well as a broader and more nuanced understanding of recovery from self-harm.

show abstract

“…Various new sensors for healthcare tracking have been devised in the recent past for different purposes [27][28][29][30][31] and these devices have been used for various applications [32,33]. The commonly acknowledged applications of these wearable devices include mental health assessment [34] and sleep monitoring [35]. There exist substantial research considering technological advancements in development of frameworks for long term mental health monitoring using wearable devices [36,37].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

2020

View full text Add to dashboard Cite

Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

show abstract

Perceived acceptability of wearable devices for the treatment of mental health problems

Cited by 32 publications

References 54 publications

Rapid telepsychology deployment during the COVID‐19 pandemic: A special issue commentary and lessons from primary care psychology training

Rapid telepsychology deployment during the COVID‐19 pandemic: A special issue commentary and lessons from primary care psychology training

mHealth Interventions for Self-Harm: Scoping Review

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

Contact Info

Product

Resources

About