The Use of Sensors to Detect Anxiety for In-the-Moment Intervention: Scoping Review

Tackling anxiety through innovation: Development and pilot study of an in-the-moment messaging intervention for young adults

Objective

Anxiety disorders are highly prevalent around the world and are associated with significant disability, comorbidity and costs to society. Digital tools have the potential to deliver equitable, convenient and sustainable interventions for anxiety. We aimed to co-design and pilot-test an in-the-moment intervention to help young people manage anxiety. Young people with lived experience of anxiety were recruited to participate in co-design workshops with the results informing the development of the Tackling Anxiety through Innovation (TAI) intervention. TAI delivers personally tailored messaging prompting engagement in self-care behaviours at times of high anxiety/stress.

Methods

Young people aged 16 to 24 years were recruited into a four-week pilot study. Participants completed the Depression Anxiety Stress Scale - 21 Items (DASS-21) at baseline and four-week follow up, as well as a survey to assess the acceptability of the intervention and its components.

Results

Participants (n = 24) reported the intervention to be highly acceptable and useful. Participants scheduled an average of eight events over the 4 weeks. DASS-21 scores improved significantly from baseline to follow up: anxiety (mean difference 2.42 (SD = 3.65), p = 0.004), depression (3.79 (SD = 3.65), p < 0.001) and stress (3.50 (SD = 3.30), p < 0.001). Participants particularly liked the ability to personalise the messages, the timing and format of the messages, and the reminders to engage in helpful behaviours.

Conclusions

This study adds to the evidence for the use of digital tools in delivering tailored anxiety self-management support to young people. Further refinement and expansion of the intervention are needed to determine its efficacy on a larger scale.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Use of Consumer Wearables in Health Research: Issues and Considerations

As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.

Asynchronous Technologies in Mental Health Care and Education

Purpose of review

Patients, providers, and trainees should understand the current types of asynchronous technologies that can be used to enhance the delivery and accessibility of mental health care. Asynchronous telepsychiatry (ATP) removes the need for real time communication between the clinician and patient, which improves efficiency and enables quality specialty care. ATP can be applied as distinct consultative and supervisory models in clinician-to-clinician, clinician-to-patient, and patient-to-mobile health settings.

Recent findings

This review is based on research literature and the authors' clinical and medical training, using experiences with asynchronous telepsychiatry from before, during, and after the COVID-19 pandemic. Our studies demonstrate that ATP provides positive outcomes in the clinician-to-patient model with demonstrated feasibility, outcomes and patient satisfaction. One author's medical education experience in the Philippines during COVID-19 highlights the potential to utilize asynchronous technology in areas with limitations to online learning. We emphasize the need to teach media skills literacy around mental health to students, coaches, therapists, and clinicians when advocating for mental well-being. Several studies have demonstrated the feasibility of incorporating asynchronous e-tools such as self-guided multimedia and artificial intelligence for data collection at the clinician-to-clinician and patient-to-mobile health level. In addition, we offer fresh perspectives on recent trends in asynchronous telehealth in wellness, applying concepts such as "tele-exercise" and "tele-yoga."

Summary

Asynchronous technologies continue to be integrated into mental health care services and research. Future research must ensure that the design and the usability of this technology puts the patient and provider first.