Technological developments in e-mental health represent an exciting new frontier for psychologists. While these developments bring opportunities, they also pose numerous challenges. The rise of mental health technologies targeted directly at consumers is at risk of becoming a growing pipeline of alternative treatments with little or no supporting evidence. In recent years, this issue has been well examined in relation to mental health apps (Neary & Schueller, 2018). Alongside apps, though, we are now beginning to see wearable devices targeted at mental health outcomes. Most of these monitor specific physiological signals thought to interact with psychological phenomena (such as breathing or skin conductance). They typically interface wirelessly with a mobile app and feed information back to the user that may help them to make adaptive changes to their cognitions and behaviours.
In our recently published review in Clinical Psychology: Science and Practicewe aimed to identify wearable devices that could plausibly be used in the treatment of anxiety symptoms, examine any supporting evidence for these devices, and consider the implications for using them in clinical practice. We categorised 14 devices into five psychophysiological modalities through which they were assumed to work: electrodermal activity biofeedback, electroencephalographic neurofeedback, entrainment, heart rate variability (HRV) biofeedback, and respiration biofeedback. Where available, we examined recent systematic reviews for these modalities, as well as searching for literature evaluating specific devices.
Our findings suggested that support for all five device modalities was limited. Evidence for HRV biofeedback was more developed, but most studies identified by past systematic reviews of HRV were not of high quality. It was also not clear how much lab-based research into these modalities could tell us, since the sensitivity of lab-based measuring apparatus employed in highly controlled environments may not carry over into consumer-grade technology used in the real world. Research into specific devices was at an early stage, with small participant pools and experimental designs that had significant limitations to validity. One of the most critical issues that research to date has involved healthy adults or university students, a population which is qualitatively different from clinical populations encountered in practice. These limitations are consistent with pre-existing challenges in e-mental health evaluation (Mohr, Lyon, Lattie, Reddy, & Schueller, 2017).
The second part of our review considered the clinical implications of using wearable devices as therapeutic adjuncts. In the past, clinicians have generally been able to determine which interventions they introduce in clinical practice, and so it has been possible to select only treatments with adequate empirical support. However, since wearable devices are (like apps) often marketed directly to consumers, clinicians may increasingly encounter clients who have made their own decision adopt these devices. Clinicians may also feel a need to increase technology use in their practice for efficiency reasons, as well as a desire to be offering cutting edge treatments. With this in mind, the potential risks and unexpected effects that might be associated with wearable devices were considered. While little work has been done to monitor and evaluate these effects, possible concerns included somatic biofeedback-related side-effects, devices becoming anxiety-inducing themselves, devices being used as an avoidance strategy for unpleasant emotions, compromises to the therapeutic alliance, and the use of devices as a substitute for professional support. Devices could also compromise treatment in complex cases or where clients’ safety is at risk.
Since the range of wearable devices is continuing to evolve, making it difficult for clinicians to keep up, we considered whether there were any practical methods for evaluating the suitability of a device for a particular clinical scenario. Though we did not identify any evaluation frameworks developed specifically for wearable devices, the American Psychiatric Association app evaluation model (Torous et al., 2018) can be readily adapted for assessing wearables. The model involves five stages in which background information, risks, evidence, ease of use, and interoperability are considered. The advantage of this framework is its systematic approach to a variety of factors, creating an opportunity to collaboratively make decisions with clients on device use, and serving to inform consent.
Future wearable device technologies could play an important role in the treatment of anxiety. For now, clinicians should adopt a cautious but pragmatic approach, by evaluating the benefits and risks for each individual treatment situation. We hope to see further research in this emerging area to better inform the use of wearable devices in clinical practice.
- When a client wishes to use a wearable device with limited empirical evidence, is it more helpful to try to support the client’s preference while also promoting evidence-based treatments, or to discourage the use of these devices altogether? In what kinds of cases might benefits to the therapeutic alliance and client self-efficacy outweigh the risks?
- Is it reasonable for clinicians to be optimistic with clients about the effects of wearable devices, given limited evidence and the need for informed consent? How can this best be balanced, knowing that pessimism about a treatment may result in reduced adherence and poorer outcomes?
Hunkin, H., King, D. L., & Zajac, I. T. (2019). Wearable devices as adjuncts in the treatment and management of anxiety related symptoms: A narrative review of five device modalities and implications for clinical practice. Clinical Psychology: Science and Practice. https://doi.org/10.1111/cpsp.12290
Hugh Hunkin is currently undertaking a combined Doctor of Philosophy/Master of Psychology (Clinical) program at the University of Adelaide, in partnership with the Commonwealth Scientific and Industrial Research Organisation (CSIRO). His thesis relates to the feasibility and effectiveness of wearable devices in clinical practice, from both clinician and client perspectives.
Mohr, D. C., Lyon, A. R., Lattie, E. G., Reddy, M., & Schueller, S. M. (2017). Accelerating digital mental health research from early design and creation to successful implementation and sustainment. Journal of Medical Internet Research, 19(5), 1–14. https://doi.org/10.2196/jmir.7725
Neary, M., & Schueller, S. M. (2018). State of the Field of Mental Health Apps. Cognitive and Behavioral Practice, 25(4), 531–537. https://doi.org/10.1016/j.cbpra.2018.01.002
Torous, J. B., Chan, S. R., Gipson, S. Y.-M. T., Kim, J. W., Nguyen, T.-Q., Luo, J., & Wang, P. (2018). A hierarchical framework for evaluation and informed decision making regarding smartphone apps for clinical care. Psychiatric Services, 69(5), 498–500. https://doi.org/10.1176/appi.ps.201700423