Pain is an unpleasant sensation which we all experience throughout the course of life, but it serves the useful evolutionary purpose of alerting us to danger and allowing us to protect our bodies. However, chronic pain does not serve this purpose and can persist in the absence of injury or tissue damage. Chronic pain is an umbrella term for any pain that persists for longer than 3 months, and includes a wide range of conditions such as osteo and rheumatoid arthritis, fibromyalgia, complex regional pain syndrome (CRPS), headaches and migraines, and chronic back pain [1]. In the UK 62% of people over 75 live with some form of chronic pain, and in at least 10% this pain is moderately to severely disabling [2]. Worldwide, long-term pain effects over 30% of the adult population [3], however despite this prevalence, little is understood about its neurological mechanisms. Due to increased awareness of the drawbacks of opioid use, recent NICE guidelines for treating chronic pain emphasise building a collaborative treatment plan between patients and clinicians [4]. This project aims to use emerging technologies from robotics, sensing, and machine learning to aid in the detection and management of chronic pain.

Chronic pain can be difficult to detect and diagnose, as pain levels fluctuate for an individual daily, so sensors in the home may be suitable for monitoring the effects of pain over a period much longer than a clinical consultation. This could also help with detection in changing levels of health or mobility over time [5]. However, if we are to place sensors within the home, we need to understand how they can collect data which is sufficient to inform the user and clinician about health status, whilst still being acceptable to that end user [6].

If pain can be sensed and understood, there may be the opportunity to develop assistive technology that can then respond appropriately and be integrated with pain management plans. Assistive robots may be able to aid in pain management by providing distraction (through pain gating) [7] or using soothing touch, which release oxytocin to reduce pain [8]. Robotic pain management interventions using these mechanisms have shown promise, but little research has been conducted to understand the desires of older adults experiencing pain, despite this being the largest pain demographic [9].

Therefore, alongside the industry partner, ExtraCare Charitable trust, this project will include a series of co-design activities alongside older adults living with pain at ExtraCare retirement villages. Using surveys, interviews, focus groups, and other design workshops, we hope to understand the needs of this demographic to develop assistive technologies for pain management, with a particular focus on acceptability, usability and accessibility.

References

[1] J. Hawthorn and K. Redmond, Pain : Causes and Management. Malden, Mass: John Wiley and Sons, Inc, 1998. [Online].

[2] A. Fayaz, P. Croft, R. M. Langford, L. J. Donaldson, and G. T. Jones, ‘Prevalence of chronic pain in the UK: A systematic review and meta-analysis of population studies’, BMJ Open, vol. 6, no. 6, 2016, doi: 10.1136/bmjopen-2015-010364.

[3] S. P. Cohen, L. Vase, and W. M. Hooten, ‘Chronic pain: an update on burden, best practices, and new advances’, The Lancet, vol. 397, no. 10289, pp. 2082–2097, May 2021, doi: 10.1016/S0140-6736(21)00393-7.

[4] S. Carville, M. Constanti, N. Kosky, C. Stannard, and C. Wilkinson, ‘Chronic pain (primary and secondary) in over 16s: summary of NICE guidance’, bmj, vol. 373, 2021.

[5] D. Naranjo-Hernández, J. Reina-Tosina, and L. M. Roa, ‘Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review’, Sensors, vol. 20, no. 2, p. 365, Jan. 2020, doi: 10.3390/s20020365.

[6] H. R. Boeije and H. J. M. Vrijhoef, ‘Factors influencing acceptance of technology for aging in place : A systematic review’, Int. J. Med. Inf., vol. 83, no. 4, pp. 235–248, 2014, doi: 10.1016/j.ijmedinf.2014.01.004.

[7] M. H. Johnson, ‘How does distraction work in the management of pain?’, Curr. Pain Headache Rep., vol. 9, no. 2, pp. 90–95, 2005.

[8] M. V. Mohr, C. Krahe, B. Beck, and A. Fotopoulou, ‘The social buffering of pain by affective touch: A laser-evoked potential study in romantic couples’, Soc. Cogn. Affect. Neurosci., vol. 13, no. 11, pp. 1121–1130, 2018, doi: 10.1093/scan/nsy085.

[9] A. Higgins, A. Llewellyn, E. Dures, and P. Caleb-Solly, ‘Robotics Technology for Pain Treatment and Management: A Review’, in Social Robotics: 14th International Conference, ICSR 2022, Florence, Italy, December 13–16, 2022, Proceedings, Part I, 2023, pp. 534–545.