Mapping the Design Space of Technology-Based Solutions for Better Chronic Pain Care: Introducing the Pain Tech Landscape. Review uri icon

Overview

abstract

  • OBJECTIVES: Technology has substantial potential to transform and extend care for persons with chronic pain, a burdensome and costly condition. To catalyze the development of impactful applications of technology in this space, we developed the Pain Tech Landscape (PTL) model, which integrates pain care needs with characteristics of technological solutions. METHODS: Our interdisciplinary group representing experts in pain and human factors research developed PTL through iterative discussions. To demonstrate one potential use of the model, we apply data generated from a narrative review of selected pain and technology journals (2000-2020) in the form of heat map overlays, to reveal where pain tech research attention has focused to date. RESULTS: The PTL comprises three two-dimensional planes, with pain care needs on each x axis (measurement to management) and technology applications on the y axes according to a) user agency (user- to system-driven), b) usage time frame (temporary to lifelong), and c) collaboration (single-user to collaborative). Heat maps show that existing applications reside primarily in the "user-driven/management" quadrant (e.g., self-care apps). Examples of less developed areas include artificial intelligence and Internet of Things (i.e., Internet-linked household objects), and collaborative/social tools for pain management. CONCLUSIONS: Collaborative development between the pain and tech fields in early developmental stages using the PTL as a common language could yield impactful solutions for chronic pain management. The PTL could also be used to track developments in the field over time. We encourage periodic reassessment and refinement of the PTL model, which can also be adapted to other chronic conditions.

publication date

  • April 3, 2023

Research

keywords

  • Chronic Pain

Identity

PubMed Central ID

  • PMC10523878

Scopus Document Identifier

  • 85170295072

Digital Object Identifier (DOI)

  • 10.1097/PSY.0000000000001200

PubMed ID

  • 37010232

Additional Document Info

volume

  • 85

issue

  • 7