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Pediatric chronic pain places a significant burden on children, their families, and healthcare services. Effective pain measurement is needed for both clinical management and research. Digital pain measurement tools have been developed for adult and adolescent populations however less is known about measurement in younger children. In this systematic review, we aimed to identify, describe, and evaluate (in terms of acceptability) digital tools for the assessment of chronic pain in children (5–11 years). We searched five databases (Cochrane Library, EMBASE, MEDLINE, PsycINFO, and CINAHL), between January 2014 and January 2022. We included empirical studies which included digital tool/s to assess pain in children aged between 5–11 years with chronic pain conditions. We independently double‐screened the papers to determine eligibility. We followed PRISMA guidelines for reporting. A total of five papers, covering four digital tools, were included. The digital tools used ranged from a static online survey to a highly interactive, personalized tablet application. Two studies were cross‐sectional and two collected longitudinal pain data via electronic devices outside the clinical setting. Digital features of the tools included: dynamic testing (n = 2), notifications/prompts (n = 1), data transmission (n = 1), remote monitoring (n = 1), accessibility (n = 1), data visualization/feedback (n = 1), personalization/customization (n = 1), gamification (n = 1) and data labeling (n = 1). Qualitative usability data was only available for one of the tools, which indicated its acceptability and highlighted preferred features/functions by child users (creative and personalizable features, gamification features), and parental users (symptom tracking). This review has highlighted the limited number of digital assessment tools available for children with chronic pain aged 5–11. This review identified some examples of technology enabling the capture of longitudinal, repeated measurement of multiple dimensions of pain (intensity, location, quality). We suggest directions for future research. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies., Objective: The ATMOSPHERE study aimed to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualized seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modeling. The first aim was to collaboratively design the prototype (work completed). The second aim is to conduct an "in-the-wild" study to assess usability and refine the prototype (planned research)., Methods: This study uses a person-based approach to design and test the usability of a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and health care professionals. Sessions explored users' requirements for the prototype, followed by iterative design of low-fidelity, static prototypes. Phase 2 (planned research) will be an "in-the-wild" usability study involving the deployment of a mid-fidelity, functional prototype for 4 weeks, with the collection of mixed methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in 3 rounds of deployment and data collection, aiming to recruit 5 participants per round, with prototype refinement between rounds., Results: The phase-1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, functional prototype based on identified requirements, including the tracking of evidence-based and personalized seizure precipitants. The upcoming phase-2 usability study is expected to provide insights into the prototype's real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of phase 2 is the last quarter of 2024., Conclusions: The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centered, noninvasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalized machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life, through increased predictability and seizure management., International Registered Report Identifier (irrid): DERR1-10.2196/60129., (©Emily E V Quilter, Samuel Downes, Mairi Therese Deighan, Liz Stuart, Rosie Charles, Phil Tittensor, Leandro Junges, Peter Kissack, Yasser Qureshi, Aravind Kumar Kamaraj, Amberly Brigden. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 19.09.2024.)