Nicole Emma Andrews

Background: Nonpharmacological self-management interventions are often recommended for general practice patients with persistent pain—a distressing, costly, and heterogeneous condition with variable responses to treatment interventions. Single-case experimental designs (“self-experiments”) evaluate intervention effectiveness at the individual level. StudyU is an open-source digital platform designed to help patients undertake these self-experiments. Objective: This study aims to investigate the feasibility and acceptability (for patients, practitioners, and other practice staff) of integrating digitally enabled, withdrawal/reversal self-experiments in the general practice care of patients with persistent pain. Methods: We will recruit 50 patients from a large Australian general practice. Participants will trial a self-selected, self-management intervention (such as physical activity, mindfulness practice, or online self-guided cognitive behavioral therapy) approved by their general practitioner (GP) and use the StudyU app to rate the daily impact of their pain over the 10-week study period. The primary clinical outcome of the self-experiments is pain interference (measured using the modified Brief Pain Inventory), testing for its mean difference between usual routine and intervention conditions. Clinical reports will be generated for the patient and their GP. We will use validated measures of app usability and acceptance, pre-post measures of patient self-efficacy, quality of life, health service use, self-reported health; individual interviews informed by the Normalization Process Theory; and a nested process evaluation to examine the feasibility and acceptability for patients and practice staff of embedding these self-experiments in general practice care. Results: The research was funded in September 2023, funding and other agreements were completed by December 2024, patient recruitment commenced in January 2025, and publication of results is anticipated by January 2027. Conclusions: Digitally enabled self-experiments testing nonpharmacological treatment effectiveness may empower patients to self-manage persistent pain and adopt personally effective nonpharmacological interventions in partnership with their GPs and provide a model for integrating other new technologies into the general practice care of patients with other chronic conditions. Trial Registration: Australia and New Zealand Clinical Trials Registry ACTRN12624000459527; https://tinyurl.com/bdhabjd2 International Registered Report Identifier (IRRID): DERR1-10.2196/79810

Objectives: There is limited research exploring the age-related difference in communication when describing pain experiences. This project aimed to identify key differences between adolescents’, young adults’ and adults’ (i) preferred communication method, (ii) language content and (iii) lexical amount and variety when discussing their persistent pain experience using chatbot technology. Design: An exploratory concurrent nested mixed-methods design using a comparative analysis. Setting: Participants were recruited through a convenience sampling strategy from two tertiary multidisciplinary pain centres in Australia. Participants: 20 adolescents, 20 young adults and 20 adults completed a pain history assessment using the Dolores application. The inclusion criteria for this study were (i) persistent non-cancer pain for at least 3 months, (ii) 10 years of age and over and (iii) adequate expressive and receptive language skills to complete the required tasks in English, as determined by clinicians in the treating team. Results: Drawn, voice and typed responses provided by participants during the chatbot interaction were analysed using content analysis. Fisher’s exact tests and χ ² tests were used to examine differences between age groups. Strong effect size estimates generated from comparative analyses suggested that adolescents were the most likely age group to utilise drawing (p=0.153, Cramer’s V=0.268), preferred typing over speaking (p== 0.007, Cramer’s V=0.433). Young adults used the greatest number and variety of words in response to chatbot questions including evaluative language (p=0.097, Cramer’s V=0.296). Adults tended to use more metaphors (p = 0.085, Cramer’s V=0.305) and had a strong preference for speaking over typing (p=<0.001, Cramer’s V=0.470). Conclusions: The results of this study provide insights into age-related differences in communication and preferences when using technology to communicate about persistent pain. Future research exploring individualised age-related approaches to pain assessment, supported by the findings of this study, in comparison to current standardised assessments administered by clinicians are warranted.

Introduction: It is widely recognised that night shifts can cause fatigue, negatively impact cognitive performance and increase the risk of accidents. This quality improvement project aimed to better understand factors impacting on fatigue levels of medical imaging technologists who perform night shifts at metropolitan hospitals in Australia. Methods: All medical imaging technologists (i.e., radiographers and sonographers) employed at two Australian metropolitan hospitals who had performed night shifts within the last year were invited to complete a custom‐made electronic survey. Quantitative survey responses were examined using a combination of descriptive and comparative statistics. An applied thematic analysis was used to examine qualitative findings from open‐ended questions. Results: Medical imaging technologists perceive roster patterns with more consecutive night shifts and more rostered days off following night shifts to be less fatiguing and more sustainable. Fatiguing factors, including being awake at night and longer shift lengths, were perceived as significantly more fatiguing during the first 3 days of night shifts. The self‐reported time taken to return to a normal sleep routine following night shifts was a mean of 3.2 days. Workload was rated as the factor that caused the highest levels of fatigue throughout a night shift roster. The majority of participants expressed that organisation‐level changes are needed to reduce fatigue levels. Conclusion: Organisation level strategies to: (1) reduce workloads, and (2) enable rosters with more consecutive night shifts are needed to help medical imaging technologists effectively manage fatigue levels. The small sample size may limit the generalisability of findings.

BACKGROUND Nonpharmacological self-management interventions are often recommended for general practice patients with persistent pain—a distressing, costly, and heterogeneous condition with variable responses to treatment interventions. Single-case experimental designs (“self-experiments”) evaluate intervention effectiveness at the individual level. StudyU is an open-source digital platform designed to help patients undertake these self-experiments. OBJECTIVE This study aims to investigate the feasibility and acceptability (for patients, practitioners, and other practice staff) of integrating digitally enabled, withdrawal/reversal self-experiments in the general practice care of patients with persistent pain. METHODS We will recruit 50 patients from a large Australian general practice. Participants will trial a self-selected, self-management intervention (such as physical activity, mindfulness practice, or online self-guided cognitive behavioral therapy) approved by their general practitioner (GP) and use the StudyU app to rate the daily impact of their pain over the 10-week study period. The primary clinical outcome of the self-experiments is pain interference (measured using the modified Brief Pain Inventory), testing for its mean difference between usual routine and intervention conditions. Clinical reports will be generated for the patient and their GP. We will use validated measures of app usability and acceptance, pre-post measures of patient self-efficacy, quality of life, health service use, self-reported health; individual interviews informed by the Normalization Process Theory; and a nested process evaluation to examine the feasibility and acceptability for patients and practice staff of embedding these self-experiments in general practice care. RESULTS The research was funded in September 2023, funding and other agreements were completed by December 2024, patient recruitment commenced in January 2025, and publication of results is anticipated by January 2027. CONCLUSIONS Digitally enabled self-experiments testing nonpharmacological treatment effectiveness may empower patients to self-manage persistent pain and adopt personally effective nonpharmacological interventions in partnership with their GPs and provide a model for integrating other new technologies into the general practice care of patients with other chronic conditions. CLINICALTRIAL Australia and New Zealand Clinical Trials Registry ACTRN12624000459527; https://tinyurl.com/bdhabjd2 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/79810

There are numerous behavioural, social and environmental factors that influence the symptomatology of a chronic health condition. These factors and how they manifest are often very specific to the individual, which creates challenges for applying macro population health approaches and insights to guide treatment. An artificial intelligence system, referred to as a non-axiomatic reasoning system (NARS), is presented. Learning in NARS is incremental and ongoing. A practical application of NARS in chronic pain management is demonstrated, as NARS can establish associations with behavioural activities that might exacerbate pain levels and revise the strengths of these associations over time. The system has potential application in any condition requiring patient-centric adaption.

An innovative chatbot incorporates a drawing tool allowing users to draw pictures that symbolise the nature of their chronic pain. Rather than simply ask ‘What did you draw?” what if the chatbot could engage the user to provide insight into symbols and images related to pain and suffering? The system was tested on images that included features of clinical interest in a first step toward a chatbot able to ‘see’ and discuss visual input. Such a chatbot has the potential for retrospective and prospective applications. *Contains childhood sexual abuse references*

Background: Musculoskeletal injuries are common after road traffic crash (RTC) and can lead to poor work-related outcomes. This review evaluated the impact of interventions on work-related (e.g. sick leave), health, and functional outcomes in individuals with a RTC-related musculoskeletal injury, and explored what factors were associated with work-related outcomes. Methods: Searches of seven databases were conducted up until 9/03/2023. Eligible interventions included adults with RTC-related musculoskeletal injuries, a comparison group, and a work-related outcome, and were in English. Meta-analyses were conducted using RevMan and meta-regressions in Stata. Results: Studies (n = 27) were predominantly conducted in countries with third-party liability schemes (n = 26), by physiotherapists (n = 17), and in participants with whiplash injuries (94%). Pooled effects in favour of the intervention group were seen overall (SMD = − 0.14, 95% CI: − 0.29, 0.00), for time to return to work (− 17.84 days, 95% CI: − 24.94, − 10.74), likelihood of returning to full duties vs. partial duties (RR = 1.17, 95% CI: 1.01, 1.36), decreased pain intensity (− 6.17 units, 95% CI: − 11.96, − 0.39, 100-point scale), and neck disability (− 1.77 units, 95% CI: − 3.24, − 0.30, 50-point scale). Discussion: Interventions after RTC can reduce time to return to work and increase the likelihood of returning to normal duties, but the results for these outcomes were based on a small number of studies with low-quality evidence. Further research is needed to evaluate a broader range of interventions, musculoskeletal injury types, and to include better quality work-related outcomes.

Background: The 6-minute walk test (6MWT) is a commonly used method to assess the exercise capacity of people with many health conditions, including persistent pain. However, it is conventionally performed with in-person supervision in a hospital or clinic, therefore requiring staff resources. It may also be difficult when in-person supervision is unavailable, such as during the COVID-19 pandemic, or when the person is geographically remote. A potential solution to these issues could be to use GPS to measure walking distance. Objective: The primary aim of this study was to assess the validity of a GPS-based smartphone app to measure walking distance as an alternative to the conventional 6MWT in a population with persistent pain. The secondary aim of this study was to estimate the difference between the pain evoked by the 2 test methods. Methods: People with persistent pain (N=36) were recruited to complete a conventional 6MWT on a 30-m shuttle track and a 6MWT assessed by a smartphone app using GPS, performed on outdoor walking circuits. Tests were performed in random order, separated by a 15-minute rest. The 95% limits of agreement were calculated using the Bland-Altman method, with a specified maximum allowable difference of 100 m. Pain was assessed using an 11-point numerical rating scale before and after each walk test. Results: The mean 6-minute walk distance measured by the GPS-based smartphone app was 13.2 (SD 46; 95% CI −2.7 to 29.1) m higher than that assessed in the conventional manner. The 95% limits of agreement were 103.9 (95% CI 87.4-134.1) m and −77.6 (95% CI −107.7 to −61) m, which exceeded the maximum allowable difference. Pain increased in the conventional walk test by 1.1 (SD 1.0) points, whereas pain increased in the app test by 0.8 (SD 1.4) points. Conclusions: In individuals with persistent pain, the 2 methods of assessing the 6MWT may not be interchangeable due to limited validity. Potential reasons for the differences between the 2 methods might be attributed to the variation in track layout (shuttle track vs continuous circuit); poor GPS accuracy; deviations from the 30-m shuttle track; human variability in walking speed; and the potential impact of a first test on the second test due to fatigue, pain provocation, or a learning effect. Future research is needed to improve the accuracy of the GPS-based approach. Despite its limitations, the GPS-based 6MWT may still have value as a tool for remote monitoring that could allow individuals with persistent pain to self-administer frequent assessments of their functional capacity in their home environment.

Background: The delivery of education on pain neuroscience and the evidence for different treatment approaches has become a key component of contemporary persistent pain management. Chatbots, or more formally conversation agents, are increasingly being used in health care settings due to their versatility in providing interactive and individualized approaches to both capture and deliver information. Research focused on the acceptability of diverse chatbot formats can assist in developing a better understanding of the educational needs of target populations. Objective: This study aims to detail the development and initial pilot testing of a multimodality pain education chatbot (Dolores) that can be used across different age groups and investigate whether acceptability and feedback were comparable across age groups following pilot testing. Methods: Following an initial design phase involving software engineers (n=2) and expert clinicians (n=6), a total of 60 individuals with chronic pain who attended an outpatient clinic at 1 of 2 pain centers in Australia were recruited for pilot testing. The 60 individuals consisted of 20 (33%) adolescents (aged 10-18 years), 20 (33%) young adults (aged 19-35 years), and 20 (33%) adults (aged >35 years) with persistent pain. Participants spent 20 to 30 minutes completing interactive chatbot activities that enabled the Dolores app to gather a pain history and provide education about pain and pain treatments. After the chatbot activities, participants completed a custom-made feedback questionnaire measuring the acceptability constructs pertaining to health education chatbots. To determine the effect of age group on the acceptability ratings and feedback provided, a series of binomial logistic regression models and cumulative odds ordinal logistic regression models with proportional odds were generated. Results: Overall, acceptability was high for the following constructs: engagement, perceived value, usability, accuracy, responsiveness, adoption intention, esthetics, and overall quality. The effect of age group on all acceptability ratings was small and not statistically significant. An analysis of open-ended question responses revealed that major frustrations with the app were related to Dolores’ speech, which was explored further through a comparative analysis. With respect to providing negative feedback about Dolores’ speech, a logistic regression model showed that the effect of age group was statistically significant (χ22=11.7; P=.003) and explained 27.1% of the variance (Nagelkerke R2). Adults and young adults were less likely to comment on Dolores’ speech compared with adolescent participants (odds ratio 0.20, 95% CI 0.05-0.84 and odds ratio 0.05, 95% CI 0.01-0.43, respectively). Comments were related to both speech rate (too slow) and quality (unpleasant and robotic). Conclusions: This study provides support for the acceptability of pain history and education chatbots across different age groups. Chatbot acceptability for adolescent cohorts may be improved by enabling the self-selection of speech characteristics such as rate and personable tone.