John Bradley Arnold

Purpose : This study sought to determine the associations between global positioning system-derived match demands and technical performance in elite Australian Football League Women’s athletes. Methods : Global positioning system match demands (total distance, speed, acceleration, high-intensity running [>19 km·h ⁻¹ ], very high-intensity running [>21 km·h ⁻¹ ], sprint distances [>24 km·h ⁻¹ ]) were measured with Catapult Vector S7 global positioning system units during the 2023 and 2024 seasons. Champion Data ranking points and best and fairest votes for each game were considered technical performance outcomes. Linear mixed effects regression models examined associations between match demands and technical performance, adjusting for season, ground location, and playing position. Results : Five hundred sixty-six player match profiles from 32 athletes across the 2 seasons were analyzed. Ranking points were significantly ( P < .001) associated with total distance ( R ² = .29 [95% CI .22–.40]), speed ( R ² = .01 [.00–.15]), and acceleration ( R ² = .09 [.00–.23]). Best and fairest votes were significantly ( P < .001) associated with total distance ( R ² = .23 [.17–.34]), speed ( R ² = .01 [.00–.11]), acceleration ( R ² = .05 [.00–.17]), and very high-intensity running distance per minute ( R ² = .00 [.00–.13]). Conclusions : Total distance, speed, acceleration, and very high-intensity running distance per minute were associated with Champion Data ranking points and best and fairest votes across Australian Football League Women’s seasons 2023 and 2024. This suggests that athletes who cover greater total distances and achieve higher average acceleration get more opportunities to be involved in match-play and demonstrate higher levels of technical performance.

Purpose: To investigate the changes in match demands of Australian Football League Women’s (AFLW) athletes over the past 3 seasons (2022–2023). It was hypothesized that match demands would increase across all positional groups. Methods: Forty-two athletes from 1 AFLW club who played at least 1 match across seasons 6 to 8 (2022–2023) were included. Match-day running demands including total distance (TD), speed, acceleration, high-intensity running (HIR; >19 km/h), very HIR (VHIR; >21 km/h), and sprint distances (>24 km/h) were exported from Catapult Vector S7 global positioning system units. Raw speed (in meters per minute) and acceleration (in meters per second squared) files were exported to calculate 1-minute rolling peak periods. Linear mixed-effects regression models were conducted with effect sizes (d) to examine changes in match demands over time for the cohort and playing-position groups. Results: TD increased by 344 m (d = 0.33) between seasons 6 and 7 and 971 m (d = 0.72) between seasons 7 and 8 (P < .001). HIR, VHIR, rolling 1-minute peak speed, and acceleration increased between seasons 6 and 7 by 70 m (d = 0.38), 36 m (d = 0.31), 5.1 m/min (d = 0.15), and 0.04 m/s² (d = 0.46), respectively (P < .001), while no changes were seen between seasons 7 and 8. Total player load increased by 28 AU (d = 0.29) between seasons 6 and 7 and 80 AU (d = 0.54) between seasons 7 and 8 (P < .001). Conclusions: TD increased across seasons 6 to 8, whereas HIR, VHIR, rolling 1-minute peak speed, and acceleration only increased between seasons 6 and 7. While changes in TD may encompass factors such as rule changes, relative metrics including HIR and VHIR changed in this cohort. These findings provide a comprehensive evaluation of current AFLW match demands and will assist in future AFLW athlete preparation.

Private practice physiotherapists are critically positioned to integrate pain science education (PSE) into patient care but face challenges integrating PSE into real-world care. To address this, a co-design approach was used to develop resources/strategies to improve PSE integration into routine practice. Co-design promotes meaningful end-user involvement, ensuring that outputs align with real-world needs. A multistage iterative co-design methodology, guided by the Double Diamond design process, involved people with chronic pain, private practice physiotherapists, and researchers as equal partners. Three workshop rounds used participatory strategies to promote collaboration and reduce perceived hierarchies. Workshop 1 (Brainstorm) explored opportunities to integrate PSE, workshop 2 (Prototype) involved rapid prototyping, and workshop 3 (Refine) refined the prototypes. Both coresearcher-led and researcher-led analysis were iteratively fed into subsequent workshops. Twenty coresearchers (n = 9 patients with chronic pain, n = 6 physiotherapists without chronic pain, n = 5 physiotherapists with chronic pain; 75% female; mean age 47.6 [SD 13.5] years) and 3 researchers participated. Workshop 1 identified 4 key timepoints and associated formats for PSE integration. Prototyping in workshop 2 led to consensus on infographic sheets and personalised action plans. Refinement in workshop 3 highlighted the importance of affordability, accessibility, individualisation, and pacing. The final resource, “PainPLaN: Personalised Learning and Navigation to pain education resources,” includes a personalised action plan builder linked to infographic sheets for collaborative use by physiotherapists and patients. Co-design ensured authentic end-user involvement at all design/development stages, providing a strong foundation for future resource testing and PSE implementation in clinical care.

Physiotherapists are critically positioned to integrate education into patient care, including pain science education (PSE) to enhance management and outcomes. Anecdotally, many physiotherapists report difficulty providing PSE in private practice settings. Here, we aimed to explore current PSE use, knowledge, and barriers to implementation. A nationwide online (Qualtrics) survey of PSE-trained Australian private practice physiotherapists assessed use, knowledge (revised Neurophysiology of Pain Questionnaire [rNPQ]), concept application, implementation barriers, and resource awareness/use/preferences, exploring associations with individual/clinic-level factors (clinical experience, PSE training, work location). A total of 278 physiotherapists (62.9% female, 39.4 [11.8] years, 15.2 [11.6] years of experience, 37.2% rural/remote) completed the survey. Pain science knowledge (rNPQ: mean 10.4 [2.2]/13) and perceived PSE competence was high, although 30% supported inaccurate PSE concepts. Pain science education training via professional development course was associated with higher knowledge relative to university training (multivariable; β = 1.337, P < 0.001). Physiotherapists reported providing PSE to 61% of patients, with patient-related barriers (expecting other treatments: 94%; previous negative/contradictory PSE experiences: 89%), clinic-level barriers (time constraints: 77%; insufficient billing schedules: 57%), and clinician-specific barriers (difficulties identifying/addressing patient maladaptive beliefs/behaviours: 53%; cultural/demographic translation challenges: 46%) reported. Fewer years of clinical experience was associated with heightened worry that providing PSE might go wrong (multivariable; β = −0.034, P = 0.010) and/or damage therapeutic relationships (multivariable; β = −0.049, P < 0.001). Physiotherapists were aware of over 100 PSE resources, with varying levels of perceived use/effectiveness, yet were largely unaware of educational strategies. Physiotherapists called for reduced complexity and greater ability to individualise PSE resources. Findings will guide improvements in PSE training/resources, to maximise physiotherapists' confidence and preparedness to effectively implement PSE.

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = −1.44 ± 0.90; p = 0.004), recovery (SMD = −1.83 ± 0.82; p less than 0.001), performance (SMD = −0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r² = 0.55) and recovery (r² = 0.55) and increased stress (r² = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training.

Background: Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings. Objective: This study aimed to develop a consensus-based framework for the optimal use of WATs in health care. Methods: A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants’ opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist’s clarity, utility, and appropriateness. Results: Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients’ data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4. Conclusions: This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector.

This systematic review and meta-analysis critically examined the evidence for peer support interventions to reduce pain and improve health outcomes in community-dwelling adults with chronic musculoskeletal pain (PROSPERO CRD42022356850). A systematic search (inception—January 2023) of electronic databases and grey literature was undertaken to identify relevant randomised controlled trials, with risk of bias and GRADE assessments performed on included studies. Meta-analyses used a generic, inverse-variance, random-effects model, calculating mean difference (MD) or standardised mean difference (SMD). Of 16,445 records identified, 29 records reporting on 24 studies (n = 6202 participants) were included. All evidence had unclear/high risk of bias and low-very low certainty. Peer support interventions resulted in small improvements in pain (medium-term: MD −3.48, 95% CI −6.61, −0.35; long-term: MD −1.97, 95% CI −3.53, −0.42), self-efficacy (medium-term: SMD 0.26, 95% CI 0.16, 0.36; long-term: SMD 0.21, 95% CI 0.07, 0.36), and function (long-term: SMD −0.10, 95% CI −0.19, −0.00) relative to usual care and greater self-efficacy (medium-term: SMD 0.36, 95% CI 0.20, 0.51) relative to waitlist control. Peer support interventions resulted in similar improvement as active (health professional led) interventions bar long-term self-efficacy (MD −0.41, 95% CI −0.77, −0.05), which favoured active interventions. No point estimates reached minimal clinically important difference thresholds. Pooled health service utilisation outcomes showed unclear estimates. Self-management, quality of life, and social support outcomes had mixed evidence. Despite low-very low evidence certainty, peer support interventions demonstrated small improvements over usual care and waitlist controls for some clinical outcomes, suggesting that peer support may be useful as an adjunct to other treatments for musculoskeletal pain.

BACKGROUND Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings. OBJECTIVE This study aimed to develop a consensus-based framework for the optimal use of WATs in health care. METHODS A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants’ opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist’s clarity, utility, and appropriateness. RESULTS Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients’ data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4. CONCLUSIONS This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector. CLINICALTRIAL