Objectives To investigate if manual workers with and without a history of LBP show different trunk muscle activity during repeated lifting? And explore if pain or fatigue is related to trunk muscle activity in people with LBP during repeated lifting? Methods This study compared peak and average muscle activity between manual workers with LBP (n=21) and without a history of LBP (n=16) during lifting. A box of 10% of the participant’s body mass was lifted from the floor at a self-selected speed and posture. A 12-sensor trunk muscle surface electromyography (sEMG) protocol was used to compare peak and average muscle activity over 100 lifts. This lifting task induced pain and fatigue; therefore, associations between trunk muscle activity, pain and fatigue in manual workers with LBP could be explored. Peak and average muscle activity over a 100-lift task were used. Results There were no differences in trunk muscle activity between groups. Associations between muscle activity and pain or muscle activity and fatigue in manual workers with LBP were rare (4 of 96 tests of association were statistically significant). Peak abdominal muscle activation was consistently low at 12 to 31% of maximal voluntary contraction (MVC) throughout the lifting task in both groups, whereas peak paraspinal muscle activation was around 33 to 59% of MVC. Conclusion At a group level, trunk muscle activity during lifting does not differ between people with and without a history of LBP. In this study, neither repetition, pain, nor fatigue had a consistent effect on trunk muscle activity during lifting. These sEMG results question the importance of trunk muscle activity during lifting in people with lifting-related LBP with low levels of pain and disability.

Aim: This study compared posture and movement classification accuracy and agreement of two thigh-worn accelerometers (SENS and Axivity) among children aged 3-14 years using the same classification algorithm. Method: Forty-eight children attended a structured 1-hr laboratory session, of which 46 provided valid data. The session was video recorded and accelerometer data were collected from accelerometers worn on the front of the thigh. Human-coded video provided the reference standard to calculate accuracy of ActiMotus algorithm posture and movement classifications. Agreement between devices was compared using multiple metrics. Accelerometer variables (acceleration variation and sensor orientation metrics) used for classification were also compared between devices. Results: The devices showed comparable classification accuracy (F1-score = 66%-68%; balanced accuracy = 82%-83%) with a trend for SENS to have slightly higher accuracy, but the only significant difference was found for running where SENS had lower metrics (F1-score = 55%; balanced accuracy = 77%) than Axivity (F1-score = 56%; balanced accuracy = 81%). Agreement in posture and movement duration estimates was highest for lying, sitting, and standing (85%-88%), but lower for dynamic movements, particularly running (65%) and stair climbing (53%). When comparing the accelerometer variables, the greatest relative difference was observed for the SD of x-axis for running (mean absolute percent error = 18.4%, SD = 8.9). Conclusions: We observed differences between SENS and Axivity for posture and movement classification accuracy and agreement which may be linked to differences in hardware features and thereby derived accelerometer variables. The results suggest that hardware choice might influence estimates of postures and movements among children even when using the same placement and algorithm.

Background: Reduced knee confidence is common in people with knee osteoarthritis and may influence how they engage with physical activity. A lack of confidence may lead an individual to adopt compensatory movement strategies, such as reduced vertical knee loading or movement in the sagittal plane, which may contribute to disability. As such, reduced confidence may be an important treatment target for clinicians working to improve function in people with knee osteoarthritis. Research question: Is there an association between knee confidence and vertical knee loading or sagittal-plane knee movement during tasks of daily living, in individuals with knee osteoarthritis? Methods: 51 people with knee osteoarthritis participated in this cross-sectional investigation. Knee confidence was assessed using a single item from the Knee Injury and Osteoarthritis Outcome Score. Sagittal plane knee kinematics and moments, vertical knee force and vertical ground reaction force were output for walking, sit-to-stand, step-up and step-down. Force data were normalised to bodyweight, and knee moments to bodyweight and height. Linear regression was used to estimate the association between confidence and kinematic and kinetic peak measures, and statistical parametric mapping regression analysis was used to estimate the association between confidence and the time-series measures. Participants were also classified into three subgroups based on their walking knee flexion/extension moment (biphasic, flexor, extensor) and multinomial logistic regression was used to estimate the association between confidence and subgroup membership. All models were adjusted for knee pain, sex, and speed of movement. Results: Knee confidence was not associated with peak or time-series kinematic or kinetic measures during the four tasks. There was no association between confidence and knee moment subgroup. Significance: In people with knee osteoarthritis, vertical knee loading and sagittal-plane knee movement are not associated with knee confidence, as measured by a single item commonly used to assess this construct. The lack of association may be explained by the inadequate assessment of confidence, or the individual and task-dependent nature of compensatory movement strategies.

Background:Technology use by children has implications for their physical, mental, and social health and development. There are many challenges in measuring modern screen use by children. To better understand the potential impact of technology on children, it is crucial to have robust methods to measure technology use. Visual recording data may be particularly well suited to capture modern digital technology use (eg, rapid transitions, multitasking, and device switching). It would be valuable to directly compare the 2 commonly used methods (wearable camera images and room video frames) to determine what is captured best by each and how they compare when both are coded by human annotation. Objective:This study aimed to compare wearable camera images and room video frames to understand what can be captured from each source. Methods:In a laboratory study, children (aged 3-14 years) performed various tasks while wearing a wearable camera and being video recorded by room video cameras. Comparisons between the coding of the wearable camera images and room video frames were performed on a second-by-second basis. Confusion matrices were generated to identify patterns of differences in classifications between wearable camera images and room video frame coding. Percent agreement between the wearable camera image and room video frame coding was calculated based on the confusion matrix. Results:Across 44 participants, 182,451 contemporaneous wearable camera images and room video frames were coded and merged. There were generally low levels of agreement between the codes. The highest agreement was found for "desktop" and "handheld gaming," and the lowest agreement was found for "smartwatch." The wearable camera and room video methods varied in sampling rate, camera movement, and field of view, leading to differences in what was captured. Conclusions:Room video might better capture technology that is within a set space and the context of the technology use, while wearable cameras could better capture active technology use, the content of technology, and different locations. Capturing technology use by children is difficult, with no gold standard and no one ideal method.

Accurate measurements of children’s digital technology use is essential for understanding its potential implications on health and wellbeing. Wearable cameras can provide such measurements, but the image coding is a high researcher burden. Machine learning based object recognition models have the potential to reduce this burden by identifying images containing technologies. To evaluate the performance of an object recognition model, the Square Eyes model, as a screening tool for identifying technologies in wearable camera images among children for further human review. Additionally, to examine the potential influence of face-blurring methods on the model’s performance. This study used data collected on 48 children (3-14 years of age) during a ~1 hour laboratory session. The children performed various technology related tasks while wearing a camera. A total of 211,226 images were coded by humans and processed through the Square Eyes model. The performance of the Square Eyes model as a screening tool was evaluated by 1) assessing agreement between the model and human coding; 2) evaluating the N-back algorithm, an algorithm embedded in the model aimed to flag images requiring human review, and 3) examining the potential influence of facial-blurring on model performance. Humans detected technology in 92,745 (41.9%) images The Square Eyes model detected technologies with an overall accuracy of 78.0%. When considering specific technologies, agreement between the model and human coders was the highest for Television (54.3%) and Laptop (44.5%) and lowest for smaller devices such as Smartphone (31.3%) and Tablet (25.1%). The model’s N-back algorithm effectively flagged images that required further human review, with only 7,144 images (3.2% of all images) not flagged for screening containing a human-coded technology. An explorative analysis indicated that using a square face-blurring with border could have reduced the model’s ability to accurately detect technologies. The Square Eyes model demonstrated overall satisfying accuracy in detecting technologies and successfully flagged images that required further review by humans. These findings suggest that the model could be used as an effective screening tool for reducing the burden of human coding. However, the model could be improved for accurately detecting smaller devices and the form of facial blurring in images should be considered.

BACKGROUND:Chronic low back pain (CLBP) frequently involves lifting as a pain-provoking activity. Pain-related cognitions, such as pain-related fear, pain catastrophizing, and pain self-efficacy, are suggested to influence lifting behavior. However, the longitudinal relationship between changes in lifting kinematics and pain-related cognitions within individuals remains unexplored. OBJECTIVE:To investigate longitudinal within-person relationships between changes in lifting kinematics and changes in pain-related cognitions in people with lifting-related CLBP. METHODS:Five adults (2 females, aged 26-47 years) with lifting-related CLBP participated in a single-case design study. Weekly assessments were conducted during a 4-6-week baseline period, a 12-week Cognitive Functional Therapy intervention (up to 10 sessions), and a 3-month follow-up. Sagittal plane lifting kinematics of the spine and lower extremities were measured using wearable sensors, and pain-related cognitions (pain-related fear, pain catastrophizing, pain self-efficacy) were assessed via online questionnaires. Cross-correlation analyses examined within-person relationships between changes in lifting kinematics and changes in pain-related cognitions. RESULTS:Relationships between changes in lifting kinematics and pain-related cognitions were individual-specific. Changes in lifting techniques were frequently associated with reduced pain-related fear (15/25 relationships, 60%) and pain catastrophizing (16/25 relationships, 64%), and occasionally with improved pain self-efficacy (12/25 relationships, 48%). Where relationships were observed, reduced pain-related fear and pain catastrophizing, along with improved pain self-efficacy, were usually (39/43 relationships, 91%) associated with increased trunk flexion and velocity, decreased knee flexion and velocity, and faster lifting speed. CONCLUSIONS:For individuals with lifting-related CLBP, reduced pain-related fear and pain catastrophizing, along with improved pain self-efficacy, often occurred alongside a transition from squat-like to more semi-squat or stoop-like lifting techniques, accompanied by faster movement speed.

Aim: To evaluate the criterion validity of the SENS Motion Activity algorithm to classify postures and movements among children aged between 3 and 14 years in a laboratory setting by comparing with human-coded video. Method: Data were collected on 48 Australian children who attended a structured similar to 1 hr data collection session at a laboratory with their caregivers. The session was video recorded, and thigh acceleration was measured using a SENS accelerometer. Data from the accelerometer were processed and classified into four postures and movements using the SENS algorithm. Human-coded video provided the reference standard to calculate the performance metrics sensitivity, specificity, precision, F1-score, and balanced accuracy. Results: Overall, the SENS Motion Activity algorithm classified postures and movements with performance metrics F1-score of 73% and balanced accuracy of 87%. Lying/sitting had the highest F1-score and balanced accuracy (99%). Standing, walking, and running had somewhat lower performance metrics (F1-score and balanced accuracy of 69%-77%, 57%-92%, and 68%-82%, respectively). Difficulties with human coding likely contributed to lower performance. A higher balanced accuracy was found for boys compared with girls for running. The algorithm had better performance for children older than 11 years compared with younger children overall and for walking and running. Conclusion: Our results suggest that the SENS Motion Activity algorithm can provide accurate measurements for detecting lying/sitting, standing, walking, and running among children. Further research could usefully explore algorithm development for classifying movement in sitting and standing postures and brief sporadic movements, especially among children younger than 11 years.

Background : Children with cerebral palsy (CP) who are nonambulatory are particularly vulnerable to the negative health impacts of physical inactivity and sedentary behavior. Yet, there is a paucity of validated methods to measure movement behaviors. Purpose : To develop and test a simple decision tree (DT) model for classifying activity type—sit/lie, active sitting, standing utilitarian movements, stepping, and cycling using data from a single thigh-mounted accelerometer in nonambulant children with CP. Methods : Twenty-one children (mean age 9.4 ± 3.6 years) with CP completed a series of unstructured data acquisition sessions while wearing a SENS motion accelerometer on the least affected thigh. Triaxial accelerometer data (12.5 Hz) were segmented into 4-s windows and annotated with ground truth labels derived from a bespoke direct observation scheme. Four time-domain features served as inputs to a handcrafted DT model. Feature thresholds were identified through visual inspection of probability density plots and receiver operating characteristic (ROC) curve analyses in the training sample and evaluated for accuracy in the testing sample. Results : Within the testing sample, the DT exhibited substantial agreement with ground truth activity with F1 statistics exceeding 0.70. In comparison, the SENS proprietary model exhibited moderate agreement. When applied to data from an independent sample performing different activities, the DT demonstrated excellent recognition of sit/lie, stepping, and cycling. However, seated throwing and catching and manual wheelchair propulsion were predominantly misclassified as sedentary. Conclusion : This study provides a significant advance toward accurate, interpretable, and accessible device-based measurement of movement behaviors in children with CP who are nonambulatory.

Background:Only 10% of Australian children meet the recommended daily physical activity guidelines. Augmented reality (AR) is increasingly being used in primary education and clinical rehabilitation, with high enjoyment and motivation to participate frequently reported. AR has increased physical activity participation in adult populations, but whether AR can increase physical activity in young children has yet to be investigated. Objective:This study aimed to determine if an indoor AR-enhanced playground is enjoyed by young children and prompts physical activity in both low- and high-structured scavenger hunts. Methods:Seventeen pairs of 5- to 8-year-olds participated in 2 animal search tasks (ie, AR and non-AR) in 2 activity structure levels (ie, low-structured and high-structured) in a 2×2 repeated measures design in an indoor laboratory playground. Children searched for either AR animals (a custom AR app on a smartphone) or toy animals and followed a set obstacle course route (high-structured) or moved wherever they wished (low-structured). Questionnaires assessed child enjoyment, perceived physical activity, and caregiver perception of enjoyment. Thigh-worn accelerometers (SENS; SENS Innovation ApS) assessed postures and movements, and a video camera recorded engagement time. Results:Children rated AR conditions (low-structured: mean 4.4, SD 1.0, and high-structured: mean 4.5, SD 0.9) as more enjoyable than high-structured non-AR (mean 4.1, SD 1.0; P=.03). When asked which condition was the most enjoyable, 15 chose the low-structured AR, followed by the high-structured AR (n=11) and low-structured non-AR (n=8). Caregiver perception of children's enjoyment ratings generally aligned. Ratings of perceived physical activity level were the same in all conditions (mean 4.3, SD 0.7; P>.05). Accelerometry showed that a greater percentage of time was spent in low-intensity postures and movements during AR conditions (AR: mean 50%, SD 13% vs non-AR: mean 35%, SD 14%; P<.001), namely in sitting and standing, and in high-intensity movements during non-AR (AR: mean 21%, SD 12% vs non-AR: mean 32%, SD 18%; P<.001). During low-structured conditions, engagement time was significantly longer with the AR animals compared to the toy animals (AR: mean 263.1, SD 65.7 seconds vs non-AR: mean 197.3, SD 76.5 seconds; P=.002). Conclusions:While the intensity of physical activity was lower during AR, the greater enjoyment and longer engagement time may lead to greater overall accumulation of active play by motivating young children to go to and stay longer at playgrounds. The high-structured AR conditions resulted in higher-intensity physical activity compared to low-structured AR conditions; however, enjoyment ratings from children and caregivers were generally higher in the low-structured AR. Therefore, AR may be suitable to implement in both low- and high-structured play environments. Future research should investigate whether these findings hold true at outdoor playgrounds and examine the impact of novelty over time.

BACKGROUND:Body position and movement during sleep is assessed for both clinical and research purposes. A diverse array of both assessment tools and classification systems are used to capture and code sleep biomechanics data. OBJECTIVES:The aim of this scoping review was to identify the assessment tools and classification systems used to examine sleep biomechanics, and the strengths and limitations of current approaches. METHODS:MEDLINE, EMBASE and CINAHL databases were searched from inception to July 2024, from which 73 publications were selected that assessed body position distribution and/or repositioning rate, for at least one night of sleep. Qualitative content analysis was completed to extract strengths and limitations of current approaches. RESULTS:Nearly half (44 %) of studies assessed rate of repositioning; 26 % of studies assessed position distribution, and 30 % studies examined both rate of repositioning and position distribution. Common assessment tools were Wearable Accelerometry (26 %) and Polysomnography (22 %). The most frequent repositioning rate classification system was 90° trunk rotation (10 %) and the most frequent position distribution classification system was Supine/Left Side Lying/Right Side Lying/Prone (29 %). Strengths included richness of data set (wearable accelerometry), while limitations included cost and unfamiliar sleep environment (e.g. polysomnography). CONCLUSIONS:While different methods are needed to accommodate various research and clinical needs, a frequent challenge is the lack of detail in how sleep biomechanics are recorded and coded. Wearable sensors offer significant advantages in ease of implementation and granularity of data capture. These devices, in combination with a detailed sleep biomechanics coding system, show potential as future research tools to overcome this limitation.

BACKGROUND:Chronic low back pain (CLBP) is often provoked by lifting activities, but the relationship between changes in lifting technique and clinical outcomes while undergoing intervention remains unclear. This study examined the within-person relationships between changes in lifting technique and changes in pain and functional limitation in people with lifting-related CLBP. METHODS:Five participants with lifting-related CLBP completed repeated measures of their lifting techniques, pain and functional limitation across baseline (4-6 weeks), intervention (12 weeks) and follow-up periods (3 months). Participants received up to 10 sessions of Cognitive Functional Therapy (CFT) during the intervention period. Wearable sensors measured the spinal and lower extremities' range of motion (ROM) and velocity during a repeated lift task. Pain and functional limitation were assessed via online questionnaires. Within-person relationships were estimated using cross-correlation analyses. RESULTS:All participants demonstrated changes in lifting technique throughout the study, though with varied timing and direction. Changes in lifting technique were frequently related to changes in functional limitation (18/25 relationships, 72%) and sometimes to changes in pain (13/25 relationships, 52%). When relationships were observed, reductions in pain and functional limitation were predominantly (27/31 relationships, 87%) associated with a transition along a continuum from squat-like towards semi-squat-like and stoop-like lifting techniques with faster lifting movements. CONCLUSIONS:Within-person changes in lifting technique varied among individuals. Greater trunk ROM and velocity, lower knee ROM and velocity, and faster lifting movements often co-occurred with lower levels of pain and functional limitation. This reflects a transition along a continuum from squat-like towards semi-squat-like and stoop-like lifting techniques. SIGNIFICANCE STATEMENT:Our study reveals that changes in lifting technique may relate to clinical improvements in people with lifting-related CLBP on an individual basis. Contrary to conventional clinical and ergonomic advice promoting 'safe' squat lifting techniques, our findings suggest that transition from squat-like towards semi-squat-like and stoop-like lifting techniques with faster lifting movements often corresponds with reductions in pain and functional limitation. These findings challenge pervasive recommendations for squat lifting and support an individualised multi-dimensional approach to managing people with lifting-related CLBP.

AIM:Examine the feasibility of wearable sensors to measure sleep biomechanics in typically developing (TD) children and children with cerebral palsy (CP) and compare sleep biomechanics between each group. MATERIALS AND METHODS:Eleven children with CP (4 male, Gross Motor Function Classification System (GMFCS) I-II: n = 7, GMFCS IV-V: n = 4), and 19 TD children (11 male) aged 5-18 yrs wore sensors during sleep for five nights. Body rotation was coded using the Body Orientation During Sleep Framework to measure repositioning profiles. Feasibility (e.g. comfort, reliability) was assessed against endpoints, and mixed models compared sleep biomechanics across TD, CP-GMFCS I-II, and CP-GMFCS IV-V groups. RESULTS:Wearable sensors were well tolerated (>80 % satisfaction). There were no significant differences between TD and GMFCS I-II profiles. The GMFCS IV-V group demonstrated significantly (p < 0.001) fewer repositionings per hour than the TD group (TD: mean 2.72 ± 0.38, CP-GMFCS I-II: 2.7 ± 0.65, GMFCS IV-V: 0.46 ± 0.61). INTERPRETATION:Children with CP with higher gross motor impairment had reduced rates of repositioning in sleep. This has been proposed as contributing to the development of Body Shape Distortion; more research is needed. Sleep biomechanics recorded using wearable sensors could provide a low-cost approach to the long-term measurement and monitoring of repositioning during sleep.

OBJECTIVES:Lifting is a common self-reported aggravating factor associated with individuals experiencing low back pain (LBP). However, there is a paucity of longitudinal studies with multiple measures on lifting spinal kinematics, pain, disability and pain-related cognitions. This study investigated the relationship between changes in these factors in people with lifting-related chronic LBP undergoing cognitive functional therapy (CFT). METHODS:141 people with chronic LBP aggravated by lifting received a course of CFT over 13 weeks (average 4.8 sessions) and performed the same lifting task before each treatment session. Measures included range of motion (ROM) and velocity from trunk and pelvis inertial measurement units independently and the relative angle between units (lumbar angle). Participants reported (1) average pain intensity, (2) pain-related disability and (3) pain-related cognitions (pain catastrophising and pain self-efficacy) via online questionnaires at baseline, 3, 6 and 13 weeks. Multivariate multilevel models evaluated associations between individual rates of changes over time for three selected kinematic measures (trunk velocity, trunk ROM or lumbar ROM) with pain, disability, pain catastrophising and pain self-efficacy. RESULTS:Increased trunk velocity showed potentially large correlations with reduced disability (r=-0.60, 95% CI: -0.90, 0.09) and improved pain self-efficacy (r=0.64, 95% CI: -0.17, 0.93) over time, although there was statistical uncertainty for these estimates. CONCLUSIONS:In people with lifting-related LBP undergoing CFT, increased trunk velocity during lifting showed potentially large correlations with reductions in disability and improvements in pain self-efficacy. These findings highlight the potential clinical importance of trunk velocity as both a monitoring measure and treatment target.

BackgroundSmartwatch activity trackers are devices that measure physical activity levels with features that aim to encourage physically active behaviors. These devices have shown promise for increasing physical activity levels and reducing sedentary behaviors among school-aged children, adolescents, and adults. Recently, commercially available products have been adapted so that they are suitable for use by preschool-aged children. However, it is unclear whether the intended use of these devices is feasible and effective in young children. ObjectiveThe purpose of this study was to explore parents’ perspectives on the use of smartwatch activity trackers by young children. MethodsSemistructured interviews were conducted with 22 parents (17/22, 77% female) of children aged 3-5 years. Interviews explored perspectives on the feasibility of their children wearing the devices, implications of use by young children, and how families could make use of these devices to support their children’s physically active behaviors. Interviews were audio-recorded, transcribed verbatim, and data analyzed using thematic analysis. ResultsParents perceived that the use of these devices by young children is feasible, with developmental stage or abilities and personality or temperament being important individual determinants of feasibility. However, parents expressed concerns related to the devices providing extrinsic motivation to move, being disruptive or distracting, being a burden on parents, and for the safety and privacy of their child’s information. Most parents believed that young children are inherently active and do not need devices to support physical activity. Furthermore, most parents expressed an interest in knowing how physically active their children were and thought that there may be a role for these devices for children who are less physically active. ConclusionsParents reported developmental stage or abilities and temperament as relevant considerations related to the feasibility of smartwatch activity tracker use by young children. Parents also indicated that there is a potential role for these devices in young, less active children.

AIM:This review aimed to summarise the evidence related to the feasibility, efficacy for increasing physical activity, and implications of smartwatch activity tracker use in preschool-aged children. METHODS:In November 2023, we searched Medline, PsycINFO, CINAHL, Web of Science, ProQuest Central and SPORTDiscus. From an initial 7449 studies, we included 15 studies that explored the use of smartwatch activity trackers in preschool children aged three to 5 years. Data were extracted on feasibility, efficacy, and reported advantages and disadvantages of use. We used the preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS:Studies that reported adherence to wear indicate good feasibility. No studies evaluated the efficacy of activity trackers for influencing physical activity and sedentary behaviours. Potential limitations of activity tracker use by preschool children include discomfort and a loss of interest. CONCLUSION:Smartwatch activity trackers may be feasible for use by preschool children, but no studies have explored whether these devices can increase physical activity in this age group. Short-term use of these devices may encourage physical activity by promoting family discussions and increasing awareness of physical activity, but discomfort and a loss of interest over time may be barriers to long-term use by young children.

OBJECTIVE: To investigate whether improvements in forward bending were related to reductions in pain catastrophizing (PC) and improvements in pain self-efficacy (PSE) in people with chronic low back pain (CLBP) who were undergoing cognitive functional therapy (CFT). DESIGN: Longitudinal observational study. METHODS: Two hundred sixty-one participants with CLBP received CFT. Forward bending was assessed at each treatment session over 13 weeks (average of 4.3 time points per participant [range, 1-8]). Inertial measurement units placed on T12 and S2 measured spinal range of movement (ROM) and velocity. Participants completed the Pain Catastrophizing Scale and the Pain Self-Efficacy Questionnaire online at 0, 3, 6, and 13 weeks. Multivariate, multilevel models evaluated the associations between individual rates of change over time for 3 spinal movement measures (trunk velocity, trunk ROM, and lumbar ROM) as well as PC/PSE. RESULTS: Strong correlations were observed for increased trunk velocity with reduced PC (r = -0.56; 95% confidence interval [CI]: -0.82, -0.01) and increased PSE (r = 0.63; 95% CI: 0.18, 0.87). There was no evidence of an association between changes in trunk ROM and PC (r = -0.06; 95% CI: 0.38, 0.28) or PSE (r = 0.36; 95% CI: -0.27, 0.65) as well as no evidence of an association between lumbar ROM and PC (r = -0.07; 95% CI: -0.63, 0.55) or PSE (r = 0.16; 95% CI: -0.49, 0.69). CONCLUSION: Improvements in PC and PSE were strongly correlated with increased trunk velocity-but not trunk or lumbar ROM-in people with CLBP who were undergoing CFT. These findings are consistent with CFT that explicitly trains "nonprotective" spinal movement in conjunction with positively reframing pain cognitions. J Orthop Sports Phys Ther 2025;55(4):1-11. Epub 12 March 2025. doi:10.2519/jospt.2025.13114.

BACKGROUND:Lifting is a functional movement commonly assessed and targeted in the treatment of people with low back pain (LBP). OBJECTIVE:To investigate changes in spinal range of motion (ROM) and velocity during lifting in people with lifting-related LBP over the course of Cognitive Functional Therapy (CFT), and to compare these changes between CFT-only and CFT-with-biofeedback. DESIGN:Longitudinal observational study. METHOD:One hundred and forty-one people with lifting-related LBP received CFT and performed a lifting task prior to each treatment session. Measures included ROM and velocity from trunk and pelvis sensors independently and the intersensor angle. Multilevel models estimated the average amount of change and inter-individual variability. Time-group interaction was used to test the differences in the mean change between CFT-only and CFT-with-biofeedback. RESULTS:During the 13-week intervention period, the average trunk and pelvis ROM increased significantly between week 1 and week 8 (10.6°, 95% CI: 5.9, 15.4; 10.4°, 95% CI: 6.9, 14.0), while the average intersensor ROM did not change over 13 weeks (-0.79°, 95% CI: -3.74, 2.16). The average trunk, pelvis and intersensor velocity increased significantly up to weeks 9 or 10 (17.8°/sec, 95% CI: 14.0, 21.6; 10.8°/sec, 95% CI: 8.3, 13.4; 6.0°/sec, 95% CI: 3.7, 8.3). There was no evidence for differences in change in ROM or velocity measures between CFT-only and CFT-with-biofeedback (P = 0.14-0.64). CONCLUSIONS:People with lifting-related LBP demonstrated increases in trunk and pelvis ROM and all velocity measures but not intersensor ROM during lifting over the course of CFT. Biofeedback did not augment changes in lifting kinematics.

Chronic low back pain (CLBP) is an urgent global health priority given its high prevalence and impact as the leading cause of disability. While several efficacious treatments exist, most have modest effects. Improving outcomes requires a better understanding of treatment mechanisms to enable optimisation. This study explored the mechanisms of cognitive functional therapy (CFT), a biopsychosocial intervention with large, durable effects for adults with disabling CLBP. A longitudinal mediation analysis was performed on data from the RESTORE multisite clinical trial comparing CFT (n = 327) to usual care (n = 165). Mediators (self-efficacy, fear, catastrophising, and pain intensity) were specified based on behavioural theories underlying CFT and previous research. The joint mediation of treatment effects on disability (Roland Morris Disability Questionnaire) and pain intensity (numerical rating scale), were examined using a counterfactual framework for mediation analysis. As hypothesised, earlier changes in self-efficacy, fear, catastrophising, and pain intensity mediated improvements in disability at the end of treatment and at 12-month follow-up, explaining up to 61 % of the effect. Similarly, self-efficacy, fear, and catastrophising mediated the effect of CFT on pain intensity, explaining up to 62 % of the effect. Results are consistent with previous CLBP mediation research highlighting self-efficacy, fear, and catastrophising as likely common mechanisms among effective biopsychosocial treatments. CFT demonstrates large, durable, and clinically important effects on these mechanisms. Findings shed light for clinicians and researchers on how CFT works, although the role of other mechanisms such as movement changes requires further exploration, along with research analysing how different treatment components activate these mechanisms.

OBJECTIVE: To investigate forward bending range of motion (ROM) and velocity in patients with low back pain who were receiving Cognitive Functional Therapy and determine (1) the amount and timing of change occurring at the trunk and pelvis (global angles), and lumbar spine (intersensor angle), and (2a) differences in changes between participants with and without sensor biofeedback, and (2b) participants with and without baseline movement limitation. DESIGN: Observational study. METHODS: Two hundred sixty-one participants attended Cognitive Functional Therapy treatment and wore sensors at the T12 and S2 spine levels while performing forward bending. Measures included ROM and velocity from both sensors, and the intersensor angle. Regression models estimated changes over time. Time-group interactions tested participants who were subgrouped by treatment and baseline movement. RESULTS: During the 90-day evaluation period, most change occurred in the first 21 days. Changes in ROM observed at T12 (3.3 degrees, 95% CI: 1.0 degrees, 5.5 degrees; P = .001) and S2 (3.3 degrees, 95% CI: 1.2 degrees, 5.4 degrees; P = .002) were similar. Intersensor angle remained similar (0.2 degrees, 95% CI: -2.0 degrees, -1.6 degrees; P = .81). Velocity measured at T12 and S2, and the intersensor angle increased 8.5 degrees/s (95% CI: 6.7 degrees/s, 10.3 degrees/s; P<.0001), 5.3 degrees/s (95% CI: 4.0 degrees/s, 6.5 degrees/s; P<.0001), and 3.4 degrees/s (95% CI: 2.4 degrees/s, 4.5 degrees/s; P<.0001), respectively, for 0 to 21 days. There were minimal differences in participants who received biofeedback. Larger increases occurred in participants with restricted ROM and slower velocity at baseline. CONCLUSION: During 0 to 21 days, we observed changes at the trunk and pelvis (especially in people with reduced ROM), and velocity changes across all measures (especially in people with baseline movement limitations). Biofeedback did not augment the changes. When targeting forward bending in people with low back pain, clinicians should monitor changes in velocity and global ROM.