Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.

PURPOSE: Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when developing prognostic models in individuals with neck pain. METHODS: A total of 3001 participants suffering from neck pain were included into a clinical registry database. Three dichotomous outcomes of a clinically meaningful improvement in neck pain, arm pain, and disability at 3 months follow-up were used. There were 26 predictors included, five numeric and 21 categorical. Seven modelling techniques were used (logistic regression, least absolute shrinkage and selection operator [LASSO], gradient boosting [Xgboost], K nearest neighbours [KNN], support vector machine [SVM], random forest [RF], and artificial neural networks [ANN]). The primary measure of model performance was the area under the receiver operator curve (AUC) of the validation set. RESULTS: The ML algorithm with the greatest AUC for predicting arm pain (AUC = 0.765), neck pain (AUC = 0.726), and disability (AUC = 0.703) was Xgboost. The improvement in classification AUC from stepwise logistic regression to the best performing machine learning algorithms was 0.081, 0.103, and 0.077 for predicting arm pain, neck pain, and disability, respectively. CONCLUSION: The improvement in prediction performance between ML and logistic regression methods in the present study, could be due to the potential greater nonlinearity between baseline predictors and clinical outcome. The benefit of machine learning in prognostic modelling may be dependent on factors like sample size, variable type, and disease investigated.

Understanding the Psychophysiological Mechanisms Related to Widespread Pressure Pain Hyperalgesia Underpinning Carpal Tunnel Syndrome: A Network Analysis Approach.

OBJECTIVE: Current evidence suggests that carpal tunnel syndrome (CTS) involves widespread pressure pain sensitivity as a manifestion of central sensitization. This study aimed to quantify mechanisms driving widespread pressure pain hyperalgesia in CTS by using network analysis. DESIGN: Cross-sectional. SETTING: Urban hospital. SUBJECTS: Women with CTS (n=120) who participated in a previous randomized clinical trial. METHODS: Pain intensity, related function, symptom severity, depressive levels, and pressure pain threshold (PPTs) over the median, radial, and ulnar nerves, as well as the cervical spine, the carpal tunnel, and the tibialis anterior, were collected. Network analysis was used to quantify the adjusted correlations between the modeled variables and to determine the centrality indices of each variable (i.e., the degree of connection with other symptoms in the network). RESULTS: The estimated network showed several local associations between clinical variables and the psychophysical outcomes separately. The edges with the strongest weights were those between the PPT over the median nerve and the PPT over the radial nerve (P=0.34), between function and depressive levels (P=0.30), and between the PPT over the carpal tunnel and the PPT over the tibialis anterior (P=0.29 ). The most central variables were PPT over the tibialis anterior (the highest Strength centrality) and PPT over the carpal tunnel (the highest Closeness and Betweenness centrality). CONCLUSIONS: This is the first study to apply network analysis to understand the multivariate mechanisms of individuals with CTS. Our findings support a model in which clinical symptoms, depression, and widespread pressure pain sensitivity are connected, albeit within separate clusters. The clinical implications of the present findings, such as the development of treatments targeting these mechanisms, are also discussed.

Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders.

PURPOSE: To evaluate whether a set of pre-accident demographic, accident-related, post-accident treatment and psychosocial factors assessed in people with acute/subacute whiplash-associated disorders (WAD) mediate the association between pain intensity and: (1) pain interference and (2) expectations of recovery, using Bayesian networks (BNs) analyses. This study also explored the potential mediating pathways (if any) between different psychosocial factors. METHODS: This was a cross-sectional study conducted on a sample of 173 participants with acute/subacute WAD. Pain intensity, pain interference, pessimism, expectations of recovery, pain catastrophizing, and self-efficacy beliefs were assessed. BN analyses were conducted to analyse the mediating effects of psychological factors on the association between pain intensity and pain-related outcomes. RESULTS: The results revealed that self-efficacy beliefs partially mediated the association between pain intensity and pain interference. Kinesiophobia partially mediated the association between self-efficacy and pain catastrophizing. Psychological factors did not mediate the association between pain intensity and expectations of recovery. CONCLUSION: These results indicate that individuals with acute/subacute WAD may present with lesser pain interference associated with a determined pain intensity value when they show greater self-efficacy beliefs. As the cross-sectional nature of this study limits firm conclusions on the causal impact, researchers are encouraged to investigate the role that patient's self-efficacy beliefs play in the transition to chronic WAD via longitudinal study designs.

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE: To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS: Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS: Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text] = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] = 0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION: The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material.

Effects of two neuromuscular training programs on running biomechanics with load carriage: a study protocol for a randomised controlled trial.

BACKGROUND: In recent years, athletes have ventured into ultra-endurance and adventure racing events, which tests their ability to race, navigate, and survive. These events often require race participants to carry some form of load, to bear equipment for navigation and survival purposes. Previous studies have reported specific alterations in biomechanics when running with load which potentially influence running performance and injury risk. We hypothesize that a biomechanically informed neuromuscular training program would optimize running mechanics during load carriage to a greater extent than a generic strength training program. METHODS: This will be a two group, parallel randomized controlled trial design, with single assessor blinding. Thirty healthy runners will be recruited to participate in a six weeks neuromuscular training program. Participants will be randomized into either a generic training group, or a biomechanically informed training group. Primary outcomes include self-determined running velocity with a 20 % body weight load, jump power, hopping leg stiffness, knee extensor and triceps-surae strength. Secondary outcomes include running kinetics and kinematics. Assessments will occur at baseline and post-training. DISCUSSION: To our knowledge, no training programs are available that specifically targets a runner's ability to carry load while running. This will provide sport scientists and coaches with a foundation to base their exercise prescription on. TRIAL REGISTRATION: ANZCTR ( ACTRN12616000023459 ) (14 Jan 2016).

Exploring the complexity of commonly held attitudes and beliefs of low back pain-a network analysis.

Objectives: The current study used a network analysis approach to explore the complexity of attitudes and beliefs held in people with and without low back pain (LBP). The study aimed to (1) quantify the adjusted associations between individual items of the Back Pain Attitudes Questionnaire (Back-PAQ), and (2) identify the items with the strongest connectivity within the network. Methods: This is a secondary data analysis of a previously published survey using the Back-PAQ (n = 602). A nonparametric Spearman's rank correlation matrix was used as input to the network analysis. We estimated an unregularised graphical Gaussian model (GGM). Edges were added or removed in a stepwise manner until the extended Bayesian information criterion (EBIC) did not improve. We assessed three measures of centrality measures of betweenness, closeness, and strength. Results: The two pairwise associations with the greatest magnitude of correlation were between Q30-Q31 [0.54 (95% CI 0.44 to 0.60)] and Q15-Q16 [0.52 (95% CI 0.43 to 0.61)]. These two relationships related to the association between items exploring the influence of attentional focus and expectations (Q30-Q31), and feelings and stress (Q15-Q16). The three items with the greatest average centrality values, were Q22, Q25, and Q10. These items reflect beliefs about damaging the back, exercise, and activity avoidance, respectively. Conclusion: Beliefs about back damage, exercise, and activity avoidance are factors most connected to all other beliefs within the network. These three factors may represent candidate targets that clinicians can focus their counseling efforts on to manage unhelpful attitudes and beliefs in people experiencing LBP.

Quantifying lumbar sagittal plane kinematics using a wrist-worn inertial measurement unit.

Wearable sensors like inertial measurement units (IMUs), and those available as smartphone or smartwatch applications, are increasingly used to quantify lumbar mobility. Currently, wearable sensors have to be placed on the back to measure lumbar mobility, meaning it cannot be used in unsupervised environments. This study aims to compare lumbar sagittal plane angles quantified from a wrist-worn against that of a lumbar-worn sensor. Twenty healthy participants were recruited. An IMU was placed on the right wrist and the L3 spinal level. Participants had to position their right forearm on their abdomen, parallel to the floor. Three sets of three consecutive repetitions of flexion, and extension were formed. Linear mixed models were performed to quantify the effect of region (lumbar vs. wrist) on six outcomes [minimum, maximum, range of motion (ROM) of flexion and extension]. Only flexion ROM was significantly different between the wrist and lumbar sensors, with a mean of 4.54° (95% CI = 1.82°-7.27°). Across all outcomes, the maximal difference between a wrist-worn and lumbar-worn sensor was <8°. A wrist-worn IMU sensor could be used to measure gross lumbar sagittal plane mobility in place of a lumbar-worn IMU. This may be useful for remote monitoring during rehabilitation.

Strategies to optimise machine learning classification performance when using biomechanical features.

Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging. This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets - a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653). Machine-learning based strategies for managing the challenging issue of limited sample size for biomechanical ML-based problems, could benefit the development of alternative prediction models in healthcare, especially when time-series data are involved.

Neuromechanical stabilisation of the centre of mass during running.

BACKGROUND: Stabilisation of the centre of mass (COM) trajectory is thought to be important during running. There is emerging evidence of the importance of leg length and angle regulation during running, which could contribute to stability in the COM trajectory The present study aimed to understand if leg length and angle stabilises the vertical and anterior-posterior (AP) COM displacements, and if the stability alters with running speeds. METHODS: Data for this study came from an open-source treadmill running dataset (n = 28). Leg length (m) was calculated by taking the resultant distance of the two-dimensional sagittal plane leg vector (from pelvis segment to centre of pressure). Leg angle was defined by the angle subtended between the leg vector and the horizontal surface. Leg length and angle were scaled to a standard deviation of one. Uncontrolled manifold analysis (UCM) was used to provide an index of motor abundance (IMA) in the stabilisation of the vertical and AP COM displacement. RESULTS: IMAAP and IMAvertical were largely destabilising and always stabilising, respectively. As speed increased, the peak destabilising effect on IMAAP increased from -0.66(0.18) at 2.5 m/s to -1.12(0.18) at 4.5 m/s, and the peak stabilising effect on IMAvertical increased from 0.69 (0.19) at 2.5 m/s to 1.18 (0.18) at 4.5 m/s. CONCLUSION: Two simple parameters from a simple spring-mass model, leg length and angle, can explain the control behind running. The variability in leg length and angle helped stabilise the vertical COM, whilst maintaining constant running speed may rely more on inter-limb variation to adjust the horizontal COM accelerations.

Network analyses reveal the interaction between physical features, fear of movement and neck pain and disability in people with acute and chronic whiplash-associated disorders.

BACKGROUND AND OBJECTIVE: A network analysis can be used to quantitatively assess and graphically describe multiple interactions. This study applied network analyses to determine the interaction between physical and pain-related factors and fear of movement in people with whiplash-associated disorders (WAD) during periods of acute and chronic pain. METHODS: Physical measurements, including pressure pain-thresholds (PPT) over neural structures, cervical range of motion, neck flexor and extensor endurance and the cranio-cervical flexion test (CCFT), in addition to subjective reports including the Tampa Scale of Kinesiophobia (TSK-11), Neck Disability Index (NDI) and neck pain and headache intensity, were assessed at baseline in 47 participants with acute WAD. TSK-11, NDI and pain intensity were assessed for the same participants 6 months later (n = 45). Two network analyses were conducted to estimate the associations between features at baseline and at 6 months and their centrality indices. RESULTS: Both network analyses revealed that the greatest weight indices were found for NDI and CCFT at baseline and for neck pain and headache intensity and NDI and TSK-11 at both time points. Associations were also found betweeen cervical muscle endurance and neck pain intensity in the acute phase. Cervical muscle endurance assesssed during the acute phase was also associated with NDI after 6 months - whereas PPT measured at baseline was associsated with headache intensity after 6 months. CONCLUSION: The strongest associations were found for headache and neck pain intensity and neck disability and fear of movement, both during acute pain and when mesured 6 months later. The extent of neck endurance and measures of PPT at baseline may be associated with neck disability and headache, respectively, 6 months after a whiplash injury. SIGNIFICANCE: Through two network analyses, we evaluated the interaction between pain-related factors, fear of movement, neck disability and physical factors in people who had experienced a whiplash injury. We demonstrated that physical factors may be involved in the maintenance and development of chronic pain after a whiplash injury. Nevertheless, the strongest associations were found for headache and neck pain intensity and neck disability and fear of movement, both during acute and chronic phases.

Words matter: Effects of instructional cues on pressure pain threshold values in healthy people.

BACKGROUND: Pressure pain threshold (PPT) measurements require standardised verbal instructional cues to ensure that the increasing pressure is stopped at the correct time consistently. This study aimed to compare how PPT values and their test-retest reliability were affected by different instructional cues. METHODS: At two separate sessions, two PPT measurements were taken at the anterior knee for each of four different instructional cues: the cue of the German Neuropathic Research Network instructions ('DFNS'), the point where pressure first feels uncomfortable ('Uncomfortable'), 3/10 on the numerical pain rating scale ('3NPRS'), and where pain relates to an image from the pictorial-enhanced NPRS scale ('Pictorial'). Linear mixed modeling was used to quantify differences between pairs of instructional cues. Test-retest reliability was estimated using intraclass correlation coefficients (ICC[2,1] and ICC[2,k]). RESULTS: Twenty participants were recruited. The cue resulting in greatest PPT value was DFNS (394.32 kPa, 95%CI [286.32 to 543.06]), followed by Pictorial (342.49 kPa, 95%CI [248.68 to 471.68]), then Uncomfortable (311.85 kPa, 95%CI [226.43 to 429.48]), and lastly 3NPRS (289.78 kPa, 95%CI [210.41 to 399.09]). Five of six pairwise contrasts were statistically significant. Regardless of the cues, the point estimates of ICC (2,1) ranged from 0.80 to 0.86, and the ICC (2,k) values ranged from 0.89 to 0.93. No statistically significant differences were found between any pairwise contrasts of reliability indices. CONCLUSION: Words matter when instructing people when to stop testing in pressure algometry. Clinicians should use the same instructional cue when assessing pain thresholds to ensure reliability.

Quantifying lumbar mobility using a single tri-axial accelerometer.

Background: Lumbar mobility is regarded as important for assessing and managing low back pain (LBP). Inertial Measurement Units (IMUs) are currently the most feasible technology for quantifying lumbar mobility in clinical and research settings. However, their gyroscopes are susceptible to drift errors, limiting their use for long-term remote monitoring. Research question: Can a single tri-axial accelerometer provide an accurate and feasible alternative to a multi-sensor IMU for quantifying lumbar flexion mobility and velocity? Methods: In this cross-sectional study, 18 healthy adults performed nine repetitions of full spinal flexion movements. Lumbar flexion mobility and velocity were quantified using a multi-sensor IMU and just the tri-axial accelerometer within the IMU. Correlations between the two methods were assessed for each percentile of the lumbar flexion movement cycle, and differences in measurements were modelled using a Generalised Additive Model (GAM). Results: Very high correlations (r > 0.90) in flexion angles and velocities were found between the two methods for most of the movement cycle. However, the accelerometer overestimated lumbar flexion angle at the start (-4.7° [95 % CI -7.6° to -1.8°]) and end (-4.8° [95 % CI -7.7° to -1.9°]) of movement cycles, but underestimated angles (maximal difference of 4.3° [95 % CI 1.4° to 7.2°]) between 7 % and 92 % of the movement cycle. For flexion velocity, the accelerometer underestimated at the start (16.6°/s [95%CI 16.0 to 17.2°/s]) and overestimated (-12.3°/s [95%CI -12.9 to -11.7°/s]) at the end of the movement, compared to the IMU. Significance: Despite the observed differences, the study suggests that a single tri-axial accelerometer could be a feasible tool for continuous remote monitoring of lumbar mobility and velocity. This finding has potential implications for the management of LBP, enabling more accessible and cost-effective monitoring of lumbar mobility in both clinical and research settings.

Association between fat and fat-free body mass indices on shock attenuation during running.

High amplitudes of shock during running have been thought to be associated with an increased injury risk. This study aimed to quantify the association between dual-energy X-ray absorptiometry (DEXA) quantified body composition, and shock attenuation across the time and frequency domains. Twenty-four active adults participated. A DEXA scan was performed to quantify the fat and fat-free mass of the whole-body, trunk, dominant leg, and viscera. Linear accelerations at the tibia, pelvis, and head were collected whilst participants ran on a treadmill at a fixed dimensionless speed 1.00 Fr. Shock attenuation indices in the time- and frequency-domain (lower frequencies: 3-8 Hz; higher frequencies: 9-20 Hz) were calculated. Pearson correlation analysis was performed for all combinations of DEXA and attenuation indices. Regularised regression was performed to predict shock attenuation indices using DEXA variables. A greater power attenuation between the head and pelvis within the higher frequency range was associated with a greater trunk fat-free mass (r = 0.411, p = 0.046), leg fat-free mass (r = 0.524, p = 0.009), and whole-body fat-free mass (r = 0.480, p = 0.018). For power attenuation of the high-frequency component between the pelvis and head, the strongest predictor was visceral fat mass (ß = 48.79). Passive and active tissues could represent important anatomical factors aiding in shock attenuation during running. Depending on the type and location of these masses, an increase in mass may benefit injury risk reduction. Also, our findings could implicate the injury risk potential during weight loss programs.

Prognostic Factors of Perceived Disability and Perceived Recovery After Whiplash: A Longitudinal, Prospective Study With One-year Follow-up.

OBJECTIVES: The understanding of the role that cognitive and emotional factors play in how an individual recovers from a whiplash injury is important. Hence, we sought to evaluate whether pain-related cognitions (self-efficacy beliefs, expectation of recovery, pain catastrophizing, optimism, and pessimism) and emotions (kinesiophobia) are longitudinally associated with the transition to chronic whiplash-associated disorders in terms of perceived disability and perceived recovery at 6 and 12 months. METHODS: One hundred sixty-one participants with acute or subacute whiplash-associated disorder were included. The predictors were: self-efficacy beliefs, expectation of recovery, pain catastrophizing, optimism, pessimism, pain intensity, and kinesiophobia. The 2 outcomes were the dichotomized scores of perceived disability and recovery expectations at 6 and 12 months. Stepwise regression with bootstrap resampling was performed to identify the predictors most strongly associated with the outcomes and the stability of such selection. RESULTS: Baseline perceived disability, pain catastrophizing, and expectation of recovery were the most likely to be statistically significant, with an overage frequency of 87.2%, 84.0%, and 84.0%, respectively. CONCLUSION: Individuals with higher expectations of recovery and lower levels of pain catastrophizing and perceived disability at baseline have higher perceived recovery and perceived disability at 6 and 12 months. These results have important clinical implications as both factors are modifiable through health education approaches.

Exploring Interactions Between Sex, Pain Characteristics, Disability, and Quality of Life in People With Chronic Spinal Pain: A Structural Equation Model.

In people with nonspecific chronic spinal pain (nCSP), disability and quality of life are associated with clinical, cognitive, psychophysical, and demographic variables. However, evidence regarding the interactions between these variables is only limited to this population. Therefore, this study aims to explore path models explaining the multivariate contributions of such variables to disability and quality of life in people with nCSP. This secondary analysis uses baseline data from a randomized controlled trial including 120 participants with nCSP. Structural equation modeling was used to explore path models for the Pain Disability Index (PDI), the Short Form 36-item physical (SF-36 PC), and mental (SF-36 MC) component scores. All models included sex, pain catastrophizing, kinesiophobia, hypervigilance, and pain intensity. Additionally, the PDI and SF-36 PC models included pressure pain thresholds (PPTs) at the dominant pain site (ie, neck or low back). Significant associations were found between sex, pain cognitions, pain intensity, and PPTs. Only pain catastrophizing significantly directly influenced the PDI (P ≤ .001) and SF-36 MC (P = .014), while the direct effects on the SF-36 PC from kinesiophobia (P = .008) and pain intensity (P = .006) were also significant. However, only the combined effect of all pain cognitions on the SF-36 PC was mediated by pain intensity (P = .019). Our findings indicate that patients' pain-related cognitions have an adverse effect on their physical health-related quality of life via a negative influence on their pain intensity in people with nCSP. PERSPECTIVE: This secondary analysis details a network analysis confirming significant interactions between sex, pain cognitions, pain intensity, and PPTs in relation to disability and health-related quality of life in people with chronic spinal pain. Moreover, its findings establish the importance of pain cognitions and pain intensity for these outcomes. TRIALS REGISTRATION: Clinicaltrials.gov (NCT02098005).

Redirected Attention and Impaired Recognition Memory during Exhaustive Cycling Has Implications for Information Processing Models of Effort-Regulation.

Perception of internal and external cues is an important determinant of pacing behaviour, but little is known about the capacity to attend to such cues as exercise intensity increases. This study investigated whether changes in attentional focus and recognition memory correspond with selected psychophysiological and physiological parameters during exhaustive cycling. METHODS: Twenty male participants performed two laboratory ramped cycling tests beginning at 50 W and increasing by 0.25 W/s until volitional exhaustion. Ratings of perceived exertion, heart rate and respiratory gas exchange measures were recorded during the first test. During the second test, participants listened to a list of spoken words presented through headphones at a rate of one word every 4 s. Afterwards, their recognition memory for the word pool was measured. RESULTS: Recognition memory performance was found to have strong negative correlations with perceived exertion (p < 0.0001), percentage of peak power output (p < 0.0001), percentage of heart rate reserve (p < 0.0001), and percentage of peak oxygen uptake (p < 0.0001). CONCLUSIONS: The results show that, as the physiological and psychophysiological stress of cycling intensified, recognition memory performance deteriorated. This might be due to impairment of memory encoding of the spoken words as they were presented, or because of a diversion of attention away from the headphones, perhaps towards internal physiological sensations as interoceptive sources of attentional load increase with exercise intensity. Information processing models of pacing and performance need to recognise that an athlete's capacity to attend to and process external information is not constant, but changes with exercise intensity.

Characterising the interventions designed to affect the reporting of musculoskeletal imaging: a scoping review protocol using the COM-B model.

INTRODUCTION: Attributing musculoskeletal (MSK) pain to normal and commonly occurring imaging findings, such as tendon, cartilage and spinal disc degeneration, has been shown to increase people's fear of movement, reduce their optimism about recovery and increase healthcare costs. Interventions seeking to reduce the negative effects of MSK imaging reporting have had little effect. To understand the ineffectiveness of these interventions, this study seeks to scope their behavioural targets, intended mechanisms of action and theoretical underpinnings. This information alongside known barriers to helpful reporting can enable researchers to refine or create new more targeted interventions. METHODS AND ANALYSIS: The scoping review will be conducted in accordance with the JBI methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Search terms will be devised by the research team. Searches of MEDLINE, EMBASE, CINAHL, AMED and PsycINFO from inception to current day will be performed. The review will include studies, which have developed or evaluated interventions targeting the reporting of MSK imaging. Studies targeting the diagnosis of serious causes of MSK pain will be excluded. Two independent authors will extract study participant data using predefined extraction templates and intervention details using the Template for Intervention Description and Replication checklist. Interventions will be coded and mapped to the technique, mechanism of action and behavioural target according to the Capability, Opportunity, Motivation-Behaviour (COM-B) model categories. Any explicit models or theories used to inform the selection of interventions will be extracted and coded. The study characteristics, behaviour change techniques identified, behavioural targets according to the COM-B and context specific theories within the studies will be presented in narrative and table form. ETHICS AND DISSEMINATION: The information from this review will be used to inform an intervention design process seeking to improve the communication of imaging results. The results will also be disseminated through a peer-reviewed publication, conference presentations and stakeholder events.

Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain.

This study aims to compare the variable selection strategies of different machine learning (ML) and statistical algorithms in the prognosis of neck pain (NP) recovery. A total of 3001 participants with NP were included. Three dichotomous outcomes of an improvement in NP, arm pain (AP), and disability at 3 months follow-up were used. Twenty-five variables (twenty-eight parameters) were included as predictors. There were more parameters than variables, as some categorical variables had >2 levels. Eight modelling techniques were compared: stepwise regression based on unadjusted p values (stepP), on adjusted p values (stepPAdj), on Akaike information criterion (stepAIC), best subset regression (BestSubset) least absolute shrinkage and selection operator [LASSO], Minimax concave penalty (MCP), model-based boosting (mboost), and multivariate adaptive regression splines (MuARS). The algorithm that selected the fewest predictors was stepPAdj (number of predictors, p = 4 to 8). MuARS was the algorithm with the second fewest predictors selected (p = 9 to 14). The predictor selected by all algorithms with the largest coefficient magnitude was "having undergone a neuroreflexotherapy intervention" for NP (ß = from 1.987 to 2.296) and AP (ß = from 2.639 to 3.554), and "Imaging findings: spinal stenosis" (ß = from -1.331 to -1.763) for disability. Stepwise regression based on adjusted p-values resulted in the sparsest models, which enhanced clinical interpretability. MuARS appears to provide the optimal balance between model sparsity whilst retaining high predictive performance across outcomes. Different algorithms produced similar performances but resulted in a different number of variables selected. Rather than relying on any single algorithm, confidence in the variable selection may be increased by using multiple algorithms.

Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study.

OBJECTIVES: To understand the physical, activity, pain, and psychological pathways contributing to low back pain (LBP) -related disability, and if these differ between subgroups. METHODS: Data came from the baseline observations (n = 3849) of the "GLA:D Back" intervention program for long-lasting nonspecific LBP. 15 variables comprising demographic, pain, psychological, physical, activity, and disability characteristics were measured. Clustering was used for subgrouping, Bayesian networks (BN) were used for structural learning, and structural equation model (SEM) was used for statistical inference. RESULTS: Two clinical subgroups were identified with those in subgroup 1 having worse symptoms than those in subgroup 2. Psychological factors were directly associated with disability in both subgroups. For subgroup 1, psychological factors were most strongly associated with disability (ß = 0.363). Physical factors were directly associated with disability (ß = -0.077), and indirectly via psychological factors. For subgroup 2, pain was most strongly associated with disability (ß = 0.408). Psychological factors were common predictors of physical factors (ß = 0.078), pain (ß = 0.518), activity (ß = -0.101), and disability (ß = 0.382). CONCLUSIONS: The importance of psychological factors in both subgroups suggests their importance for treatment. Differences in the interaction between physical, pain, and psychological factors and their contribution to disability in different subgroups may open the doors toward more optimal LBP treatments.