Characteristics of the Sexual Networks of Men Who Have Sex With Men in Montréal, Toronto, and Vancouver: Insights from Canada's 2022 Mpox Outbreak.

BACKGROUND: The 2022-2023 global mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBM). We investigated differences in GBM's sexual partner distributions across Canada's 3 largest cities and over time, and how they shaped transmission. METHODS: The Engage Cohort Study (2017-2023) recruited GBM via respondent-driven sampling in Montréal, Toronto, and Vancouver (n = 2449). We compared reported sexual partner distributions across cities and periods: before COVID-19 (2017-2019), pandemic (2020-2021), and after lifting of restrictions (2021-2023). We used Bayesian regression and poststratification to model partner distributions. We estimated mpox's basic reproduction number (R0) using a risk-stratified compartmental model. RESULTS: Pre-COVID-19 pandemic distributions were comparable: fitted average partners (past 6 months) were 10.4 (95% credible interval: 9.4-11.5) in Montréal, 13.1 (11.3-15.1) in Toronto, and 10.7 (9.5-12.1) in Vancouver. Sexual activity decreased during the pandemic and increased after lifting of restrictions, but remained below prepandemic levels. Based on reported cases, we estimated R0 of 2.4 to 2.7 and similar cumulative incidences (0.7%-0.9%) across cities. CONCLUSIONS: Similar sexual partner distributions may explain comparable R0 and cumulative incidence across cities. With potential for further recovery in sexual activity, mpox vaccination and surveillance strategies should be maintained.

Context-informed incremental learning improves both the performance and resilience of myoelectric control.

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.

This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.The intrinsic physiological mechanisms limiting practical implementations of myoelectric deviceswere explored: the limb position effect and the contraction intensity effect. The degradationof electromyography (EMG) pattern recognition in the presence of these factors was demonstratedon six datasets, where classification performance was 13% and 20% lower than the controlledsetting for the limb position and contraction intensity effect, respectively. The experimental designsof limb position and contraction intensity literature were surveyed. Current state-of-the-art trainingstrategies and robust algorithms for both effects were compiled and presented. Recommendationsfor future limb position effect studies include: the collection protocol providing exemplars of at least 6positions (four limb positions and three forearm orientations), three-dimensional space experimentaldesigns, transfer learning approaches, and multi-modal sensor configurations. Recommendationsfor future contraction intensity effect studies include: the collection of dynamic contractions, nonlinearcomplexity features, and proportional control.

Foxn4 promotes gene expression required for the formation of multiple motile cilia.

Multiciliated cell (MCC) differentiation involves extensive organelle biogenesis required to extend hundreds of motile cilia. Key transcriptional regulators known to drive the gene expression required for this organelle biogenesis are activated by the related coiled-coil proteins Multicilin and Gemc1. Here we identify foxn4 as a new downstream target of Multicilin required for MCC differentiation in Xenopus skin. When Foxn4 activity is inhibited in Xenopus embryos, MCCs show transient ciliogenesis defects similar to those seen in mutants of Foxj1, a known key regulator of genes required for motile ciliation. RNAseq analysis indicates that Foxn4 co-activates some Foxj1 target genes strongly and many Foxj1 targets weakly. ChIPseq suggests that whereas Foxn4 and Foxj1 frequently bind to different targets at distal enhancers, they largely bind together at MCC gene promoters. Consistent with this co-regulation, cilia extension by MCCs is more severely compromised in foxn4 and foxj1 double mutants than in single mutants. In contrast to Foxj1, Foxn4 is not required to extend a single motile cilium by cells involved in left-right patterning. These results indicate that Foxn4 complements Foxj1 transcriptionally during MCC differentiation, thereby shaping the levels of gene expression required for the timely and complete biogenesis of multiple motile cilia.

Physiotherapy Rehabilitation for People With Progressive Multiple Sclerosis: A Systematic Review.

OBJECTIVE: To assess the efficacy of physiotherapy interventions, including exercise therapy, for the rehabilitation of people with progressive multiple sclerosis. DATA SOURCES: Five databases (Cochrane Library, Physiotherapy Evidence Database [PEDro], Web of Science Core Collections, MEDLINE, Embase) and reference lists of relevant articles were searched. STUDY SELECTION: Randomized experimental trials, including participants with progressive multiple sclerosis and investigating a physiotherapy intervention or an intervention containing a physiotherapy element, were included. DATA EXTRACTION: Data were independently extracted using a standardized form, and methodologic quality was assessed using the PEDro scale. DATA SYNTHESIS: Thirteen studies (described by 15 articles) were identified and scored between 5 and 9 out of 10 on the PEDro scale. Eight interventions were assessed: exercise therapy, multidisciplinary rehabilitation, functional electrical stimulation, botulinum toxin type A injections and manual stretches, inspiratory muscle training, therapeutic standing, acupuncture, and body weight-supported treadmill training. All studies, apart from 1, produced positive results in at least 1 outcome measure; however, only 1 article used a power calculation to determine the sample size and because of dropouts the results were subsequently underpowered. CONCLUSIONS: This review suggests that physiotherapy may be effective for the rehabilitation of people with progressive multiple sclerosis. However, further appropriately powered studies are required.

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition.

Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to 'confounding factors' (i.e. factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1)limb position variability, (2)cross-day use, and a newly identified confound faced by discrete systems (3)gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded (p<0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the end-user using a small amount of new data (only 10% , 20% , and 40% of the new user data). Using a deep multimodal convolutional neural network, consisting of two CNN models, one with high-density (HD) EMG and one with motion data recorded by an Inertial Measurement Unit (IMU), our proposed TL technique significantly improved force modeling compared to leave-one-subject-out (LOSO) and even intra-subject scenarios. The TL approach increased the average R squared values of the force modeling task by 60.81%, 190.53%, and 199.79% compared to the LOSO case, and by 13.4%, 36.88%, and 45.51% compared to the intra-subject case for isotonic, isokinetic and dynamic conditions, respectively. These results show that it is possible to adapt to a new user with minimal data while improving performance significantly compared to the intra-subject scenario. We also show that TL can be used to generalize on a new experimental condition for a new user.

Electrical signals in the ER are cell type and stimulus specific with extreme spatial compartmentalization in neurons.

The endoplasmic reticulum (ER) is a tortuous organelle that spans throughout a cell with a continuous membrane containing ion channels, pumps, and transporters. It is unclear if stimuli that gate ER ion channels trigger substantial membrane potential fluctuations and if those fluctuations spread beyond their site of origin. Here, we visualize ER membrane potential dynamics in HEK cells and cultured rat hippocampal neurons by targeting a genetically encoded voltage indicator specifically to the ER membrane. We report the existence of clear cell-type- and stimulus-specific ER membrane potential fluctuations. In neurons, direct stimulation of ER ryanodine receptors generates depolarizations that scale linearly with stimulus strength and reach tens of millivolts. However, ER potentials do not spread beyond the site of receptor activation, exhibiting steep attenuation that is exacerbated by intracellular large conductance K+ channels. Thus, segments of ER can generate large depolarizations that are actively restricted from impacting nearby, contiguous membrane.

Novel Wearable HD-EMG Sensor With Shift-Robust Gesture Recognition Using Deep Learning.

In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyography (HD-EMG) sensor called EMaGer. The stretchable, wearable sensor adapts to different forearm sizes while maintaining uniform electrode density around the limb. Leveraging this uniformity, we propose novel array barrel-shifting data augmentation (ABSDA) approach used with a convolutional neural network (CNN), and an anti-aliased CNN (AA-CNN), that provides shift invariance around the limb for improved classification robustness to electrode movement, forearm orientation, and inter-session variability. Signals are sampled from a 4×16 HD-EMG array of electrodes at a frequency of 1 kHz and 16-bit resolution. Using data from 12 non-amputated participants, the approach is tested in response to sensor rotation, forearm rotation, and inter-session scenarios. The proposed ABSDA-CNN method improves inter-session accuracy by 25.67% on average across users for 6 gesture classes compared to conventional CNN classification. A comparison with other devices shows that this benefit is enabled by the unique design of the EMaGer array. The AA-CNN yields improvements of up to 63.05% accuracy over non-augmented methods when tested with electrode displacements ranging from -45 ° to +45 ° around the limb. Overall, this article demonstrates the benefits of co-designing sensor systems, processing methods, and inference algorithms to leverage synergistic and interdependent properties to solve state-of-the-art problems.

Deep Cross-User Models Reduce the Training Burden in Myoelectric Control.

The effort, focus, and time to collect data and train EMG pattern recognition systems is one of the largest barriers to their widespread adoption in commercial applications. In addition to multiple repetitions of motions, including exemplars of confounding factors during the training protocol has been shown to be critical for robust machine learning models. This added training burden is prohibitive for most regular use cases, so cross-user models have been proposed that could leverage inter-repetition variability supplied by other users. Existing cross-user models have not yet achieved performance levels sufficient for commercialization and require users to closely adhere to a training protocol that is impractical without expert guidance. In this work, we extend a previously reported adaptive domain adversarial neural network (ADANN) to a cross-subject framework that requires very little training data from the end-user. We compare its performance to single-repetition within-user training and the previous state-of-the-art cross-subject technique, canonical correlation analysis (CCA). ADANN significantly outperformed CCA for both intact-limb (86.8-96.2%) and amputee (64.1-84.2%) populations. Moreover, the ADANN adaptation computation time was substantially lower than the time otherwise devoted to conducting a full within-subject training protocol. This study shows that cross-user models, enabled by deep-learned adaptations, may be a viable option for improved generalized pattern recognition-based myoelectric control.

Effects of Exercise Training on Neurotrophic Factors and Subsequent Neuroprotection in Persons with Multiple Sclerosis-A Systematic Review and Meta-Analysis.

BACKGROUND: Evidence indicates that exercise holds the potential to counteract neurodegeneration experienced by persons with multiple sclerosis (pwMS), which is in part believed to be mediated through increases in neurotrophic factors. There is a need to summarize the existing evidence on exercise-induced effects on neurotrophic factors alongside neuroprotection in pwMS. AIM: To (1) systematically review the evidence on acute (one session) and/or chronic (several sessions) exercise-induced changes in neurotrophic factors in pwMS and (2) investigate the potential translational link between exercise-induced changes in neurotrophic factors and neuroprotection. METHODS: Five databases (Medline, Scopus, Web of Science, Embase, Sport Discus) were searched for randomized controlled trials (RCT) examining the effects of exercise (all modalities included) on neurotrophic factors as well as measures of neuroprotection if reported. The quality of the study designs and the exercise interventions were assessed by use of the validated tool TESTEX. RESULTS: From N = 337 identified studies, N = 14 RCTs were included. While only N = 2 of the identified studies reported on the acute changes in neurotrophic factors, all N = 14 RCTs reported on the chronic effects, with N = 9 studies revealing between-group differences in favor of exercise. This was most prominent for brain-derived neurotrophic factor (BDNF), with between-group differences in favor of exercise being observed in N = 6 out of N = 12 studies. Meta-analyses were applicable for three out of 10 different identified neurotrophic factors and revealed that exercise can improve the chronic levels of BDNF (delta changes; N = 9, ES = 0.78 (0.27; 1.28), p = 0.003, heterogeneity between studies) and potentially also ciliary neurotrophic factor (CNTF) (N = 3, ES = 0.24 (-0.07; 0.54), p = 0.13, no heterogeneity between studies) but not nerve growth factor (NGF) (N = 4, ES = 0.28 (-0.55; 1.11), p = 0.51, heterogeneity between studies). Indicators of neuroprotection (e.g., with direct measures of brain structure assessed by MRI) were assessed in N = 3 of the identified studies only, with N = 2 partly supporting and thus indicating a potential translational link between increases in neurotrophic factors and neuroprotection. CONCLUSION: The present study reveals that exercise can elicit improvements in chronic levels of BDNF in pwMS, whereas the effects of exercise on chronic levels of other neurotrophic factors and on acute levels of neurotrophic factors in general, along with a potential translational link (i.e., with exercise-induced improvements in neurotropic factors being associated with or even mediating neuroprotection), are sparse and inconclusive. There is a need for more high-quality studies that assess neurotrophic factors (applying comparable methods of blood handling and analysis) concomitantly with neuroprotective outcome measures. Review Registration: PROSPERO (ID: CRD42020177353).

The effect of exercise on quality of life and activities of daily life in frail older adults: A systematic review of randomised control trials.

While the positive effects of exercise on frailty are well documented, the effect of exercise on quality of life (QoL) and activities of daily living (ADL) in frail older adults remains less certain. Therefore, this paper aimed to systematically review the literature investigating the effect of exercise on QoL and ADL in this group. Embase, MEDLINE, CENTRAL, PEDro and Web of Science Core Collections were searched systematically using relevant MeSH terms. The inclusion criteria were: controlled trial design, published in English, population included frail older adults, frailty measured quantitatively, interventions that included exercise, and QoL or ADL measurements (PROSPERO: CRD42018106173). After screening, 15 studies were eligible for inclusion in the qualitative synthesis (total n: 2467; mean age range: 70-85 years). There was a positive effect on QoL or ADL measures in 10 out of the 15 studies. QoL and ADLs only improved in studies that also reported improved physical outcomes. These results reflect the multi-factoral nature of frailty and how physical capability and QoL are interlinked. Heterogeneity precluded formal meta-analysis. Future trials in frail older adults should focus on interventions that include exercise, measure physical outcomes and use consistent study design to enable meta-analysis to be conducted.

Resistance exercise training at different loads in frail and healthy older adults: A randomised feasibility trial.

OBJECTIVES: This trial aimed to determine the feasibility of recruitment, retention, adherence, and safety of a resistance training (RT) intervention to skeletal muscle failure in both frail and non-frail older adults. DESIGN: An 8-week randomised feasibility trial. SETTING AND PARTICIPANTS: Older adults, with and without frailty, recruited from both clinics and community. METHODS: Recruitment was based on the number of participants enrolled from those provided with a Patient Information Sheet (PIS). Retention was based on the number of participants who completed the trial. Adherence was based on the number of RT sessions attended out of 16. Outcomes included frailty (Fried criteria), muscle strength (maximal voluntary contraction), functional abilities (Short Physical Performance battery), quality of life (EQ-5D-5L), activities of daily living (LIADL) and safety (diary). RESULTS: Recruitment target (n = 60) was achieved within 15 months, 58 were randomised to high (n = 30) or low repetition-load (n = 28) groups. Mean age of participants was 72 years (range 65-93). Adherence and retention rate for the RT intervention was ≥70%. There was one serious adverse experience due to the RT intervention. There were no differences (P > 0.05) in effects of RT on outcome variables between low and high repetition-load groups. CONCLUSIONS AND IMPLICATIONS: Recruitment of frail people was challenging. Older adults performing supervised RT to skeletal muscle failure was feasible and safe, with appropriate caution, and the repetition-load did not appear to influence its efficacy. Future research into the effectiveness of this simplified model of RT is warranted.

All-Optical Electrophysiology in hiPSC-Derived Neurons With Synthetic Voltage Sensors.

Voltage imaging and "all-optical electrophysiology" in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

Feasibility of Data-driven EMG Signal Generation using a Deep Generative Model.

Despite recent advancements in the field of pattern recognition-based myoelectric control, the collection of a high quality training set remains a challenge limiting its adoption. This paper proposes a framework for a possible solution by augmenting short training protocols with subject-specific synthetic electromyography (EMG) data generated using a deep generative network, known as SinGAN. The aim of this work is to produce high quality synthetic data that could improve classification accuracy when combined with a limited training protocol. SinGAN was used to generate 1000 synthetic windows of EMG data from a single window of six different motions, and results were evaluated qualitatively, quantitatively, and in a classification task. Qualitative assessment of synthetic data was conducted via visual inspection of principal component analysis projections of real and synthetic feature space. Quantitative assessment of synthetic data revealed 11 of 32 synthetic features had similar location and scale to real features (using univariate two-sample Lepage tests); whereas multivariate distributions were found to be statistically different (p <0.05). Finally, the addition of these synthetic data to a brief training set of real data significantly improved classification accuracy in a cross-validation testing scheme by 5.4% (p <0.001).

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.

Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. Nevertheless, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN (Adaptive Domain Adversarial Neural Network), which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, this work provides the first topological data analysis of EMG-based gesture recognition for the characterization of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. In the later layers, the learned features are inclined to instead adopt a one-vs.-all strategy for a given class. Furthermore, by using convolutional network visualization techniques, it is revealed that learned features actually tend to ignore the most activated channel during contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.

Context-dependent plasticity of adult-born neurons regulated by cortical feedback.

In a complex and dynamic environment, the brain flexibly adjusts its circuits to preferentially process behaviorally relevant information. Here, we investigated how the olfactory bulb copes with this demand by examining the plasticity of adult-born granule cells (abGCs). We found that learning of olfactory discrimination elevates odor responses of young abGCs and increases their apical dendritic spines. This plasticity did not occur in abGCs during passive odor experience nor in resident granule cells (rGCs) during learning. Furthermore, we found that feedback projections from the piriform cortex show elevated activity during learning, and activating piriform feedback elicited stronger excitatory postsynaptic currents in abGCs than rGCs. Inactivation of piriform feedback blocked abGC plasticity during learning, and activation of piriform feedback during passive experience induced learning-like plasticity of abGCs. Our work describes a neural circuit mechanism that uses adult neurogenesis to update a sensory circuit to flexibly adapt to new behavioral demands.

Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals.

In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features. In this work, a deep exploration of pain-based emotion classification was conducted to better understand differences in the results of the related literature. In total, 155 different time domain and frequency domain features were explored, derived from electromyogram (EMG), skin conductance levels (SCL), and electrocardiogram (ECG) readings taken from the 85 subjects in response to heat-induced pain. To address the inconsistency in the optimal feature sets found in related works, an exhaustive and interpretable feature selection protocol was followed to obtain a generalizable feature set. Associations between features were then visualized using a topologically-informed chart, called Mapper, of this physiological feature space, including synthesis and comparison of results from previous literature. This topological feature chart was able to identify key sources of information that led to the formation of five main functional feature groups: signal amplitude and power, frequency information, nonlinear complexity, unique, and connecting. These functional groupings were used to extract further insight into observable autonomic responses to pain through a complementary statistical interaction analysis. From this chart, it was observed that EMG and SCL derived features could functionally replace those obtained from ECG. These insights motivate future work on novel sensing modalities, feature design, deep learning approaches, and dimensionality reduction techniques.

High intensity interval training for people with multiple sclerosis: A systematic review.

BACKGROUND: Aerobic high intensity interval training (HIIT) is safe in the general population and more efficient in improving fitness than continuous moderate intensity training. The body of literature examining HIIT in multiple sclerosis (MS) is expanding but to date a systematic review has not been conducted. The aim of this review was to investigate the efficacy and safety of HIIT in people with MS. METHODS: A systematic search was carried out in September 2017 in EMBASE, MEDline, PEDro, CENTRAL and Web of Science Core collections using appropriate keywords and MeSH descriptors. Reference lists of relevant articles were also searched. Articles were eligible for inclusion if they were published in English, used HIIT, and included participants with MS. Quality was assessed using the PEDro scale. The following data were extracted using a standardised form: study design and characteristics, outcome measures, significant results, drop-outs, and adverse events. RESULTS: Seven studies (described by 11 articles) were identified: four randomised controlled trials, one randomised cross-over trial and two cohort studies. PEDro scores ranged from 3 to 8. Included participants (n = 249) were predominantly mildly disabled; one study included only people with progressive MS. Six studies used cycle ergometry and one used arm ergometry to deliver HIIT. One study reported six adverse events, four which could be attributed to the intervention. The other six reported that there were no adverse events. Six studies reported improvements in at least one outcome measure, however there were 60 different outcome measures in the seven studies. The most commonly measured domain was fitness, which improved in five of the six studies measuring aspects of fitness. The only trial not to report positive results included people with progressive and a more severe level of disability (Extended Disability Status Scale 6.0-8.0). CONCLUSION: HIIT appears to be safe and effective in increasing fitness in people with MS and low levels of disability. Further research is required to explore the effectiveness of HIIT in people with progressive MS and in those with higher levels of disability.