Reliability of patient-specific gait profiles with inertial measurement units during the 2-min walk test in incomplete spinal cord injury.

Most established clinical walking tests assess specific aspects of movement function (velocity, endurance, etc.) but are generally unable to determine specific biomechanical or neurological deficits that limit an individual's ability to walk. Recently, inertial measurement units (IMU) have been used to collect objective kinematic data for gait analysis and could be a valuable extension for clinical assessments (e.g., functional walking measures). This study assesses the reliability of an IMU-based overground gait analysis during the 2-min walk test (2mWT) in individuals with spinal cord injury (SCI). Furthermore, the study elaborates on the capability of IMUs to distinguish between different gait characteristics in individuals with SCI. Twenty-six individuals (aged 22-79) with acute or chronic SCI (AIS: C and D) completed the 2mWT with IMUs attached above each ankle on 2 test days, separated by 1 to 7 days. The IMU-based gait analysis showed good to excellent test-retest reliability (ICC: 0.77-0.99) for all gait parameters. Gait profiles remained stable between two measurements. Sensor-based gait profiling was able to reveal patient-specific gait impairments even in individuals with the same walking performance in the 2mWT. IMUs are a valuable add-on to clinical gait assessments and deliver reliable information on detailed gait pathologies in individuals with SCI.Trial registration: NCT04555759.

Data-driven characterization of walking after a spinal cord injury using inertial sensors.

BACKGROUND: An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson's disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity. METHODS: The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters. RESULTS: Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient's walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors. CONCLUSIONS: In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.

Machine-Learning-Based Methodology for Estimation of Shoulder Load in Wheelchair-Related Activities Using Wearables.

There is a high prevalence of shoulder problems in manual wheelchair users (MWUs) with a spinal cord injury. How shoulder load relates to shoulder problems remains unclear. This study aimed to develop a machine-learning-based methodology to estimate the shoulder load in wheelchair-related activities of daily living using wearable sensors. Ten able-bodied participants equipped with five inertial measurement units (IMU) on their thorax, right arm, and wheelchair performed activities exemplary of daily life of MWUs. Electromyography (EMG) was recorded from the long head of the biceps and medial part of the deltoid. A neural network was trained to predict the shoulder load based on IMU and EMG data. Different cross-validation strategies, sensor setups, and model architectures were examined. The predicted shoulder load was compared to the shoulder load determined with musculoskeletal modeling. A subject-specific biLSTM model trained on a sparse sensor setup yielded the most promising results (mean correlation coefficient = 0.74 ± 0.14, relative root-mean-squared error = 8.93% ± 2.49%). The shoulder-load profiles had a mean similarity of 0.84 ± 0.10 over all activities. This study demonstrates the feasibility of using wearable sensors and neural networks to estimate the shoulder load in wheelchair-related activities of daily living.

Accuracy and comparison of sensor-based gait speed estimations under standardized and daily life conditions in children undergoing rehabilitation.

BACKGROUND: Gait speed is a widely used outcome measure to assess the walking abilities of children undergoing rehabilitation. It is routinely determined during a walking test under standardized conditions, but it remains unclear whether these outcomes reflect the children's performance in daily life. An ankle-worn inertial sensor provides a usable opportunity to measure gait speed in the children's habitual environment. However, sensor-based gait speed estimations need to be accurate to allow for comparison of the children's gait speed between a test situation and daily life. Hence, the first aim of this study was to determine the measurement error of a novel algorithm that estimates gait speed based on data of a single ankle-worn inertial sensor in children undergoing rehabilitation. The second aim of this study was to compare the children's gait speed between standardized and daily life conditions. METHODS: Twenty-four children with walking impairments completed four walking tests at different speeds (standardized condition) and were monitored for one hour during leisure or school time (daily life condition). We determined accuracy by comparing sensor-based gait speed estimations with a reference method in both conditions. Eventually, we compared individual gait speeds between the two conditions. RESULTS: The measurement error was 0.01 ± 0.07 m/s under the standardized and 0.04 ± 0.06 m/s under the daily life condition. Besides, the majority of children did not use the same speed during the test situation as in daily life. CONCLUSION: This study demonstrates an accurate method to measure children's gait speed during standardized walking tests and in the children's habitual environment after rehabilitation. It only requires a single ankle sensor, which potentially increases wearing time and data quality of measurements in daily life. We recommend placing the sensor on the less affected side, unless the child wears one orthosis. In this latter case, the sensor should be placed on the side with the orthosis. Moreover, this study showed that most children did not use the same speed in the two conditions, which encourages the use of wearable inertial sensors to assess the children's walking performance in their habitual environment following rehabilitation.

Objectively measured adherence to physical activity among patients with coronary artery disease: Comparison of the 2010 and 2020 World Health Organization guidelines and daily steps.

Background: Tailored recommendations for patients after percutaneous coronary interventions (PCI) need physical activity (PA) to be objectively measured and assessed for adherence to guidelines. The recent WHO guidelines removed the daily recommended bout duration, while the potential impact of this change on patients after PCI remains unclear. Aim: We evaluated prevalence estimates of adherence to PA recommendations among patients after PCI across the 2010 [≥30 min moderate- to vigorous-intensity PA (MVPA) at ≥ 10-min bout duration] and 2020 WHO guidelines (≥30 min of MVPA of any bout duration), as well as 7,500 and 10,000 steps. Methods: We conducted an observational longitudinal single-center study with patients after PCI for chronic or acute coronary syndrome (ACS); maximal age 80 years. Wrist-worn accelerometers recorded participants' PA data from the evening of hospital discharge over the next 18 days. Results: We analyzed data from 282 participants with sufficient minimum wear time (7 days of ≥12 h), including 45 (16%) women; and 249 (88%) with ACS. Median wear time was 18 (17, 18) days. Median participant age was 62 (55, 69) years. Fifty-two participants (18.4%) fulfilled 2010 WHO guidelines and 226 (80.1%) fulfilled the 2020 WHO guidelines. Further, 209 (74.1%) participants achieved ≥7,500 steps/day and 155 (55.0%) performed ≥10,000 steps/day. Conclusion: Among participants after PCI, most MVPA was accumulated in bouts <10 min, leading to a fourfold discrepancy between participants fulfilling the 2010 and 2020 WHO PA recommendations. The number of steps/day may be a valid proxy to recent WHO PA recommendations as it is not dependent on the bout-length definition. Clinical trial registration: [ClinicalTrials.gov], identifier [NCT04663373].

Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke.

Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our study, 21 patients performed movement tasks of the Action Research Arm Test (ARAT), one of the most widely used clinical tests of upper limb motor functioning, while trained evaluators scored each task on pre-defined criteria. We collected data with just two wrist-worn inertial sensors to guarantee applicability across the continuum of care and used machine learning algorithms to estimate the ARAT task scores from sensor-derived features. Tasks scores were classified with approximately 80% accuracy. Linear regression between summed clinical task scores (across all tasks per patient) and estimates of sum task scores yielded a good fit (R 2 = 0.93; range reported in previous studies: 0.61-0.97). Estimates of the sum scores showed a mean absolute error of 2.9 points, 5.1% of the total score, which is smaller than the minimally detectable change and minimally clinically important difference of the ARAT when rated by a trained evaluator. We conclude that it is feasible to obtain accurate estimates of ARAT scores with just two wrist worn sensors. The approach enables administration of the ARAT in an objective, minimally supervised or remote fashion and provides the basis for a widespread use of wearable sensors in neurorehabilitation.

The Relationship Between Dietary Intake and Dietary-Focused Lifestyle Interventions on Risk Factors Associated with Cardiovascular Disease in Firefighters.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) is the leading cause of death to on-duty firefighters and is a substantial health concern. Preventative measures, including dietary interventions, may improve CVD in firefighters. The purpose of this review is to examine the literature on the relationship between dietary intake, dietary lifestyle interventions, and risk of CVD in firefighters. RECENT FINDINGS: Existing evidence, albeit limited, suggests that firefighters do not meet dietary guidelines. Lifestyle modifications featuring dietary interventions are effective in improving risk factors associated with CVD in firefighters. Unfortunately, no consistent nutrition-related trend was identified across the retrieved studies and only a limited number of randomized controlled trials have examined the efficacy of lifestyle intervention studies on CVD in firefighters. Although this review suggests dietary lifestyle interventions may facilitate decreases in risk factors associated with CVD in firefighters, current evidence is limited, and additional research is needed.

Validation of open-source step-counting algorithms for wrist-worn tri-axial accelerometers in cardiovascular patients.

BACKGROUND: Accurate quantification of daily steps in a cardiovascular patient population is of high importance for primary and secondary prevention. While sensor derived step counts have been sufficiently validated for hip-worn devices and commercial wrist-worn devices, there is a lack of knowledge on validity of freely available step counting algorithms for raw acceleration data collected at the wrist. RESEARCH QUESTION: How accurate are step-counting algorithms for wrist worn tri-axial accelerometers in a cardiac rehabilitation training setting? METHODS: Two step counting algorithms (Windowed Peak Detection, Autocorrelation) for tri-axial accelerometers (Axivity AX-3), were tested. Steps were recorded by chest-mounted GoPro video cameras as gold standard. Cardiovascular patients without neurological impairments enrolled in an ambulatory rehabilitation program were recruited. Recordings were performed during one 45-90 min outdoor physical therapy session of which 5-min segments of six movement categories, namely Walking, Running, Nordic, Stairs, Arm Movement [AM] With [+] and Without [-] Walking [W] were identified and analyzed. Mean absolute difference and mean absolute percentage error [MAPE] with regard to true steps measured from video are reported to report accuracy. RESULTS: Training sessions of 22 patients were recorded and analyzed. Steps were overestimated during AM-W and underestimated during Walking, Running and Stairs. Windowed Peak Detection algorithm was more accurate during AM+W and AM-W and Autocorrelation performed better during Nordic. A MAPE of close or below 10% was achieved by both algorithms for the categories: Walking, Running, Stairs and Nordic. SIGNIFICANCE: Both algorithms provided accurate results for estimation of step counts in a controlled setting of a cardiovascular patient population. The quantification of daily number of steps recorded by wrist-worn accelerometers delivering raw data analyzed by freely available algorithms is a cost-effective option for research studies.

Towards a Mobile Gait Analysis for Patients with a Spinal Cord Injury: A Robust Algorithm Validated for Slow Walking Speeds.

Spinal cord injury (SCI) patients suffer from diverse gait deficits depending on the severity of their injury. Gait assessments can objectively track the progress during rehabilitation and support clinical decision making, but a comprehensive gait analysis requires far more complex setups and time-consuming protocols that are not feasible in the daily clinical routine. As using inertial sensors for mobile gait analysis has started to gain ground, this work aimed to develop a sensor-based gait analysis for the specific population of SCI patients that measures the spatio-temporal parameters of typical gait laboratories for day-to-day clinical applications. The proposed algorithm uses shank-mounted inertial sensors and personalized thresholds to detect steps and gait events according to the individual gait profiles. The method was validated in nine SCI patients and 17 healthy controls walking on an instrumented treadmill while wearing reflective markers for motion capture used as a gold standard. The sensor-based algorithm (i) performed similarly well for the two cohorts and (ii) is robust enough to cover the diverse gait deficits of SCI patients, from slow (0.3 m/s) to preferred walking speeds.

Complementing Clinical Gait Assessments of Spinal Cord Injured Individuals using Wearable Movement Sensors.

Today's standard clinical practice to assess the walking ability of patients with neurological disorders during rehabilitation is based on simple gait tests such as the six-minute walking test (6MWT). Since the outcome of these tests is the average walking speed only, the aim of this work was to show that the application of movement sensors during a standardized walking test for the population of spinal cord injured (SCI) patients provides additional information on gait quality not directly described by the average speed. Hence, gait features that are related to quantitative and qualitative aspects of gait were extracted from the ankle sensor recordings of 29 SCI subjects and 19 healthy controls performing the 6MWT. The subjects were clustered into groups based on these gait features, and six gait features were selected to demonstrate the key differences between the clusters. The correlation of these features to the outcome of the 6MWT is discussed with their implications on gait quality.

Temperature-acclimated brown adipose tissue modulates insulin sensitivity in humans.

In rodents, brown adipose tissue (BAT) regulates cold- and diet-induced thermogenesis (CIT; DIT). Whether BAT recruitment is reversible and how it impacts on energy metabolism have not been investigated in humans. We examined the effects of temperature acclimation on BAT, energy balance, and substrate metabolism in a prospective crossover study of 4-month duration, consisting of four consecutive blocks of 1-month overnight temperature acclimation (24 °C [month 1] → 19 °C [month 2] → 24 °C [month 3] → 27 °C [month 4]) of five healthy men in a temperature-controlled research facility. Sequential monthly acclimation modulated BAT reversibly, boosting and suppressing its abundance and activity in mild cold and warm conditions (P < 0.05), respectively, independent of seasonal fluctuations (P < 0.01). BAT acclimation did not alter CIT but was accompanied by DIT (P < 0.05) and postprandial insulin sensitivity enhancement (P < 0.05), evident only after cold acclimation. Circulating and adipose tissue, but not skeletal muscle, expression levels of leptin and adiponectin displayed reciprocal changes concordant with cold-acclimated insulin sensitization. These results suggest regulatory links between BAT thermal plasticity and glucose metabolism in humans, opening avenues to harnessing BAT for metabolic benefits.

Irisin and FGF21 are cold-induced endocrine activators of brown fat function in humans.

Rediscovery of cold-activated brown adipose tissue (BAT) in humans has boosted research interest in identifying BAT activators for metabolic benefits. Of particular interest are cytokines capable of fat browning. Irisin, derived from FNDC5, is an exercise-induced myokine that drives brown-fat-like thermogenesis in murine white fat. Here we explored whether cold exposure is an afferent signal for irisin secretion in humans and compared it with FGF21, a brown adipokine in rodents. Cold exposure increased circulating irisin and FGF21. We found an induction of irisin secretion proportional to shivering intensity, in magnitude similar to exercise-stimulated secretion. FNDC5 and/or FGF21 treatment upregulated human adipocyte brown fat gene/protein expression and thermogenesis in a depot-specific manner. These results suggest exercise-induced irisin secretion could have evolved from shivering-related muscle contraction, serving to augment brown fat thermogenesis in concert with FGF21. Irisin-mediated muscle-adipose crosstalk may represent a thermogenic, cold-activated endocrine axis that is exploitable in obesity therapeutics development.

Swedish sperm donors are driven by altruism, but shortage of sperm donors leads to reproductive travelling.

BACKGROUND: Swedish legislation requires that sperm donors are identifiable to offspring. In Denmark sperm donors remain anonymous. The aim of this study was to examine sperm donation in Sweden by identifying socio-demographic backgrounds, motivations and attitudes among donors and to describe options and plans of sperm recipients. Furthermore, the willingness of Swedish health care providers to assist in treatment abroad, where sperm from an anonymous donor were to be used, was assessed. The extent of travelling to Denmark for reproductive purposes was also examined. METHODS: Thirty Swedish sperm donors completed a questionnaire and were interviewed about their backgrounds, motivations and attitudes. Thirty couples where the infertility workup had shown azoospermia were interviewed about their options for achieving parenthood. The willingness to assist in fertility treatment abroad and the extent of reproductive cross border travelling were assessed by interviewing health care providers and by contacting Danish clinics. RESULTS: Almost all donors were Caucasian. The main motivation for sperm donors was to help others. Owing to shortage of sperm donors many Caucasian recipients intended to have treatment abroad. For most non-Caucasian recipients sperm from a donor of appropriate ethnicity were not available in Sweden. Whether the sperm donor was anonymous or identifiable was not of major importance to most sperm recipients. Health care providers expressed unanimous willingness to assist in treatment with sperm from an anonymous donor. Our inquiry indicated that more than 250 Swedish sperm recipients travel to Denmark annually. CONCLUSIONS: Identifiable sperm donors are driven by altruistic motives, but shortage of sperm donors leads to reproductive travelling. Recruitment strategies to increase the number of sperm donors in Sweden are therefore warranted.

Electro-acupuncture versus conventional analgesia: a comparison of pain levels during oocyte aspiration and patients' experiences of well-being after surgery.

BACKGROUND: The primary aims were to compare the pain-relieving effect and post-operative well-being between electro-acupuncture analgesia (EA) and conventional analgesia (CA) comprising opiates. Further aims were to compare time for mobilization, and costs for time and drug consumption. METHODS: In all, 160 women undergoing IVF were randomized, according to a computer-generated list, to EA or CA. Well-being was evaluated with the State Trait Anxiety Inventory (STAI). Pain and subjective expectations and experiences were recorded on a visual analogue scale (VAS). Time and drug consumption were recorded. RESULTS: Although VAS pain ratings were significantly higher at oocyte aspiration (P < 0.0001) and after retrieval (P < 0.01) in the EA than in the CA group, they were similar 60 min after surgery. Both groups had similar STAI well-being scores. The EA group was significantly less tired and confused than the CA group after oocyte aspiration. No significant differences in time and costs for drug consumption were noted. CONCLUSION: EA cannot generally be recommended as a pain-relieving method at oocyte aspiration but might be an alternative for women desiring a non-pharmacological method. An advantage of EA is less post-operative tiredness and confusion compared with CA.