A biomechanics dataset of healthy human walking at various speeds, step lengths and step widths.

The biomechanics of human walking are well documented for standard conditions such as for self-selected step length and preferred speed. However, humans can and do walk with a variety of other step lengths and speeds during daily living. The variation of biomechanics across gait conditions may be important for describing and determining the mechanics of locomotion. To address this, we present an open biomechanics dataset of steady walking at a broad range of conditions, including 33 experimentally-controlled combinations of speed (0.7-2.0 m·s-1), step length (0.5-1.1 m), and step width (0-0.4 m). The dataset contains ground reaction forces and motions from healthy young adults (N = 10), collected using split-belt instrumented treadmill and motion capture systems respectively. Most trials also include pre-computed inverse dynamics, including 3D joint positions, angles, torques and powers, as well as intersegmental forces. Apart from raw data, we also provide five strides of good quality data without artifacts for each trial, and sample software for visualization and analysis.

TimTrack: A drift-free algorithm for estimating geometric muscle features from ultrasound images.

Ultrasound imaging is valuable for non-invasively estimating fascicle lengths and other features of pennate muscle, especially when performed computationally. Effective analysis techniques to date typically use optic flow to track displacements from image sequences, but are sensitive to integration drift for longer sequences. We here present an alternative algorithm that objectively estimates geometric features of pennate muscle from ultrasound images, without drift sensitivity. The algorithm identifies aponeuroses and estimates fascicle angles to derive fascicle lengths. Length estimates of human vastus lateralis and gastrocnemius fascicles in healthy subjects (N = 9 and N = 17 respectively) compared well (overall root-mean-square difference, RMSD = 0.52 cm) to manual estimates by independent observers (n = 3), with overall coefficient of multiple correlation (CMC) of 0.98. Our tests yielded accuracy (CMC, RMSD) and processing speed similar to or exceeding that of state-of-the-art algorithms. The algorithm requires minimal manual intervention and can optionally extrapolate fascicle lengths that extend beyond the image frame. It thus facilitates automated analysis of ultrasound images without drift.

Soft tissue deformations explain most of the mechanical work variations of human walking.

Humans perform mechanical work during walking, some by leg joints actuated by muscles, and some by passive, dissipative soft tissues. Dissipative losses must be restored by active muscle work, potentially in amounts sufficient to cost substantial metabolic energy. The most dissipative, and therefore costly, walking conditions might be predictable from the pendulum-like dynamics of the legs. If this behavior is systematic, it may also predict the work distribution between active joints and passive soft tissues. We therefore tested whether the overall negative work of walking, and the fraction owing to soft tissue dissipation, are both predictable by a simple dynamic walking model across a wide range of conditions. The model predicts whole-body negative work from the leading leg's impact with the ground (termed the collision), to increase with the squared product of walking speed and step length. We experimentally tested this in humans (N=9) walking in 26 different combinations of speed (0.7-2.0 m s-1) and step length (0.5-1.1 m), with recorded motions and ground reaction forces. Whole-body negative collision work increased as predicted (R2=0.73), with a consistent fraction of approximately 63% (R2=0.88) owing to soft tissues. Soft tissue dissipation consistently accounted for approximately 56% of the variation in total whole-body negative work, across a wide range of speed and step length combinations. During typical walking, active work to restore dissipative losses could account for 31% of the net metabolic cost. Soft tissue dissipation, not included in most biomechanical studies, explains most of the variation in negative work of walking, and could account for a substantial fraction of the metabolic cost.

The high energetic cost of rapid force development in muscle.

Muscles consume metabolic energy for active movement, particularly when performing mechanical work or producing force. Less appreciated is the cost for activating muscle quickly, which adds considerably to the overall cost of cyclic force production. However, the cost magnitude relative to the cost of mechanical work, which features in many movements, is unknown. We therefore tested whether fast activation is costly compared with performing work or producing isometric force. We hypothesized that metabolic cost would increase with a proposed measure termed force rate (rate of increase in muscle force) in cyclic tasks, separate from mechanical work or average force level. We tested humans (N=9) producing cyclic knee extension torque against an isometric dynamometer (torque 22 N m, cyclic waveform frequencies 0.5-2.5 Hz), while also quantifying quadriceps muscle force and work against series elasticity (with ultrasonography), along with metabolic rate through respirometry. Net metabolic rate increased by more than four-fold (10.5 to 46.8 W) with waveform frequency. At high frequencies, the hypothesized force-rate cost accounted for nearly half (40%) of energy expenditure. This exceeded the cost for average force (17%) and was comparable to the cost for shortening work (43%). The force-rate cost is explained by additional active calcium transport necessary for producing forces at increasing waveform frequencies, owing to rate-limiting dynamics of force production. The force-rate cost could contribute substantially to the overall cost of movements that require cyclic muscle activation, such as locomotion.

The metabolic cost of in vivo constant muscle force production at zero net mechanical work.

The metabolic cost per unit force is generally thought to increase with the mechanical work done by the muscle fibres. It is currently unclear how the metabolic cost of doing alternating positive and negative muscle fibre mechanical work relates to the metabolic cost of doing zero muscle fibre mechanical work at similar muscle force. The current study aimed to investigate this issue by comparing in vivo metabolic power between a dynamic and an isometric near-constant force production task. In both tasks, participants performed periodic movement about the knee joint in the gravitational field. Therefore, net external mechanical work was constrained to be zero. The tasks mainly differed from each other in average positive knee joint mechanical power, which was 4.3±0.5 W per leg during the dynamic task and 0.1±0.1 W per leg during the isometric task. Knee extension torque was near-constant around 15.2±1.7 N m during the dynamic task and around 15.7±1.7 N m during the isometric task. Owing to near-constant knee extension torque, quadriceps tendon length was presumably nearly constant during both tasks. Therefore, knee joint mechanical work was predominantly done by the muscle fibres in both tasks. Average gross metabolic power was 3.22±0.46 W kg-1 during the dynamic task and 2.13±0.36 W kg-1 during the isometric task. Because tasks differed mainly in the amount of positive muscle fibre mechanical work, these results imply that the metabolic cost of near-constant force production in vivo at zero net mechanical work can be reduced by minimizing positive muscle fibre mechanical work.

Are you mind-wandering, or is your mind on task? The effect of probe framing on mind-wandering reports.

The last decade has seen a dramatic rise in the number of studies that utilize the probe-caught method of collecting mind-wandering reports. This method involves stopping participants during a task, presenting them with a thought probe, and asking them to choose the appropriate report option to describe their thought-state. In this experiment we manipulated the framing of this probe, and demonstrated a substantial difference in mind-wandering reports as a function of whether the probe was presented in a mind-wandering frame compared with an on-task frame. This framing effect has implications both for interpretations of existing data and for methodological choices made by researchers who use the probe-caught mind-wandering paradigm.

Do patients' disruptive behaviours influence the accuracy of a doctor's diagnosis? A randomised experiment.

BACKGROUND: Literature suggests that patients who display disruptive behaviours in the consulting room fuel negative emotions in doctors. These emotions, in turn, are said to cause diagnostic errors. Evidence substantiating this claim is however lacking. The purpose of the present experiment was to study the effect of such difficult patients' behaviours on doctors' diagnostic performance. METHODS: We created six vignettes in which patients were depicted as difficult (displaying distressing behaviours) or neutral. Three clinical cases were deemed to be diagnostically simple and three deemed diagnostically complex. Sixty-three family practice residents were asked to evaluate the vignettes and make the patient's diagnosis quickly and then through deliberate reflection. In addition, amount of time needed to arrive at a diagnosis was measured. Finally, the participants rated the patient's likability. RESULTS: Mean diagnostic accuracy scores (range 0-1) were significantly lower for difficult than for neutral patients (0.54 vs 0.64; p=0.017). Overall diagnostic accuracy was higher for simple than for complex cases. Deliberate reflection upon the case improved initial diagnostic, regardless of case complexity and of patient behaviours (0.60 vs 0.68, p=0.002). Amount of time needed to diagnose the case was similar regardless of the patient's behaviour. Finally, average likability ratings were lower for difficult than for neutral-patient cases. CONCLUSIONS: Disruptive behaviours displayed by patients seem to induce doctors to make diagnostic errors. Interestingly, the confrontation with difficult patients does however not cause the doctor to spend less time on such case. Time can therefore not be considered an intermediary between the way the patient is perceived, his or her likability and diagnostic performance.

Why patients' disruptive behaviours impair diagnostic reasoning: a randomised experiment.

BACKGROUND: Patients who display disruptive behaviours in the clinical encounter (the so-called 'difficult patients') may negatively affect doctors' diagnostic reasoning, thereby causing diagnostic errors. The present study aimed at investigating the mechanisms underlying the negative influence of difficult patients' behaviours on doctors' diagnostic performance. METHODS: A randomised experiment with 74 internal medicine residents. Doctors diagnosed eight written clinical vignettes that were exactly the same except for the patients' behaviours (either difficult or neutral). Each participant diagnosed half of the vignettes in a difficult patient version and the other half in a neutral version in a counterbalanced design. After diagnosing each vignette, participants were asked to recall the patient's clinical findings and behaviours. Main measurements were: diagnostic accuracy scores; time spent on diagnosis, and amount of information recalled from patients' clinical findings and behaviours. RESULTS: Mean diagnostic accuracy scores (range 0-1) were significantly lower for difficult than neutral patients' vignettes (0.41 vs 0.51; p<0.01). Time spent on diagnosing was similar. Participants recalled fewer clinical findings (mean=29.82% vs mean=32.52%; p<0.001) and more behaviours (mean=25.51% vs mean=17.89%; p<0.001) from difficult than from neutral patients. CONCLUSIONS: Difficult patients' behaviours induce doctors to make diagnostic errors, apparently because doctors spend part of their mental resources on dealing with the difficult patients' behaviours, impeding adequate processing of clinical findings. Efforts should be made to increase doctors' awareness of the potential negative influence of difficult patients' behaviours on diagnostic decisions and their ability to counteract such influence.

Statistical heartburn: an attempt to digest four pizza publications from the Cornell Food and Brand Lab.

BACKGROUND: We present the results of a reanalysis of four articles from the Cornell Food and Brand Lab based on data collected from diners at an Italian restaurant buffet. METHOD: We calculated whether the means, standard deviations, and test statistics were compatible with the sample size. Test statistics and p values were recalculated. We also applied deductive logic to see whether the claims made in each article were compatible with the claims made in the others. We have so far been unable to obtain the data from the authors of the four articles. RESULTS: A thorough reading of the articles and careful reanalysis of the results revealed a wide range of problems. The sample sizes for the number of diners in each condition are incongruous both within and between the four articles. In some cases, the degrees of freedom of between-participant test statistics are larger than the sample size, which is impossible. Many of the computed F and t statistics are inconsistent with the reported means and standard deviations. In some cases, the number of possible inconsistencies for a single statistic was such that we were unable to determine which of the components of that statistic were incorrect. Our Appendix reports approximately 150 inconsistencies in these four articles, which we were able to identify from the reported statistics alone. CONCLUSIONS: We hope that our analysis will encourage readers, using and extending the simple methods that we describe, to undertake their own efforts to verify published results, and that such initiatives will improve the accuracy and reproducibility of the scientific literature. We also anticipate that the editors of the journals that published these four articles may wish to consider whether any corrective action is required.

How Can People Be so Good at Intercepting Accelerating Objects if They Are so Poor at Visually Judging Acceleration?

People are known to be very poor at visually judging acceleration. Yet, they are extremely proficient at intercepting balls that fall under gravitational acceleration. How is this possible? We previously found that people make systematic errors when trying to tap on targets that move with different constant accelerations or decelerations on interleaved trials. Here, we show that providing contextual information that indicates how the target will decelerate on the next trial does not reduce such errors. Such errors do rapidly diminish if the same deceleration is present on successive trials. After observing several targets move with a particular acceleration or deceleration without attempting to tap on them, participants tapped as if they had never experienced the acceleration or deceleration. Thus, people presumably deal with acceleration when catching or hitting a ball by compensating for the errors that they made on preceding attempts.