Stride Interval Research Articles

Comparison of Short Fast Fourier Transform and Continuous Wavelet Transform in Study of Stride Interval

Neurodegenerative diseases (NDD) are a heterogeneous group of complex diseases characterized by neuronal loss and progressive degeneration of different areas of the nervous system. Gait analysis presents an early recognition system for NDD which is important to increase the patient’s awareness of their health conditions. However, it is very difficult to identify and formulate suitable digital biomarkers from the data collected from gait experiments such as stride interval and swing. The objective of this paper is to compare the result of Short - Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) on the collected stride interval of healthy young people and healthy old people. In this paper, STFT and CWT are performed on the collected stride interval and from the result of the STFT and CWT, further features are extracted like instantaneous RMS and maximum RMS value. STFT is performed on the collected stride interval from a window length of 64 to 512 while CWT is performed on the collected stride interval from the scale of 128 to 2048. The processing time of the STFT and CWT with varied window lengths and scales respectively are collected. Besides, the actual maximum time from the time - frequency plot derived from STFT and CWT is also collected. Both STFT and CWT show that the young group has a higher maximum RMS, an indication of higher stride interval than the old group and higher variance, an indication of higher gait complexity. The suitable window lengths for STFT in analyzing the stride interval are 64 and 128 while the scale for CWT should be set to the lowest scale. Overall, STFT with a window length of 64 and 128 is better in analyzing the stride interval due to low processing time at the expense of slightly less accurate time - frequency representation.

The repeatability of stride time variability, regularity, and long-range correlations

BackgroundDetrended fluctuation analysis (DFA) and sample entropy (SE) measure the long-term correlations and regularity of gait patterns, respectively, having previously been used to identify participants at risk of falling, previous history of injury, or patients with motor diseases. Since these measures are more sensitive to gait impairment than linear measures (e.g., the standard deviation [SD] of stride time), they can be potentially used in military medicine to identify soldiers at risk of injury. However, clinometric properties are yet to be established. Research QuestionWhat is the repeatability of DFA, SE, and traditional linear measures of stride time variability (SD and coefficient of variation [CV]) under various load and speed constraints? MethodsFourteen Australian Army trainee soldiers (age: 25.6 ± 5.9 y, height: 1.74 ± 0.08 m, body mass: 77.2 ± 15.1 kg, service: 1.5 ± 1.8 y) attended three sessions over two weeks, completing four 12-minute walking trials on an instrumented treadmill in each session. Participants walked with a combination of 0 kg or 23 kg loads at a self-selected or 5.5 km/h speed. Heel contacts from the right foot were identified using treadmill-embedded force plates. From 512 stride time intervals, linear (SD and CV), and non-linear (DFA and SE) measures were obtained. To assess the between-session repeatability, intraclass correlations (ICC 2,1) were employed. Results and SignificanceThere was poor-to-moderate repeatability for the SD (ICC: 0.357–0.545) and CV (ICC: 0.371–0.529). DFA showed poor-to-moderate repeatability (ICC: 0.013–0.504), while SE had poor repeatability (ICC: 0.133–0.226). Previous studies have shown that differences of > 0.19 in DFA and > 0.66 in SE can differentiate between healthy and pathological gait. These values are greater than this study's reported standard error of measurement, indicating that clinically meaningful changes may still be detectable despite low repeatability.

The Cumulative Impacts of Fatigue during Overload Training Can Be Tracked Using Field-Based Monitoring of Running Stride Interval Correlations.

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = -1.44 ± 0.90; p = 0.004), recovery (SMD = -1.83 ± 0.82; p less than 0.001), performance (SMD = -0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r2 = 0.55) and recovery (r2 = 0.55) and increased stress (r2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training.

Smartphone usage during walking decreases the positive persistency in gait cycle variability

Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized.

Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people

This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein’s algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.

The Agreement between Wearable Sensors and Force Plates for the Analysis of Stride Time Variability.

The variability and regularity of stride time may help identify individuals at a greater risk of injury during military load carriage. Wearable sensors could provide a cost-effective, portable solution for recording these measures, but establishing their validity is necessary. This study aimed to determine the agreement of several measures of stride time variability across five wearable sensors (Opal APDM, Vicon Blue Trident, Axivity, Plantiga, Xsens DOT) and force plates during military load carriage. Nineteen Australian Army trainee soldiers (age: 24.8 ± 5.3 years, height: 1.77 ± 0.09 m, body mass: 79.5 ± 15.2 kg, service: 1.7 ± 1.7 years) completed three 12-min walking trials on an instrumented treadmill at 5.5 km/h, carrying 23 kg of an external load. Simultaneously, 512 stride time intervals were identified from treadmill-embedded force plates and each sensor where linear (standard deviation and coefficient of variation) and non-linear (detrended fluctuation analysis and sample entropy) measures were obtained. Sensor and force plate agreement was evaluated using Pearson's r and intraclass correlation coefficients. All sensors had at least moderate agreement (ICC > 0.5) and a strong positive correlation (r > 0.5). These results suggest wearable devices could be employed to quantify linear and non-linear measures of stride time variability during military load carriage.

Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running

Introduction: The seemingly periodic human gait exhibits stride-to-stride variations as it adapts to the changing task constraints. The optimal movement variability hypothesis (OMVH) states that healthy stride-to-stride variations exhibit “fractality”—a specific temporal structure in consecutive strides that are ordered, stable but also variable, and adaptable. Previous research has primarily focused on a single fractality measure, “monofractality.” However, this measure can vary across time; strideto-stride variations can show “multifractality.” Greater multifractality in stride-tostride variations would highlight the ability to tune and adjust movements more.Methods: We investigated monofractality and multifractality in a cohort of eight healthy adults during self-paced walking and running trials, both on a treadmill and overground. Footfall data were collected through force-sensitive sensors positioned on their heels and feet. We examined the effects of self-paced walking vs. running and treadmill vs. overground locomotion on the measure of monofractality, α-DFA, in addition to the multifractal spectrum width, W, and the asymmetry in the multifractal spectrum, WAsym, of stride interval time series.Results: While the α-DFA was larger than 0.50 for almost all conditions, α-DFA was higher in running and locomoting overground than walking and locomoting on a treadmill. Similarly, W was greater while locomoting overground than on a treadmill, but an opposite trend indicated that W was greater in walking than running. Larger WAsym values in the negative direction suggest that walking exhibits more variation in the persistence of shorter stride intervals than running. However, the ability to tune and adjust movements does not differ between treadmill and overground, although both exhibit more variation in the persistence of shorter stride intervals.Discussion: Hence, greater heterogeneity in shorter than longer stride intervals contributed to greater multifractality in walking compared to running, indicated by larger negative WAsym values. Our results highlight the need to incorporate multifractal methods to test the predictions of the OMVH.

Application of vibration to the soles increases long-range correlations in the stride parameters during walking

Temporal fluctuations in the stride parameters during human walking exhibit long-range correlations, but these long-range correlations in the stride parameters decrease due to aging or neuromuscular diseases. These observations suggest that any quantified index of the long-range correlation can be regarded as an indicator of gait functionality. Considering the effect of task-relevant sensory feedback on augmenting human motor performance, we devised shoes with active insoles that could deliver noisy vibration to the soles of feet and assessed their efficacy in enhancing the long-range correlations in the stride parameters for healthy young adults. The vibration could be wirelessly controlled using a smartphone. The actuators, control unit, and battery in the devised shoes were light and embedded in the shoes. By virtue of this compactness, the shoes could be easily used for daily walking outside a laboratory. We performed walking experiments with 20 healthy adults and evaluated the effects of sub- and supra-threshold vibration on long-range correlations in stride interval and length. We performed detrended fluctuation analysis to quantify the long-range correlation of temporal changes in stride interval and length. We found that supra-threshold vibration, applied to the soles with the amplitude of 130 % of the sensory threshold, significantly increased the long-range correlations in stride interval and length by 10.3 % (p = 0.009) and 10.1 % (p = 0.021), respectively. On the other hand, sub-threshold vibration with the amplitude of 90 % of the sensory threshold had no significant effect. These results demonstrate that additional somatosensory feedback through barely detectable vibrations, which are supplied by compact shoes with active insoles, can enhance the indices of “healthy” complexity of locomotor function.

Multiscale SVD entropy for the analysis of gait dynamics

This work explores the use of singular value decomposition (SVD) entropy to characterize the complexity of stride interval sequences from gait dynamics. The computation of the approximate (ApEn) and sample (SampEn) entropies require the specification of the length of the scrutinized patterns (m) and the pattern similarity tolerance (r). In contrast, the SVD entropy estimation does not require these parameters since the computations are based on the inspection of similarities among lagged vectors. In this regard, the present work aims to assess the SVD entropy to characterize gait dynamics. The gait maturation and the neurodegenerative disease (NDD) datasets from Physionet were considered. The SVDEn of the gait dynamics decreased with the age to normalized values of about 0.2. In contrast, the SampEn, which was used for comparison, decreased to values close to zero. For the gait NDD dataset, it was found that the gait dynamics of Parkinson, ASL and Huntington pathologies exhibit increased SVDEn relative to a control group, with normalized values of about 0.2 and 0.4, respectively. However, the SVDEn and the SampEn were unable to distinguish between the control and neurodisease groups (p < 0.05). Overall, the results indicated that the gait dynamics of the NDD are less regular than that of the control group. Although the SVDEn and other entropy metrics (e.g., SampEn) showed similar results, the SVDEn offers the advantage that the computation require the specification of only the scale parameter.

Visual feedback influences the consistency of the locomotor pattern in Asian elephants (Elephas maximus).

Elephants are atypical of most quadrupeds in that they maintain the same lateral sequence footfall pattern across all locomotor speeds. It has been speculated that the preservation of the footfall patterns is necessary to maintain a statically stable support polygon. This should be a particularly important constraint in large, relatively slow animals. This suggests that elephants must rely on available sensory feedback mechanisms to actively control their massive pillar-like limbs for proper foot placement and sequencing. How the nervous system of elephants integrates the available sensory information for a stable gait is unknown. Here we explored the role that visual feedback plays in the control of the locomotor pattern in Asian elephants. Four Asian elephants (Elephas maximus) walked with and without a blindfold as we measured their stride time intervals. Coefficient of variation was used to assess changes in the overall variability of the stride time intervals, while approximate entropy was used to measure the stride-to-stride consistency of the time intervals. We show that visual feedback plays a role in the stride-to-stride consistency of the locomotor pattern in Asian elephants. These results suggest that elephants use visual feedback to correct and maintain proper sequencing of the limbs during locomotion.

Temporal organization of stride-to-stride variations contradicts predictive models for sensorimotor control of footfalls during walking.

Walking exhibits stride-to-stride variations. Given ongoing perturbations, these variations critically support continuous adaptations between the goal-directed organism and its surroundings. Here, we report that stride-to-stride variations during self-paced overground walking show cascade-like intermittency-stride intervals become uneven because stride intervals of different sizes interact and do not simply balance each other. Moreover, even when synchronizing footfalls with visual cues with variable timing of presentation, asynchrony in the timings of the cue and footfall shows cascade-like intermittency. This evidence conflicts with theories about the sensorimotor control of walking, according to which internal predictive models correct asynchrony in the timings of the cue and footfall from one stride to the next on crossing thresholds leading to the risk of falling. Hence, models of the sensorimotor control of walking must account for stride-to-stride variations beyond the constraints of threshold-dependent predictive internal models.

Surrogate multivariate Hurst exponent analysis of gait dynamics

The analysis of complex patterns in human gait dynamics typically relies on measuring stride intervals. Several studies have revealed that the dynamics of stride intervals exhibit fractal characteristics that depend on health, age, and task conditions. However, with current measurement devices, other gait parameters such as the swing and stance intervals can also be measured. This prompts the question of whether multivariate analysis provides a more detailed view of gait dynamics. This work aims to use multivariate rescaled range (MR/S) analysis to characterize the fractality of the combined behavior of the stride, stance, and swing intervals in terms of the Hurst exponent, which is an index of the fractality of a time series. The MR/S method was equipped with a surrogate data analysis, to refer to a corrected Hurst exponent, which is defined as a distance to randomness. Datasets of neurodegenerative conditions (amyotrophic lateral sclerosis, Huntington's disease and Parkinson's disease) from Physionet were used to evaluate the ability of MR/S analysis to characterize different phases of gait dynamics. An index of distance to randomness relative to a 95 % confidence interval obtained from surrogate data was introduced to alleviate biased estimation of the Hurst exponent for relatively small samples. The results showed that the distance to randomness of stance and stride intervals was higher (p < 0.05) than that of swing intervals. On the other hand, the stride interval discriminated (p < 0.05) the control conditions from the neurodegenerative conditions. In contrast, the swing interval discriminated (p < 0.05) the Parkinson's disease from ALS and Huntington's disease. Overall, the results indicate that multivariate analysis is a suitable approach for a detailed characterization of the impact of different gait phases on gait dynamics.

Model of gait control in Parkinson's disease and prediction of robotic assistance.

Gait variability of healthy adults exhibits Long-Range Autocorrelations (LRA), meaning that the stride interval at any time statistically depends on previous gait cycles; and this dependency spans over several hundreds of strides. Previous works have shown that this property is altered in patients with Parkinson's disease, such that their gait pattern corresponds to a more random process. Here, we adapted a model of gait control to interpret the reduction in LRA that characterized patients in a computational framework. Gait regulation was modeled as a Linear-Quadratic-Gaussian control problem where the objective was to maintain a fixed velocity through the coordinated regulation of stride duration and length. This objective offers a degree of redundancy in the way the controller can maintain a given velocity, resulting in the emergence of LRA. In this framework, the model suggested that patients exploited less the task redundancy, likely to compensate for an increased stride-to-stride variability. Furthermore, we used this model to predict the potential benefit of an active orthosis on the gait pattern of patients. The orthosis was embedded in the model as a low-pass filter on the series of stride parameters. We show in simulations that, with a suitable level of assistance, the orthosis could help patients recovering a gait pattern with LRA comparable to that of healthy controls. Assuming that the presence of LRA in a stride series is a marker of healthy gait control, our study provides a rationale for developing gait assistance technology to reduce the fall risk associated with Parkinson's disease.

Adaptive Bayesian sum of trees model for covariate-dependent spectral analysis.

This paper introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dependencies and interactions between covariates and the power spectrum, which are often observed in studies of biomedical time series. Local power spectra corresponding to terminal nodes within trees are estimated nonparametrically using Bayesian penalized linear splines. The trees are considered to be random and fit using a Bayesian backfitting Markov chain Monte Carlo (MCMC) algorithm that sequentially considers tree modifications via reversible-jump MCMC techniques. For high-dimensional covariates, a sparsity-inducing Dirichlet hyperprior on tree splitting proportions is considered, which provides sparse estimation of covariate effects and efficient variable selection. By averaging over the posterior distribution of trees, the proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates. Empirical performance is evaluated via simulations to demonstrate the proposed method's ability to accurately recover complex relationships and interactions. The proposed methodology is used to study gait maturation in young children by evaluating age-related changes in power spectra of stride interval time series in the presence of othercovariates.

Ensemble entropy: A low bias approach for data analysis

To quantify the irregularity of data, there are a number of entropy measures each with its own advantages and disadvantages. In this pilot study, a new concept, namely ensemble entropy, is introduced and used to generate more stable and low bias signal patterns for entropy estimation. We propose ensemble versions of sample entropy (SampEn), permutation entropy, dispersion entropy (DispEn), fluctuation DispEn (FDispEn) based on the combination of different parameters initialization for an original entropy method. Also, ensemble Shannon and conditional entropy methods based on the entropy values obtained by different entropy algorithms are developed in this study. We applied the techniques to different synthetic and three biomedical datasets to investigate the behavior of the ensemble methods on the changes in the data dynamics. The results suggest that ensemble approaches are able to distinguish different kinds of noises and the degrees of randomness in our generated MIX process. Ensemble SampEn, unlike SampEn, does not result in undefined values for short signals. Ensemble DispEn needs a smaller number of samples for distinguishing different kinds of noise. The majority of ensemble methods result in larger differences between younger and older subjects using their RR intervals as well as healthy young vs. elderly children using their walking stride interval data based on Hedges’ g effect size. The ensemble algorithms lead to more stable results (lower coefficients of variations) for the synthetic data (different kinds of noises and mixed processes) and discriminated different types of physiological signals better than their corresponding original entropy approaches. The Matlab code used in this paper is available at https://github.com/HamedAzami/.

Adiabatic Invariant of Center-of-Mass Motion during Walking as a Dynamical Stability Constraint on Stride Interval Variability and Predictability.

Simple SummaryHuman walking exhibits properties of both stability and variability. On the one hand, the variability of the interval of time between heel strikes is autocorrelated, i.e., not randomly organized. On the other hand, walking is highly stereotyped and arguments from general mechanics suggest that the stability of gait can be assessed according to invariant properties. This study aims at proposing one of those invariants. Participants walked for 10 min at a natural pace, with and without a metronome indicating participants’ preferred step frequency. In both cases, we use different parameters to assess both the variability and stability of walking. We verify a known result: the metronome strongly alters the variability of the motion. However, despite the large variability changes, our proposed adiabatic invariant is preserved in both conditions, demonstrating the stability of gait. It appears as though our model reveals dynamical constraints that are “hidden” beyond apparent walking variability.Human walking exhibits properties of global stability, and local dynamic variability, predictability, and complexity. Global stability is typically assessed by quantifying the whole-body center-of-mass motion while local dynamic variability, predictability, and complexity are assessed using the stride interval. Recent arguments from general mechanics suggest that the global stability of gait can be assessed with adiabatic invariants, i.e., quantities that remain approximately constant, even under slow external changes. Twenty-five young healthy participants walked for 10 min at a comfortable pace, with and without a metronome indicating preferred step frequency. Stride interval variability was assessed by computing the coefficient of variation, predictability using the Hurst exponent, and complexity via the fractal dimension and sample entropy. Global stability of gait was assessed using the adiabatic invariant computed from averaged kinetic energy value related to whole-body center-of-mass vertical displacement. We show that the metronome alters the stride interval variability and predictability, from autocorrelated dynamics to almost random dynamics. However, despite these large local variability and predictability changes, the adiabatic invariant is preserved in both conditions, showing the global stability of gait. Thus, the adiabatic invariant theory reveals dynamical global stability constraints that are “hidden” behind apparent local walking variability and predictability.

A novel method to assess correlation structure among stride intervals using a low-back accelerometer: A validation study in older adults

A novel method to assess correlation structure among stride intervals using a low-back accelerometer: A validation study in older adults

Reliability of a novel method to assess correlation structure among consecutive stride intervals using a low-back accelerometer: A test-retest study

Reliability of a novel method to assess correlation structure among consecutive stride intervals using a low-back accelerometer: A test-retest study

Wearable gait monitoring for diagnosis of neurodegenerative diseases

Wearable gait monitoring is of significance to improve the quality of clinical diagnosis of neurodegenerative diseases. However, the symptoms of neurodegenerative diseases are always diverse for different subjects. To address this issue, wireless gait monitoring system detecting plantar bend and impact force is proposed. PVDF sensors are arranged as the sensing part. Signal conditioning circuit, WiFi transmission module, and feature signal processing unit are also integrally designed. The proposed system is utilized to 6 healthy adults for normal single walking and cognitive activity interfered walking respectively. Experimental results show that walking interfered by cognitive activities has a smaller plantar bend while larger stride intervals and asymmetry index than normal single walking. Furthermore, the designed system is validated by patients affected with Parkinson’s disease. Results indicate that the proposed method can be implemented as a medical decision support system for diagnosis of neurodegenerative diseases in daily life.

On Selection of Threshold Values for Symbolic Entropy Analysis of Human Gait.

Human gait is a complex system affected by many other processes of human physiology. It has multiple inputs and multiple outputs. Due to its complex nature, signals obtained from this system also exhibit complexity and variability. It has been analyzed in many ways to extract the information inhabited by these signals. Entropy based methods showed a significant impact on analysis of gait signals. Threshold based symbolic entropy analysis is one of the entropy based method applied to human gait signals. In this method Normalized Corrected Shannon Entropy (NCSE) is calculated to compare the spontaneous output of the human locomotors system during different walking conditions. Selection of the threshold values is an important task and sometimes it depends upon the type and size of the data. Results are dependent on the proper selection of the threshold. In this paper, different threshold selection methods are discussed and their impact on the results are presented. It was observed that, variation in stride interval has performed better as a threshold value as compare to the other methods. It provided maximum separation among different groups of gait data used in this study. We concluded with the recommendations for the proper selection of the threshold values to apply symbolic entropy methods on human gait signals. Clinical relevance Various gait related problems are common in older adults that increase with age and are associated with reduced gait speed increased fall risk and other impairments. Consequently objective gait assessment in the clinics depending upon the size of the available data has become increasingly important for the classification of gait. It was found that while applying symbolic entropy method proper selection of threshold result into improved classification of different types of gait data which will help the clinician for better decision-making regarding treatment.