Which physical fitness test assess how fast a person can respond to a stimulus?

Agility, Balance, and Reaction Time

For activities requiring agility, balance, and good reaction time, the physical fitness profile should include these items. Balance testing is especially important in the elderly population.155 O’Brien155 advocated the Sharpened Romberg test, functional reach test, timed get-up-and-go test (seeChapters 2 and11), and Tinetti assessment tool for balance and gait. The functional reach test involves the patient reaching forward as far as possible without falling forward or taking a step while the examiner measures for distance horizontally. The Tinetti assessment tool has two parts. The first part measures static and dynamic sitting and standing balance (Table 17.14) and the second part assesses gait (Table 17.15).175 Ideally, testing should be related to the specific activity.Agility is defined as the ability to change directions rapidly when one is moving ata high rate of speed.3 Agility and balance tests are often measured by time or accuracy (e.g., correct two out of three).3,176

Agility and Balance Tests

Carioca

Run-and-cut drills

Backpedal and throw at stationary or moving target

Kick at stationary or moving target (different distances)

One-arm spin

Shuttle drills

Pivoting drills

Blocking drills

Figure-eight running

Front-to-back and side-to-side hops

Sidestep tests

Beam-walking tests

Sharpened Romberg Test152,155

With eyes open, stand with feet together for 10 seconds.

Repeat step 1 with eyes closed.

With eyes open, place one foot halfway in front of the other for 10 seconds.

Repeat step 3 with eyes closed.

With eyes open, place one foot directly in front of the other for 10 seconds.

Repeat step 5 with eyes closed.

Historical Background

The first systematic assessment of the relations between adult age and reaction time (RT) was performed by Galton in the late 1800s, although analyses of his data were not published until much later. There were sporadic investigations of the relations between age and RT until about 1950, when interest in this topic increased because of an assumption that an individual's RT might be informative about the status of his or her neurological system. A number of studies then appeared in which RT was the focus of the research.

Beginning in the 1960s, there was an increase in the use of RT as a primary dependent variable because it was assumed to reflect the duration of interesting mental processes. RT measurement in the context of mental chronometry has been a valuable tool in the information-processing perspective on cognition, and a very large number of studies have been reported within this tradition. In recent years a major issue in RT research has been the extent to which RT measures reflect general or specific age-related influences.

Response Time

EMS response time is often used as a benchmark of overall system performance, with accepted standards of 4 minutes for BLS and 8 minutes for ALS arrival.59 Specifically, national guidelines recommend medical intervention within 4 to 6 minutes of EMS activation for patients in cardiac or respiratory arrest, although such intervention may include dispatcher-assisted bystander CPR or AED use.60 Studies of set response time criteria have shown mixed results. Retrospective and post hoc analyses of urban EMS transport show a possible survival benefit for patients transported to hospitals in 4 minutes or less, although the advantage was not significant for those not in cardiac arrest.59,61 E911 systems—in which caller location is available instantly—can decrease response times because they help plan more direct routes to the scene and reduce the likelihood of ambulance crews becoming lost.16 Furthermore, response times should be targeted to specific regional needs and may be maximized by a strong community-wide first-responder presence, strategic placement of ambulances based on historical call data, mobile mapping or global positioning systems, and adequate fleet maintenance.5,61

From their earliest beginnings as purely military organizations to the complex heterogeneous models in place today, EMS systems have evolved into critical providers of health care and a critical component of modern emergency medicine. In 2010, EMS was approved by the American Board of Medical Specialties as the sixth subspecialty of emergency medicine, with development of standard examination and certification procedures underway. This further solidifies EMS as a distinct clinical subspecialty involving the prehospital care of sick and injured patients, regardless of specific local or regional system design.

Definition and History

Reaction time may be defined simply as the time between a stimulus and a response. Three basic reaction time paradigms have been described: (1) simple reaction time has a single stimulus and a single predefined response, (2) recognition reaction time has several false stimuli mixed with one correct stimulus prompting the response, and (3) choice reaction time involves multiple stimuli and differing responses for each stimulus. Serial reaction time is a combination of recognition and choice reaction time, where the stimulus is a repeating sequence that the subject must learn to predict and then to respond in a prescribed fashion.

An example of simple reaction time would be the time from a buzzing sound to moving a finger. Adding false chime or ring sounds would convert the model to recognition reaction time, while creating different responses for the chime or other ring sounds would convert the model to a measurement of choice reaction time.

Reaction times are usually recorded as a mean of several trials following a practice period (cueing) to minimize practice effects and to reduce variability of the response. Reaction times are very situation specific, and can vary according to choice of device, stimulus, or response. In designing a paradigm for measuring the reaction time, it is important to select the test instrument carefully and to compare trends between studies rather than absolute times if different devices are used. In general, simple reaction times are faster than recognition reaction times and recognition reaction times are faster than choice reaction times. Multiple factors, including age, sex, IQ, handedness, fatigue, sleep deprivation, and medications may influence the reaction time, and it is of paramount importance for studies to control for these variables. Many reaction time instruments filter results to eliminate responses that are shorter than physiologic (anticipated) or uncharacteristically long (distraction). However, such filtering techniques can introduce other errors.

Reaction Time

Clinicians' awareness that TBI alters reaction time is important to daily rounds, because slowed processing may be misconstrued as abulia, aphasia, or disorientation when, in actuality, the patient may eventually come up with the answer if given enough time. From the literature, we know that severe TBI is associated with prolonged simple and choice reaction times when patients are presented with visual, auditory, and tactile stimuli.16 Likewise, functional magnetic resonance imaging (fMRI) studies support the hypothesis that patients with TBI require more of their brain to perform a simple task.17 Even in cases of mild TBI, such as concussion, patients react slowly, particularly when interhemispheric transfer of information is required. Clinically, this evidence translates to patients who may appear to be cognitively intact during simple tests but will break down when challenged with greater informational load or fatigue. The breakdowns manifest as delayed, erroneous, or inconsistent responses.18

Reaction time.

Measures of reaction time have been used in many different contexts to evaluate warning signs. The basic assumption is that a symbol quickly reacted to is more salient (or attention demanding) than one that is reacted to slowly. Reaction time measures are particularly useful when quick reaction times are an actual requirement of the task (as in the driving of automobiles). Their value becomes questionable when reaction time is not of essence to the task. This latter point follows because reaction time is frequently not related to other measures of sign or label quality.

6.1.1 Reaction Time

Reaction time is a simple form of speed, and depends mainly on the nervous system. It is the time interval between a signal and the reaction to it—for instance, when the starter pistol is fired at the start of the 100 m. The signal is perceived by the sensory system and the reaction evolves in the brain, then runs through the spinal cord to the muscles, resulting in contraction. Reaction time to sounds and visual information is on average 0.13–0.18 s, without consideration of speed of sound. It is determined by genetic factors and age, and it changes during effort; for instance, its value decreases/improves during loading and it is impaired by fatigue. Simple reaction time (reaction to a certain stimulus) of an average individual is 0.16–0.2. It can be improved by training; however, even the best sprinters cannot go below 0.1 s. The 100 m sprint is one of the shortest events, and the winner does not necessarily have the fastest reaction time.

Simple reaction time is the reaction time to a certain stimulus, whereas complex reaction time is the reaction to the right stimulus selected from many stimuli, and it increases reaction time.

Reaction Time

The reaction time defines the necessary time to identify the situation, decide the kind of reaction, and start the action through activation of certain muscles. The reaction time depends on many parameters, such as age, tiredness, and also on parameters which are difficult to estimate, for example, an eye blink may enlarge the reaction time by approximately 0.2 s. One aspect, which is also of greatest importance, is the visibility of the object. In the case of low contrasts or small light differences, reaction time may increase dramatically. This is the reason why so many pedestrian accidents occur during nighttime. In addition, there is a significant difference if the resulting action has to be done by the arms or by the legs. Due to the longer distance between brain and leg, an action being performed with the leg will require a significantly longer reaction time.

Typical reaction times are between 0.5 and 1.5 s. Racing drivers are known to react during the race within a time of 0.3 s.

6.2.9 Raw data analysis

PTR-TOF-MS produces a large amount of information. A quick look at a PTR-TOF-MS spectrum of ambient air or exhaled breath gas will reveal that on virtually every integer mass two or three peaks can be seen at minimum. Several studies have tried to advance the data evaluation methodologies for the Ionicon PTR-TOF-MS, in order to make use of the full potential of this technology. However, no automated universally applicable procedure has been accepted up to now, but solutions to specific problems have been developed.30,35–37,51,52 Customized tools can be found in the Internet1 or have been presented in the doctoral thesis of Thorsten Titzmann.50

In the present study, the goal of raw data analysis was to extract a summary about the peaks contained in a spectrum, their corresponding m/z and peak intensity. A certain degree of automation in raw data analysis was desired due to the large number of samples in our study, and the large number of peaks contained in one spectrum. This peak summary was used for further statistical data analysis, e.g. the comparison of two subgroups, like smokers with non-smokers in the present case. This comparison leaded to statistically significant differences between these two subgroups, pointing to chemical compounds, which might originate from cigarette smoke inhalation.

Comparison of breath gas data from non-smokers and smokers can be used as a benchmark test for the data quality, by judging the results of such a statistical analysis against the current knowledge on VOC in tobacco smoke. In such an approach it is an important prerequisite that those signals arising from the same chemical compound are compared by statistical analysis. In contrast to PTR-TOF-MS, the non-selective quadrupole PTR-MS does not fulfill this condition, but broad peaks are followed and evaluated. Chemical noise due to different chemical components of the same nominal mass might mask certain differences between two subgroups. Still, in other cases, differences between nominal peaks are still high enough, as the important compound is the main contributor to its intensity.

Even though mass resolution is high in PTR-TOF-MS, both single peaks as well as overlapping peaks can be found in a TOF spectrum. It is not clear in every case whether a peak should be treated as a single peak originating from a single ion species or not. A criterion to decide whether multi-peak analysis has to be performed is difficult to define for the whole range of TOF. Reasons are manifold, for instance, the peak broadness (RFWHM) is not constant but increases with increasing time-of-flight, due to statistical uncertainty of the absolute time-of-flight determination.53 Another difficulty comprises peaks originating from the detection system that can be found as spectral artifacts, especially when count rates are high. These artifacts must be distinguished from real peaks of ionic species.

The following approach was applied to the data presented in this chapter. In a first step, all measured breath gas TOF spectra were added to one sum spectrum. By this simple procedure, random peaks at low signal-to-noise-ratio were removed and non-random peaks became sharper. The definition of peaks that partly overlapped was improved in terms of the center (the maximum of the peak) and the full width. Based on this sum spectrum, a base set of 790 peaks was identified in the mass range of m/z 15–800. Each PTR-TOF spectrum was searched for this base set subsequently. Signal intensities and corresponding m/z were determined from Gaussian fits using Matlab (MathWorks version 7.8.0.347). Statistical significances of the differences between the means/medians of smoker’s samples and non-smoker’s samples were investigated using the Student’s t-test and the Mann-Whitney U-test (p < 0.05 was considered to be statistically significant).

Fast response

Response time of thermal sensors plays a key role in real-time measurement of signals. A fast response time enables thermal sensors to respond instantaneously to the change of temperature, flow and acceleration. Experimentally, the response time is determined from 63.2% (or 90% in some applications) of the output signal (Sosna et al., 2011). For example, the thermopile output signals from the thermal flow sensor (Ke et al., 2019) is estimated to be approximately 250 ms based on 63.2% output signal as shown in Fig. 11. The response time can also be estimated from exponential fit of the sensor temperature T and the time t using T = A − B × e− t/τ, where τ is the time constant; A and B are experimental constants . A typical response time of MEMS thermal sensors is in millisecond range or a response frequency in kHz ranges. Theoretically, the response of thermal sensors depends upon the thermal resistance Rth and thermal capacitance Cth of the sensing element, with the time constant determined by τ=Rth×Cth=LA×k×ρVCH, where L and A are the length and cross-sectional area of the thermal sensing element; V and CH are the volume and heat capacity of the sensing element; k and ρ are the thermal conductivity and density of the sensor material. To achieve a faster response, designers can consider to scale down the dimensions of the sensor or/and select materials with high thermal conductivity, low density and low heat capability.