Particle Exposure Hazards of Visiting Outdoor Smoking Areas for Patients with Asthma or COPD Even in EU Countries with Comprehensive Smokefree Laws

Smokefree laws are intended to protect against second-hand smoke (SHS) in outdoor areas. We examined if exposure to PM2.5 particles in outdoor smoking areas changed breathing rates in 60 patients with asthma (n = 30) or with COPD (n = 30), in an open, non-randomised, interventional study model in Czechia, Ireland and Spain. The patients wore a PM2.5 particle monitor (AirSpeck) and a breath monitor (RESpeck) for 24 h to determine changes in breathing rates (Br) at rest and during a visit to an outside smoking area. Spirometry and breath CO were measured before and the day after visiting an outdoor smoking area. The PM2.5 levels at the 60 venues were highly variable, ranging from ≥2000 µg/m3 (in 4 premises) to ≤10 µg/m3 (in 3 premises, which had only a single wall in the structure). At 39 venues, the mean PM 2.5 levels were ≥25 µg/m3. The breathing rate changed significantly in 57 of the 60 patients, resulting in an increase in some patients and a decrease in others. Comprehensive smokefree laws were ineffective in protecting asthma and COPD patients from exposure to high levels of SHS in outside areas of pubs and terraces, which should be avoided by these patients. These findings also support the extension of smokefree laws to outside areas.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM_2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM_2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM_2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM_2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM_2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM_2.5, which vary by season.

A conceptual model for changes in finger photoplethysmograph signals caused by hand posture and isothermic regulation

OBJECTIVE

To observe changes in baseline and pulsatile light absorbance (photoplethysmograph, PPG) in the finger-tip, by raising the hand above the horizontal plane in recumbent subjects. We applied current knowledge of the circulation to the finger-tip, particularly arteriovenous anastomoses (AVAs), and the physiology of the venous circulation.

APPROACH

We studied healthy young volunteers in a quiet thermoneutral environment. A finger plethysmograph on the non-dominant hand recorded transmission of red and infra-red light, and the values were converted into absorbance to allow comparisons within and between subjects. Breathing movements were recorded unobtrusively to assess any effect on absorbance and the pulse amplitude of the signals. All body movements were passive: the study arm was elevated in a trough to about 40° above the horizontal plane. The following conditions were studied, each for 15 minutes, using the last 10 minutes for analysis: recumbent, study arm elevated, study arm horizontal, and both legs elevated by 40°.

MAIN RESULTS

There was a substantial time-related effect, and considerable variation between subjects. Arm elevation reduced red light absorbance and increased the range of amplitudes of the PPG waveform: only in subjects with large absorbances, did waveform amplitude increase. The other main effect was that spontaneous, thermoregulatory decreases in absorbance were associated with decreases in waveform amplitude.

SIGNIFICANCE

Finger-tip vessels distend with blood when AVAs open. The vessels pulsate more strongly if venous collapse allows the vessels to become more compliant. The postcapillary circulation is likely to be an important source of pulsation.

Classifying signals from a wearable accelerometer device to measure respiratory rate

Background

Automatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60 s is needed for adequate precision.

Methods

We studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a triaxial accelerometer attached to the chest wall and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula.

Results

In the test set of patients, machine classification of the respiratory signal reduced the median absolute difference (interquartile range) from 1.25 (0.56–2.18) to 0.48 (0.30–0.78) breaths per min. 50% of the recording periods were rejected as unreliable and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6 min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time.

Conclusion

Signals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.

A machine learning method was developed to classify sections of breathing records from acutely ill patients wearing a small wireless motion sensor. This would allow accurate and automatic measurement, recording, and charting of respiratory rate.https://bit.ly/301P8XW

An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments

Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM_2.5; fine particles) and ≤0.1 μm (PM_0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM_2.5 and PM_0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides guidance to prepare and plan efficient sampling campaigns to collect sufficient PM mass for various purposes in a reasonable timeframe.

Current clinical methods of measurement of respiratory rate give imprecise values

Background

Respiratory rate is a basic clinical measurement used for illness assessment. Errors in measuring respiratory rate are attributed to observer and equipment problems. Previous studies commonly report rate differences ranging from 2 to 6 breaths·min^-1 between observers.

Methods

To study why repeated observations should vary so much, we conducted a virtual experiment, using continuous recordings of breathing from acutely ill patients. These records allowed each breathing cycle to be precisely timed. We made repeated random measures of respiratory rate using different sample durations of 30, 60 and 120 s. We express the variation in these repeated rate measurements for the different sample durations as the interquartile range of the values obtained for each subject. We predicted what values would be found if a single measure, taken from any patient, were repeated and inspected boundary values of 12, 20 or 25 breaths·min^-1, used by the UK National Early Warning Score, for possible mis-scoring.

Results

When the sample duration was nominally 30 s, the mean interquartile range of repeated estimates was 3.4 breaths·min^-1. For the 60 s samples, the mean interquartile range was 3 breaths·min^-1, and for the 120 s samples it was 2.5 breaths·min^-1. Thus, repeat clinical counts of respiratory rate often differ by >3 breaths·min^-1. For 30 s samples, up to 40% of National Early Warning Scores could be misclassified.

Conclusions

Early warning scores will be unreliable when short sample durations are used to measure respiratory rate. Precision improves with longer sample duration, but this may be impractical unless better measurement methods are used.

Automatic Calculation of Hydrostatic Pressure Gradient in Patients with Head Injury: A Pilot Study

The non-surgical management of patients with traumatic brain injury is the treatment and prevention of secondary insults, such as low cerebral perfusion pressure (CPP). Most clinical pressure monitoring systems measure pressure relative to atmospheric pressure. If a patient is managed with their head tilted up, relative to their arterial pressure transducer, then a hydrostatic pressure gradient (HPG) can act against arterial pressure and cause significant errors in calculated CPP.To correct for HPG, the arterial pressure transducer should be placed level with the intracranial pressure transducer. However, this is not always achieved. In this chapter, we describe a pilot study investigating the application of speckled computing (or "specks") for the automatic monitoring of the patient's head tilt and subsequent automatic calculation of HPG. In future applications this will allow us to automatically correct CPP to take into account any HPG.

Validation of a new non-invasive automatic monitor of respiratory rate for postoperative subjects

BACKGROUND

Respiratory rate is an important measurement in patient care but frequently poorly assessed. We set out to develop a simple non-invasive device to reliably measure respiratory movements and estimate respiratory rate, in clinical circumstances.

METHODS

Respiratory movement was detected with an encapsulated tri-axial accelerometer (Orient speck) and the data transmitted wirelessly to a computer for analysis. We studied subjects after gynaecological surgery who received opioid analgesia, and compared the derived signal with a signal from nasal cannula using directly matched breaths and within the same 5 min epoch. We analysed the signals for 5 min epochs over a 15 h recording period.

RESULTS

For matched breath analysis, the instantaneous respiratory rates matched within 2 bpm on 86% of occasions. A similar match was found between epoch averages of the respiratory rate. The mean absolute difference between the respiratory rate measured by nasal cannula and Orient speck was 0.6 bpm. The Orient speck generated reliable measures of respiratory rate every 5 min in 95.4% of epochs.

CONCLUSIONS

The Orient speck provides a reliable measure of respiratory rate at frequent intervals in subjects receiving patient-controlled morphine analgesia after surgery.