Continuous Observations Research Articles

Decadal Monitoring of the Hydrothermal System of Stromboli Volcano, Italy

AbstractIn active volcanoes, magmatic fluids rising toward the surface may interact with shallow waters, thereby forming hydrothermal systems that record variations in magma dynamics at depth. Here, we report on a data set comprising the chemical and isotopic composition of thermal waters and dissolved gases from Stromboli Island (Aeolian Volcanic Arc, Southern Italy) that spans 14 years (2004–2018) of continuous observations. We show that the shallow thermal aquifer of Stromboli results from variable mixing between meteoric water, seawater and magmatic fluids. Gas‐water‐rock interactions occur, which induce a large spectrum of variation in both water and gas chemistry. These shallow processes do not affect the 3He/4He of helium dissolved in thermal waters, which records a magmatic signature that varies in response to changes in magma supply at depth. We show that in periods of more intense volcanic activity, the helium isotopic composition of thermal waters approaches that of the gas emitted from the magma residing at 7–10 km depth. Investigation of hydrothermal waters at active volcanoes is a promising tool to examine magmatic fluids and their shallow circulation, as well as to evaluate the state of activity of a volcano, particularly when summit areas are inaccessible.

Optimality Guarantees for Particle Belief Approximation of POMDPs

Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of O(C), where C is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.

Fundamental limits for sensor-based robot control

Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot’s sensors for a given task. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano's inequality from information theory, we demonstrate that this quantity provides an upper bound on the highest achievable expected reward for one-step decision-making tasks. We then extend this bound to multi-step problems via a dynamic programming approach. We present algorithms for numerically computing the resulting bounds, and demonstrate our approach on three examples: (i) the lava problem from the literature on partially observable Markov decision processes, (ii) an example with continuous state and observation spaces corresponding to a robot catching a freely-falling object, and (iii) obstacle avoidance using a depth sensor with non-Gaussian noise. We demonstrate the ability of our approach to establish strong limits on achievable performance for these problems by comparing our upper bounds with achievable lower bounds (computed by synthesizing or learning concrete control policies).

Space Surveillance payload camera breadboard: Star tracking and debris detection algorithms

Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth’s orbits. However, SST efforts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera breadboard was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms’ tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net, to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost.

Monitoring Aerosol Optical Depth for Air Quality Through Himawari-8 in Urban Area West Java Province Indonesia

Air quality is a crucial parameter in human life. One air quality indicator can be observed through Aerosol Optical Depth (AOD). If these substances are pollutants such as particulate matter, aerosols, and ozone, it is confident that air quality will deteriorate, threatening human health and causing climate change. AOD monitoring can be used as a basis for policymakers and related parties to maintain the stability of air quality in the atmosphere. Many ground observation stations monitor air quality by obtaining data on PM2.5 and PM10 aerosol particles. However, the number of ground stations is limited, resulting in incomplete data. Fortunately, remote sensing satellites have the advantage of covering large areas and providing continuous observations, with the ability to gather information on large-scale aerosol and obtain spatiotemporal distribution. Therefore, this research aims to obtain AOD through Himawari-8 and analyze the spatiotemporal air quality in urban areas of West Java based on AOD. The research methodology used in this study is descriptive analysis with an empirical research approach. Assisted by remote sensing technology and Geographic Information Systems, this research generates AOD data extraction that can be obtained from the new generation satellite of Himawari-8. The distribution of AOD levels and spatiotemporal monitoring in urban areas of West Java is very dynamic depending on anthropogenic activity in a particular area and time. Keywords: Aerosol Optical Depth (AOD), Air Quality, Himawari-8

Magnetic-field-driven director configuration transitions in radial nematic liquid crystal droplets.

We study the director configurations of nematic liquid crystal (NLC) droplets with homeotropic anchoring in a magnetic field and report observation of a magnetic-field-driven transition from a deformed radial to an axial-with-defect configuration. Magnetic-field-induced transitions in NLC droplets differ fundamentally from the traditional planar Freedericksz transition due to the spherical droplet geometry and resulting topological defect. This transition has been studied theoretically, but the director configurations and mechanism of defect evolution in an applied magnetic field have yet to be observed experimentally. To this end, we combine polarized optical microscopy with a variable electromagnet (≤1T) for continuous observation of droplet director fields, and we employ Landau-de Gennes numerical simulations to elucidate the director configurations and first-order nature of the transition. We report a configuration transition from point defect to ring defect at a critical field, which varies inversely with droplet radius and is relatively independent of surfactant type and concentration. We also estimate anchoring strengths of commonly used surfactants at the NLC-aqueous interface.

A Retrospective Study on Previously Untreated Non-metastatic Breast Cancer Patients at a Tertiary Care Hospital

Introduction: The combination of adoxorubicin and cyclophosphamide (AC) followed by a taxane (Docetaxel or Paclitaxel) and epirubicin and cyclophosphamide (EC) followed by a taxane (Docetaxel or Paclitaxel) is a standard regimen for breast cancer patients. In this study, we compared the three-year overall survival (OS) and progression free survival (PFS) of a patient with previously untreated non-metastatic breast cancer patients. Objectives: To find out the overall survival and progression free survival among previously untreated non-metastatic breast cancer patients after standard chemotherapy. Methods: Fifty-six patients received four cycles of doxorubicin at a dose of 60mg/m2 and cyclophosphamide at a dose of 600 mg/m2 followed by four cycles of paclitaxel at a dose of 175mg/m2. Thirteen patients received AC followed by four cycles of docetaxel at a dose of 75mg/m2. Twenty-one patients received four cycles of epirubicin (75mg/m2) and cyclophosphamide (600mg/m2), followed by four cycles of paclitaxel (175mg/m2). Twelve patients were given EC followed by four cycles of docetaxel (75mg/m2). Results: The four groups of patients had similar characteristics. Overall survivalafter three years was 92% in AC followed by paclitaxel, 98% in AC followed by docetaxel, 95% and 97% in EC followed by paclitaxel and docetaxel respectively. One year of PFS in AC followed by paclitaxel and docetaxel was 98% and 100% respectively whereas in EC followed by paclitaxel and docetaxel were 98% and 98% respectively. The four arms had 96%, 98%, 97% and 96% PFS after two years respectively. This research also revealed that four chemotherapy regimens had three-year survival rates of 93%, 98%, 93% and 96% respectively. Conclusion: AC or EC followed by taxane regimens well suitable regimens for the population of patients with previously untreated non-metastatic breast cancer patients. The findings show the need for continued observation as well as taking into account the treatment of other patients throughout different periods. JAFMC Bangladesh. Vol 18, No 2 (December) 2022: 11-14

National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

Along with the development of information and positioning technologies, there emerges passively collected location data that contain location observations with time information from various types of mobile devices. Passive location data are known for their large sample size and continuous behavior observations. However, they also require careful and comprehensive data processing and modeling algorithms for privacy protection and practical applications. In the meantime, the travel demand estimation of origin–destination (OD) tables is fundamental in transportation planning and analysis. There is a lack of national OD estimation that provides time-dependent travel behaviors for all travel modes. Passively collected location data appeal to researchers for their potential of serving as the data source for estimation and monitoring of large-scale multimodal travel demand. This research proposes a comprehensive set of methods for passive location data processing including data cleaning, activity location and purpose identification, trip-level information identification, social demographic imputation, sample weighting and expansion, and demand validation. For each task, the paper evaluates the state-of-the-practice and state-of-the-art algorithms and develops an applicable method jointly considering different features of various passive location data sources, imputation accuracy, and computation efficiency. The paper further examines the viability of the method kit in a national-level case study and successfully derives the multimodal national-level OD estimates with additional data products, such as trip rate and vehicle miles traveled, at different geographic levels and temporal resolutions.

High-frequency, continuous hydrogen observations at Mace Head, Ireland from 1994 to 2022: Baselines, pollution events and ‘missing’ sources

This study analysed just under three hundred thousand 40-min hydrogen observations taken at the Mace Head baseline station on the Atlantic Ocean coastline of Ireland between 1994 and 2022. Advection of European polluted air masses to Mace Head brought elevated hydrogen mixing ratios. Annual mean European excess mixing ratios were between 2 and 4 ppb above baseline in the 1990s but have steadily declined to about 0.2 ppb in 2021, with an e-folding time of 10 years. European excess carbon monoxide mixing ratios declined in an analogous manner, confirming the cause of the declines in both hydrogen and carbon monoxide as the fitting of exhaust gas catalysts to petrol-engined motor vehicles. The annual mean baseline hydrogen mixing ratios were constant over the 1994–2010 period, averaging about 513 ppb, before an upwards trend set in bringing the annual mean up to 532 ppb in 2021. Monthly baseline mixing ratios exhibited a seasonal cycle with an average spring maximum of 531 ppb and an autumn minimum of 497 ppb. Differences between the monthly means and the 1995–2010 averages were constant up to December 2015 when an anomalous growth trend of 2–4 ppb yr−1 set in. A two-hemisphere two-box model was used to describe the hydrogen budget over the 1995–2022 period. This model highlighted that a ‘missing’ source was required to explain the anomalous growth in the Mace Head observations from 2010 onwards. Six potential candidates for this ‘missing’ source were characterised. However, there is a dearth of hydrogen observations with which to identify the real ‘missing’ source with any certainty. The anomalous growth in hydrogen since 2010 reported here may feasibly be the first evidence of the widespread seepage of natural hydrogen into the atmosphere.

ASO-DKELM: Alpine skiing optimization based deep kernel extreme learning machine for elderly stroke detection from EEG signal

Stroke is considered the third leading cause of mortality followed by cancer and heart disease worldwide. Stroke detection in advance is the only procedure to avoid health risks while it necessitates continuous monitoring and observation of subjects. Many research works already exist in the medical field to detect diseases in advance using machine learning techniques. However, their detection results are limited by several biasing factors such as more training time and imprecise detection. So a novel stroke detection system using the Deep Kernel Extreme Learning Machine based Alpine Skiing (DKELM-AS) approach is proposed to predict the probability of subjects being affected by stroke disease. The electroencephalogram (EEG) signals of normal and stroke patients are collected and utilized to investigate the proposed approach. The raw data are preprocessed and the features are extracted based on frequency ranges using the Fast Hartley Transform (FHT) technique. Finally, the proposed DKELM-AS approach classifies them as stroke-affected EEG or normal EEG based on the precursor symptoms obtained from EEG signals. The experimental results reveal that the proposed DKELM-AS approach achieves an overall accuracy rate of about 95.2% in stroke detection based on the precursor symptoms of EEG signals.

A Study to Assess Knowledge and Practices regarding Fire Safety among Health care workers in selected Hospitals of Pune City

Background: Every year, in India, about 25,000 persons die due to fires and related causes. According to India Risk Surveys 2018, outbreak of fire poses risks to business continuity and operations and ranks India at 3rd position in fire incidents, especially in Northern and Western regions of India. Past incidents show that most fire accidents take place due to three major reasons: electrical short circuit and gas cylinder/stove bursts, human negligence, and ill-formed habits. As per the ADSI-2019 report, there were 11,037 fire accidents reported across the country in 2019. The number of such reported fire accidents fell by around 16% compared to 2018. The decline was more steeped compared to the year earlier, which was around 2%. The number of persons injured in fire accidents also more than halved with 441 injured persons in 2019 compared to 1193 in 2015. Fire can happen in very severe condition and so many people and animals dying due to this. It is one of the disasters faced by India. So fire safety and fire measures are one of the important aspects. Most of the fire seen in the hospital areas is due to the gaseous and chemical substances. So, for the prevention of fire or further complications fire safety measures are must. Health care worker know about the fire safety measures and preventions. Subjects and Methods: Setting: The study was conducted at selected Hospitals of the Pune city. subject the study sample comprised 100 health care workers, three tools were used for data collection, namely: Sociodemographic data, structured knowledge questionnaire, structured practice checklist. Results: Statistically significant difference was noticed between sociodemographic variables like age, gender, profession, educational qualification, work experience with Knowledge and practices of health care workers. Conclusion: There was an appreciably good practice about fire safety but they are having poor knowledge regarding fire safety. Recommendation: The study recommended continuous observation of practices can done to evaluate practices of fire safety and interventional study can be conducted to improve knowledge of health care worker.

Very high particulate pollution over northwest India captured by a high-density in situ sensor network

Exposure to particulate matter less than 2.5 µm in diameter (PM2.5) is a cause of concern in cities and major emission regions of northern India. An intensive field campaign involving the states of Punjab, Haryana and Delhi national capital region (NCR) was conducted in 2022 using 29 Compact and Useful PM2.5 Instrument with Gas sensors (CUPI-Gs). Continuous observations show that the PM2.5 in the region increased gradually from < 60 µg m−3 in 6–10 October to up to 500 µg m−3 on 5–9 November, which subsequently decreased to about 100 µg m−3 in 20–30 November. Two distinct plumes of PM2.5 over 500 µg m−3 are tracked from crop residue burning in Punjab to Delhi NCR on 2–3 November and 10–11 November with delays of 1 and 3 days, respectively. Experimental campaign demonstrates the advantages of source region observations to link agricultural waste burning and air pollution at local to regional scales.

How Do Prebiotics Affect Human Intestinal Bacteria?-Assessment of Bacterial Growth with Inulin and XOS In Vitro.

Prebiotics are believed to exhibit high specificity in stimulating the growth or activity of a limited number of commensal microorganisms, thereby conferring health benefits to the host. However, the mechanism of action of prebiotics depends on multiple factors, including the composition of an individual's gut microbiota, and is therefore difficult to predict. It is known that different bacteria can utilize inulin and xylooligosaccharides (XOS), but an overview of which bacteria in the human gut may be affected is lacking. Detailed knowledge of how bacterial growth is affected by prebiotics is furthermore useful for the development of new synbiotics, which combine a living microorganism with a selective substrate to confer a health benefit to the host. Hence, we developed a statistical model to compare growth in vitro among typical human gut bacteria from different phylogenetic lineages. Based on continuous observation of the optical density (OD600), we compare maximal growth rates (rmax), maximal attained OD600 (ODmax), and area under the growth curve (AUC) of bacteria grown on inulin or XOS. The consideration of these three parameters suggests strain-specific preferences for inulin or XOS and reveals previously unknown preferences such as Streptococcus salivarius growth on XOS.

Consistency and uncertainty of remote sensing-based approaches for regional yield gap estimation: A comprehensive assessment of process-based and data-driven models

Quantifying the gap between actual and exploitable yields (Ya and Ye, respectively) at the regional scale is critical for understanding the scope and hotspots for future yield improvements. Remote sensing can provide geospatially continuous observations of crop growth, which is perceived as an effective means to overcome the scaling issue commonly suffered by regional yield gap (YG) studies. However, the consistency and uncertainties of different modeling paradigms (i.e., model-driven or data-driven paradigm) remain to be investigated when integrating remote sensing data into YG analysis. This study provides a comprehensive assessment of process-based and machine learning approaches for estimating rice YG over Northeast China. Three machine learning models are examined for predicting Ya, the results of which reveal a prevalent deviation in the estimation of extreme yields. A rotation scheme is thereby proposed to correct this error, and the outperforming random forest model is selected to be compared with a biophysical process-based model (BEPS). County-scale validation reveals good accuracy of both the process-based and machine learning models in estimating Ya, with RMSEs of 1355.3 kg ha−1 (17.3%) and 1205.0 kg−1 (15.4%), respectively. The process-based model reproduces well the probability distribution of Ya in peaks and tails, which guarantees the reasonable estimation of Ye, given that Ye is derived from the high percentile of Ya. In contrast, the machine learning model trained over county-scale yield statistics underestimates the heterogeneity of Ya, leading to significant shrinkage of Ye and thus YG. Degraded model consistency is found from the estimation of Ya, Ye to YG, yet the discrepancy presents limited impacts on identifying the regional priorities for closing YG. The remote sensing-based estimations under the “top-down” framework are further evaluated against the results of the Global Yield Gap Atlas (GYGA), which follows a “bottom-up” protocol. Multiscale comparisons (i.e., local, subregional and regional) with the GYGA and other reference results suggest that the pixelwise modeling of crop-growing processes constitutes a more reliable approach for estimating rice YG over Northeast China, although uncertainty exists in both remote sensing approaches in the local YG estimation. The need to train machine learning models using representative datasets at the field scale is strongly emphasized. This study contributes to the first comprehensive understanding of different modeling paradigms for YG analysis and has important implications for promoting model development and ensuring food security.

Regional climate trend analyses for Aqua MODIS land surface temperatures

ABSTRACT Land surface temperature (LST) is the skin surface temperature of the Earth’s land surface and is an essential climate variable (ECV) that critically characterizes the Earth’s climate and surface energy processes. This is the first regional trend analysis for LST with uncertainties, using a stable LST climate data record suitable for climate trend analyses: the Aqua Moderate Imaging Spectroradiometer (MODIS) dataset (MYDCCI) produced in the European Space Agency (ESA) LST Climate Change Initiative (LST_cci) Project. The quality of the trends is verified here for Western Europe, for which consistent trends per decade of 0.5 K and 0.57 K across daytime and night-time data, respectively. Eight regions representative of a range of biomes and, including the Amazon, Western U.S.A., Greenland, the Sahel, Siberia, China, India, and Australia, have been analysed. Our most significant findings show substantial LST increases for Siberia and the Amazon, as well as evidence of recent increasing trends for the Western U.S.A. Siberia showed the most substantial daytime and night-time changes per decade of +0.87 K and +0.93 K, respectively. Furthermore, the Amazon and Western U.S.A. showed significant daytime LST trend increases of +0.82 K and +0.51 K, respectively. Western Europe and Western U.S.A. show the largest night-time change of +0.57 K and +0.54 K, respectively. India presents an atypical time series with a decreasing daytime and increasing night-time trend per decade of −0.62 K and +0.44 K, respectively, consistent with reported air temperatures. Overall the results show significant positive regional LST trends, against a background of both strong seasonal cycles and intermittent disruptive events, which demonstrate the importance of continued LST observations. The results also incentivize remote sensing science to derive ever more rigorous LST datasets and to continue to investigate emissivity and error correlations to allow improved trend analyses for smaller regions and individual locations.

The Growing Dynamic of Pure Scots Pine Stands Using Different Thinning Regimes in Lithuania

The aim of this study is to relate initial stand density and thinning intensity to tree diameter, height, and volume increment based on continuous 30-year observations in thinning experimental stands of Scots pine (Pinus sylvestris L.). The thinning experiments on Scots pine were established in 1990 and 1992, and the experimental model in each trial comprised five density variants: control and four plots with tree densities up to 3.0–4.4, 2.0–2.4, 1.0–1.2, and 0.5–0.6 thousand trees per hectare after the initial thinning. Since the establishment, only the dead trees were removed in control plots, and other initial density plots were thinned according to predefined thinning treatment plans. Our study shows that the lowest increment in diameter was found in control plots and the highest in the most intensive thinning plots, in which the diameter increment was 1.5 times higher. However, the intensification of thinning and the signs of growth stabilization in the control stands cannot guarantee them the position of highest productivity and cumulative volume in the future.

An alarming rise in COVID-19-related acute macular neuroretinopathy in China: a cross-sectional multimodal imaging study.

COVID-19-related acute macular neuroretinopathy rapidly increased in the pandemic surge in China in December 2022. Possible risk factors and pathophysiological mechanisms including inflammatory responses, vascular factors, and sex hormones have been discussed. Multimodal imaging revealed structural damage and vascular deficits in the retina and choroid, which warrants continuous observation.

Analysis of urban turbulence intensity observed by Beijing 325-m tower and comparison with the IEC turbulence model for small wind turbines

The accurate assessment of turbulence intensity is crucial for the effective utilization of urban wind energy. However, the Normal Turbulence Model (NTM) prescribed by the IEC standard has not been adequately evaluated for its applicability in urban environments. To address this gap, we analyze one-year continuous turbulence observations collected at multiple levels on a 325-m meteorological tower in Beijing. The results show that: the turbulence intensity follows the Johnsonsb distribution instead of the Normal distribution in the IEC standard; it has significant diurnal variation and piecewise power-law vertical variation; the NTM underestimates turbulence intensity at most times and heights. A new turbulence intensity model with an exponential form is proposed, which significantly outperforms the NTM. Further, the exponential model fits the observations better than power-law model that outperforms the NTM. In terms of the fitting errors to the measurements, the full-year, monthly, and hourly RMSE (MAE) of this model are reduced on average by 51.81% (54.31%), 33.04% (34.38%) and 29.46% (28.24%) compared to that of the power-law model. These findings will contribute to the revision of the IEC standards governing the safe operation of small wind turbines in complex wind environments such as urban areas.

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) − the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015–2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

Explanatory sequential descriptive analysis of music therapists’ clinical practice for individuals with eating disorders

The purpose of this study was to explore music therapists’ clinical practice in the United States for individuals with eating disorders (ED). To this aim, the researcher used a two-phase explanatory sequential mixed methods research design: (a) quantitative survey of music therapists (N = 18) in the U.S. working with clients with ED, and (b) in-depth interviews with music therapists (n = 7) who provided contact information at survey conclusion. The majority of participants had a bachelor’s or master’s degree in music therapy, worked approximately eight years with individuals with ED, and most frequently used cognitive behavioral, client-centered, or dialectical behavior therapy approaches. Participants primarily worked with adolescents and adults with anorexia nervosa, bulimia nervosa, and binge-eating disorder, within groups, in a variety of settings. The five most common treatment areas were self-expression, coping skills, depression, identify feelings, and express feelings, using various receptive, improvisation, composition, and re-creative interventions. The majority of MTs reported high job satisfaction and competence, perceived services as important and client responses positive. Interviewees explained and elaborated upon the results of the survey. Interviewees described aspects of safety, harm, trauma, and self-care, and highlighted importance of continued learning, training, research, observation, supervision, and skill development.