The global smart weight, body composition, and BMI scales market, valued at $374.65 million in 2025, is projected to experience robust growth, driven by increasing health consciousness, the rising prevalence of obesity and related diseases, and the expanding adoption of wearable technology and smart home devices. Consumers are increasingly seeking convenient and accurate ways to monitor their health metrics, fueling demand for these sophisticated scales that provide detailed insights beyond simple weight measurements. The market's segmentation reveals a significant presence of both online and offline distribution channels, with a likely higher proportion of sales in the online channel due to the ease of purchase and wider reach of e-commerce platforms. The price segmentation, categorized as less than USD 100 and more than USD 100, reflects the market's diverse offerings catering to budget-conscious consumers and those seeking advanced features and higher accuracy. The competitive landscape is characterized by a mix of established players like Fitbit and Garmin, alongside numerous emerging brands focusing on innovation and specific market niches. Technological advancements, such as integration with health apps and improved data analytics, are expected to further drive market growth. Geographical expansion, particularly in developing economies with rising disposable incomes, presents significant opportunities for market expansion. However, factors such as high initial costs for advanced models and concerns regarding data privacy could act as potential restraints. The projected CAGR of 5.22% indicates a steady and consistent expansion of the market over the forecast period (2025-2033). The market's growth trajectory is anticipated to be influenced by several key factors. The increasing adoption of personalized healthcare strategies and the expanding availability of connected health platforms are likely to boost demand for smart scales. Furthermore, the development of innovative features, such as body fat percentage analysis, muscle mass measurement, and bone density assessment, is enhancing the attractiveness of these scales. The growing emphasis on preventative healthcare and the integration of these scales into broader wellness ecosystems are further propelling market growth. Geographical variations in market penetration are expected, with North America and Europe likely to maintain relatively high adoption rates due to established healthcare infrastructure and higher consumer spending power. However, significant growth potential exists in Asia-Pacific and other emerging markets as consumer awareness increases and affordability improves. The ongoing focus on product innovation, along with strategic collaborations between manufacturers and healthcare providers, will be pivotal in shaping the future of this dynamic market.

Smart Body BMI Scale Market Outlook

The global smart body BMI scale market size was valued at approximately USD 1.2 billion in 2023, and it is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The primary growth factors driving this market include the rising awareness about health and fitness, the increasing prevalence of lifestyle-related diseases, and the continuous advancements in smart technology. The integration of advanced features such as wireless connectivity, data synchronization with mobile applications, and multi-user tracking capabilities is further fueling market growth.

The increasing awareness about the importance of maintaining a healthy lifestyle is one of the major factors driving the growth of the smart body BMI scale market. As people become more health-conscious, there is a higher demand for devices that can help them monitor their health metrics easily and accurately. Smart BMI scales provide users with real-time data on their body mass index, body fat percentage, muscle mass, and other vital statistics, making it easier for individuals to track their fitness progress and make informed health decisions.

Another significant growth factor is the rise in the prevalence of lifestyle-related diseases such as obesity, diabetes, and cardiovascular conditions. These health issues are often associated with poor diet, lack of exercise, and sedentary lifestyles. Smart BMI scales play a crucial role in helping individuals monitor their weight and body composition, which is essential for managing and preventing these conditions. Healthcare professionals are also increasingly recommending the use of these devices to their patients, further driving market growth.

The continuous advancements in smart technology and the integration of innovative features into BMI scales are also contributing to the market's expansion. Modern smart BMI scales come equipped with wireless connectivity options such as Bluetooth and Wi-Fi, allowing users to sync their data with mobile apps and other wearable devices. This seamless integration enables users to have a holistic view of their health metrics, set fitness goals, and receive personalized recommendations. Additionally, the advent of artificial intelligence and machine learning in these devices is expected to enhance their accuracy and functionality.

The introduction of the Smart Bluetooth Body Fat Scale has revolutionized the way individuals monitor their health metrics. These scales not only measure weight but also provide insights into body fat percentage, muscle mass, and other critical health indicators. The integration of Bluetooth technology allows users to seamlessly sync their data with smartphones and health apps, offering a comprehensive view of their health status. This connectivity feature is particularly beneficial for those who are keen on tracking their fitness progress over time and making data-driven decisions about their health and wellness routines. As technology continues to advance, the Smart Bluetooth Body Fat Scale is expected to become an essential tool for health-conscious consumers.

Regionally, North America holds the largest share in the smart body BMI scale market due to the high adoption rate of advanced health gadgets, increasing health awareness, and the presence of key market players. Europe follows closely due to similar trends and a growing emphasis on fitness and well-being. The Asia Pacific region is anticipated to witness the fastest growth, driven by a large population base, rising disposable incomes, and increasing awareness about health and fitness. The Middle East & Africa and Latin America regions are also expected to show significant growth, albeit at a slower pace, due to improving economic conditions and growing health awareness.

Product Type Analysis

The smart body BMI scale market is segmented into digital, analog, wireless, and Bluetooth-enabled scales. Digital scales dominate the market due to their accuracy, user-friendly interface, and advanced features. These scales often come with large, easy-to-read displays and can store multiple user profiles, making them a popular choice among households. The growing preference for precise and reliable health monitoring devices is driving the demand for digital BMI scales.

Analog scales, while less popular than their digital counterparts, still hold a significant

Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: ice_duration_days, ice_on_date, ice_off_date, winter_dur_0-4, coef_var_30-60, coef_var_0-30, stratification_onset_yday, stratification_duration, sthermo_depth_mean, peak_temp, gdd_wtr_0c, gdd_wtr_5c, gdd_wtr_10c, bottom_temp_at_strat, schmidt_daily_annual_sum, mean_surf_jas, max_surf_jas, mean_bot_jas, max_bot_jas, mean_surf_jan, max_surf_jan, mean_bot_jan, max_bot_jan, mean_surf_feb, max_surf_feb, mean_bot_feb, max_bot_feb, mean_surf_mar, max_surf_mar, mean_bot_mar, max_bot_mar, mean_surf_apr, max_surf_apr, mean_bot_apr, max_bot_apr, mean_surf_may, max_surf_may, mean_bot_may, max_bot_may, mean_surf_jun, max_surf_jun, mean_bot_jun, max_bot_jun, mean_surf_jul, max_surf_jul, mean_bot_jul, max_bot_jul, mean_surf_aug, max_surf_aug, mean_bot_aug, max_bot_aug, mean_surf_sep, max_surf_sep, mean_bot_sep, max_bot_sep, mean_surf_oct, max_surf_oct, mean_bot_oct, max_bot_oct, mean_surf_nov, max_surf_nov, mean_bot_nov, max_bot_nov, mean_surf_dec, max_surf_dec, mean_bot_dec, max_bot_dec, which are defined below.

Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a handful of projects) to analytics in-the-large (dealing with hundreds of projects)? To answer this question, we recheck prior small-scale results (about process versus product metrics for defect prediction and the granularity of metrics) using 722,471 commits from 700 Github projects. We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the-large. For example, like prior work, we see that process metrics are better predictors for defects than product metrics (best process/product-based learners respectively achieve recalls of 98%/44% and AUCs of 95%/54%, median values).

That said, we warn that it is unwise to trust metric importance results from analytics in-the-small studies since those change dramatically when moving to analytics in-the-large. Also, when reasoning in-the-large about hundreds of projects, it is better to use predictions from multiple models (since single model predictions can become confused and exhibit a high variance).

Political surveys often include multi-item scales to measure individual predispositions such as authoritarianism, egalitarianism, or racial resentment. Scholars use these scales to examine group differences in these predispositions, comparing women to men, rich to poor, or Republicans to Democrats. Such research implicitly assumes that, say, Republicans' and Democrats' responses to the egalitarianism scale measure the same construct in the same metric. This research rarely evaluates whether the data possess the characteristics necessary to justify this equivalence assumption. We present a framework to test this assumption and correct scales when it fails to hold. Examining 13 commonly used scales on the 2012 and 2016 ANES, we find widespread violations of the equivalence assumption. These violations often bias the estimated magnitude or direction of theoretically important group differences. These results suggest we must reevaluate what we think we know about the causes and consequences of authoritarianism, egalitarianism, and other predispositions.

These raster data represent the results of a case study in Arizona on how vertebrate richness metrics can be used with existing state and federal guidance in wind and solar energy facility siting. Each of the four geodatabases (see Cross References) contain eight native terrestrial wildlife group models in Arizona: 1) all vertebrates, 2) amphibians, 3) reptiles, 4) birds, 5) mammals, 6) bats, 7) raptors and 8) long-distant migratory birds. An XML workbook is included that lists all terrestrial native vertebrate species in Arizona which cross-walks these species to the name of the GAP species distribution model.

Market Overview The global Professional Medical Scale market is projected to reach a market size of XXX million by 2033, exhibiting a CAGR of XX% over the forecast period (2025-2033). The market growth is primarily driven by the increasing prevalence of obesity, growing geriatric population, technological advancements in medical devices, and rising awareness of health and wellness. Key market trends include the integration of advanced technologies such as Bluetooth and Wi-Fi connectivity, as well as a focus on precision and accuracy in medical weighing. Increasing investment in healthcare infrastructure, particularly in emerging economies, is further contributing to market growth. Market Segments and Competition The Professional Medical Scale market is segmented into application (adult, baby) and type (medical mechanical scale, medical electronic scale). Adult scales dominate the market, primarily due to the high prevalence of obesity and weight management needs. Medical electronic scales are gaining popularity owing to their accuracy, ease of use, and ability to provide additional health metrics such as body fat percentage. Major companies in the market include Marsden Group, Detecto, Pelstar, Seca, and Algen Scale. These companies offer a wide range of professional medical scales tailored to meet the varying needs of healthcare facilities and consumers. Strategic partnerships, product innovations, and acquisitions are common strategies employed by market participants to maintain their competitive edge. The professional medical scale market is expected to reach $990.9 million by 2028, growing at a CAGR of 3.1% during the forecast period. The growth of the market is attributed to the increasing prevalence of obesity, the growing demand for accurate and reliable weight measurement devices, and the increasing adoption of electronic medical scales.

Chicken is, among farmed species, the target of the highest levels of antimicrobial use (AMU). There are considerable knowledge gaps on how and when antimicrobials are used in commercial small-scale chicken farms. These shortcomings arise from cross-sectional study designs and poor record keeping practiced by many such farmers. Furthermore, there is a large diversity of AMU metrics, and it is not clear how these metrics relate to each other. We performed a longitudinal study on a cohort of small-scale chicken farms (n = 102) in the Mekong Delta (Vietnam), an area regarded as a hotspot of AMU, from October 2016 to May 2018. We collected data on all medicine products administered to 203 flocks with the following aims: (1) to describe types and quantities of antimicrobial active ingredients (AAIs) used; (2) to describe critical time points of AMU; and (3) to compare AMU using three quantitative metrics: (a) weight of AAIs related to bird weight at the time of treatment (mg/kg at treatment); (b) weight of AAIs related to weight of birds sold (mg/kg sold); and (c) “treatment incidence” (TI), or the number of daily doses per kilogram of live chicken [Vietnamese animal daily dose (ADDvetVN)] per 1,000 days. Antimicrobials contained in commercial feed, administered by injection (n = N = 6), or antimicrobials for human medicine (n = N = 16) were excluded. A total of 236 products were identified, containing 42 different AAIs. A total of 76.2% products contained AAIs of “critical importance” according to the World Health Organization (WHO). On average, chickens consumed 791.8 (SEM ±16.7) mg/kg at treatment, 323.4 (SEM ±11.3) mg/kg sold, and the TI was 382.6 (SEM ±5.5) per 1,000 days. AMU was more common early in the production cycle and was highly skewed, with the upper 25% quantile of flocks accounting for 60.7% of total AMU. The observed discrepancies between weight- and dose-based metrics were explained by differences in the strength of AAIs, mortality levels, and the timing of administration. Results suggest that in small-scale chicken production, AMU reduction efforts should preferentially target the early (brooding) period, which is when birds are most likely to be exposed to antimicrobials, whilst restricting access to antimicrobials of critical importance for human medicine.

Global digital scale sales are likely to grow at a 4.5% compound annual growth rate (CAGR) from USD 590.0 million in 2025 to USD 916.5 million by 2035.

Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.https://doi.org/10.3390/rs13214378

Spatial coverage index compiled by East View Geospatial of set "Botswana 1:50,000 Scale Topographic Maps (Metric Series)". Source data from DSM (publisher). Type: Topographic. Scale: 1:50,000. Region: Africa.

The goal of CHaMP is to generate and implement a standard set of fish habitat monitoring (status and trend) methods in up to 26 watersheds across the Columbia River basin. The watersheds have been chosen to maximize the contrast in current habitat conditions and also represent a temporal gradient of expected change in condition through planned habitat actions. Surveys will be conducted in watersheds with perceived large juvenile life-stage survival gaps due to habitat impairments or that are home to existing high quality fish monitoring infrastructure. CHaMP implementation will occur on the spatial scale of the Technical Recovery Team (TRT) populations with the intention for inference on habitat quality and quantity at the fish population level. CHaMP is being built around a single habitat monitoring protocol with a program-wide approach to data collection and management. CHaMP metric data set consists of the entire suite of measurements described in the 2012 CHaMP protocol. A three-person crew surveys the topography (both the in-channel bathymetry and out-of-channel topography including the near channel floodplain) at a site and collects auxiliary data at both the channel unit scale (fish cover estimates, large woody debris, ocular estimate of substrate, pebble counts of bed material in riffles, pool tail fines, and undercut banks) and the site level scale (macroinvertebrate drift, discharge, solar input, riparian structure, alkalinity, conductivity, photographs, and water and air temperature loggers). The crews post process the data collected at each of the sites, which entails a QC of the survey point data, importing the survey point data into ArcGIS and converting the points into a TIN, and converting the topographic TIN to a DEM. Additional GIS products are also produced during the post-processing and include a water surface TIN, water surface DEM, and water depth raster.

This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/.

The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The l1 in the file name indicates that the file was used for level 1 (wetland) classification and l23 used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (https://github.com/Jean-Romain/lidR) software package. Further details related to the lidar metrics extraction can be found at https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats Github repository.

Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.

This is a shapefile of points summarizing inundation metrics and geomorphic variables for bends in the downstream-most 800 km of the Lower Missouri River. Points are located at the centroids of 10-km bends of the river. The metrics were developed through analysis of inundation maps calculated from a 1-dimensional hydraulic model for the channel and floodplain. Water-surface elevations were extended across the valley bottom and intersected with lidar-derived floodplain topography to calculate inundation depth and extent on a daily basis. Detailed methods are documented in Bulliner and others (2017). We evaluated longitudinal spatial variation by aggregating floodplain inundation estimates by Thiessen polygons centered at 1-km address points along the Lower Missouri River channel navigation line and extending to the valley walls. The Thiessen polygons sample the area of the floodplain so that the attributes within the polygon are geometrically closest to the address point. These data were then aggregated into 10-km reaches to associate inundation metrics (in average hectare-days per year) with potential explanatory variables that had been developed in previous studies (Jacobson and others, 2017; Jacobson and others, 2018). The explanatory variables in the dataset quantify hypothesized physical controls on inundation at the 10-km reach scale; that is, they do not include fine-scale details of bathymetry or topography. We summarized the inundation metrics for the existing conditions, wherein only floodplain area river-ward of the levees (batture area) is inundated, and for the entire floodplain, for both historical daily discharges (1930 – 2012) and for the future-climate-adjusted daily discharge scenario for the same time series. The data are summarized as point attributes in the released shapefile. Bulliner, E.A., Lindner, G., Bouska, K., Jacobson, R.B., and Paukert, C., 2017, Science to Inform Management of Floodplain Conservation Lands under Non-Stationary Conditions U.S. Geological Survey Data Release, https://doi.org/10.5066/F7HM56KG. Jacobson, R.B., Elliott, C.M., and Bulliner, E.A., 2017, Missouri River bend classification data sets: U.S. Geological Survey Data Release, U.S. Geological Survey data release, https://doi.org/10.5066/F7FF3R9D. Jacobson, R.B., Colvin, M.E., Bulliner, E.A., Pickard, D., and Elliott, C.M., 2018, Bend-scale geomorphic classification and assessment of the Lower Missouri River from Sioux City, Iowa, to the Mississippi River for application to pallid sturgeon management 2018-5069, 46 p., https://doi.org/10.3133/sir20185069.

Although there are a large number of software products available for calculating landscape metrics (e.g. FRAGSTATS, landscapemetrics package in R) no tools are currently available (to my knowledge) that calculate landscape metrics directly in ArcGIS Pro. Moreover, many, if not most, landscape metrics were designed with vector data in mind, but most software calculates landscape metrics from raster data due to processing time and complexity. Scaling landscape metrics can also be tedious in some instances. This toolbox was designed to calculate attributes of patches that are easily calculated on polygons in ArcGIS (i.e. area, number of patches, Landscape Shape Index, edge density, patch size, distance to the nearest patch) and scales those calculations to coarser resolutions using Block Statistics. The tool also summarizes the relationships among metrics by using Principal Component Analysis and correlation matrices to assess relationships among variables. All variables are output to a single folder.

1. Extensive research has demonstrated that urbanization strongly alters ecological processes, often perniciously. However, quantifying the magnitude of urban effects and determining how generalized they can be across systems depends on the ways in which urbanization is measured and modelled. 2. We coupled a formal literature survey with a novel conceptual framework to document and synthesize the myriad of metrics used to quantify urbanization. The framework enables clear cataloguing of urban metrics by identifying) the urban component measured, ii) the method of measurement, iii) the metric's spatial scale, and iv) the metric's temporal nature. Thus, the framework comprehensively captures the what, how, where, and when of urban metrics.3. We documented striking variability in urban metrics with respect to which urban components were measured as well as how, where, and when they were quantified. Overall, our survey revealed that they tended to be: i) be structurally-focused, ii) methodologically simplistic, iii) spatially variable, and iv) temporally static. 4. Synthesis and applications. Many metrics are used to quantify urbanization or "urban-ness". The variation in urban metrics complicates the development of theory, comparisons of findings across studies, and the implementation of management and conservation actions. To pave a clear path forward for more efficient and policy-relevant urban research, we systematically organized urban metrics using a simple, flexible, and comprehensive framework. The framework clarifies what urbanization actually means in empirical practice and identifies several crucial areas for future research, including: i) systematic assessments of urban metrics across multiple scales, ii) an increased and judicious use of more complex urban metrics aimed at evaluating both mechanistic and broad-scale correlative ecological hypotheses, and iii) an increased emphasis on the socio-economic aspects of urban effects.

[205+ Pages Report] Global workplace stress management market size & share to be worth USD 15.4 Billion by 2026, growing at a CAGR value of 8.7% during the forecast period of 2021-2026.

Precision and F1 scores of the trained dialogue segmenter on the SAMSum and the DialogSum test sets.

The Moral Injury Event Scale (MIES) is a tool for measuring exposure to potentially morally injurious event(s) and distress. While it reported satisfactory psychometric properties in its early development studies, it has since been used in multiple contexts and populations without assessment of its changing reliability or validity. A meta-analysis was conducted to assess the MIES psychometric properties across settings and to determine the factors influencing its variability. A systematic search of electronic databases (PsychINFO; PTSD Pubs; MEDLINE; Scopus; Web of Science) was undertaken to identify studies reporting MIES reliability and validity data. A total of 42 records were found up-to-April-2022. Most papers reported Cronbach's Alpha so analyses of other reliability and validity metrics (e.g., test-retest, inter-rater reliability) were not possible. The review found the MIES to be a generally internally consistent tool based on alpha estimates at both Full-scale (α=.88) and Sub-scales (⍺=.82-.92). The review uncovered high heterogeneity and inconsistencies in its administration and modification although figures generally remained above acceptable levels (⍺≥.70). Based on the review, the MIES represents an internally reliably tool for measuring potentially morally injurious events and distress at both Full and Sub-Scales according to pooled Cronbach's Alpha estimates. (Supplementary Tables)

The Moral Injury Event Scale (MIES) is a tool for measuring exposure to potentially morally injurious event(s) and distress. While it reported satisfactory psychometric properties in its early development studies, it has since been used in multiple contexts and populations without assessment of its changing reliability or validity. A meta-analysis was conducted to assess the MIES psychometric properties across settings and to determine the factors influencing its variability. A systematic search of electronic databases (PsychINFO; PTSD Pubs; MEDLINE; Scopus; Web of Science) was undertaken to identify studies reporting MIES reliability and validity data. A total of 42 records were found up-to-April-2022. Most papers reported Cronbach's Alpha so analyses of other reliability and validity metrics (e.g., test-retest, inter-rater reliability) were not possible. The review found the MIES to be a generally internally consistent tool based on alpha estimates at both Full-scale (α=.88) and Sub-scales (⍺=.82-.92). The review uncovered high heterogeneity and inconsistencies in its administration and modification although figures generally remained above acceptable levels (⍺≥.70). Based on the review, the MIES represents an internally reliably tool for measuring potentially morally injurious events and distress at both Full and Sub-Scales according to pooled Cronbach's Alpha estimates.