Training Performance Research Articles

Does gamified training improve training performance? A dual-pathway moderated mediation model

Although gamification has grown in popularity in organizational training, scientific evidence verifying the benefits of gamified training is mixed, and the underlying mechanisms of it on training outputs remain unclear. Drawing upon the technology-enhanced training effectiveness model and cognitive-affective personality system theory, this research constructs a dual-pathway moderated mediation model explaining how gamified training shapes training performance, including knowledge, skills, and attitudes (KSAs). Adopting a randomized field experiment, the results indicated that gamified training significantly promoted knowledge and attitudes via enhancing goal commitment and perceived enjoyment, but exerted no significant influence on skills. Moreover, need for achievement moderated the effect of gamified training on goal commitment and its indirect effect on KSAs via goal commitment. Furthermore, hedonic need moderated the effect of gamified training on perceived enjoyment and its indirect effect on knowledge and attitudes via perceived enjoyment. Theoretical and practical implications are discussed.

Influences of Feedback Gains on Vehicle–Bridge Coupled Vibration for a Mid-Mounted Maglev Train

Levitation control systems play a vital role in ensuring the operational performance of medium and low speed maglev trains. Improper gain values may induce excessive vibrations in vehicles and bridges, potentially leading to collisions. This research investigates the influence of feedback gains on the levitation subsystem and the vehicle-bridge coupled system, leveraging a newly designed maglev vehicle equipped with mid-mounted suspension and a state feedback control strategy. A dynamic model of the vehicle-bridge coupled system was developed, incorporating control strategies, track irregularities, and bridge flexibility, and was validated through experimental data. The dynamic responses of the bogie and bridge under varying gain values were analyzed in-depth. By examining pole locations and frequency responses, the mechanisms through which each gain impacts the coupled system were clarified. The study identifies that gains associated with acceleration, velocity, and displacement each have unique stability domains that interact with one another. To maintain system stability, the gains must adhere to the Routh-Hurwitz stability criterion while fulfilling specific bandwidth and damping requirements. Gains set too low or too high may result in levitation failures. Recommended ranges for these gains are as follows: acceleration (0.2–0.6), velocity (30–100), and displacement (5,000–10,000). These findings could provide valuable guidance for designing control systems that enhance the stability and safety of maglev train operations.

Estimating Winter Wheat Canopy Chlorophyll Content Through the Integration of Unmanned Aerial Vehicle Spectral and Textural Insights

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models.

Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a potential therapeutic agent

BackgroundOsteoarthritis (OA), characterized by progressive degeneration of cartilage and reactive proliferation of subchondral bone, stands as a prevalent condition in orthopedic clinics. However, the precise mechanisms underlying OA pathogenesis remain inadequately explored.MethodsIn this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. Based on these hub genes, an Artificial Neural Network (ANN) diagnostic model was constructed. The Drug Signatures Database (DSigDB) was utilized to screen small-molecule drugs targeting these hub genes, and molecular docking analyses and molecular dynamics simulations were employed to explore and validate the binding interactions between proteins and small-molecule drugs. Expression changes of the hub genes under inflammatory conditions were validated through in vitro experiments, including RT-qPCR and Western blotting, and the therapeutic effects of the identified small-molecule drug on chondrocytes under inflammatory conditions were further verified in vitro. Lastly, Mendelian randomization analysis was conducted to examine the causal association between progesterone levels and various OA phenotypes.ResultsIn this study, we identified three hub genes: interleukin 1 receptor-associated kinase 3 (IRAK3), integrin subunit beta-like 1 (ITGBL1), and Ras homolog family member U (RHOU). An Artificial Neural Network (ANN) diagnostic model constructed based on these hub genes demonstrated excellent performance in both training and validation phases. Screening with the Drug Signatures Database (DSigDB) identified progesterone as a small-molecule drug targeting these key proteins. Molecular docking analysis using AutoDock Vina revealed that progesterone exhibited binding energies of ≤ -7 kcal/mol with each of the key proteins, indicating strong binding affinity. Furthermore, molecular dynamics simulations validated the stability and strength of these interactions. RT-qPCR and Western blotting confirmed the downregulation of the hub genes in IL-1β-treated chondrocytes. Western blotting also demonstrated the potential therapeutic effects of progesterone on IL-1β-treated chondrocytes. Finally, Mendelian randomization analysis established a significant association between progesterone levels and multiple OA phenotypes.ConclusionIn our study, IRAK3, ITGBL1, and RHOU were identified as potential novel diagnostic and therapeutic targets for OA. Progesterone was preliminarily validated as a small-molecule drug with potential effects on OA. Further research is crucial to elucidate the pathogenesis of OA and the specific therapeutic mechanisms involved.

Effect of Single-Leg Compound Training Program on Power of College Student High-Level Basketball Player

Background and Aims: There is widespread asymmetry in the body of athletes, which can affect and limit their performance in training and competition, leading to the potential and realistic risk of injury. This paper aims to improve the asymmetry of athletes and improve their performance and prevent injuries. The single-leg compound training method was used to explore the effect and degree of the improvement of the lower limb asymmetry in basketball players. Methodology: Experimental method, measurement method and questionnaire survey method are divided into pre-experiment and formal experiment. The measurement takes 38 male high-level basketball players to measure and evaluate the strength index. The formal experiment was designed with one-factor completely randomized experiment. High-level basketball players were randomly selected as the main subjects and randomly divided into experimental group and control group. The experimental group conducted single-leg compound training intervention for 8 weeks, and the control group conducted normal teaching training. The Pre-and Posttest tested the degree of lower limb asymmetry and physical fitness performance of the lower limb asymmetry and physical fitness performance indicators between the experimental group and the control group before and after the intervention. Result: The results found that: (1) Male college basketball second-level players have the asymmetry of the lower limb strength, and the asymmetry degree of the strength index on both sides of the lower limbs is more obvious. (2) 8-week single-leg compound training can effectively improve the asymmetry of lower limb strength in male college basketball players. (3) The decrease of lower limb asymmetry in male college basketball players can significantly improve the explosive force of lower limbs. This shows that the improvement of lower limb asymmetry can improve the physical performance of basketball players. Conclusion: The study shows that male college basketball players' explosive force and overall physical performance on the court can be greatly improved by targeting their lower limb strength asymmetry through targeted training. More balanced strength distribution can result from effective training interventions, which will ultimately enhance athletic performance.

A tale of two ages: fluid reasoning as a predictor of working memory training efficacy in middle-aged and older adults

ABSTRACT Research on working memory (WM) training reveals significant variability in training effects, indicating that pretraining cognitive abilities might account for these differences. However, consensus on whether higher (magnification account) or lower (compensation account) pretraining abilities predict greater training effects remains elusive. Our study aimed to clarify the role of fluid reasoning in predicting training performance (i.e. training scores at each session) and gains on near transfer WM tasks. We conducted two studies: Study 1 focused on middle-aged adults (47–65 years) and Study 2 on older adults (65–83 years). Participants in both studies were randomly assigned to either adaptive n-back training or an active control group and have all completed three WM tasks before and after 20 training sessions – the trained n-back task and two structurally different untrained tasks. Generally, greater average training scores were found in individuals with higher fluid reasoning for both age groups, although this trend did not reach statistical significance in older adults. Similarly, higher fluid reasoning predicted greater training gains only in the sample of middle-aged adults. Further analysis showed that both, middle-aged and older participants in the training groups exhibited higher gains on the trained n-back task but not on two other WM tasks. Additionally, fluid reasoning predicted n-back gains in both the training and control group. Consistent with a growing body of research, our results show limited generalization of training effects across untrained tasks. It seems that factors beyond pretraining ability should be considered when explaining between-participant differences in training performance.

Effect of internet shutdown on postgraduate trainees in Manipur: A qualitative study

Background & objectives Internet shutdowns, although rare can be imposed by governments or affiliated entities and can intentionally disrupt online information and communication systems. While justified for maintaining public order, these shutdowns cause substantial economic losses, public suffering, and reputational damage. The internet shutdown in Manipur, initiated on May 3, 2023, due to civil unrest, has affected various sectors. The impact on doctors, amidst an era where internet access is considered as an integral part of practice, warrants deeper exploration of the challenges faced during such a prolonged shutdown period. Therefore, this study was conducted to explore the impact of internet shutdown among postgraduate trainees from two tertiary care hospitals in Manipur. Methods A study was conducted in two tertiary healthcare centers in Manipur from July to August 2023 among medical postgraduate trainees. Participants were selected using purposive sampling. Twenty-two in-depth interviews (IDIs) were conducted, recorded, and transcribed verbatim. Thematic analysis was done to identify codes, categories and themes. Results This qualitative study was undertaken among postgraduate trainees in two different academic institutes. A total of 21 IDIs were carried out. The major themes emerged from the study, namely, impact on professional life, impact on personal life, social consequences and coping. Internet shutdown affected patient care such as seeking timely opinions from consultants, delays in sharing patient reports and inconveniences in the hand-off process and the academic performance of postgraduate trainees like unavailability of study materials or keeping up with recent advancements. On a personal level, it negatively affected communication with family and friends, mental well-being and caused inconvenience in shopping and transactions. The study also found some positive impacts, such as less screen time leading to better sleep and increased physical activities. Interpretation & conclusions The findings of this study suggest that the internet shutdown significantly affected both the professional and personal aspects of postgraduate trainees’ lives in Manipur. The implementation of a targeted ban on social media platforms and the establishment of designated internet access points are proposed as potential alternatives.

Global sensitivity analysis of high-speed train dynamics system with high-dimensional inputs and multiple outputs

A high-speed train dynamics system involves numerous uncertainty parameters, and there are multiple evaluation indices for its dynamic performance. In this paper, we conduct high-dimensional and multi-output global sensitivity analysis for the dynamic performance of high-speed trains by comprehensively considering these dynamics parameters and indices. Firstly, the dynamics simulation model of the high-speed train is established and six dynamics indices for evaluating the safety and comfort of the train are obtained under operating conditions. Subsequently, considering the multi-output and high-dimensional characteristics of train sensitivity analysis, we propose a multi-output global sensitivity analysis method based on the correlation-analysis-guided Kriging model (CA-K). Finally, global sensitivity analysis of 96 uncertainty parameters on six dynamics indices is conducted under three scenarios utilising the proposed method. The results show that (1) CA-K achieves higher accuracy in approximating high-dimensional train dynamics indices than existing conventional surrogate models. (2) The multi-output global sensitivity analysis method can simultaneously identify all important dynamics parameters and exclude unimportant parameters.

Future Directions in Understanding Acute and Chronic Effects of Mental Fatigue in Sports: A Commentary on Bridging Laboratory Findings and Real-World Applications.

Mental fatigue (MF) is a psychobiological state that could negatively impact physical and cognitive performance, although the extent of this impact remains controversial. While laboratory studies have provided valuable insights into the acute effects of MF, their ecological validity in real-world sport settings remains limited. Recent research indicates that MF can naturally arise during sport activities, leading to chronic effects on endurance and perceptual-cognitive skills. These effects may persist beyond individual sessions, potentially increasing the risk of injury due to their cumulative nature. This commentary aims to guide future research by emphasizing the need to investigate the natural occurrence and chronic effects of MF in applied sport settings. Understanding how MF develops and manifests in real-world settings, particularly individual differences in perception and response, is crucial for creating more effective management strategies. Addressing these factors will enable better support for athletes who may experience prolonged periods of MF that could affect their training and competitive performance. Future research should prioritize studying MF in natural sport settings to enhance the ecological validity of findings. By focusing on the chronic and cumulative nature of MF, as well as individual variability, researchers can develop more tailored and effective strategies for managing MF. This research will not only bridge the gap between laboratory studies and real-world applications but also contribute to more precise monitoring and intervention techniques. Ultimately, these advancements will improve athletes' ability to maintain peak performance and reduce the risk of injury, thereby enhancing overall athletic well-being.

Application research on mechanical assessment and training based on intelligent physical training system in the rehabilitation of athletes’ ankle injuries

As the only hinge connection between human body and the ground, ankle joint plays an important role in sports. Based on the theory of rehabilitation medicine, this paper puts forward an intelligent physical training system integrating mechanical evaluation and training functions, and applies it to the rehabilitation of athletes’ ankle injuries. The system adopts virtual reality (VR) technology, realizes active ankle rehabilitation training through high-precision sensors and advanced computer simulation technology, and provides accurate evaluation, monitoring and personalized training scheme for ankle rehabilitation. The system creates an immersive training environment for athletes by simulating real sports scenes and processes. The experimental results show that the system is excellent in training convergence speed and performance, and the error is small. Compared with the traditional algorithm, the accuracy is improved by 19.27%. The main contribution of this paper is that the intelligent physical training system integrating mechanical evaluation and training is applied to the rehabilitation of athletes’ ankle injuries, and good rehabilitation results have been achieved.

Monitoring Sleep and Nightly Recovery with Wrist-Worn Wearables: Links to Training Load and Performance Adaptations.

Previous studies on the effects of intensified training on sleep quality/quantity have been somewhat contradictory. Moreover, recreational athletes often track various sleep metrics, and those metrics' actual connections to training adaptations are unknown. This study explored the effects of intensified training on sleep and nightly recovery along with their associations with training adaptations. A total of 24 participants (10 females) performed a 3-week baseline training period (BL), a 2-week overload period (OL), and a 1-week recovery period (REC), which were followed by test days (T1-T3). The endurance performance was assessed with a 3000 m running test. Throughout all of the periods, the nightly recovery information was monitored with a wrist-worn wearable, including sleep quantity and quality, heart rate (HR) and HR variability (HRV), and proprietary parameters combining several parameters and scaling the results individually. In addition, the perceived strain and muscle soreness were evaluated daily. The 3000 m running performance improved from T1 to T2 (-1.2 ± 1.7%, p = 0.006) and from T1 to T3 (-1.7 ± 1.2%, p = 0.002). The perceived strain and muscle soreness increased (p < 0.001) from the final week of the BL to the final week of the OL, but the subjective sleep quality and nightly recovery metrics remained unchanged. The OL average of the proprietary parameter, autonomic nervous system charge ("ANS charge", combining the HR, HRV, and breathing rate), as well as the change in the sleep HR and HRV from the BL to the OL, were associated (p < 0.05) with a change in the 3000 m running time. In conclusion, the subjective recovery metrics were impaired by intensified training, while the sleep and nightly recovery metrics showed no consistent changes. However, there were substantial interindividual differences in nightly recovery, which were also associated with the training adaptations. Therefore, monitoring nightly recovery can help in recognizing individual responses to training and assist in optimizing training prescriptions.

Out-of-Distribution Detection via outlier exposure in federated learning.

Among various out-of-distribution (OOD) detection methods in neural networks, outlier exposure (OE) using auxiliary data has shown to achieve practical performance. However, existing OE methods are typically assumed to run in a centralized manner, and thus are not feasible for a standard federated learning (FL) setting where each client has low computing power and cannot collect a variety of auxiliary samples. To address this issue, we propose a practical yet realistic OE scenario in FL where only the central server has a large amount of outlier data and a relatively small amount of in-distribution (ID) data is given to each client. For this scenario, we introduce an effective OE-based OOD detection method, called internal separation & backstage collaboration, which makes the best use of many auxiliary outlier samples without sacrificing the ultimate goal of FL, that is, privacy preservation as well as collaborative training performance. The most challenging part is how to make the same effect in our scenario as in joint centralized training with outliers and ID samples. Our main strategy (internal separation) is to jointly train the feature vectors of an internal layer with outliers in the back layers of the global model, while ensuring privacy preservation. We also suggest an collaborative approach (backstage collaboration) where multiple back layers are trained together to detect OOD samples. Our extensive experiments demonstrate that our method shows remarkable detection performance, compared to baseline approaches in the proposed OE scenario.

Tele-mentorship versus in-person mentorship for trainee performance of cricothyroidotomy

Introduction Physicians and other first-line medical care providers may find themselves in an airway management emergency where they cannot ventilate, cannot oxygenate, and have limited resources to aid them. The rise in use of tele-health and other virtual interfaces creates the opportunity to bring experienced guidance to a resource-poor critical airway situation. We aimed to compare in-person mentoring (IPM) by an emergency medicine (EM) physician to tele-mentoring (TM) by a trauma surgeon for an emergent surgical airway. Methods Thirty EM residents were randomized to cricothyroidotomy on a model with IPM or TM. The IPM preceptor was an EM attending, and TM preceptors were trauma surgeons. The procedure was timed and graded on a Global Rating Scale (GRS). Post-procedure, mentor and trainee completed a NASA Task Load Index (TLX). Results There was no difference in procedure success; however, the IPM group took longer (163.6 vs. 107.6 s, p &lt; 0.05). TM trainees experienced greater temporal demand (4.27 vs. 2.73, p = 0.03). Telementors recorded improved performance compared to in-person mentors (2.30 vs. 4.17, p &lt; 0.05). Comparing mentors and trainees in each arm, IPM trainees felt more temporal demand than their mentors (4.63 vs. 2.73, p &lt; 0.05) while TM trainees felt they had worse performance than their mentors perceived (4.23 vs. 2.3, p = 0.01). Discussion Performance based on GRS between groups was equal; however, those with telementors secured the airway more rapidly. Telementors reported higher performance and lower time pressure than in-person preceptors. This data suggests that TM is effective for precepting cricothyroidotomy and may be the superior option in some settings.

The Impact of Breakfast Consumption or Omission on Exercise Performance and Adaptations: A Narrative Review.

Breakfast is often termed the most important meal of the day. However, its importance to acute and chronic adaptations to exercise is currently not well summarized throughout the literature. A narrative review of the experimental literature regarding breakfast consumption's impact on acute and chronic exercise performance and alterations in body composition prior to November 2024 was conducted. To be included in this review, the selected investigations needed to include some aspect of either endurance or resistance training performance and be conducted in humans. These findings suggest that breakfast consumption may benefit acute long-duration (>60 min) but not short-duration (<60 min) morning endurance exercise. Evening time trial performance was consistently inhibited following breakfast omission despite the resumption of eating midday. No or minimal impact of breakfast consumption was found when examining acute morning or afternoon resistance training or the longitudinal adaptations to either resistance or endurance training. Favorable changes in body composition were often noted following the omission of breakfast. However, this was primarily driven by the concomitant reduced kilocalorie intake. Consuming breakfast may aid endurance athletes regularly performing exercise lasting >60 min in length. However, the morning meal's impact on resistance training and changes in body composition appears to be minimal. Although, as the body of literature is limited, future investigations are needed to truly ascertain the dietary practice's impact.

The mediating effect of employee’s ability, motivation, and opportunity to participate (AMO) on the relationship between investment in Lean Six Sigma training and service recovery performance

The mediating effect of employee’s ability, motivation, and opportunity to participate (AMO) on the relationship between investment in Lean Six Sigma training and service recovery performance

Defining Running Intensity Domains from Critical Speed Derived from a 3-Minute All-Out Running Test

Background/Objectives: This study examines the utility of the 3-min all-out test (3MT) in determining exercise intensity domains through critical speed (CS), offering a practical alternative to traditional VO2max-based methods. Methods: Eighteen trained runners completed both the 3MT and a graded treadmill test. CS, VO2max, and HRmax were measured and compared to markers such as ventilation threshold (VT), gas exchange threshold (GET), lactate threshold (LT), onset of blood lactate accumulation (OBLA), CS, and VO2max, which are threshold markers for defining six exercise intensity domains. Results: Results demonstrate that CS reliably distinguishes among six exercise intensity domains with greater precision and less variability than heart rate (HR) or VO2-derived metrics. Notably, HR was unable to differentiate between high-intensity domains, highlighting its limitations for performance prescription. Compared to VO2max testing, the 3MT offers a simpler, non-invasive, and time-efficient approach to identifying training zones and monitoring performance. CS effectively delineated moderate-to-heavy, heavy-to-severe, and severe-to-extreme intensity domains, with strong correlations to performance thresholds. The findings emphasize the accessibility and reliability of the 3MT, particularly in overcoming the logistical challenges of traditional testing methods. Conclusions: This research underscores the potential of the running 3MT as a valuable tool for individualized training prescription and performance monitoring in both research and applied sports contexts, paving the way for a broader adoption in athletic training methodologies.

Spatial Coupling Relationship Between Water Area and Water Level of Dongting Lake Based on Multiple Temporal Remote Sensing Images Data at Its Several Hydrological Stations

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes.

Remaining useful life prediction of rolling bearings using a residual attention network with multi-scale feature extraction and temporal dependency enhancement

ABSTRACT Predicting the remaining useful life (RUL) of rolling bearings is crucial for industrial machinery maintenance, non-destructive testing and evaluation (NDT). To address the challenges posed by noise interference and redundant information, this paper proposes a novel approach utilising residual attention networks and multi-scale feature extraction. The method enhances feature extraction by combining shallow and deep convolutional layers while employing bidirectional LSTM to capture both short-term and long-term dependencies in time series data. The incorporation of a residual attention fusion module further enhances the model’s ability to focus on important features, ensuring more stable training and better prediction performance. After validation on PHM2012, IMS, and laboratory self-constructed datasets, the proposed model demonstrates superior performance compared to existing methods, significantly reducing prediction errors. The practical application of this work lies in its potential integration into industrial predictive maintenance systems, providing a solid foundation for expanding predictive maintenance strategies in complex real-world industrial environments.

The opportunities in implementing assistive technology in oral health care for individuals with sensory impairments: a systematic review.

People with sensory impairments (SI) face unique challenges in out-of-home care, especially in oral health (OH) care, compared to the general population. Various assistive technologies (ATs) and media are used to influence OH behaviors and outcomes for individuals with SI. This systematic review (SR) aimed to identify the types of ATs and assess their effectiveness for individuals with SI. A comprehensive search strategy was applied across Scopus, PubMed, Cochrane Library, ProQuest, and Scopus databases, using expert-validated keywords, with manual searches for relevant articles published from June 2013 to June 2023. The SR followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Eligible studies focused on the use of ATs as OH education tools for individuals with SI and assessed their impact on OH behavior and status. Eighteen of the 1298 identified studies were included in the data synthesis. ATs, such as images, software, multimedia, audio, video, audio-tactile performance, and combination methods, were effective for OH care in individuals with SI. However, this SR limitations include study heterogeneity in sample sizes, intervention types, and outcomes, as well as the exclusion of non-English studies. Oral health education using a combination of video training and audio-tactile performance is particularly effective for individuals with SI, engaging multiple senses for learning. Future research should focus on standardizing methodologies and incorporating larger sample sizes to enhance the efficacy of these technologies in improving OH care for individuals with SI. PROSPERO REG. NO.: CRD42023485523.

Evaluation of keratometric and total corneal astigmatism measurements from optical biometers and anterior segment tomographers and mapping to reconstructed corneal astigmatism vector components.

To investigate different measures for corneal astigmatism in the context of reconstructed corneal astigmatism (recCP) as required to correct the pseudophakic eye, and to derive prediction models to map measured corneal astigmatism to recCP. Retrospective single centre study of 509 eyes of 509 cataract patients with monofocal (MX60P) IOL. Corneal power measured with the IOLMaster 700 keratometry (IOLMK), and Galilei G4 keratometry (GK), total corneal power (TCP2), and Alpin's integrated front (CorT) and total corneal power (CorTTP). Feedforward shallow neural network (NET) and linear regression (REG) prediction models were derived to map the measured C0 and C45 power vector components to the respective recCP components. Both the NET and REG models showed superior performance compared to a constant model correcting the centroid error. The mean squared prediction errors for the NET/REG models were: 0.21/0.33 dpt for IOLMK, 0.23/0.36 dpt for GK, 0.24/0.35 for TCP2, 0.23/0.39 dpt for CorT and 0.22/0.36 dpt for CorTTP respectively (training data) and 0.27/0.37 dpt for IOLMK, 0.26/0.37 dpt for GK, 0.38/0.42 dpt for TCP2, 0.35/0.36 dpt for CorT, and 0.44/0.45 dpt for CorTTP respectively on the test data. Crossvalidation with model optimisation on the training (and validation) data and performance check on the test data showed a slight overfitting especially with the NET models. Measurement modalities for corneal astigmatism do not yield consistent results. On training data the NET models performed systematically better, but on the test data REG showed similar performance to NET with the advantage of easier implementation.