Game Performance Research Articles

Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games

Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the possible instability in the training process. We propose that a reinforcement learning algorithm can train more efficiently and achieve improved performance in a two-player game if it leverages the knowledge from the single-player version of the same game. This study examines the proposed idea in ten different Atari 2600 environments using the Atari 2600 RAM as the input state. We discuss the advantages of using transfer learning from a single-player training process over training in a two-player setting from scratch, and demonstrate our results in a few measures such as training time and average total reward. We also discuss a method of calculating RAM complexity and its relationship to performance.

Teaching Games for Whakawhānaukataka:

The purpose of this study was to use the Traditional Indigenous Game (TIG) or taonga tākaro, ‘Ki-o-Rahi’ (KoR) to explicitly promote a better understanding of wellbeing among students. Often in Physical Education (PE) settings, the Teaching Games for Understanding (TGfU) model is employed to educate students in games’ play about playing the game(s). For example, the creators of the TGfU model stated that “the primary purpose of teaching any game [TGfU] should be to improve students’ game performance and to advance their enjoyment and participation in games, which might lead to a healthier lifestyle” (Thorpe & Bunker, 1996, p.30). However, this study reversed this position and instead emphasised promoting Hauora (wellbeing) improvements to students’ lifestyles firstly, with the secondary outcome that it might lead to better game performance (but this was not a primary objective). Essentially, our aim was to determine if the KoR Unit (‘KoRU’) could be used to re-frame a traditional ‘TGfU’ unit and double-down on the ‘Understanding’ to privilege the promotion of Hauora (wellbeing) and to assess if this re-positioning led to improvements in rangatahi lives?

Will the COVID-19 infection affect the performance of top basketball players? A data-driven analysis

PurposeTo investigate the changes in the game performance of high-level basketball players in NBA league before and after the COVID-19 infection.MethodsAthletic statistics were collected from official database for 68 players (19 forwards, 29 guards, 20 centers; mean age 27.14 ± 3.65 years) in the NBA league for 10 games before and after infection with COVID-19. We used a the Kruskal-Wallis analysis of variance to examine the impact caused by COVID-19.ResultsAmong all 68 players, a statistically significant decrease in plus/minus (Z = −2.392, p = 0.017) and free throw shooting (FT% Z = −2.153, p = 0.031), occurred compared to the pre-infection with COVID-19. Among the interior players, we found a statistically significant decrease in free throw shooting FT% (Z = 2.674, p = 0.021), Plus/Minus (Z = −2.371, p = 0.018). Among centers and forwards, there was no statistically significant change.ConclusionThe impact of COVID-19 on players’ fatigue and cardiorespiratory and muscular endurance may have contributed to the decline in performance, and the impact of detraining due to isolation should not be ignored.

Forecasting extremes of football players’ performance in matches

This study evaluates the use of simple linear or piecewise linear predictive models to predict extreme performance metrics in soccer matches, based on historical training and to match data of soccer players from RKS Raków Częstochowa football club. The data were collected from January to June 2023. The collected training and matched data average is 9000 records per month. A standard workweek at the RKS Academy consisted of 5 training units and at least 1 match. The best individual models found predict selected game performance metrics with a relative error of 2.3%, suggesting an excellent model fit between prediction and the actual value. This is illustrated by input data metric called “Metabolic Time Zone 5 and 6 Per Distance”, and output data by “Decelarations Total Distance in Zone 5 and 6 Per Distance”—calculated for in 3 min sliding window and characterized by the highest value of the generated parameter based on High Metabolic Load Distance (HMLD). The result concerns models run on aggregated performance metrics developed in APEX-PRO system using expert knowledge in soccer training, while raw GPS location-based models performing worse but still acceptably. Although we believe that the accuracy of the models still has limited reliability, their clarity and up-to-date quality make them useful in the daily planning of training activities and the management of workloads that affect player performance in the upcoming match, as well as the tactical decisions of the coach. More accurate predictions are given by individual models compared to aggregated models (player position), but there are exceptions where group models also perform very well. Adding a second metric to the input did not show a significant difference in the analyzed examples (the results are very similar). Our findings indicate that the model based on metrics from the last match also effectively predict extreme motor performances occurring in the game. In the case of the analyzed player, it was at the input “Accelerations Total Time Per Distance in Zone 6” at the output “Distance in Zone 6”. Specific training or match parameters can be key in predicting exceptional soccer performance, but they can also vary depending on the analyzed player. This confirms the need for further analysis of this issue.

Tactical games model in physical education: A systematic review.

Since the Tactical Games Model (TGM) was adopted and popularised by Oslin, Griffin, and Mitchell, research interest in this model has surged, increasing its application in teaching and research. However, much-existing research is fragmented and lacks a comprehensive review. This systematic review aims to fill that gap by thoroughly analysing the literature on TGM within the context of physical education, highlighting current trends and developments. We systematically searched four online databases, identifying 38 relevant articles for inclusion, which were then evaluated across eight domains following PRISMA guidelines. The analysis indicates a steady increase in TGM-focused research within physical education, particularly at the K-12 level. However, there is a notable absence of studies addressing higher education, as well as teachers and coaches. The review also found that the majority of TGM research is concentrated on team sports and invasion games, with limited exploration of other sports. The research objectives often centred on extrinsic factors, such as game performance, skill level, and physical activity (PA) level, as well as intrinsic factors, intrinsic motivation, stress, and enjoyment. The TGM has shown varying levels of success in improving these factors. Despite the recognised benefits of TGM in physical education, its impact on cognitive aspects and decision-making remains underexplored. This systematic review may serve as a valuable resource for educators and researchers, supporting the broader application of TGM across different areas of physical education and potentially leading to enhanced student outcomes.

Enhancing stability and explainability in reinforcement learning with machine learning

Abstract. In the field of reinforcement learning, training agents using machine learning algorithms to learn and perform tasks in complex environments has become a prevalent approach. However, reinforcement learning faces challenges such as training instability and decision opacity, which limit its feasibility in real-world applications. To solve the problems of stability and transparency in reinforcement learning, this project will use advanced algorithms like Proximal Policy Optimization (PPO), Q-DAGGER, and Gradient Boosting Decision Trees to set up reinforcement learning agents in the OpenAI Gymnasium environment. Specifically, the study selected the Atari game Breakout as the testbed, enhancing training efficiency and game performance by refining reward structures and decision-making processes, and integrating interpretable models to provide explanations for agent decisions. This study has successfully developed robust reinforcement learning agents that excel in complex environments. By employing advanced algorithms like PPO, Q-DAGGER, and Gradient Boosting Decision Trees, the study has addressed issues of training instability, and improved game performance through optimized reward structures and decision processes. Additionally, by integrating interpretable models, the study has provided insights into the learned strategies of the agents, thereby enhancing decision transparency. These findings provide crucial support for the broader application of reinforcement learning in real-world scenarios and offer valuable insights for tackling other complex tasks.

Individual dynamics mathematical patterns of qualified basketball players competitive performance

Background and purpose Since the goal in sport is to overcome one's limitations and take one's functional state to a qualitatively new level, forecasting is an integral part of managing any process, including the athlete's training process. Purpose: to develop an algorithm and identify patterns of individual dynamics in competitive performance of eligible basketball players. Materials and methods The main team players of the men's basketball team of H.S. Skovoroda Kharkiv National Pedagogical University. Twelve matches of the experimental team playing against national teams of other universities were analyzed. Observations were made on the players during the championships played by student teams from Kharkiv. For each player, the number of shots and hits from short, mid and long range, goal assists, shots and hits from the penalty line, and turnovers were recorded. To identify individual patterns of competitive performance dynamics, indicators such as 'total positive points in a match', which most accurately reflect a player's 'positive' contribution to the outcome of a match, were used. To identify individual regularities in competitive performance dynamics, indicators such as 'total positive points in the match', which most accurately reflect the 'positive' contribution of players to the outcome of the match, were used. Results It was determined that the process of change in competitive performance should be treated as a variable process. The most appropriate function to describe this model is the sine function. It was shown that the regression model of the individual dynamics of competitive activity of the players of the Ukrainian national basketball team has a sine dependence, which is defined by the regression equation S + = a + bsin ((2π / t) (Т-c)). Where the coefficient a is the average value of a player's match performance, b is the amplitude of the change in a player's match performance, t is the previous period of a player's match performance and c is the value period in the first analyzed match. Conclusion The application of the regression sinusoidal model proved to be effective in practical studies. This is because, using only data from technical reports, it is possible to very quickly predict periods of 'rise' and 'fall' in an individual's game performance, which is useful for adjusting training programs and identifying psychophysiological individual characteristics of players.

Forecasts of Video Game Sales based on the ARIMA Model

Abstract. With the rapid development of the digital age, video games have become an important component of the cultural industry, and their sales forecasting has become a focus of industry attention. This article aims to predict the sales of video games based on the ARIMA model and explore effective methods to improve prediction accuracy. Through in-depth research, this article found that the ARIMA model performs well in capturing the time series characteristics of sales data and can accurately predict future sales trends. The ARIMA model provides a systematic and scientific method for predicting the future market performance of video games by comprehensively considering the trends, seasonality, and random changes in historical sales data. This research achievement not only provides strong decision support for game developers, publishers, and platform operators but also promotes the optimization of resource allocation and market risk avoidance in the video games industry, which is of great significance for promoting the healthy and sustainable development of the industry.

The effects of self-explanation on game-based learning: Evidence from eye-tracking analyses

The purpose of this study was to examine the effect of self-explanation prompts on game performance using eye-tracking techniques. We adopted an experimental design for the study. A total of 40 4th graders participated and were randomly assigned to either the experimental or control group. While the experimental group played a computer game with self-explanation prompts, the control group played the same game without any prompts. A Tobii 4C eye tracker with a sampling rate of 90 Hz was used to record the eye movements of the participants while they were playing the game. A pre-test and post-test were used to assess the participants’ understanding of the target concepts of the game before and after their game-playing, respectively. The results indicated an interaction effect between self-explanation and prior knowledge on the learning outcomes. Regarding the participants with lower-level prior knowledge, the experimental group gained significantly better scores on the posttest than the control group. In contrast, with respect to the participants with higher-level prior knowledge, the self-explanation prompts did not demonstrate a positive effect on the experimental group. Moreover, the results of the lag sequential analysis provide solid evidence of the differences in visual transitional patterns between the participants with different levels of prior knowledge while they were reacting to the self-explanation prompts. These results could account for the different learning outcomes achieved by the participants with different levels of prior knowledge in the experimental group.

Visualization of players' game performance based on random forest and particle swarm models

Visualization of players' game performance based on random forest and particle swarm models

Eye movements and stress during eye-tracking gaming performance in children with dyskinetic cerebral palsy.

https://onlinelibrary.wiley.com/doi/full/10.1111/dmcn.15237

The interaction of relative age with maturation and body size in female handball talent selection.

The relative age effects (RAEs) and biological maturation are two distinct factors that have been identified to affect talent identification and selection. Previous research has suggested that talent selection should include sport-specific technical tasks instead of body size and/or physical test measurements, assuming that the technical tasks are less influenced by variations in maturation. Our purpose was to examine the prevalence of RAEs and to assess biological maturity, body size, and body composition within a single talent selection program for female handball players. Team coaches' recommendations, handball-specific drills, and in-game performance were the selection criteria. Birth distribution of all U14 female handball players were analysed (N = 3198) grouped in two-year age cohort. Measurements of body size, body composition (InBody 720), and bone age were performed in all players who were selected to participate in the selection program (n = 264) (mean±sd age: 13.1±0.6 years) and in a sample of not-selected players (n = 266) (mean±sd age: 13.2±0.6 years). Players were grouped in quarter-year intervals based on their date of birth. Chi-square was used to examine quartile distributions, differences between quartiles were tested with one-way analysis of variance, whereas differences between the selected and not-selected groups were examined with independent sample t-test (Cohen d effect size). Binary logistic regression was used to determine the effects of the predictors on the selection. In terms of all registered players, there was no difference in birth distribution. RAEs appeared at the first selection stage and were evident at all following stages. Quartiles differed only between the first and the last quartiles in body size and muscle mass. Only bone age differed between consecutive quartile or semi-year groups. Body size, body composition, and maturity had a significant, but of moderate power, effect on the selection. Larger body height increased the likelihood of selection by about 12%, larger muscle mass by 12% to 25%, larger bone age by 350-400%, while larger percent body fat decreased selection chances by 7%. The sport-specific criteria could not eliminate the prevalence of the RAEs. Relative age was connected to bone age, but not convincingly to body size and muscle mass. Although bone age had the largest effect on the selection, this was not associated with larger body size or muscle mass. Early maturation increased selection chances mainly during the coaches' subjective evaluation, but not convincingly when sport-specific tasks were applied. Given that differences were mostly evident between players of more than 1.5-year variation in their chronological age, one-year age cohorts within talent selection or the rotation of the cut-off dates of the bi-annual age grouping could be a promising strategy while also including maturation status and relative age in performance evaluations.

Validation and comparison of virtual reality and 3D mobile games for cognitive assessment against ACE-III in 82 young participants

Current medical and clinical ecosystem for dementia detection is inadequate for its early detection. Traditional cognitive assessments are introduced after cognitive impairment has begun to disrupt the real-world functioning of the person. Moreover, these tools are paper-pen based and fail to replicate the real-world situations wherein the person ultimately lives, acts and grows. The lack of tools for early detection of dementia, combined with absence of reliable pharmacological cure compound the problems associated with dementia diagnosis and care. Advancement of technology has facilitated early prediction of disease like cancer, diabetes, heart disease, but hardly any such translation has been observed for dementia or cognitive impairment. Given this background, we examine the potential of Virtual Reality (VR) and 3D Mobile-based goal-oriented games for cognitive assessment. We evaluate three games (2 in VR, one in mobile) among 82 young participants (aged 18–28 years) and compare and contrast the game-based results with their Addenbrooke Cognitive Examination (ACE-III) scores. Three main analysis methods are used: Correlative, Z-score and Regression analysis. Positive correlation was observed for ACE-III and game-based scores. Z-scores analysis revealed no difference between the two scores, and stronger statistical significance was found between game scores and cognitive health factors like age, smoking compared to ACE-III. Specific game performances also revealed about real-world traits of participants, like hand-use confusion and direction confusion. Results establish the plausibility of using goal-oriented games for more granular, time-based, and functional cognitive assessment.

Volleyball: similar game for men and women? Factors characterising successful performance in Olympic Games 2021 regarding genders

ABSTRACT The goal of this study was to survey the effectiveness of the factors characterising successful performance in volleyball between men and women in games played in the Olympic Games 2021 tournaments. Seventy-six matches (38 matches per gender) were sampled, and data were collected from N = 560 sets (N = 292 for men and N = 268 for women). Principal component analysis was conducted on 15 performance indicators with orthogonal rotation (varimax). The principal component scores saved from the analysis were run in binomial logistic regression (Win/Loss) forced entry regression analysis using a data splitting method on a random selection of 50% of the data (training and test sample). The models produced were then used to predict the probability of winning the set using the same variables for the remaining 50% data. The results provide evidence that between genders there are common important factors which affect the evolution of a set and therefore if improved can increase the probability of winning, however with the same level of improvement, the odds differentiate per gender. These findings and inferences can be used as guidelines for coaches of teams of both genders concerning the skills that require further improvement.

The Influence of Social Competition and Maths Anxiety on Game Performance

Digital Game-Based Learning (DGBL) uses serious games to deliver educational content. DGBL has the potential to aid children’s development of maths skills, providing educators with alternatives to traditional mathematics teaching. However, the efficacy of serious games can be affected by their attributes, including whether they are single or multi-player. While competition in a multi-player environment may enhance players’ interest and motivation, potential social pressure may lead to anxiety and affect performance. In maths education, there are additional challenges. Mathematics Anxiety (MA), the negative emotional response to mathematics, may interact with game features and impact players’ experience. This study assesses players' performance in a digital maths game called ’Seven Spells’ across two different game modes (vs. Human and vs. CPU), and it also investigates whether MA levels impacted players’ performance. 43 children from two classes in an Irish primary school participated in thisstudy. Each class played a different game mode of the ‘Seven Spells’ game, one group playing against computer-controlled characters (vs. CPU) and the other playing against their classmates (vs. Human). The game mechanics were identical between the two competition game modes. The children completed the modified Abbreviated Mathematics Anxiety Scale (mAMAS) and a mathematics questionnaire before participating in the intervention, which included 4 sessions. They also participated in a focus group session at the end of the intervention. Data on their gameplay were collected throughout the first three sessions and analysed through multiple linear regression models to investigate possible connections between MA, maths knowledge, game modes and game performance. The results of the multiple linear regressions indicate that game performance was positively influenced by the duration of gameplay and the complexity of moves — measured by the number of moves and cards used — while being negatively affected by MA. However, a significant interaction between MA and the competition game mode was observed, suggesting that MA had a more pronounced negative impact on players in the vs. Human game mode compared to the vs. CPU game mode.

Analysing Game Data to Evaluate Children’s Learning Progress with the EDURINO App

Game data offers a unique way to examine children’s learning progress in a gameplay context. Despite its relevance in evaluating the efficacy of game-based learning, this area is less researched than engagement and usability study. This paper presents a quantitative investigation of EDURINO, an educational gaming app targeting children aged 4-8. We implemented learning metrics to track children’s performance in EDURINO games. Specifically, the measurements included standardised variables from educational and experimental psychology, such as error count and duration. Here we reported data from a total of 18 EDURINO minigames in Literacy and Maths. Game data was collected by the learning metrics tracking system. A total of 66,463 players’ data was analysed using generalised linear mixed models with the lm4 package in R. Overall, children have shown improvements while playing the EDURINO app. We examined the relationships between children’s game performance, player demographics and game-specific features and discovered different patterns. First, the significant main effects of age and gender indicated that EDURINO’s educational content was developmentally sensitive and age appropriate. Second, children made fewer errors and spent less time when they were in the later rounds within a game or have played the same game more than once. Third, higher difficulty level undermined game performance. Worth noting is that the effect of game-specific factors varied among different educational content, and such discrepancy could be attributed to the nature of game design. When presented with engaging and interactive content, children may have chosen to deliberately make mistakes for fun effects. These findings are discussed in relation to game-based assessment for learning in EdTech research and industry. We also considered the interpretation of learning progress in EDURINO’s gameplay context while highlighting the need for further efficacy research.

Physical fitness, psychological characteristics, and game performance in youth male soccer players of different levels of competition

ABSTRACT This cross-sectional study examined the differences and associations regarding physical fitness, psychological characteristics, and game performance between elite (n = 64), sub-elite (n = 82), and non-elite (n = 97) youth male soccer players aged 16.2 ± 0.5 years. Physical fitness parameters were assessed using field tests, psychological characteristics were evaluated using validated questionnaires and technical – tactical data were recorded during a competitive soccer match using a multicamera, semiautomatic optical tracking system. Elite players demonstrated superior performance in cardiorespiratory fitness, countermovement jump (CMJ), 10 m sprint, and agility, compared to the sub-elite and the non-elite players. Also, the elite and sub-elite players had faster 40 m sprints than the non-elite players. Game performance was significantly better in the elite group than the other groups and was associated with coping with the adversity and coachability, tension-anxiety, anger-hostility, and fatigue-inertia subscales. Among the physical fitness parameters examined, only agility and CMJ were associated with game performance. In conclusion, elite youth soccer players performed significantly better in various physical fitness and psychological parameters and demonstrated a higher game performance indicator score compared to their sub-elite and non-elite peers. Results suggested that physical fitness and psychological parameters are essential predictors of game performance.

The complexity of agility testing in water polo: Reliability and validity analysis of sport-specific protocols

ABSTRACT This study aimed at evaluating the reliability and discriminant validity of a newly developed in-water agility test through a progression in complexity. Thirty-eight young male water polo players were divided into elite (n = 20; 16.2 ± 0.5 years) and non-elite (n = 18; 15.8 ± 0.6 years) groups. Players performed 20 m Freestyle Sprint Swimming Test (20 m FSST), Change of Direction Speed (CODS), and the Functional Agility Test without (FAT) and with shooting (FATS). The cognitive (CD) and technical deficits (TD) were calculated by subtracting time results of CODS from FAT and of FAT from FATS respectively. Excellent reliability was found for the 20 m FSST, CODS, and FAT (ICC from 0.954 to 0.982) and good reliability for the FATS (ICC = 0.838). Elite players outperformed non-elite players in 20 m FSST (g = 1.25), CODS (g = 1.08), and FAT (g = 1.14). There were no differences in FATS (g = 0.29), CD (g = 0.14), TD (g = 0.50) and shooting efficiency (g = 0.04). Increasing protocol’s complexity resulted in similar performance between groups eliminating differences in physicality. Although designed to mimic in-game performance, the results suggest discriminant validity only for physical attributes among young players, raising the need for further improvements in the testing protocol to better meet the cognitive and technical demands.

Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy.

This study aims to develop a machine learning-based approach to predict adherence to gamified cognitive training using a variety of baseline measures (demographic, attitudinal, and cognitive abilities) as well as game performance data. We aimed to: (1) identify the cognitive games with the strongest adherence prediction and their key performance indicators; (2) compare baseline characteristics and game performance indicators for adherence prediction, and (3) test ensemble models that use baseline characteristics and game performance data to predict adherence over ten weeks. Using machine learning algorithms including logistic regression, ridge regression, support vector machines, classification trees, and random forests, we predicted adherence from weeks 3 to 12. Predictors included game performance metrics in the first two weeks and baseline measures. These models' robustness and generalizability were tested through five-fold cross-validation. The findings indicated that game performance measures were superior to baseline characteristics in predicting adherence. Notably, the games "Supply Run," "Ante Up," and "Sentry Duty" emerged as significant adherence predictors. Key performance indicators included the highest level achieved, total game sessions played, and overall gameplay proportion. A notable finding was the negative correlation between initial high achievement levels and sustained adherence, suggesting that maintaining a balanced difficulty level is crucial for long-term engagement. Conversely, a positive correlation between the number of sessions played and adherence highlighted the importance of early active involvement. The insights from this research inform just-in-time strategies to promote adherence to cognitive training programs, catering to the needs and abilities of the aging population. It also underscores the potential of tailored, gamified interventions to foster long-term adherence to cognitive training.

Detection and Characterization of Online Substance Use Discussions Among Gamers: Qualitative Retrospective Analysis of Reddit r/StopGaming Data.

Video games have rapidly become mainstream in recent decades, with over half of the US population involved in some form of digital gaming. However, concerns regarding the potential harms of excessive, disordered gaming have also risen. Internet gaming disorder (IGD) has been proposed as a tentative psychiatric disorder that requires further study by the American Psychological Association (APA) and is recognized as a behavioral addiction by the World Health Organization. Substance use among gamers has also become a concern, with caffeinated or energy drinks and prescription stimulants commonly used for performance enhancement. This study aimed to identify substance use patterns and health-related concerns among gamers among a population of Reddit users. We used the public streaming Reddit application programming interface to collect and analyze all posts from the popular subreddit, r/StopGaming. From this corpus of posts, we filtered the dataset for keywords associated with common substances that may be used to enhance gaming performance. We then applied an inductive coding approach to characterize substance use behaviors, gaming genres, and physical and mental health concerns. Potential disordered gaming behavior was also identified using the tentative IGD guidelines proposed by the APA. A chi-square test of independence was used to assess the association between gaming disorder and substance use characteristics, and multivariable logistic regression was used to analyze whether mental health discussion or the mention of any substance with sufficient sample size was significantly associated with IGD. In total, 10,551 posts were collected from Reddit from June 2017 to December 2022. After filtering the dataset for substance-related keywords, 1057 were included for further analysis, of which 286 mentioned both gaming and the use of ≥1 substances. Among the 286 posts that discussed both gaming and substance use, the most mentioned substances were alcohol (n=132), cannabis (n=104), and nicotine (n=48), while the most mentioned genres were role-playing games (n=120), shooters (n=90), and multiplayer online battle arenas (n=43). Self-reported behavior that aligned with the tentative guidelines for IGD was identified in 66.8% (191/286) posts. More than half, 62.9% (180/286) of the posts, discussed a health issue, with the majority (n=144) cited mental health concerns. Common mental health concerns discussed were depression and anxiety. There was a significant association between IGD and substance use (P<.001; chi-square test), and there were significantly increased odds of IGD among those who self-reported substance use (odds ratio 2.29, P<.001) and those who discussed mental health (odds ratio 1.64, P<.03). As gaming increasingly becomes highly prevalent among various age groups and demographics, a better understanding of the interplay and convergence among disordered gaming, substance use, and negative health impacts can inform the development of interventions to mitigate risks and promote healthier gaming habits.