Specific approaches are needed to reach and support people with a lower socioeconomic position (SEP) to achieve healthier eating behaviours. There is a growing body of evidence suggesting that digital health tools exhibit potential to address these needs because of its specific features that enable application of various behaviour change techniques (BCTs). The aim of this scoping review is to identify the BCTs that are used in diet-related digital interventions targeted at people with a low SEP, and which of these BCTs coincide with improved eating behaviour. The systematic search was performed in 3 databases, using terms related to e/m-health, diet quality and socioeconomic position. A total of 17 full text papers were included. The average number of BCTs per intervention was 6.9 (ranged 3–15). BCTs from the cluster ‘Goals and planning’ were applied most often (25x), followed by the clusters ‘Shaping knowledge’ (18x) and ‘Natural consequences’ (18x). Other frequently applied BCT clusters were ‘Feedback and monitoring’ (15x) and ‘Comparison of behaviour’ (13x). Whereas some BCTs were frequently applied, such as goal setting, others were rarely used, such as social support. Most studies (n = 13) observed a positive effect of the intervention on eating behaviour (e.g. having breakfast) in the low SEP group, but this was not clearly associated with the number or type of applied BCTs. In conclusion, more intervention studies focused on people with a low SEP are needed to draw firm conclusions as to which BCTs are effective in improving their diet quality. Also, further research should investigate combinations of BCTs, the intervention design and context, and the use of multicomponent approaches. We encourage intervention developers and researchers to describe interventions more thoroughly, following the systematics of a behaviour change taxonomy, and to select BCTs knowingly.
Equestrianism is currently facing a range of pressing challenges. These challenges, which are largely based on evolving attitudes to ethics and equine wellbeing, have consequences for the sport’s social licence to operate. The factors that may have contributed to the current situation include overarching societal trends, specific aspects of the equestrian sector, and factors rooted in human nature. If equestrianism is to flourish, it is evident that much needs to change, not the least,human behaviour. To this end, using established behaviour change frameworks that have been scientifically validated and are rooted in practice — most notably, Michie et al.’s COM-B model and Behaviour Change Wheel — could be of practical value for developing and implementing equine welfare strategies. This review summarises the theoretical underpinnings of some behaviour change frameworks and provides a practical, step-by-step approach to designing an effective behaviour change intervention. A real-world example is provided through the retrospective analysis of an intervention strategy that aimed to increase the use of learning theory in (educational) veterinary practice. We contend that the incorporation of effective behaviour change interventions into any equine welfare improvement strategy may help to safeguard the future of equestrianism.
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Even though mango productivity in Ethiopia is low due to moisture stress, there is no report on how such constraint could alleviate using Cocoon water-saving technology. Cocoon is small water reservoir technology which uses for plant growth in dry season. The objectives of this study were to introduce and evaluate effectiveness of water-saving techniques on mango seedlings survival and growth in Mihitsab-Azmati watershed, northern Ethiopia. In this experiment, five treatments of water-saving techniques with mango seedlings were evaluated. These were: Cocoon sprayed by tricel (T1), Cocoon painted by used engine oil (T2), Cocoon without tricel and oil (T3), manually irrigated seedlings (T4) and mango seedlings planted during rainy season (T5). The survival and growth performance of mango seedlings were recorded at six months and one-year after transplanting. Data on plant survival, height, number of leaves per plant, shoot length, stem diameter and crown width were subjected to analysis of variance and t-test. There were significant differences in the treatment effects on mango seedlings transplanted survival, plant height, number of leaves per plant, shoot length, stem diameter and crown width measured at six months and one-year after transplanting. The lowest survival rate (20 %) was found during both data collection time in T5. Six months after transplanting, the highest growth parameters were measured from T1 whereas the lowest was from T5. However, one-year after transplanting, the highest growth parameters were measured from T3. Plant heights increments between the two measurement periods for T3, T2, T1, T4 and T5 were 45.1, 38.5, 24.8, 9.8 and 7.0 cm, respectively; indicating that T3 performed better than the other treatments. The t-test on mean differences between the same growth parameter measured at 12 and six months after transplanting also showed significant differences. The Cocoon water-saving technology was superior in improving mango seedlings survival and growth in the study area. This study generalized that Cocoon seems promising, sustainable and highly scalable with mango seedlings at large-scale in the study area conditions. However, this technology should not be assumed to perform uniformly well in all environmental conditions and with all tree species before demonstrated on a pilot study.
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Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
