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Item type:Publication, Quantifying trunk neuromuscular control using seated balancing and stability threshold(Elsevier, 2020-11-09)Performance during seated balancing is often used to assess trunk neuromuscular control, including evaluating impairments in back pain populations. Balancing in less challenging environments allows for flexibility in control, which may not depend on health status but instead may reflect personal preferences. To make assessment less ambiguous, trunk neuromuscular control should be maximally challenged. Thirty-four healthy subjects balanced on a robotic seat capable of adjusting rotational stiffness. Subjects balanced while rotational stiffness was gradually reduced. The rotational stiffness at which subjects could no longer maintain balance, defined as critical stiffness (kCrit), was used to quantify the subjects’ trunk neuromuscular control. A higher kCrit reflects poorer control, as subjects require a more stable base to balance. Subjects were tested on three days separated by 24 hours to assess test–retest reliability. Anthropometric (height and weight) and demographic (age and sex) influences on kCrit and its reliability were assessed. Height and age did not affect kCrit; whereas, being heavier (p < 0.001) and female (p = 0.042) significantly increased kCrit. Reliability was also affected by anthropometric and demographic factors, highlighting the potential problem of inflated reliability estimates from non-control related attributes. kCrit measurements appear reliable even after removing anthropometric and demographic influences, with adjusted correlations of 0.612 (95%CI: 0.433–0.766) versus unadjusted correlations of 0.880 (95%CI: 0.797–0.932). Besides assessment, trainers and therapists prescribing exercise could use the seated balance task and kCrit to precisely set difficulty level to a percentage of the subject's stability threshold to optimize improvements in trunk neuromuscular control and spine health. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stability threshold during seated balancing is sensitive to low back pain and safe to assess(Elsevier, 2021-08-26)Challenging trunk neuromuscular control maximally using a seated balancing task is useful for unmasking impairments that may go unnoticed with traditional postural sway measures and appears to be safe to assess in healthy individuals. This study investigates whether the stability threshold, reflecting the upper limits in trunk neuromuscular control, is sensitive to pain and disability and is safe to assess in low back pain (LBP) patients. Seventy-nine subjects with non-specific LBP balanced on a robotic seat while rotational stiffness was gradually reduced. The critical rotational stiffness, KCrit, that marked the transition between stable and unstable balance was used to quantify the individual's stability threshold. The effects of current pain, 7-day average pain, and disability on KCrit were assessed, while controlling for age, sex, height, and weight. Adverse events (AEs) recorded at the end of the testing session were used to assess safety. Current pain and 7-day average pain were strongly associated with KCrit (current pain p < 0.001, 7-day pain p = 0.023), reflecting that people experiencing more pain have poorer trunk neuromuscular control. There was no evidence that disability was associated with KCrit, although the limited range in disability scores in subjects may have impacted the analysis. AEs were reported in 13 out of 79 total sessions (AE Severity: 12 mild, 1 moderate; AE Relatedness: 1 possibly, 11 probably, 1 definitely-related to the study). Stability threshold is sensitive to pain and appears safe to assess in people with LBP, suggesting it could be useful for identifying trunk neuromuscular impairments and guiding rehabilitation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine-Supported Decision-Making to Improve Agricultural Training Participation and Gender Inclusivity(Public Library of Science, 2023-05-01)Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Speaking Their Language: Language Inclusion and YouTube Agricultural Content Engagement in Africa(Wiley, 2026-03-01)Efficiently educating farmers in effective agricultural practices is critical in resource‐limited developing countries. YouTube, with its broad accessibility and built‐in viewership tracking, presents a potential scalable platform for agricultural education. This study assesses how language inclusion policies affect engagement with agricultural YouTube content. We conducted a case study using a video campaign to address post‐harvest loss in Africa, featuring an educational animation translated into 14 Ghanaian, 35 Kenyan, and 16 Nigerian languages. The campaign was distributed through paid YouTube ads. Two inclusion policies were evaluated through computational simulations using real‐world data: equal opportunity (i.e., equal spending across languages) and equal outcome (i.e., adjusted spending to equalize viewership across languages). We also estimated viewership under two additional scenarios: using only the official language and using the most cost‐effective language for each country. The most cost‐effective campaigns coincided with the official language of English in Ghana (8.9 viewers per USD) and Nigeria (3.1 viewers per USD), but not in Kenya, where the most effective language campaign was Kikuyu (16.2 viewers/USD). Overall, the equal spending policy reduced viewership by 43%, while the equal outcome policy reduced viewership by 66% compared to campaigns in official languages. However, results show country‐specific trends. Differences in viewership between inclusion policies were minimal in Ghana and Nigeria. Conversely, in Kenya, the discrepancy in inclusion policy impact was more pronounced, suggesting that in certain regions, more inclusive policies are likely to significantly influence viewership levels. These findings highlight the importance of localized evaluations of inclusion policies in digital agricultural education.1