Search Results (88)

Smartphones have revolutionized our daily lives, becoming portable pocket computers with easy internet access. India, the second-highest smartphone and internet user, experienced a significant rise in smartphone usage between 2013 and 2024. Prolonged smartphone use, exceeding 20 min at a time, can lead to physical and mental health issues, including psychophysiological disorders. Digital devices and their extended exposure to blue light cause digital eyestrain, sleep disorders and visual-related problems. This research examines the impact of 1 h smartphone usage on visual fatigue among young Indian adults. A portable, low-cost system has been developed to measure visual activity to address this. The developed visual activity measurement system measures blink rate, inter-blink interval, and pupil diameter. Measured eye activity was recorded during 1 h smartphone usage of e-book reading, video watching, and social-media reels (short videos). Social media reels show increased screen variations, affecting pupil dilation and reducing blink rate due to continuous screen brightness and intensity changes. This reduction in blink rate and increase in inter-blink interval or pupil dilation could lead to visual fatigue. Full article

Objective: This cross-sectional content analysis aimed to investigate how oil pulling is portrayed on YouTube Shorts, focusing on the types of speakers, claims made, and alignment with scientific evidence. The study further explored how the content may influence viewer perception, health behaviors, and the potential spread of misinformation. Methods: On 28 January 2025, a systematic search of YouTube Shorts was performed using the term “oil pulling” in incognito mode to reduce algorithmic bias. English language videos with at least 1000 views were included through purposive sampling. A total of 47 Shorts met the inclusion criteria. Data were extracted using a structured coding framework that recorded speaker type (e.g., dentist, hygienist, influencer), engagement metrics, stated benefits, oil type and regimen, the use of disclaimers or citations, and stance toward oil pulling rated on a 5-point Likert scale. Speaker background and nationality were determined through publicly available channel descriptions or linked websites, with user identities anonymized and ethical approval deemed unnecessary due to the use of publicly available content. In total, 47 videos met the inclusion criteria. Results: Of the 47 YouTube Shorts that met the inclusion criteria, most were posted by influencers rather than dental professionals. These videos predominantly encouraged oil pulling, often recommending coconut oil for 10–15 min daily and citing benefits such as reduced halitosis and improved gum health. However, a smaller subset advanced more extreme claims, including reversing cavities and remineralizing enamel. Notably, US-licensed dentists and dental hygienists tended to discourage or express skepticism toward oil pulling, assigning lower Likert scores (1 or 2) to influencers and alternative health practitioners (often 4 or 5). Conclusions: YouTube Shorts largely promote oil pulling through anecdotal and testimonial-driven content, often diverging from evidence-based dental recommendations. The findings reveal a disconnect between professional dental guidance and popular social media narratives. While some benefits like halitosis reduction may have limited support, exaggerated or misleading claims may result in improper oral hygiene practices. Greater engagement from dental professionals and improved health communication strategies are needed to counteract misinformation and reinforce oil pulling’s role, if any, as an adjunct—not a replacement—for standard oral care. Future studies should explore viewer interpretation, behavioral influence, and cross-platform content patterns to better understand the impact of short-form health videos. Full article

Police officers work in high-stress environments that demand emotional resilience, interpersonal skills, and effective communication. Occupational stress can negatively impact their motivation, conflict resolution abilities, and professional effectiveness. Loving-Kindness Meditation (LKM), a mindfulness-based intervention focused on cultivating compassion and empathy, has shown promise in enhancing prosocial attitudes and emotional regulation. With the rise of short video platforms, digital interventions like video-guided LKM may offer accessible mental health support for law enforcement. This study examines the effects of short video app-guided LKM on police officers’ mindfulness, public service motivation (PSM), conflict resolution skills (CRSs), and communication skills (CSSs). It aims to determine whether LKM can enhance these psychological and professional competencies. A randomized controlled trial (RCT) was conducted with 110 active-duty police officers from a metropolitan police department in China, with 92 completing the study. Participants were randomly assigned to either the LKM group (n = 46) or the waitlist control group (n = 46). The intervention consisted of a 6-week short video app-guided LKM program with daily 10 min meditation sessions. Pre- and post-intervention assessments were conducted using several validated scales: the Mindfulness Attention Awareness Scale (MAAS), the Public Service Motivation Scale (PSM), the Conflict Resolution Styles Inventory (CRSI), and the Communication Competence Scale (CCS). A 2 (Group: LKM vs. Control) × 2 (Time: Pre vs. Post) mixed-design MANOVA was conducted to analyze the effects. Statistical analyses revealed significant group-by-time interaction effects for PSM (F(4,177) = 21.793, p < 0.001, η² = 0.108), CRS (F(4,177) = 20.920, p < 0.001, η² = 0.104), and CSS (F(4,177) = 49.095, p < 0.001, η² = 0.214), indicating improvements in these areas for LKM participants. However, no significant improvement was observed for mindfulness (F(4,177) = 2.850, p = 0.930, η² = 0.016). Short video app-guided LKM improves public service motivation, conflict resolution skills, and communication skills among police officers but does not significantly enhance mindfulness. These findings suggest that brief, digitally delivered compassion-focused programs can be seamlessly incorporated into routine in-service training to strengthen officers’ prosocial motivation, de-escalation competence, and public-facing communication, thereby fostering more constructive police–community interactions. Full article

Although the benefits of regular physical activity are widely recognized, many university students fail to sustain consistent exercise behaviors. This phenomenon may be attributed to cognitive and motivational barriers, particularly perceptions of goal attainability and attribution styles, which are believed to significantly influence students’ health beliefs and intentions to engage in physical activity. This research aimed to examine the independent and combined effects of goal attainability and attribution style on Chinese university students’ health beliefs and willingness to exercise. The study also investigated how shifts in attribution style may influence these outcomes under different levels of goal attainability. Two between-subjects experiments were conducted. In Experiment 1 (N = 146), a 2 (goal attainability: high vs. low) × 2 (attribution style: internal vs. external) design was used. Participants were exposed to tailored exercise advertisements and completed standardized questionnaires measuring health beliefs and exercise intentions. Experiment 2 (N = 130) adopted a 2 (goal attainability: high vs. low) × 2 (attributional shift: external-to-internal vs. internal-to-external) design, utilizing visual priming and short video interventions to manipulate attributional orientation. In Experiment 1, both high goal attainability and internal attribution independently enhanced participants’ health beliefs and exercise willingness. A significant interaction effect was observed only for exercise willingness, with the highest intentions found in the high attainability × internal attribution group. In Experiment 2, shifting attribution from external to internal significantly increased both health beliefs and exercise willingness, while shifting from internal to external resulted in substantial decreases. An interaction effect was again found only for exercise willingness, suggesting that the effectiveness of attributional shift depended on goal attainability. By integrating the Health Belief Model with Attribution Theory, this study offers a deeper understanding of how cognitive and motivational factors influence exercise behavior, and provides a theoretical foundation for developing adaptive interventions. Full article

Background/Objectives: Hispanic immigrants (HIs) in the U.S.A. are disproportionately affected by cervical cancer compared to other groups, at least partly due to low HPV vaccination rates. The aim of this study was to investigate strategies to improve HPV vaccine attitudes and intent of HIs in the U.S.A. by developing and testing the effectiveness of video-based interventions. Methods: This study employed a two-phase mixed-methods approach. In the first phase, focus groups with new and established HIs explored perspectives, concerns about HPV vaccination, types of information to include in a video intervention, and how an effective intervention should be designed. Findings from the focus groups guided the creation of seven short educational videos, including a summary video and a testimonial-based video, addressing key questions about HPV and its vaccine. The second phase, which involved a nationwide survey of 1500 Spanish-speaking HIs, revealed a significant change in overall HPV vaccine attitude generally, and a significant increase in both HPV vaccine intent and attitudes among parents of unvaccinated children. Results: Regression analysis revealed general vaccine attitudes (β = 0.620, p < 0.001), English proficiency (β = 0.066, p = 0.01), and gender (β = −0.072, p = 0.002), as significant predictors of attitudinal changes. Notably, females exhibited less favorable post-intervention attitudes compared to males. Additionally, perceived care from video creators was a strong predictor of normalized gains in vaccine attitudes (β = 0.270, p < 0.001). Video content effectiveness varied; the video addressing vaccine side effects demonstrated the highest impact on attitude improvement. Testimonials and the summary video were also effective in fostering positive changes in attitudes. Despite differences in trust levels between new and established immigrants, both groups valued culturally tailored, Spanish-language information from credible sources. Conclusion: Addressing language and cultural barriers can improve trust in healthcare interventions among Hispanic immigrants in the U.S.A. Public health initiatives should consider these factors to more effectively reduce HPV vaccine hesitancy in this population. Full article

Background: Law enforcement is a high-stress profession, with officers frequently exposed to traumatic events, leading to mental health challenges such as communication anxiety, post-traumatic stress disorder (PTSD), anger management difficulties, and mood disorders. Mindfulness meditation (MM), particularly when guided through short video applications, has shown promise in addressing these issues by enhancing emotional regulation and resilience. Objective: This study explores the effects of an 8-week MM intervention, delivered via short video apps, on communication anxiety, PTSD, anger management, and mood disorders in police officers. Methods: A randomized controlled trial (RCT) was conducted with 110 full-time police officers aged 25–55 in China. The final 92 eligible participants were divided into two groups: the MM group (n = 46) and the control group (n = 46). The intervention consisted of daily 10–15 min video-guided MM sessions. Pre- and post-intervention measures included validated questionnaires assessing communication anxiety (PRCA-24), PTSD (PCL-5), anger management (STAXI-2), and mood disorders (DASS-21). Data analysis was performed using MANOVA. Results: The intervention group showed significant improvements in communication anxiety (F = 8.505, p = 0.004), PTSD (F = 25.831, p < 0.001), anger management (F = 4.968, p = 0.027), and mood disorders (F = 13.058, p < 0.001) compared to the control group. These improvements were supported by significant interaction effects between group and time, indicating that the MM intervention had a positive impact on these mental health variables. Conclusions: Video-guided MM delivered via short video apps significantly reduced communication anxiety, PTSD symptoms, and mood disorders, and improved anger management among police officers. These findings highlight the potential of digital MM interventions as a scalable and accessible tool for enhancing mental well-being and resilience in law enforcement personnel. Full article

With the growing interest in health and fitness, whey protein supplements are becoming increasingly popular among fitness enthusiasts and athletes. The surge in demand for whey protein supplements highlights the need for cost-effective methods to characterise product quality throughout the food supply chain. This study presents a rapid and low-cost method for authenticating sports whey protein supplements using smartphone video imaging (SVI) combined with machine learning. A gradient of colours ranging from purple to red is displayed on the front screen of a smartphone to illuminate the sample. The colour change on the sample surface is captured in a short video by the front-facing camera. Then, the video is split into frames, decomposed into RGB colour channels, and converted into spectral data. The relationship between video data and sample labels is established using machine learning models. The proposed method is tested on five tasks, including identifying 15 brands of whey protein concentrate (WPC), quantifying fat content and energy levels, detecting three types of adulterants, and quantifying adulterant levels. Moreover, the performance of SVI was compared to that of hyperspectral imaging (HSI), which has an equipment cost of around 80 times that of SVI. The proposed method achieves accuracies of 0.933 and 0.96 in WPC brand identification and adulterant detection, respectively, which are only around 0.05 lower than those of HSI. It obtains coefficients of determination of 0.897, 0.906 and 0.963 for the quantification of fat content, energy levels and milk powder adulteration, respectively. Such results demonstrate that the combination of smartphones and machine learning offers a low-cost and viable preliminary screening tool for verifying the authenticity of whey protein supplements. Full article

In recent years, artificial intelligence (AI) has increasingly utilized speech and video signals for emotion recognition, facial recognition, and depression detection, playing a crucial role in mental health assessment. However, the AI-driven research on detecting depression severity remains limited, and the existing models are often too large for lightweight deployment, restricting their real-time monitoring capabilities, especially in resource-constrained environments. To address these challenges, this study proposes a lightweight and accurate multimodal method for detecting depression severity, aiming to provide effective support for smart healthcare systems. Specifically, we design a multimodal detection network based on speech and video signals, enhancing the recognition of depression severity by optimizing the cross-modal fusion strategy. The model leverages Long Short-Term Memory (LSTM) networks to capture long-term dependencies in speech and visual sequences, effectively extracting dynamic features associated with depression. Considering the behavioral differences of respondents when interacting with human versus robotic interviewers, we train two separate sub-models and fuse their outputs using a Mixture of Experts (MOE) framework capable of modeling uncertainty, thereby suppressing the influence of low-confidence experts. In terms of the loss function, the traditional Mean Squared Error (MSE) is replaced with Negative Log-Likelihood (NLL) to better model prediction uncertainty and enhance robustness. The experimental results show that the improved AI model achieves an accuracy of 83.86% in depression severity recognition. The model’s floating-point operations per second (FLOPs) reached 0.468 GFLOPs, with a parameter size of only 0.52 MB, demonstrating its compact size and strong performance. These findings underscore the importance of emotion and facial recognition in AI applications for mental health, offering a promising solution for real-time depression monitoring in resource-limited environments. Full article

Pig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking efficiency, we propose a pig-tracking method that is based on skeleton feature similarity, which we named GcnTrack. We used YOLOv7-Pose to extract pig skeleton key points and design a dual-tracking strategy. This strategy combines IOU matching and skeleton keypoint-based graph convolutional reidentification (Re-ID) algorithms to track pigs continuously, even when pigs return from outside the FOV. Three identical FOV sets of data that separately included long, medium, and short duration videos were used to test the model and verify its performance. The GcnTrack method achieved a Multiple Object Tracking Accuracy (MOTA) of 84.98% and an identification F1 Score (IDF1) of 82.22% for the first set of videos (short duration, 87 s to 220 s). The tracking precision was 74% for the second set of videos (medium duration, average 302 s). The pigs entered the scene 15.29 times on average, with an average of 6.28 identity switches (IDSs) per pig during the tracking experiments on the third batch set of videos (long duration, 14 min). In conclusion, our method contributes an accurate and reliable pig-tracking solution applied to scenarios with incomplete camera FOV. Full article

Background: Physical activities in childhood are important. However, a lack of exercise among children and adolescents is becoming a global reality. Moreover, following the coronavirus disease 2019 pandemic, the increase in time spent at home has led to qualitative changes, such as at-home exercises and the use of YouTube content. This study aimed to conduct qualitative assessments of YouTube-based exercise education programs, such as video content and exercise education programs. Methods: A Python-based (version 3.11.6) video data crawl of YouTube using the keywords “children + exercise”, “kid + exercise”, “child + physical activity”, and “kid + physical activity” was conducted on 27 November 2023. Duplicate, non-English, outdated (over 5 years old), short (<60 s) or long (>30 min) videos, and irrelevant content were excluded. Basic video characteristics, video popularity metrics, and qualitative analyses (m-DISCERN, GQS, i-CONTENT, CONTENT, CERT) were collected and assessed. Results: Of the 2936 retrieved videos, 126 were selected. Approximately 10% of the videos were uploaded by health professionals, and most videos covered aerobic and muscle-strengthening exercises. A qualitative analysis of the video content showed moderate to high quality, while only a few videos satisfied the criteria of an effective exercise program, especially in terms of “Type and timing of outcome assessment”, “Qualified supervisor”, “Patient eligibility”, “Adherence to the exercise program”, and “Dosage parameters (frequency, intensity, time)”. In the correlation analysis of video content and exercise program quality, only a few items showed a statistically significant correlation. Conclusions: YouTube exercise-related educational content targeting children may be inadequate and is not correlated with video popularity. Although an overall weak to moderate correlation was observed between the quality evaluation of exercise education and video content, the use of video quality assessment tools to evaluate exercise program quality was insufficient. Full article

Background and Purpose: The proliferation of short-form video content on social media platforms has led to increased user engagement but also raised concerns about potential addictive behaviors and cognitive consequences, particularly among youth. This study explored the prevalence of short-form video addiction (SVA) among Jordanian youth, its correlates, and its impact on attention and memory function, with an emphasis on understanding the mediating and moderating role of attention in the relationship between SVA and memory. Methods: Utilizing a cross-sectional survey design, data were collected from 1029 university students across 25 higher-education institutions in Jordan. Results: Half of the participants exhibited moderate to high levels of SVA. The findings indicated a significant increase in SVA scores among female students (p = 0.003), those of a younger age (p = 0.045), those with lower GPAs (p = 0.013), and those who dedicated fewer hours to study (p = 0.006). Notably, there was a significant and large correlation between SVA scores and students’ perceptions of user-generated content (p < 0.001). Attention partially mediated the relationship between SVA and memory function with excellent model fit indices (χ²(12) = 14.11, p = 0.05, RMSEA = 0.03, GFI = 0.99, IFI = 0.99, TLI = 0.98, CFI = 0.99). However, attention did not moderate this relationship, suggesting that the impact of SVA on memory is consistent across varying levels of attention. Discussion: The findings underscore the significant engagement of Jordanian youth with short-form video content and the potential cognitive risks associated with SVA. Interventions to manage attention could mitigate the adverse effects of SVA on cognitive functions. This study calls for a comprehensive approach to address SVA among youth, including the development of digital literacy programs, mental health support services, and policy interventions that promote a balanced digital ecosystem and responsible media consumption. Full article

Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging devices like phone cameras, webcams, or surveillance systems. Methods: This study introduces a hybrid deep learning model aimed at estimating heart rate (HR), blood oxygen saturation level (SpO2), and blood pressure (BP) from facial videos. The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. Given the temporal variability of HR and BP, emphasis is placed on temporal resolution during feature extraction. The CNN processes video frames one by one while convLSTM and ViViT handle sequences of frames. These high-resolution temporal features are fused to predict HR, BP, and SpO2, capturing their dynamic variations effectively. Results: The dataset encompasses 891 subjects of diverse races and ages, and preprocessing includes facial detection and data normalization. Experimental results demonstrate high accuracies in predicting HR, SpO2, and BP using the proposed hybrid models. Discussion: Facial images can be easily captured using smartphones, which offers an economical and convenient solution for vital sign monitoring, particularly beneficial for elderly individuals or during outbreaks of contagious diseases like COVID-19. The proposed models were only validated on one dataset. However, the dataset (size, representation, diversity, balance, and processing) plays an important role in any data-driven models including ours. Conclusions: Through experiments, we observed the hybrid model’s efficacy in predicting vital signs such as HR, SpO2, SBP, and DBP, along with demographic variables like sex and age. There is potential for extending the hybrid model to estimate additional vital signs such as body temperature and respiration rate. Full article

Noise pollution is a known health risk factor and evidence for cardiovascular diseases associated with traffic noise is growing. At least 20% of the European Union’s population lives in noise-polluted areas with exposure levels exceeding the recommended limits of the World Health Organization, which is considered unhealthy by the European Environment Agency. This results in the annual loss of 1.6 million healthy life years. Here, we investigated the protective effects of cardiovascular drug interventions against aircraft noise-mediated cardiovascular complications such as elevated oxidative stress or endothelial dysfunction. Using our established mouse exposure model, we applied mean sound pressure levels of 72 dB(A) for 4 d. C57BL/6 mice were treated with the beta-blocker propranolol (15 mg/kg/d s.c. for 5 d) or the alpha-blocker phenoxybenzamine (1.5 mg/kg/d s.c. for 5 d) and noise-exposed for the last 4 d of the drug administration. Short-term noise exposure caused hypertension (measured by tail-cuff blood pressure monitoring) and impaired endothelial function (measured by isometric tension recording in the aorta and video microscopy in cerebral arterioles in response to acetylcholine). Noise also increased markers of oxidative stress and inflammation. Treatment of mice with propranolol and phenoxybenzamine prevented endothelial and microvascular dysfunction, which was supported by a decrease in markers of inflammation and oxidative stress in heart tissue and the brain. Amelioration of noise-induced hypertension (systolic blood pressure) was not observed, whereas pulse pressure was lowered by trend. This study provides a novel perspective mitigating the adverse effects of noise pollution, especially in vulnerable groups with medication, a rationale for further pharmacological human studies. Full article

Background: Although cervical cancer (CC) is highly preventable through appropriate screening methods like the Papanicolaou (Pap) test, which enables early detection of malignant and precancerous lesions, access to such screening has not been equitable across social groups. Sex workers and people with records of incarceration are among the most under-screened populations in Ontario. Little is known about the acceptability and feasibility of HPV self-sampling (HPV-SS) as an alternative cervical cancer screening method for these groups. This online, community-based mixed-methods pilot study aimed to address this knowledge gap. Methods: Eighty-four under- and never-screened sex workers and ex-prisoners aged 25–69 years and residing in the Greater Toronto Area, were recruited by community peer associates. Participants completed an online survey and viewed short videos about CC and screening with Pap and HPV-SS. Those who opted for HPV-SS conducted the test at one of two collaborating organizations. Results: The median age of participants was 36.5 years. Most had limited knowledge about CC and screening. Approximately 13% identified as non-binary, and 5% as two-spirit or trans men, with the majority having completed secondary education. Of the participants, 88% chose HPV-SS, and one-third tested positive for high-risk HPV types. The ability to self-sample without judgment from healthcare providers was noted as a key advantage. However, there was a need for training on proper HPV-SS techniques. Conclusions: To improve cervical cancer screening among sex workers, increasing awareness through participatory community co-creation of sexual health education is essential. Additionally, offering HPV-SS as a screening option is crucial, given its demonstrated acceptability and feasibility within this population, many of whom lack a primary care provider and face discriminatory attitudes in healthcare settings. Full article

Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios. Hence, we propose a lightweight multi-frequency network named MFF-Net to measure heart rhythm via facial videos in a short time. Firstly, we propose a multi-frequency mode signal fusion (MFF) mechanism, which can separate the characteristics of different modes of the original rPPG signals and send them to a processor with independent parameters, helping the network recover blood volume pulse (BVP) signals accurately under a complex noise environment. In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). The TMSC-module can expand the receptive field of the signal-refining network, obtain more abundant multiscale information, and transmit it to the signal reconstruction network. The SSA-module can help a signal reconstruction network locate the obvious inferior parts in the reconstruction process so as to make better decisions when merging multi-dimensional signals. Finally, in order to solve the over-fitting phenomenon that easily occurs in the network, we propose an over-fitting sampling training scheme to further improve the fitting ability of the network. Comprehensive experiments were conducted on three benchmark datasets, and we estimated HR and HRV based on the BVP signals derived by MFF-Net. Compared with state-of-the-art methods, our approach achieves better performance both on HR and HRV estimation with lower computational burden. We can conclude that the proposed MFF-Net has the opportunity to be applied in many real-world scenarios. Full article