Wrist-worn devices hold significant potential for health monitoring, particularly for older people. However, the adoption and continuous usage of these devices are heavily affected by wearing comfort. Currently, there is still a lack of validated assessment tools specifically developed to evaluate the comfort of such devices. Additionally, while pressure is critical for both proper device placement and maintaining comfort, quantitative references for suitbale pressure levels are scarce. Moreover, the impact of age difference on comfort perceptions have been largely overlooked. To address these gaps, we conducted two experiments involving 18 older and 18 younger participants. In Experiment 1, we gathered comfort evaluations from participants while wearing the devices in both home/office and exercise scenarios. In Experiment 2, we focused on pressure, measuring the pressure on participants’ wrists under three strap material conditions and collecting their corresponding comfort evaluations. The results established three dimensions of comfort: movement comfort, thermophysiological comfort, and contact comfort, with their relative importance varying by age and use scenario. Also, we identified comfort-discomfort pressure ranges for both age groups, highlighting the influence of strap material and age on comfort perceptions. These findings offer valuable design guidance for manufacturers and enhance understanding of wrist-mounted device comfort.

Although short video applications are prevalent among young and senior populations, how their engagement behaviors are influenced remains unclear. To address this, we developed a research model grounded in affordance theory and perceived value theory. This model integrates four social media affordances (recommending, searching, meta-voicing, and persistence), three perceived values (utilitarian, hedonic, and social), and three user engagement behaviors (consuming, participating, and producing). Data were collected from 283 young participants and 344 senior participants (aged over 55 and retired) and analyzed using partial least squares structural equation modeling (PLS-SEM). Our findings indicated that the recommending, searching, and persistence affordances significantly positively affect perceived values across both age groups while meta-voicing affordance does not significantly influence any values in the senior group. Moreover, social value consistently drives all three levels of engagement across both age groups. These results offer valuable theoretical and practical insights for researchers and practitioners.

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human-computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

Physical activity (PA) is critical to health, but PA level declines dramatically in young adulthood. This study aims to compare the effectiveness of different social gamification designs (i.e. cooperation and competition) in mobile fitness applications among occasional exercisers among young adults. A mobile fitness application featuring competitive, cooperative, or individualistic gamification was designed and their effectiveness in promoting PA behaviours and socio-cognitive factors among occasional exercisers was compared through a four-week (i.e. one week for baseline and three weeks for use) field experiment involving 49 participants. The participants used one of the gamified applications with one or more friends in daily life during the experimental period. The results showed that cooperation and competition stimulate different types of social support: cooperation improves companion support, whereas competition improves appraisal support. Both gamification significantly increased daily steps in the first week, but after three-week usage, the increase of the cooperation groups reclined, whereas that of the competition groups was maintained. The application usage in cooperation also declined significantly after three weeks, while that of the other two conditions did not change significantly. The current study provided insights into gamification design for occasional exercisers among Chinese young adults.

Post-purchase trust is formed after an online transaction is completed and the product or service is experienced. It influences consumers’ repurchase intention and the reputation of vendors. The aim of the current study was to deepen our understanding of post-purchase trust in e-Commerce and to develop a text mining-based assessment method by mining consumers’ online comments. By combining the expectancy-confirmation theory and product evaluation theory, this study proposes a comprehensive model of post-purchase trust encompassing consumers’ evaluation of product, delivery, service, and website. The model was verified by a survey involving 249 consumers. The results indicate that both product evaluation factors and transaction supporting factors have positive impacts on post-purchase trust. Based on this theoretical model, we proposed a text mining-based method to measure these factors through text-mining of consumers’ comments. To demonstrate the feasibility and benefits of the method, the method was applied to analyze 1,015,484 consumers’ comments on personal computer products from jd.com, a major Chinese e-Commerce website. The results suggest that the proposed method can provide practitioners with diagnostic suggestions to promote post-purchase trust and understand consumers better. HIGHLIGHTSIntegration of the consumers’ product evaluation model and expectation-confirmation theory is proposed to investigate post-purchase trust in e-commerce.The confirmation of delivery, service, website, and perceived value of products affect consumer satisfaction, thereby affect post-purchase trust.Perceived value is positively affected by perceived price and perceived quality, which can be increased by consumers’ evaluation of the appearance, authenticity, and brand reputation of the product.The proposed model can be used to design text mining-based tools to monitor important factors of post-purchase trust via text mining.

SDespite the enormous potential of wearables, the adoption rate of wearables among older people is low. To better understand the aspects influencing the adoption of wearables by older Chinese adults, this study investigates the changes in the usage behaviors, perceptions, and attitudes of older people toward wearables over four weeks. Twenty older people (age > 60 years) were included, and each of them was assigned a device for use at home. Information about features they desired and used, their perceptions of functional (i.e., affordances) and experiential qualities, perceived values, and adoption intention of wearables were collected before actual use and at weekly meetings during use. Results showed that health data monitoring was the most expected use of wearables before actual use and considered the most valued affordance throughout the period, although the less-than-anticipated actual experience reduced the usage. Interestingly, notification/reminders were not considered as important initially; however, the increasing usage increased its importance. Regression analysis showed that perceived utilitarian value was influential throughout the study, while hedonic value influenced the adoption intention only before actual use. These findings facilitate understanding how older people adopt wearables and provide practical design suggestions that could benefit the elderly population.