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Cognitive decline in older adults is a major public health threat. This study aimed to explore the association of participation in cards or mahjong with cognitive function in older adults. A total of 7308 older adults were selected from the 2018 Chinese Longitudinal Healthy Longevity Survey. A modified Mini-Mental State Examination (MMSE) was used to assess cognitive function. The participants were classified according to the frequency of playing cards or mahjong into the “regularly” (R) group, “occasionally” (O) group, or “never” (N) group. The results showed that older persons in the R group and O group had better cognitive function than those in the N group. Specifically, significant differences were found in attention and calculation, language, and total MMSE score between the R group and the N group. However, significant differences were not observed for adults aged 60–69 years old. Regression analysis indicated that playing cards or mahjong, together with age, educational level, sex, marital status, and occupation before age 60 could explain the cognitive function. The findings suggest that there is an association between participation in cards or mahjong and cognitive function in the population of Chinese older adults, and that the frequency of participation plays an important role in the association.

Objectives: Studies have shown that the frequent participation of the elderly in cognitive stimulation activities is associated with a reduced risk of dementia, but the prospective evidence of this association is limited. Methods: We used data from a prospective cohort study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), and included 11821 community-living Chinese individuals aged 65 years or older at 2008 baseline who were free of dementia, and were followed up every 2–3 years until 2018. Cox proportional hazards models were applied to generate the hazard ratios (HRs) and 95% confidence intervals (CIs) for analyzing the associations between the frequency of playing cards/mahjong and the incidence of dementia. Results: A total of 821 participants were diagnosed with dementia during the 10-year follow-up. The average age of patients with dementia and non dementia were 89 and 90 years old, respectively. Compared with participants who rarely or never played cards/mahjong, participants who played cards/mahjong almost every day had a significantly lower risk of dementia (HR=0.63; 95%CI, 0.42-0.95) after the multivariable-adjusted model. Similar results were observed in subgroup analyses based on sex (male:HR=0.52, 0.28-0.96; female:HR=0.62, 0.36-0.98), age(<85years:HR=0.55, 0.32-0.89), regularly exercise (yes: HR =0.44, 0.28–0.87) and MMSE score [above median (25): HR=0.66, 0.41-0.92]. Conclusions: Playing cards/mahjong in the elderly may contribute to reducing the risk of dementia.

Objective Mahjong, a traditional Chinese tile-based game, has been widely reported to be closely associated with better cognitive function. However, its effects on the cognitive function of patients with schizophrenia have not yet been studied. Method In a pilot study, 49 patients diagnosed with schizophrenia were randomly assigned to the intervention group (Mahjong combined with standard treatment) and the control group (standard treatment). The intervention group engaged in cognitive training through Mahjong for 2 h per day, 4 days per week for 12 weeks. Primary cognitive outcomes were assessed using Cambridge Neuropsychological Test Automated Battery (CANTAB), while secondary outcomes include quality of life, clinical symptoms, anhedonia, treat side effects, and personal and social functioning. Assessments were conducted at baseline (T0), the 4th week (T1), the 8th week (T2), and the 12th week (T3). Results The intervention group exhibited progressive improvements in both reaction time and movement time throughout the study. No significant differences were found between the intervention and control groups regarding visual memory, novel learning, strategy utilization, spatial memory performance or complex visual task accuracy. The intervention group demonstrated gradual improvements in quality of life, whereas no significant changes were noted in other secondary outcomes. Conclusion While this exploratory study suggests that Mahjong intervention may benefit certain cognitive functions and quality of life in patients with schizophrenia, these findings should be interpreted with caution. Further research with larger, more diverse samples and longer intervention is necessary to confirm and extend these findings. Trial registration The trial is registered with https://www.chictr.org.cn/ under registration number ChiCTR2400080268 on January 25th, 2024.

Background Engagement in leisure activities such as playing cards or mahjong has been linked to cognitive and physical benefits among older adults. However, the relationship between playing cards/mahjong and all-cause mortality remains unclear. This study aims to explore the association between playing cards/mahjong and all-cause mortality in a cohort of older Chinese adults. Methods We analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) collected between 2002 and 2018. A total of 29,254 participants aged 65 or older were included in the study. Participants were categorized based on their frequency of playing cards/mahjong: almost every day, at least once a week, at least once a month or sometimes, and never. Cox proportional hazards models were applied to assess the association between playing cards/mahjong and all-cause mortality. Interaction and sensitivity analyses were conducted to explore potential effect modifications and the robustness of the findings. Results During a mean follow-up of 4.6 years, 7,651 participants died. Compared to participants who never played cards/mahjong, those who played almost every day had a significantly lower risk of all-cause mortality (HR = 0.88; 95% CI, 0.82–0.95). Participants who played at least once a week (HR = 0.84; 95% CI, 0.78–0.91) similarly demonstrated reduced mortality. Participants who consistently played cards/mahjong at least once a week at both baseline and follow-up had a significantly lower all-cause mortality risk compared to never-players (HR = 0.67; 95% CI: 0.52–0.86). Interaction analysis revealed that the protective effect of playing cards/mahjong was more pronounced in participants aged 80 or older (P-interaction < 0.001). Sensitivity analyses confirmed the robustness of these associations. Conclusion Older adults who regularly play cards/mahjong have a lower risk of all-cause mortality, especially in the oldest groups. These leisure activities may promote longevity, but further research is needed to understand why.

As a testbed for the development of artificial intelligence (AI) techniques, Mahjong has become a hot spot in computer game research due its characteristics of imperfect information. An important aspect of the game is the evaluation of the quality of a hand, which significantly influences the decision-making process for players, including discarding tiles, pong, kong, and so on. In this paper, an effective and efficient algorithm is proposed to measure the quality (i.e., deficiency) of a Mahjong hand by introducing a hierarchical branch and bound method. In the proposed approach, an updating knowledge-based octree search method is first developed to explore all possible quasi-decompositions for one hand to ensure the correctness of the result. Meanwhile, an updated knowledge-based evaluation method is designed to calculate the cost of each quasi-decomposition, and a hierarchical branch and bound method is presented to accelerate the efficiency of getting its deficiency by layering the quasi-decompositions according to the number of their tiles. Moreover, the block decomposition is further adopted in the process of finding all quasi-decompositions to enhance its efficiency. Compared to existing methods, the proposed algorithm not only demonstrates computational efficiency but also provides the exact deficiency in all cases. Experimental results on different types of hands show its effectiveness and efficiency.

Games have long been benchmarks and testbeds for AI research. In recent years, with the development of new algorithms and the boost in computational power, many popular games played by humans have been solved by AI systems. Mahjong is one of the most popular games played in China and has been spread worldwide, which presents challenges for AI research due to its multi-agent nature, rich hidden information, and complex scoring rules, but it has been somehow overlooked in the community of game AI research. In 2020 and 2022, we held two AI competitions of Official International Mahjong, the standard variant of Mahjong rules, in conjunction with a top-tier AI conference called IJCAI. We are the first to adopt the duplicate format in evaluating Mahjong AI agents to mitigate the high variance in this game. By comparing the algorithms and performance of AI agents in the competitions, we conclude that supervised learning and reinforcement learning are the current state-of-the-art methods in this game and perform much better than heuristic methods based on human knowledge. We also held a human-versus-AI competition and found that the top AI agent still could not beat professional human players. We claim that this game can be a new benchmark for AI research due to its complexity and popularity among people.

Public parks are critical resources for physical activity (PA). Pocket parks within neighborhoods play an essential role in the elder’ daily life. A deep understanding of pocket park use and park-based PA for older adults supports the better design of outdoor spaces. Gender differences have been confirmed in previous studies. Indeed, it was observed that more males visit parks than females, and males are likely to be vigorously active. However, almost all the studies were conducted in western countries, and none has yet focused on the Chinese elderly. The main objective of this study is to investigate gender differences in park-based PA in the Chinese middle-aged and elderly population. In Zongbei Park (Chengdu, China), video recording is used from 07:00 a.m. to 07:00 p.m. to document numbers, gender, age group, and type of activity. Demographic variations of park use patterns and energy expenditure are analyzed. The study findings indicated that most users (92.05%) were middle-aged and elderly. Significant gender variations were found in the number of total and five types of activities ( p < 0.01), respectively. Although more old males than females were observed in Zongbei Park, the total METs and mean METs per male were lower than females. A majority (65.66%) of the older adults were seen playing cards/mahjong. It will lead to a significant number of sedentary older adults. As China faces severe healthcare challenges due to the problem of aging, it becomes essential to construct age-friendly design pocket parks. More attention should be paid to address the park-based PA gender gap.

The deficiency number of one hand, i.e., the number of tiles needed to change in order to win, is an important factor in the game Mahjong, and plays a significant role in the development of artificial intelligence (AI) for Mahjong. However, it is often difficult to compute due to the large amount of possible combinations of tiles. In this paper, a novel discrete differential evolution (DE) algorithm is presented to calculate the deficiency number of the tiles. In detail, to decrease the difficulty of computing the deficiency number, some pretreatment mechanisms are first put forward to convert it into a simple combinatorial optimization problem with varying variables by changing its search space. Subsequently, by means of the superior framework of DE, a novel discrete DE algorithm is specially developed for the simplified problem through devising proper initialization, a mapping solution method, a repairing solution technique, a fitness evaluation approach, and mutation and crossover operations. Finally, several experiments are designed and conducted to evaluate the performance of the proposed algorithm by comparing it with the tree search algorithm and three other kinds of metaheuristic methods on a large number of various test cases. Experimental results indicate that the proposed algorithm is efficient and promising.

To examine whether leisure activities can alleviate depressive symptoms among nursing home residents with very mild to mild dementia. A cluster-randomized open-label controlled design. Thirty-six residents with at least moderate depressive symptoms were randomized by home into three conditions—mahjong (a.k.a. mah-jongg), tai chi, and handicrafts (placebo). Activities were conducted three times weekly for 12 weeks. Outcome measure was Geriatric Depression Scale (GDS) administered at baseline, posttreatment, and at 6 months. Repeated-measures analysis of variance showed a group by time interaction on the GDS. Unlike control and tai chi participants whose scores remained relatively unchanged, the mahjong group reported a drop of 3.25 points (95% confidence interval: 1.00–5.50) on the GDS at posttreatment but gained back 2.83 points (95% confidence interval: 1.95–5.47) at 6 months. Activity discontinuation might be the reason for depression to return to baseline. Mahjong can lower depressive symptoms in those with mild dementia, but activity maintenance may be essential for long-term effects.

The game theory is widely acknowledged to benefit a lot from recent advances in deep learning, and intelligent competition strategies have been proposed for both complete information games and incomplete information games in recent years. In this paper, the four-players Chinese Mahjong game, which is a typical incomplete information game, is emphasized, a low-level semantic pseudo image generated based on game related prior knowledge and a novel deep residual network-based competition strategy are introduced to play the Chines Mahjong game. Technically, the deep learning within this new competition strategy is realized by a series of “GoBlock”, which is a new deep learning model structure introduced in this paper. Also, the “GoBlock” is further made up of several “Inception+” sub-structures, which is novel as well. Comprehensive experiments are conducted to reveal the superiority of this new competition strategy. A great number of the Chinese Mahjong game data have been collected from an online Chinese Mahjong company to construct the dataset in this study, and the newly proposed competition strategy has been compared with several shallow learning-based methods as well as deep learning-based methods. Both qualitative and quantitative analysis have been conducted based on outcomes obtained by all compared methods, and the superiority of the new competition strategy over others are suggested. Furthermore, an interesting competition among the new AI competition strategy and three real senior players are also introduced in this paper. The effectiveness and efficiency of the new competition strategy over real senior human players are also revealed by quantitative analysis based on four measures, from the statistical point of view. It is also necessary to point out that, this work is the first attempt to tackle the Mahjong game, which is a typical incomplete information game, from the deep learning perspective.