Designing a gamified social platform for people living with dementia and their live-in family caregivers

In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects. © 2018 Association for Computing Machinery.Peer ReviewedPostprint (author's final draft

Creativity Support in a Serious Game for Dementia Care

This paper advocates the use of computer-based serious games as a form of creativity support tool. Whilst the use of serious games has grown considerably in recent years, support for players to think creatively is often implicit in the game, and does not exploit the wide range of creativity techniques and software tools available. This paper makes the case for explicit creativity support in serious games, explores how implicit creativity support can be delivered in game play, and extends one reported model of serious game play with activities in which players deploy different forms of supported creative thinking. The model is then applied to inform 2 versions of a serious game developed to train carers in creativity techniques to deliver more person-centered care to people with dementia. Each version of the game was delivered as a prototype to support playtesting of the game and its effect on carer training

USE OF SERIOUS GAMES FOR THE ASSESSMENT OF MILD COGNITIVE IMPAIRMENT IN THE ELDERLY

This study investigated the use of computer games to detect the symptoms of mild cognitive impairment (MCI), an early stage of dementia, in the elderly. To this end, three serious games were used to measure the visio-perception coordination and psycho-motor abilities, spatial memory, and short-term digit span memory. Subsequently, the correlations between the results of the games and the results of the Korean Mini-Mental State Examination (K-MMSE), a dementia screening test, were analyzed. In addition, the game results of normal elderly persons were compared with those of elderly patients who exhibited MCI symptoms. The results indicated that the game play time and the frequency of errors had significant correlations with K-MMSE. Significant differences were also found in several factors between the control group and the group with MCI. Based on these findings, the advantages and disadvantages of using serious games as tools for screening mild cognitive impairment were discussed

Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records

Preservation of private user data is of paramount importance for high Quality of Experience (QoE) and acceptability, particularly with services treating sensitive data, such as IT-based health services. Whereas anonymization techniques were shown to be prone to data re-identification, synthetic data generation has gradually replaced anonymization since it is relatively less time and resource-consuming and more robust to data leakage. Generative Adversarial Networks (GANs) have been used for generating synthetic datasets, especially GAN frameworks adhering to the differential privacy phenomena. This research compares state-of-the-art GAN-based models for synthetic data generation to generate time-series synthetic medical records of dementia patients which can be distributed without privacy concerns. Predictive modeling, autocorrelation, and distribution analysis are used to assess the Quality of Generating (QoG) of the generated data. The privacy preservation of the respective models is assessed by applying membership inference attacks to determine potential data leakage risks. Our experiments indicate the superiority of the privacy-preserving GAN (PPGAN) model over other models regarding privacy preservation while maintaining an acceptable level of QoG. The presented results can support better data protection for medical use cases in the future

Age and sex effects on SuperG performance are consistent across internet devices

There have been recent advances in the application of online games that assess motor skill acquisition/learning and its relationship to age and biological sex, both of which are associated with dementia risk. While this online motor learning assessment (called Super G), along with other computer-based cognitive tests, was originally developed to be completed on a computer, many people (including older adults) have been shown to access the internet through a mobile device. Thus, to improve the generalizability of our online motor skill learning game, it must not only be compatible with mobile devices but also yield replicable effects of various participant characteristics on performance relative to the computer-based version. It is unknown if age and sex differentially affect game performance as a function of device type (keyboard versus touchscreen control). Thus, the purpose of this study was to investigate if device type modifies the established effects of age and sex on performance. Although there was a main effect of device on performance, this effect did not alter the overall relationship between performance vs. age or sex. This establishes that Super G can now effectively be extended to both computer and mobile platforms to further test for dementia risk factor

The use of motion-based technology for people living with dementia or mild cognitive impairment: a literature review

Background: The number of people living with dementia and mild cognitive impairment (MCI) is increasing substantially. Although there are many research efforts directed toward the prevention and treatment of dementia and MCI, it is also important to learn more about supporting people to live well with dementia or MCI through cognitive, physical, and leisure means. While past research suggests that technology can be used to support positive aging for people with dementia or MCI, the use of motion-based technology has not been thoroughly explored with this population. Objective: The aim of this study was to identify and synthesize the current literature involving the use of motion-based technology for people living with dementia or MCI by identifying themes while noting areas requiring further research. Methods: A systematic review of studies involving the use of motion-based technology for human participants living with dementia or MCI was conducted. Results: A total of 31 articles met the inclusion criteria. Five questions are addressed concerning (1) context of use; (2) population included (ie, dementia, MCI, or both); (3) hardware and software selection; (4) use of motion-based technology in a group or individual setting; and (5) details about the introduction, teaching, and support methods applied when using the motion-based technology with people living with dementia or MCI. Conclusions: The findings of this review confirm the potential of motion-based technology to improve the lives of people living with dementia or MCI. The use of this technology also spans across several contexts including cognitive, physical, and leisure; all of which support multidimensional well-being. The literature provides evidence that people living with dementia or MCI can learn how to use this technology and that they enjoy doing so. However, there is a lack of information provided in the literature regarding the introduction, training, and support methods applied when using this form of technology with this population. Future research should address the appropriate introduction, teaching, and support required for people living with dementia or MCI to use the motion-based technology. In addition, it is recommended that the diverse needs of these specific end-users be considered in the design and development of this technology