Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization. 

https://www.nature.com/articles/s41531-023-00494-0.pdf

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Whilst Parkinson’s disease (PD) is typically thought of as a motor disease, a significant number of individuals also experience cognitive impairment (CI), ranging from mild-CI to dementia. One technique that may prove effective in delaying the onset of CI in PD is cognitive training (CT); however, evidence to date is variable. This may be due to the implementation of CT in this population, with the motor impairments of PD potentially hampering the ability to use standard equipment, such as pen-and-paper or a computer mouse. This may, in turn, promote negative attitudes toward the CT paradigm, which may correlate with poorer outcomes. Consequently, optimizing a system for the delivery of CT in the PD population may improve the accessibility of and engagement with the CT paradigm, subsequently leading to better outcomes. To achieve this, the NeuroOrb Gaming System was designed, coupling a novel accessible controller, specifically developed for use with people with motor impairments, with a “Serious Games” software suite, custom-designed to target the cognitive domains typically affected in PD. The aim of the current study was to evaluate the usability of the NeuroOrb through a reiterative co-design process, in order to optimize the system for future use in clinical trials of CT in individuals with PD. Individuals with PD (n = 13; mean age = 68.15 years; mean disease duration = 8 years) were recruited from the community and participated in three co-design loops. After implementation of key stakeholder feedback to make significant modifications to the system, system usability was improved and participant attitudes toward the NeuroOrb were very positive. Taken together, this provides rationale for moving forward with a future clinical trial investigating the utility of the NeuroOrb as a tool to deliver CT in PD.

/pdf/development-and-co-design-of-neuroorb-a-novel-serious-gaming-ld8mi7en.pdf

Development and Co-design of NeuroOrb: A Novel “Serious Gaming” System Targeting Cognitive Impairment in Parkinson’s Disease

Functionality is a crucial aspect of aging that is vital to one’s health and well-being. Older adults often struggle with mobility issues, which increases their risk of injury from falls and other problems. Dancing has the potential to be a physically stimulating activity that may be tailored to older individuals’ ages, physical conditions, and cultural preferences. The study aimed to determine whether dancing programs can improve older adults’ physical and mental health by using technology. Sixty women were divided into two groups at random: a dance group (N = 33; mean age 62.24) and a dance group using technology (N = 27, mean age 67.37). The intervention lasted six months and was performed twice a week for 75-min sessions. Dances were chosen from all over Greece. Participants’ physical and cognitive status was evaluated before and after the intervention. The results show that the dance group improved balance (p = 0.001), lower body strength (p = 0.006) and aerobic capacity (p = 0.006), while the dance group with the use of technology shower greater improvement in the same tests (p = 0.002, p < 0.0001 and p < 0.0001). Both groups improved on walking balance and danger of falling (p < 0.0001). Depression (p = 0.007) and sociability (p = 0.001) significantly improved in the dance group. Dance, an enjoyable activity, contributes to the well-being of older adults by maintaining their physical status and functional capacity at acceptable levels.

Effects of Engaging Older Adults in Technology-Based Dance Programs

Mobile gaming is a part of worldwide Information and Communication Technology (ICT) characterized by new paradigms and rapid growth (ICT). Integrating mobile games into military training equips trainee officers with the requisite effectiveness, efficiency, and enjoyment to accomplish training assignments. To ensure the optimization and quality of mobile gaming, it is vital to study key success factors (CSFs) to design app user interfaces. This study aims to develop a CSFs model of mobile gaming apps (MG apps) and assess the applicability of the model in military training. This research intends to examine the CSFs of MG apps, build a structure of CSFs of MG apps, and develop a CSFs model of MG apps for the purpose of military training. In the study, a sample of hundreds of cadet officers from the Malaysian Military Training Academy was utilized. The research was conducted in three phases: theoretical analysis, empirical study, and development of the proposed CSFs model of MG apps in military training. This paradigm is helpful to sectors of ICT and Defence Technology as an alternative way to ensure the success of the development of MG apps for military and armed forces education and training. This strategy also provides a new successful mechanism and assurance for using MG apps in military and armed forces education and training, as well as ICT-savvy cadet officers from Malaysia. 

pdf/critical-success-factors-csfs-model-for-military-training-3l4lvjv1l3.pdf

Critical Success Factors (CSFs) Model for Military Training Mobile Gaming Apps (MG apps)

BACKGROUND
Balance impairments are common in people with multiple sclerosis (MS), with reduced ability to maintain position and delayed responses to postural adjustments. Pilates is a popular alternative method for balance training that may reduce the rapid worsening of symptoms and the increased risk of secondary conditions (eg, depression) that are frequently associated with physical inactivity.


OBJECTIVE
In this paper, we aimed to describe the design, development, and usability testing of MS Fitness Intervention Training (MS-FIT), a Kinect-based tool implementing Pilates exercises customized for MS.


METHODS
MS-FIT has been developed using a user-centered design approach (design, prototype, user feedback, and analysis) to gain the target user's perspective. A team composed of 1 physical therapist, 2 game programmers, and 1 game designer developed the first version of MS-FIT that integrated the knowledge and experience of the team with MS literature findings related to Pilates exercises and balance interventions based on exergames. MS-FIT, developed by using the Unity 3D (Unity Technologies) game engine software with Kinect Sensor V2 for Windows, implements exercises for breathing, posture, and balance. Feedback from an Italian panel of experts in MS rehabilitation (neurologists, physiatrists, physical therapists, 1 statistician, and 1 bioengineer) and people with MS was collected to customize the tool for use in MS. The context of MS-FIT is traveling around the world to visit some of the most important cities to learn the aspects of their culture through pictures and stories. At each stay of the travel, the avatar of a Pilates teacher shows the user the exercises to be performed. Overall, 9 people with MS (n=4, 44% women; mean age 42.89, SD 11.97 years; mean disease duration 10.19, SD 9.18 years; Expanded Disability Status Scale score 3.17, SD 0.75) were involved in 3 outpatient user test sessions of 30 minutes; MS-FIT's usability was assessed through an ad hoc questionnaire (maximum value=5; higher the score, higher the usability) evaluating easiness to use, playability, enjoyment, satisfaction, and acceptance.


RESULTS
A user-centered design approach was used to develop an accessible and challenging tool for balance training. All people with MS (9/9, 100%) completed the user test sessions and answered the ad hoc questionnaire. The average score on each item ranged from 3.78 (SD 0.67) to 4.33 (SD 1.00), which indicated a high usability level. The feedback and suggestions provided by 64% (9/14) of people with MS and 36% (5/14) of therapists involved in the user test were implemented to refine the first prototype to release MS-FIT 2.0.


CONCLUSIONS
The participants reported that MS-FIT was a usable tool. It is a promising system for enhancing the motivation and engagement of people with MS in performing exercise with the aim of improving their physical status.

An Internet- and Kinect-Based Multiple Sclerosis Fitness Intervention Training With Pilates Exercises: Development and Usability Study.

Human-Computer Interaction (HCI) and games set a new domain in understanding people's motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people's health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson's Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients' quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.

/pdf/assistive-hci-serious-games-co-design-insights-the-case-2tba21xbjw.pdf

Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease.

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Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease.