Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study

Effect of an Internet-Based Pilates Telerehabilitation Intervention in People With Multiple Sclerosis: Protocol for a Randomized Controlled Trial

BACKGROUND

Physical activity (PA) has been recommended in multiple sclerosis (MS) to maintain good physical fitness and mental health, reduce the severity of symptoms and risk of relapse, and improve quality of life. Pilates has been suggested as an ideal PA to manage physical, cognitive, and psychological symptoms of MS and a useful method to maintain and improve balance and gait.

OBJECTIVE

This paper presents the protocol for a study that aims to evaluate the efficacy on the physical domain (specifically balance and gait) of a home-based, self-managed PA intervention delivered through the MS-FIT exergame (HELAGLOBE Società a responsabilità limitata). In addition, measures of cognitive performance, quality of life, and well-being will be considered.

METHODS

This is a 2-arm, multicenter, randomized controlled trial with 3 assessment points (baseline, 12 weeks postintervention, and 6 weeks follow-up). People with MS with mild disability, low risk of falling, preserved cognitive functions, and low anxiety and depression are potential eligible participants. The experimental group (MS-FIT) will self-administer the MS-FIT exergame at home in addition to their leisure-time physical activities. MS-FIT is an internet- and Pilates-based tool that uses the Microsoft Kinect Sensor V2. Participants in the control group will only have access to their leisure-time physical activities. Participants in the MS-FIT group will train at home with MS-FIT for 12 weeks and will be required to perform the exercises for a total of 30 minutes/day for at least 3 days/week. The primary outcome is the Timed Up and Go, a test designed to assess walking. We will also administer additional tests for motor function (visual analog scale 0-10, Timed 25-Foot Walk, Ambulation Index, 2-minute walk test, Twelve Item Multiple Sclerosis Walking Scale, Nine-Hole Peg Test), cognition (Brief International Cognitive Assessment for Multiple Sclerosis), fatigue (Modified Fatigue Impact Scale), quality of life (Multiple Sclerosis Quality of Life-54), well-being (Psychological Well-Being Scales), and PA (International Physical Activity Questionnaire and Minnesota Leisure Time Physical Activity Questionnaire). Acceptance and satisfaction with the intervention received (Client Satisfaction Questionnaire and an adapted version of the Tele-healthcare Satisfaction Questionnaire - Wearable Technology) and subjective impressions of changes in performance (Patients' Global Impression of Change) will also be assessed.

RESULTS

Recruitment for the trial started on March 16, 2022, and the first participant was randomized the same day. Data analysis and results are expected to be published in 2025.

CONCLUSIONS

Pilates has proven beneficial in several neurological diseases such as MS. With this study, we will provide evidence for the use in clinical practice of a digital tool for self-administered Pilates exercises at home as a complement to rehabilitation and for the continuity of care in MS.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04011579; https://tinyurl.com/2p9n4d2t.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/58026.

Categorizing digital data collection and intervention tools in health and wellbeing living lab settings: A modified Delphi study

BACKGROUND

Health and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges and promote overall wellbeing. A knowledge gap exists in categorizing and understanding the types of ICT tools and technical devices employed by Living Labs.

AIM

Define a comprehensive taxonomy that effectively categorizes and organizes the digital data collection and intervention tools employed in Health and Wellbeing Living Lab research studies.

METHODS

A modified consensus-seeking Delphi study was conducted, starting with a pre-study involving a survey and semistructured interviews (N=30) to gather information on existing equipment. The follow-up three Delphi rounds with a panel of living lab experts (R1 N=18, R2 - 3 N=15) from 10 different countries focused on achieving consensus on the category definitions, ease of reading, and included subitems for each category. Due to the controversial results in the 2nd round of qualitative feedback, an online workshop was organized to clarify the contradictory issues.

RESULTS

The resulting taxonomy included 52 subitems, which were divided into three levels as follows: The first level consists of 'devices for data monitoring and collection' and 'technologies for intervention.' At the second level, the 'data monitoring and collection' category is further divided into 'environmental' and 'human' monitoring. The latter includes the following third-level categories: 'biometrics,' 'activity and behavioral monitoring,' 'cognitive ability and mental processes,' 'electrical biosignals and physiological monitoring measures,' '(primary) vital signs,' and 'body size and composition.' At the second level, 'technologies for intervention' consists of 'assistive technology,' 'extended reality - XR (VR & AR),' and 'serious games' categories.

CONCLUSION

A common language and standardized terminology are established to enable effective communication with living labs and their customers. The taxonomy opens a roadmap for further studies to map related devices based on their functionality, features, target populations, and intended outcomes, fostering collaboration and enhancing data capture and exploitation.

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

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.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.