Investigating the Comprehensive Inventory of Thriving (CIT) as a rehabilitation outcome measure

Reliable and valid outcome measures are needed in community rehabilitation settings following acquired neurological injury. The Comprehensive Inventory of Thriving (CIT) (Su, Tay and Diener, 2013) was investigated for this purpose. The CIT is a 54 item self-report measure that provides 18 subscales and seven main scales of thriving: Relationships, Engagement, Mastery, Autonomy, Meaning, Optimism and Subjective Well-being. Participants (n=76) were administered the CIT on admission to a community rehabilitation service. The mean age of participants was 54.8 (SD = 17.7), with 43% being male. The main diagnostic groups were cerebrovascular disease (28%), traumatic brain injury (17%) and Parkinson's disease (12%). Internal consistency was moderate to high (α =.6 to .9) for all subscales with the exception of Support (Relationships) and Skills (Mastery); and high (α=.79-.93) for all indexes with the exception of Subjective Wellbeing. Correlational analyses supported the scale groupings. However, the subscales of Support (Relationships) and Skills (Mastery) did not correlate significantly with any subscales. Additionally the Subjective Well-being scale should not be calculated, but instead its three subscales (Negative Feelings, Life Satisfaction, Positive Feelings) used individually. In terms of demographic variables, there were no significant gender differences on CIT scales. Age had low correlations with two Relationships subscales only (Trust r=.23, p=.04; Loneliness r=-.25, p=.03). Diagnostic group minimally influenced CIT scores. Significant between-group differences were only found for Accomplishment (Mastery), with post-hoc analyses indicating higher levels for the cerebrovascular group. The CIT shows considerable promise in rehabilitation outcomes as a reliable and valid multi-component measure of wellbeing

Backward Walking: A Novel Marker Of Fall Risk, Cognitive Dysfunction, And Myelin Damage In Persons With Multiple Sclerosis

Multiple sclerosis (MS) is a progressive, neurologic disease of the central nervous system that causes debilitating motor, sensory and cognitive impairments. As a result, persons with MS are at an increased risk for falls and falls represent a serious public health concern for the MS population. The current clinical measures used to assess fall risk in MS patients lack sensitivity and predictive validity for falls and are limited in their ability to capture to multiple functional domains (i.e., motor, sensory, cognitive and pathological domains) that are impaired by MS. Backward walking sensitively detects falls in the elderly and other neurologic diseases. However, backward walking and falls has never been explored in the MS population and the underlying reasons as to why backward walking sensitively detects falls remains unknown. Identification of a quick, simply and clinically feasible fall risk measures related to multiple functions impacted by MS and related to fall risk, which can detect falls before they occur is critical for fall prevention and timely and targeted intervention. Therefore, this dissertation examines backward walking as a novel marker of fall risk and its cognitive and pathological underpinnings to support its clinical utility. Our results indicate that backward walking is a sensitive marker of fall risk in the MS population, regardless of co-morbid cognitive deficits, and that examining underlying brain regions likely to contribute to backward walking performance including the corticospinal tract, corpus callosum and cerebellum, with neuroimaging tools sensitive to myelin (i.e., Myelin Water Imaging) demonstrate potential to identify underlying mechanisms of backward walking performance in the MS population. This work is the critical first step in establishing backward walking as a sensitive marker of fall risk for the MS population and leads the way to more personalized fall prevention therapies and interventions to improve clinical outcomes and decrease fall rates in the MS population

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201

Understanding Older Adults' Perceptions and Challenges in Using AI-enabled Everyday Technologies

Artificial intelligence (AI)-enabled everyday technologies could help address age-related challenges like physical impairments and cognitive decline. While recent research studied older adults' experiences with specific AI-enabled products (e.g., conversational agents and assistive robots), it remains unknown how older adults perceive and experience current AI-enabled everyday technologies in general, which could impact their adoption of future AI-enabled products. We conducted a survey study (N=41) and semi-structured interviews (N=15) with older adults to understand their experiences and perceptions of AI. We found that older adults were enthusiastic about learning and using AI-enabled products, but they lacked learning avenues. Additionally, they worried when AI-enabled products outwitted their expectations, intruded on their privacy, or impacted their decision-making skills. Therefore, they held mixed views towards AI-enabled products such as AI, an aid, or an adversary. We conclude with design recommendations that make older adults feel inclusive, secure, and in control of their interactions with AI-enabled products.Comment: The Tenth International Symposium of Chinese CHI (Chinese CHI 2022

Digital innovation in Multiple Sclerosis Management

Due to innovation in technology, a new type of patient has been created, the e-patient, characterized by the use of electronic communication tools and commitment to participate in their own care. The extent to which the world of digital health has changed during the COVID-19 pandemic has been widely recognized. Remote medicine has become part of the new normal for patients and clinicians, introducing innovative care delivery models that are likely to endure even if the pendulum swings back to some degree in a post-COVID age. The development of digital applications and remote communication technologies for patients with multiple sclerosis has increased rapidly in recent years. For patients, eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. For HCPs, eHealth technology may facilitate the assessment of clinical disability, analysis of lab and imaging data, and remote monitoring of patient symptoms, adverse events, and outcomes. It may allow time optimization and more timely intervention than is possible with scheduled face-to-face visits. The way we measure the impact of MS on daily life has remained relatively unchanged for decades, and is heavily reliant on clinic visits that may only occur once or twice each year.These benefits are important because multiple sclerosis requires ongoing monitoring, assessment, and management.The aim of this Special Issue is to cover the state of knowledge and expertise in the field of eHealth technology applied to multiple sclerosis, from clinical evaluation to patient education