Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Project vital: Community revitalization industry report

ViTAL (Vitality Through Active Living) FIJIAN PROJECT The ViTAL project aims to aid in the reduction of non-communicable diseases (NCDs) through investment in the health and well-being of women by increasing the level of participation in physical activity (PA) and health eating across the community. Women are agents of change in families, communities and countries. The ViTAL program aims to support, other community-based programs such as community gardens, healthy cooking classes, healthy cookbooks, walking groups and other ongoing physical activity programs

A review of the effectiveness of lower limb orthoses used in cerebral palsy

To produce this review, a systematic literature search was conducted for relevant articles published in the period between the date of the previous ISPO consensus conference report on cerebral palsy (1994) and April 2008. The search terms were 'cerebral and pals* (palsy, palsies), 'hemiplegia', 'diplegia', 'orthos*' (orthoses, orthosis) orthot* (orthotic, orthotics), brace or AFO

Toward understanding ambulatory activity decline in Parkinson disease

BACKGROUND: Declining ambulatory activity represents an important facet of disablement in Parkinson disease (PD). OBJECTIVE: The primary study aim was to compare the 2-year trajectory of ambulatory activity decline with concurrently evolving facets of disability in a small cohort of people with PD. The secondary aim was to identify baseline variables associated with ambulatory activity at 1- and 2-year follow-up assessments. DESIGN: This was a prospective, longitudinal cohort study. METHODS: Seventeen people with PD (Hoehn and Yahr stages 1-3) were recruited from 2 outpatient settings. Ambulatory activity data were collected at baseline and at 1- and 2-year annual assessments. Motor, mood, balance, gait, upper extremity function, quality of life, self-efficacy, and levodopa equivalent daily dose data and data on activities of daily living also were collected. RESULTS: Participants displayed significant 1- and 2-year declines in the amount and intensity of ambulatory activity concurrently with increasing levodopa equivalent daily dose. Worsening motor symptoms and slowing of gait were apparent only after 2 years. Concurrent changes in the remaining clinical variables were not observed. Baseline ambulatory activity and physical performance variables had the strongest relationships with 1- and 2-year mean daily steps. LIMITATIONS: The sample was small and homogeneous. CONCLUSIONS: Future research that combines ambulatory activity monitoring with a broader and more balanced array of measures would further illuminate the dynamic interactions among evolving facets of disablement and help determine the extent to which sustained patterns of recommended daily physical activity might slow the rate of disablement in PD.This study was funded primarily by the Davis Phinney Foundation and the Parkinson Disease Foundation. Additional funding was provided by Boston University Building Interdisciplinary Research Careers in Women's Health (K12 HD043444), the National Institutes of Health (R01NS077959), the Utah Chapter of the American Parkinson Disease Association (APDA), the Greater St Louis Chapter of the APDA, and the APDA Center for Advanced PD Research at Washington University. (Davis Phinney Foundation; Parkinson Disease Foundation; K12 HD043444 - Boston University Building Interdisciplinary Research Careers in Women's Health; R01NS077959 - National Institutes of Health; Utah Chapter of the American Parkinson Disease Association (APDA); Greater St Louis Chapter of the APDA; APDA Center for Advanced PD Research at Washington University

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R) and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy in Madrid, Spain, The foundation de l’avenir, and the Fondation de France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network (TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015– 69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P. Moreno-Muñoz has been supported by FPI grant BES-2016-07762