Parkinson disease causes several changes in gait, such as postural stability, which consequently induces fall risk factors and loss of quality of life. Alternative forms of treatment through rhythmic and dance stimuli have been used to minimize the Parkinson disease effects, which have been shown to be effective in improving gait and providing social well-being and quality of life in the patient. Aim: This review aims to demonstrate the efficiency of music and dance for gait improvement and symptom alleviation in Parkinson disease. Methodology: Studies that analyzed sound stimuli and dance in gait improvement in Parkinson disease were searched through PubMed, Scopus, Doaj, MEDLINE, and ScienceDirect databases from November 2017 to April 2018 and repeated in September 2018. Results and Discussion: Forty-five studies met the inclusion criteria to synthesize the findings on dance and music performance as a treatment for classical symptoms of Parkinson disease. Five reviews and 40 experimental papers have shown that rhythmic stimulation and dance provide the motor, cognitive, and quality of life benefits for participants with Parkinson disease. Thus, sound stimuli and dance offer satisfactory effects for gait, improving cognitive abilities such as motor control and adjustment and spatial memory. In addition, these new treatment modalities stimulate the elderly population to practice physical exercise, generating well-being and helping self-esteem. Conclusion: Dance and music therapy interventions are noninvasive, simple treatment options, which promote gait and cognition.

Background: Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.Objective: This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.Methods: We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.Results: A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.Conclusions: This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

Purpose: We sought to verify if alterations in prefrontal cortex (PFC) activation and psychological responses would play along with impairments in pacing and performance of mentally fatigued cyclists. Materials and Methods: Eight recreational cyclists performed two preliminary sessions to familiarize them with the rapid visual information processing (RVP) test, psychological scales and 20 km cycling time trial (TT_20km) (session 1), as well as to perform a VO_2MAX test (session 2). Thereafter, they performed a TT_20km either after a RVP test (30 min) or a time-matched rest control session (session 3 and 4 in counterbalanced order). Performance and psychological responses were obtained throughout the TT_20km while PFC electroencephalography (EEG) was obtained at 10 and 20 km of the TT_20km and throughout the RVP test. Increases in EEG theta band power indicated a mental fatigue condition. Repeated-measures mixed models design and post-hoc effect size (ES) were used in comparisons. Results: Cyclists completed the trial ~2.7% slower in mental fatigue (34.3 ± 1.3 min) than in control (33.4 ± 1.1 min, p = 0.02, very large ES), with a lower W_MEAN (224.5 ± 17.9 W vs. 240.2 ± 20.9 W, respectively; p = 0.03; extremely large ES). There was a higher EEG theta band power during RVP test (p = 0.03; extremely large ES), which remained during the TT_20km (p = 0.01; extremely large ES). RPE increased steeper in mental fatigue than in control, together with isolated reductions in motivation at 2th km (p = 0.04; extremely large ES), felt arousal at the 2nd and 4th km (p = 0.01; extremely large ES), and associative thoughts to exercise at the 6th and 16th km (p = 0.02; extremely large ES) of the TT_20km. Conclusions: Mentally fatigued recreational cyclists showed impaired performance, altered PFC activation and faster increase in RPE during a TT_20km.

Different treatments for stroke patients have been proposed; among them the mirror therapy and motion imagery lead to functional recovery by providing a cortical reorganization. Up today the basic concepts of the current literature on mirror neurons and the major findings regarding the use of mirror therapy and motor imagery as potential tools to promote reorganization and functional recovery in post-stroke patients. Bibliographic research was conducted based on publications over the past thirteen years written in English in the databases Scielo, Pubmed/MEDLINE, ISI Web of Knowledge. The studies showed how the interaction among vision, proprioception and motor commands promotes the recruitment of mirror neurons, thus providing cortical reorganization and functional recovery of post-stroke patients. We conclude that the experimental advances on Mirror Neurons will bring new rational therapeutic approaches to post-stroke rehabilitation.

Amyotrophic lateral sclerosis (ALS), Charcot’s disease or Lou Gehrig’s disease, is a term used to cover the spetrum of syndromes caracterized by progressive degeneration of motor neurons, a paralytic disorder caused by motor neuron degeneration. Currently, there are approximately 25,000 patients with ALS in the USA, with an average age of onset of 55 years. The incidence and prevalence of ALS are 1-2 and 4-6 per 100,000 each year, respectively, with a lifetime ALS risk of 1/600 to 1/1000. It causes progressive and cumulative physical disabilities, and leads to eventual death due to respiratory muscle failure. ALS is diverse in its presentation, course, and progression. We do not yet fully understand the causes of the disease, nor the mechanisms for its progression; thus, we lack effective means for treating this disease. In this chapter, we will discuss the diagnosis, treatment, and how to cope with impaired function and end of life based on of our experience, guidelines, and clinical trials. Nowadays ALS seems to be a more complex disease than it did two decades – or even one decade – ago, but new insights have been plentiful. Clinical trials should be seen more as experiments on pathogenic mechanisms. A medication or combination of medications that targets more than one pathogenic pathway may slow disease progression in an additive or synergistic fashion.