ABSTRACT
Gait disturbance is a serious problem in Parkinson's disease (PD). Exercise therapy is used to alleviate this complaint. We investigated the effects of a videotaped arm swinging-based home exercise program on gait in PD patients. The subjects included 25 outpatients aged between 55 and 77 years (average age 68.9 years) with PD in Hoehn and Yahr stages II-IV. We prepared a master video tape in which a trainer was shown to be performing an arm swinging exercise or steps in synchrony with a metronome. The exercise menu consisted of 4 parts : arm swinging in a seated position, arm swinging in an upright position without stepping, arm swinging with stepping, and walking on the floor or taking a walk. The patients performed the arm swinging exercise while watching a video tape everyday for a period of 3.4 weeks. Of the 25 patients, 22 completed the exercise program. Following the program, these patients showed significant improvements in their walking times for a 20m walk (10 m, return, 10 m) (before/after=29.6/24.3 sec, <i>p</i>=0.002) and in the step length (before/after=43.7/50.1 cm, <i>p</i><0.001). Frozen gait was a negative factor to complete the programs. Improvement of gait speed after the exercise was related with less frequency of cadence. Loss of arm swinging in PD may be associated with a disturbance in the internal rhythm formation process. Arm swinging exercises are one of the exercise methods that use rhythmic cues. This study suggests that a videotaped arm swinging-based home exercise therapy is effective in such patients.
ABSTRACT
OBJECTIVE: We implemented automatic online medical consultation software. It infers disease of patients with knowledge about symptoms and the epidemiologic data. And we compared its performance of inference with that of human doctors. METHODS: This software accepts information about users' age, sex, and symptoms, lists up diseases compatible with these information, and sorts diseases by probability. We implement this software with Ruby and C. RESULTS: We compared diseases listed up by this software with those that by two human doctors, and found that 1) 90% of confirmed diagnoses was included in the list this software inferred, and 2) more than 50% of diseases in the list this software inferred are same diseases as ones both of two human doctors inferred. CONCLUSION: This software can not determine final diagnosis. But this software lists up probable diseases only by interview. Then we believe this software will be useful for patients when they want to check themselves before consulting their doctor. We believe that this software will be useful for patients to check their health status.