پديد آورندگان :
نبوي نيك، حسين دانشگاه خوارزمي، تهران، ايران - دانشكدهٔ تربيت بدني و علوم ورزشي - گروه بيومكانيك و آسيب شناسي ورزشي , صادقي، حيدر دانشگاه خوارزمي، تهران، ايران - دانشكدهٔ تربيت بدني و علوم ورزشي - گروه بيومكانيك و آسيب شناسي ورزشي , كبروي، حميدرضا دانشگاه آزاد اسلامي مشهد، ايران - گروه مهندسي پزشكي , براتي، اميرحسين دانشگاه تربيت دبير شهيد رجايي، تهران، ايران - دانشكدهٔ تربيت بدني و علوم ورزشي - گروه آسيب شناسي ورزشي و حركات اصلاحي , حدادنژاد، مليحه دانشگاه خوارزمي، تهران، ايران - دانشكدهٔ تربيت بدني و علوم ورزشي - گروه بيومكانيك و آسيب شناسي ورزشي
چكيده فارسي :
زمينه و هدف: تغييرهاي سيستم عصبي و تارهاي عضلاني با فعاليت بدني رخ مي‌دهد و الگوي راه‌رفتن متأثر از تغييرات در اين دو قسمت است؛ بنابراين محقق به مطالعهٔ اثر تمرين‌هاي بدني بر سينرژي‌هاي عضلاني در الگوي راه‌رفتن پرداخت.
روش‌بررسي: جامعهٔ آماري اين پژوهش نيمه‌آزمايشي را مردان جوان فعال آموزشكدهٔ فني‌و‌حرفه‌اي امام‌علي تشكيل دادند. 26 آزمودني در دو گروه فعال و غيرفعال به‌صورت نمونهٔ دردسترس در اين پژوهش شركت كردند. به‌منظور اندازه‌گيري الگوي فعاليت عضلاني، دستگاه الكتروميوگرافي بايومتريك (هشت‌كاناله، مدل PS850، ساخت انگلستان) به‌كار رفت و پس از اندازه‌گيري اين الگو در فعاليت راه‌رفتن، سينرژي عضلاني ازطريق الگوريتم HALS استخراج شد. از آمار توصيفي جهت توصيف داده‌ها و از آزمون يومن‌ويتني در سطح معناداري 0٫05≥p به‌منظور مقايسهٔ تعداد سينرژي‌هاي دو گروه و نيز نرم‌افزار SPSS نسخهٔ 16 استفاده شد.
يافته‌ها: نتايج مشخص كرد كه ميانگين تعداد سينرژي‌ها در گروه فعال (0٫37±3٫16) كمتر از گروه غيرفعال (0٫48±4٫68) است؛ ضمن اينكه ناپايداري بيشتري در بار عاملي و الگوي زماني هر سينرژي در گروه غيرفعال ديده مي‌شود. نتايج مقايسه نيز اختلاف معناداري بين الگوي سينرژي عضلاني گروه فعال و غيرفعال نشان مي‌دهد (0٫05≥p).
نتيجه‌گيري: يافته‌هاي سينرژي در گروه فعال بيانگر كارايي بيشتر سيستم يا كارآمدي سيستم حركتي است و تعداد سينرژي‌هاي بيشتر و ناپايداري سهم عضلات در گروه غيرفعال، ناكارآمدي سيستم حركتي را معين مي‌كند.
چكيده لاتين :
Background & Objective: Some researchers studied on gait pattern; however, the complex neuromuscular system has led to extensive research in this area. A concept recently developed to understand the gait pattern is muscle synergy, which is defined as muscle coordination in order to
achieve a specific neural target. Identifying basic neural patterns can be effective in understanding the mechanism and development of the movement patterns. On the other hand, changes in the nervous system and muscle fibers occur with physical activity. Considering that walking
patterns are affected by changes in the nervous system and muscle fibers, the effect of exercise on muscle synergies has not yet been clarified.
In this research, the effect of exercise on muscle synergies in walking patterns studied.
Methods: Young men in Imam Ali’s technical and vocational school formed the statistical population of this semi–experimental study. Twenty–
six subjects participated in two active and inactive groups as available samples in this study. The active and inactive subjects did not have a
history of neurological problems, traumatic injury, visual impairment, balance, or any history of athletic injury. Before starting the research, the
method of implementation and the necessary explanations were provided about the experiment. After the completion of the consent form, the
measurement process was started. In order to measure the pattern of muscle activity during walking, a biometric electromyography system
(eight–channel model; PS850; UK) was used. Target areas of eight muscles of the gluteus medius, vastus lateralis, rectus femoris, medial
hamstring, lateral hamstring, tibialis anterior, gastrocnemius and soleus were determined using Seniam instructions. After determining the
electrode placement, the areas were cleaned with alcohol in order to reduce the resistance of the skin. In the next step, chloride–silver electrodes
were placed at inter–electrode distance of 20 mm. Data collection was done at a frequency of 1000 Hz. Participants were asked to practice
walking more times to familiarize with the test. Then the main test was performed with five repetitions. Three appropriate efforts were eventually
selected as the main test. MVC test was performed to normalize the process of muscle activity pattern, at the end of the measurement. Data
processing was carried out by means of MATLAB R2014b. The fourth order Betterworth low–pass filter was used with a cut–off frequency of
10–500 Hz. EMG signals were rectified and the maximum value of MVC was normalized. A fourth order Butterworth low–pass filter with a
cut–off frequency of 10–500 Hz was used. Again, a fourth order low–pass filter with a cut off frequency of 10 Hz was used and stored as a
matrix of 8×100 to calculate muscle synergy. Muscle synergy was extracted using the HALS algorithm, after measuring the muscle activity
pattern and preparing the 8×100 matrix. Descriptive statistics were used to describe the data and the Mann–Whitney test was used to compare
the number of synergy vector in the two groups at a significant level of p<0.05 using SPSS software version 16.
Results: The results showed that the mean number of synergies in the active group is 3.16±0.37 and the active group is 4.68 ± 0.48. More
instability was observed in the load of each synergy and the time pattern of any synergy in the inactive group. In addition, the RMS values
between the muscle activity pattern and the reconstructed pattern in all muscles of two groups was smaller than 0.02, which confirm accuracy of
synergies extraction. The results of Mann–Whitney test did not show a significant difference when synergies were compared (p<0.05).
Conclusion: The results of synergy revealed that the system was more efficient in the active group. It seems synergy support some kind of
integrity of the motor system. In the inactive group, it also showed the inefficiency of the motor system, which confirmed the possibility of
increased load on the musculoskeletal system and the risk of its disorders. These findings point out again that the experience can affect neurological health and factors.
