Status Of Healthy People Research Articles

Relationship between body awareness level and musculoskeletal pain complaints, physical activity level and emotional status in healthy people.

A total of 289 healthy individuals between the ages of 18 and 25 were included in the study. Body awareness level was using Body Awareness Questionnaire (BAQ), musculoskeletal pain complaint using The Nordic Musculoskeletal Questionnaire (NMQ), physical activity levels using International Physical Activity Questionnaire (IPAQ-short form) and emotional status using Beck Depression Inventory (BDI) were assessed. The mean age of the participants was 19.50 ± 1.55years. The mean pain intensity was 2.48 ± 1.99. The BAQ score average was 91.87 ± 15.55. Thirty percent of the participants had a mild level and 14.9% had a moderate level of depression risk. While there was a weak positive relationship between the Disease Startup sub-dimension of BAQ and the moderate-level IPAQ score (r = 0.135, p = 0.022). Also there was a weak negative relationship between Disease Startup sub-dimension and the level of depression risk. A weak positive relationship was found between the changes in the body process and attention to subjects sub-dimension and the level of depression risk (r = 0.127, p = 0.030). There was a positive correlation between level of depression risk and pain intensity (r = 0.237, p = 0.000). Body awareness levels of individuals who had back pain in the last 1month were higher than those who did not experience pain (p = 0.029). The results of the study showed that physical activity positively affected body awareness level. The body awareness level was directly related to the pain associated with the musculoskeletal system and emotional state. There is a need more studies examining the relationship between body awareness and pain, physical activity and emotional status with subdimensions of the BAQ.

Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning

Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p ​< ​0.001). The mean absolute error of the predicted age was reduced from 13.89 to 4.78 years in Cohort 1, 8.34 to 5.35 years in Cohort 2, and 8.74 to 5.64 years in Cohort 3. The test–retest reliability of the transferred model was verified using dMRI data acquired at two timepoints (intraclass correlation coefficient ​= ​0.950). Clinical sensitivity of the brain age prediction model was investigated by estimating the brain age in patients with schizophrenia. The prediction made by the transferred model was not significantly different from that made by the reference model. Both models predicted significant brain aging in patients with schizophrenia as compared with healthy controls (p ​< ​0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and −0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.

Vitamin E status in healthy population in Asia: A review of current literature

Vitamin E is a lipid soluble antioxidant which mainly circulates as α-tocopherol in the human plasma. Its deficiency is associated with ataxia, neuropathy, anaemia and several other health conditions. Although substantial data on vitamin E status has been published worldwide, there is paucity of data on the extent of deficiency from most Asian countries, including India. Part of the problem is lack of validated biomarkers for vitamin E and no consensus on cut offs for defining deficiency and sufficiency. Thus, interpretation of the data on the vitamin E status is difficult. Limited available data from 31 studies on vitamin E status in healthy people from Asia, the most populated continent, has been collated for the purpose of this review. Broadly, the results suggest inadequate vitamin E status in most age groups, with the prevalence of deficiency reaching 67%, 80%, 56% and 72% in infants, children and adolescents, adults, elderly and pregnant women, respectively, based on varying cut offs. The findings are not surprising as both, vitamin E intakes and its status have not received too much attention in the past. Lack of conclusive data accentuates the need for more research on the vitamin E status across all age groups and to define age, gender and physiological state specific cut offs for vitamin E levels.

Pathologies of the oral cavity in patients with non-controlled diabetes type 1 and type 2--analysis of periodontal status and periodontal treatment needs.

Decompensated diabetes is a major risk factor in the development of periodontal diseases. This leads to disturbances of carbohydrates, protein, and fat and morphological changes in many organs. These changes also apply to the masticatory system, especially periodontal tissues. The aim of the study was to assess the periodontal status and periodontal treatment needs in patients with non-controlled diabetes type 1 and type 2 (HbA1c > 7%), and to compare the results with the data obtained in a group of generally healthy patients. The study included 275 patients, 155 of them were patients with non-controlled diabetes during hospitalisation (study group), while 120 subjects constituted the control group of healthy people. The study excluded edentulous people. CPITN index (according to Ainamo et al.) was used to assess the periodontal state and periodontal treatment needs. The average level of glycated haemoglobin HbA1C among patients in the study group was 9.43% in women and slightly more at 9.57% in men. The periodontal status in healthy people was satisfactory, dominated by the maximum values of CPITN = 0, CPITN = 1, and CPITN = 2. The study group more frequently revealed the maximum values of CPITN = 3 and CPITN = 4. This shows the more advanced periodontal changes in this group. Due to the bad condition of the periodontium, the periodontal treatment needs proved to be far greater in the study group and related primarily to comprehensive specialist treatment (TN3). Decompensated diabetes may be an important cause of changes in periodontal tissues and may cause a significant loss of masticatory function in patients.