Hacettepe Üniversitesi Açık Erişim Sistemi (HÜAES)
- HÜAES, Hacettepe Üniversitesi bünyesinde üretilen kitap, makale, tez, bildiri, rapor gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.
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Üst Yaylı Çalgı Çalan Müzisyenlerde Omurga ve Pelvisin Açısal Parametreleri ile Gövde ve Servikal Bölgenin Endurans, Stabilite, Pozisyon Hissi ve Fonksiyonel Düzeyinin İncelenmesi
(Sağlık Bilimleri Enstitüsü, 2025-08-25) Tanımlı, Yeliz Gülce; Fizyoterapi ve Rehabilitasyon
This study aimed to examine musculoskeletal, postural, pain,
muscle endurance, stabilization strength, joint range of motion, joint position sense,
functional level, and body awareness parameters in professional upper string
instrument players depending on their playing posture. The study included 38
professional musicians (27 females, 11 males) who had been playing the violin (n=24)
or viola (n=14) for an average of 22.13 ± 13.40 years and had a minimum of 6 years
of playing experience (mean age: 32.60 ± 12.92 years), and a control group of 38
healthy individuals (27 females, 11 males; mean age: 33.60 ± 11.91 years).Participants
were assessed using the Visual Analog Scale (VAS), DIERS 4D postural analysis
system, trunk and cervical muscle endurance tests, stabilization strength
measurements, joint range of motion and joint position sense evaluations, scapular
dyskinesis tests, muscle shortening tests, grip strength measurements, as well as the
DASH, Neck Disability Index (NDI), Oswestry Disability Index, Fremantle Back and
Neck Awareness Questionnaires, and the Kenny Music Performance Anxiety
Inventory. Additionally, standing and sitting instrument-playing postures of the
musician group were compared using the DIERS 4D system. Relationships between
years of instrument playing and all clinical parameters were also investigated.As a
result, the musician group showed significantly higher values in scoliosis angle and
incidence, pain levels during day, night, and rest, shortening ratios of pectoral and
lumbar extensor muscles, positive scapular dyskinesis test results, and scores on the
DASH and Neck Disability Index (p<0.05). Conversely, the musician group
demonstrated higher trunk anterior, posterior, left lateral, and cervical muscle
endurance, as well as longer contraction durations of the Transversus Abdominis,
Multifidus, and cervical muscles. Range of motion in cervical right/left lateral flexion
and lumbar lateral flexion/extension was also found to be greater in musicians.Errors
in joint position sense during cervical right and left rotation were higher in musicians,
while the error in left lateral flexion was lower. In the standing playing posture, sagittal
imbalance, kyphosis, lordosis, and pelvic inclination values were elevated; whereas in
the sitting posture, trunk length was found to be greater. A significant negative
correlation was identified between years of instrument playing and the following
parameters: error in cervical left rotation joint position sense, Neck Disability Index
score, Fremantle Back and Neck Awareness scores, VAS pain scores during night,
day, post-playing, during concerts and practice, and Kenny Music Performance
Anxiety score (p<0.05). Based on the findings, it is recommended that professional
musicians receive early postural awareness training under the guidance of
physiotherapists, with the inclusion of individualized exercise programs to prevent
muscle imbalances and support spinal symmetry, scapular control training, and
ergonomic adjustments integrated into music education programs.
Persistent Postural Perceptual Dizziness Hastalarında Vestibüler Rehabilitasyon Programının Etkinliğinin İncelenmesi
(Sağlık Bilimleri Enstitüsü, 2025-07-23) Cömert, Aycan; Odyoloji
The aim of this study was to prepare a systematic desensitization-based
vestibular rehabilitation program specific to individuals diagnosed with Persistent
Postural Perceptual Dizziness (PPPD) and to examine the effectiveness of this
program. For this purpose, 12 individuals (42.00 ± 11.63 years) diagnosed with PPPD
(42.00 ± 11.63 years) underwent an 8-session vestibular rehabilitation program, each
session lasting an average of 40 minutes and conducted face-to-face with a clinician.
To examine the effectiveness of the vestibular rehabilitation program, the Dizziness
Handicap Inventory (DHI), Beck Anxiety Inventory (BAI) and Beck Depression
Inventory (BDI) were administered to all individuals before and after the program.
Anxiety levels before vestibular rehabilitation were significantly higher than
depression levels (p = 0.017). After vestibular rehabilitation, a significant decrease was
found in all scale scores (p < 0.05). When the DHI subcomponents were analyzed, a
significant decrease was observed especially in the functional subtest scores. There
was no significant correlation between gender and DHI, BAI and BDI scores (p >
0.05). The findings obtained in our study suggest that a symptom-specific structured
vestibular rehabilitation program is effective in individuals with PPPD. It is thought
that this vestibular rehabilitation program we developed will serve as a concrete guide
for specialists working in the field on the management of PPPD and rehabilitation
program planning for these individuals.
Doğal Afetlerde Çevrimiçi Davranış Verilerine Dayalı İhtiyaç Tahmini ve Kaynak Tahsis Optimizasyonu: 2023 Kahramanmaraş Depremleri Uygulaması
(Fen Bilimleri Enstitüsü, 2025) Demir Mehmet; Endüstri Mühendisliği
Natural disasters are events that deeply shake the physical, economic and social structure
of societies with their sudden occurrence and devastating effects. After disasters such as
earthquakes, floods and forest fires, there is an intense demand for basic needs such as
search and rescue, health services, food and shelter in a short time. Meeting these
demands in a timely and effective manner is one of the most critical elements of the
disaster management process.
After a disaster, it is necessary to deliver basic aid such as search and rescue, equipment,
law enforcement, health services to the regions in need. However, determining the levels
of need in the regions affected by the disaster is very difficult due to limited access to
information, damaged communication infrastructure and rapid change in demand
dynamics. Especially if the disaster is not limited to a single center but has affected a wide
geographical area, the problem of allocating resources to which regions and to what extent
becomes more complex. It is not possible to wait for the determination of real demands
to mobilize resources. Therefore, early and accurate prediction of potential need regions and estimated demand levels in these regions is of critical importance for the effective
and timely allocation of resources.
In this study, we address the demand uncertainty in natural disasters that require
simultaneous intervention and the resource allocation problems that become complicated
due to this uncertainty. For the solution, we develop a two-stage “predict-then-optimize”
approach based on online behavioral data. We apply our approach to real data belonging
to the earthquake centered in Kahramanmaraş that occurred on February 6, 2023 in the
southeast of Turkey. In the first stage, the “predict” stage, we address the demand
uncertainty problem using machine learning models. In this context, we first develop a
demand index that uses social media shares (X, formerly known as Twitter, posts) and
search engine trends (Google search trend data) in the early post-disaster period. In order
to identify the data containing calls for help in X posts, we manually label 5000 X posts
as “contains calls for help” (1) or “does not contain” (0). We divide the dataset into
training, test and validation; We develop a text classification model based on BERT based
on this structure. First, we clean the text data from punctuation marks, special characters,
and user labels, and standardize the texts by converting them to lowercase. After these
pre-processing steps, we compare the performance using both traditional machine
learning algorithms (Support Vector Machines, Logistic Regression, Random Forest) and
a BERT-based deep learning model capable of learning contextual meaning. Then, we
combine X posts and Google search trend data to create a regional demand popularity
index. Then, we develop Ridge regression models that determine the relative demand
levels in disaster-affected regions using the index we developed and various demographic
and structural variables belonging to disaster regions. While developing the regression
models, we apply logarithmic, square root, inverse, square and cube transformations and
binary/triple interactions to the demand popularity index and various demographic and
physical variables of the provinces affected by the disaster, creating a total of 113
features. Then, in order to reduce the multicollinearity within this large feature set, we
conduct a target-oriented variable elimination process with a correlation threshold of 0,95
and reduce the number of features to 33. In the modeling phase, we perform demand
forecasts for February 07, 08, 09 and 10 using the Ridge regression method with multiple
target variables. In the second phase, in the “optimize” phase, we develop a network flow
optimization model that allocates multiple resources to disaster areas in an effective and
balanced manner based on these forecasts. Thus, we determine the temporal and regional distribution plans of a limited number of teams such as search and rescue, health and
logistics. In recent years, machine learning and artificial intelligence-based forecasting
models have been used in decision support processes in uncertain environments such as
disasters. On the other hand, resource management or allocation problems are among the
basic problem classes with extensive application in the operations research literature. This
thesis develops a solution approach in which prediction and optimization processes work
in an integrated manner by adopting the predict-then-optimize paradigm, and applies this
method using real data from the Kahramanmaraş earthquake, thus contributing to
literature and practice.
Jinekolojik Kanserli Kadınlarda Hastalıkta Belirsizliğin Yaşam Kalitesi ile İlişkisinde Başa Çıkmanın Aracılık Etkisinin Belirlenmesi
(Sağlık Bilimleri Enstitüsü, 2025-07-28) Yaman, Sanem; Doğum ve Kadın Hastalıkları Hemşireliği
This study was conducted to determine the predictors of quality of life in women diagnosed with gynecological cancer and to examine the mediating role of coping in the relationship between uncertainty in illness and quality of life. This descriptive and cross-sectional study was carried out in the gynecologic oncology clinic of a public hospital. A total of 157 women with gynecological cancer participated in the study. Data were collected using the Descriptive Characteristics Form, Mishel Uncertainty in Illness Scale, Cancer Coping Questionnaire, and the Functional Assessment of Cancer Therapy-General (FACT-G) and ECOG Performance Scale. Descriptive statistics, correlation analysis, multiple regression, and mediation analysis using Hayes’ PROCESS macro (Model 4) were used for data analysis. The participants’ mean age was 51.34±10.27 years. The average quality of life score was 68.17±13.25, uncertainty in illness was 55.18±8.05, and coping score was 53.46±8.18, indicating levels. There was a significant negative correlation between uncertainty and quality of life (r = -0.351; p < 0.001) and a significant positive correlation between coping and quality of life (r = 0.312; p < 0.001). According to regression analysis, educational level (β = 0.217; p = 0.004), duration of illness (β = 0.194; p = 0.010), uncertainty (β = -0.283; p < 0.001), and coping (β = 0.278; p < 0.001) were identified as significant predictors of quality of life. Mediation analysis revealed that coping partially mediated the relationship between uncertainty in illness and quality of life, and this indirect effect was statistically significant (indirect effect = -0.132; 95% CI: -0.152 to -0.018). These findings highlight the importance of reducing uncertainty and supporting effective coping strategies in efforts to improve quality of life in women with gynecologic cancer.
Koklear İmplant Kullanan Çocuklarda Vestibüler-Proprioseptif Sistemlerin; Okuma, Yazma ve Dil Becerileri Üzerindeki Etkisinin Değerlendirilmesi
(Sağlık Bilimleri Enstitüsü, 2025-07-28) İçöz, Betül; Odyoloji
This thesis aims to evaluate the relationship
between vestibular-proprioceptive systems and reading, writing, and language skills
in children using cochlear implants. A total of 48 children (24 unilateral and 24
bilateral CI users) attending 1st and 2nd grade participated in the study. Language
skills were assessed using “the Turkish School-Age Language Development Test
(TODIL)”, reading and writing skills with “the Reading and Writing Assessment
Battery (OYAB)”, and vestibular-proprioceptive systems with “the Ayres Sensory
Integration Assessment (EASI)”. Results revealed a strong and significant correlation
between TODIL and OYAB composite scores, although no significant group
differences were found. In the EASI, normative performance rates were higher in
Proprioception, Ocular Motor, and Ocular Praxis, whereas below-norm performance
was more frequent in Vestibular Nystagmus and Balance, with no group differences
in any domain. Significant associations emerged between Vestibular Nystagmus,
Proprioception, Oculo-Motor scores and TODIL composite scores. Positive
correlations were also found between EASI subtests and Reading, Reading
Comprehension, and OYAB composite scores; however, the Writing composite score
was significantly correlated with all EASI subtests except Proprioception. Text
Structure Quality was positively associated with Proprioception. These findings
highlight not only the influence of vestibular-proprioceptive systems on language
and literacy skills in children with CIs but also underscore the importance of
supporting these systems in rehabilitation for comprehensive development.