6-Minute Walk Test Assessment Via Mobile App

This study aims to develop an Android-based 6-Minute Walk Test (6MWT) application to facilitate quick and efficient functional capacity assessment in clinical settings. The project will be carried out by a multidisciplinary team of software engineers and physiotherapists, with algorithms developed for both indoor and outdoor environments. As GPS signals may be lost indoors, the program will utilize the phone's gyroscope for distance measurement, while GPS signals will be used outdoors. The study will include healthy individuals aged 18-65, without any exercise restrictions. Participants' demographic data will be recorded, and the 6MWT will be conducted according to traditional protocols. During the test, parameters such as heart rate, oxygen saturation, and walking distance will be measured and compared with traditional methods. The Android application will provide auditory and vibration cues at the start and end of the test and will report health data in real-time. The data will be presented as mean ± standard deviation for continuous variables, and the test reliability will be assessed using the test-retest method. Results will be reported as age- and gender-adjusted z-scores for objective functional decline.

The 6-minute walk test (6MWT) is a widely used test for the objective assessment of functional exercise capacity in the management of patients with moderate to severe lung disease.Unlike pulmonary function testing, 6DYT captures the often coexisting extrapulmonary manifestations of chronic respiratory disease, including cardiovascular disease, frailty, sarcopenia and cancer. Unlike the cardiopulmonary exercise stress test, this test does not require complex equipment or technical expertise. In this low-complexity, safe test, the patient is asked to walk as far as possible along a 30 m corridor for a 6-min period, with the primary outcome measure being the 6-min walking distance in metres.In a healthcare setting, measurement of exercise capacity is important to understand a person's current status and to assess rehabilitation progress. The 6-minute walk test (6MWT), which measures the distance walked in 6 minutes, is a widely used clinical tool for this purpose. A smartphone with integrated sensors provides a suitable platform for wearable biomechanical applications. With a wearable system for 6DYT and minimal additional setup, additional information can be obtained and clinically useful and immediate output can be provided to assess physical function and gait characteristics without the need to purchase specialised medical equipment.Wearable sensors allow a person to walk freely at a self-selected and natural pace that is more representative of everyday life than some laboratory conditions.Many studies have used accelerometers to detect gait and calculate gait parameters such as cadence, step timing and symmetry. Accelerometers can also be used to assess physical activity levels in relation to 6DYT results.In a systematic review published in 2015, 47 applications related to smoking cessation, 7 applications related to hernia, 42 applications related to diabetes, 103 applications related to asthma, 19 applications related to personal health records, 69 applications related to HIV, 29 applications related to colorectal diseases, 46 applications related to cancer, 104 applications related to pain management and 26 applications related to bariatric surgery were used.When the literature was reviewed, studies on the android-based application of 6DYT were conducted, but no application was found in Android application markets. Therefore, the aim of our study is to develop an Android-based 6 Minute Walk Test and to investigate its validity and reliability.

Eligibility Criteria:
Inclusion Criteria:

* Not having a disease that prevents walking
* To be able to use a smartphone

Exclusion Criteria:

* Exercise restriction for any reason
* Pregnancy or any known cardiovascular, pulmonary, neurological, metabolic or haematological disease
* Having any problem that prevents communication