Rintasyöpä ja liikuntaharjoittelu : ryhmä rintasyöpää sairastaneille

Rintasyöpä ja sen hoidot saattavat aiheuttaa sairastuneelle monia fyysisiä, psyykkisiä ja sosiaalisia haittavaikutuksia. Liikunta voi toimia rintasyöpään sairastuneen kuntoutumisen tukena auttaen toimintakyvyn palautumisessa sekä vähentäen haittavaikutuksia. Liikunnalla on useiden tutkimusten mukaan myös monia muita positiivisia vaikutuksia rintasyöpää sairastaneelle. Opinnäytetyö on toiminnallinen opinnäytetyö ja työn tilaajana toimi Pohjois-Savon Syöpäyhdistys ry. Opinnäytetyön tarkoituksena oli järjestää liikuntaharjoitteluryhmä rintasyöpää sairastaneille naisille, joilla syöpähoidot ovat jo päättymässä tai päättyneet. Ryhmä toteutui neljä kertaa syksyllä 2015 ja ryhmäkertojen aiheina olivat kestävyys-, voima- ja liikkuvuusharjoittelu sekä rentoutuminen. Tavoitteenamme oli antaa ryhmäläisille tietoa liikunnan merkityksestä ja sen tutkituista hyötyvaikutuksista rintasyöpää sairastaneelle sekä tarjota ohjattua ja turvallista liikuntaharjoittelua. Lisäksi tavoitteenamme oli syventää omaa asiantuntijuuttamme aihealueelta. Ryhmäläisiltä saamamme palautteen perusteella järjestämämme liikuntaharjoitteluryhmän kaltaiselle toiminnalle olisi tarvetta. Rintasyöpää sairastaneet kokevat usein ongelmaksi liikuntaan liittyvän ohjauksen ja tiedon puutteen. Ohjaus ja neuvonta voi olla vähäistä tai ristiriitaista. Aihe on tärkeä, sillä liikuntaan liittyvä ohjeistus saattaa madaltaa kynnystä aloittaa liikunta rintasyöpähoitojen jälkeen toipumisvaiheessa.Breast cancer with treatment may cause many negative side effects for physical, psychological and social functioning. Exercising after breast cancer is one evidence-based way to help the rehabilitation process and exercising has many other positive impacts on better survival. This thesis was functional and was made in co-operation with Pohjois-Savon Syöpäyhdistys ry. The purpose of this thesis was to create an exercise group for breast cancer survivors whose cancer treatment were almost over. The group met four times in fall 2015 and the subjects were aerobic, strength, flexibility training and relaxation. The aim of this work was to give information about the effects of exercise for breast cancer survivors. The aim was also to offer supervised, effective and suitable exercise for the group. According to our group members this kind of exercise group for breast cancer survivors could be necessary in the future.Lack of guidance and information related to physical activity is often experienced as a problem among breast cancer survivors. Information may be insufficient or contradictory. The topic of this thesis is important because physical activity counseling may lower the step to begin physical activity after breast cancer treatment

Wearable sensors to detect seizures and attacks:challenges of data gathering

Abstract The wearable sensor market is currently one of the most rapidly growing area in consumer electronics. One of the most interesting application field for wearable sensors is early detection of diseases, seizures and attacks as it would save a huge amount of money from society. In order to train reliable recognition models for these purposes, a high quality training data needs to be collected. This study concentrates on challenges on gathering such data set based on our own experiences when collecting data using wearable sensors to detect migraine attacks

Electrodermal activity asymmetry in sleep:a case study for migraine detection

Abstract Migraine is a poorly understood disease and it is estimated that 15% of people in Europe alone are affected by it. In this study skin electrodermal activity (EDA) signals of left and right wrists were collected from a migraine patient to see if the asymmetry associated with EDA signals will affect to migraine detection based on wearable sensors. In the study, nighttime EDA storm epochs were detected and visual inspection on total time of EDA storm epochs and timing of EDA storms between wrists were done. Also filtered EDA signals of nights that preceded migraine attacks were visually checked. According to the results, EDA measurements from one wrist are enough to detect changes before a migraine attack because the EDA asymmetry measured between wrists might play not a significant role in migraine prediction

Using sleep time data from wearable sensors for early detection of migraine attacks

Abstract The migraine is a chronic, incapacitating neurovascular disorder, characterized by attacks of severe headache and autonomic nervous system dysfunction. Among the working age population, the costs of migraine are 111 billion euros in Europe alone. The early detection of migraine attacks would reduce these costs, as it would shorten the migraine attack by enabling correct timing when taking preventive medication. In this article, whether it is possible to detect migraine attacks beforehand using wearable sensors is studied, and t preliminary results about how accurate the recognition can be are provided. The data for the study were collected from seven study subjects using a wrist-worn Empatica E4 sensor, which measures acceleration, galvanic skin response, blood volume pulse, heart rate and heart rate variability, and temperature. Only sleep time data were used in this study. A novel method to increase the number of training samples is introduced, and the results show that, using personal recognition models and quadratic discriminant analysis as a classifier, balanced accuracy for detecting attacks one night prior is over 84%. While this detection rate is high, the results also show that balance accuracy varies greatly between study subjects, which shows how complicated the problem actually is. However, at this point, the results are preliminary as the data set contains only seven study subjects, so these do not cover all migraine types. If the findings of this article can be confirmed in a larger population, it may potentially contribute to early diagnosis of migraine attacks

Wrist-worn wearable sensors to understand insides of the human body:data quality and quantity

Abstract Wearable sensors have become more commonly used in everyday basis and powerful in terms of computational capacity and sensing resources, including capability to collect data from different bio-signals. The data collected from everyday wearables offers huge opportunities to monitor people’s everyday life without expensive laboratory measurements, including also behaviours and conditions only rarely seen in controlled laboratory environments. So far wearable sensors have mostly been used to monitor motion, but bio-sensor powered wearables can do a lot more: they can be used to monitor physiological reactions inside the human body as well as some psycho-physical reactions such as affection and stress. This development enables multiple interesting and important applications, such as early detection of diseases, seizures, and attacks. With stock wearables worn in everyday basis, one of the biggest challenges for such applications is the sensing data itself. In order to train reliable recognition and prediction models, high quality training data with labels needs to be collected. This paper focuses on lessons learned of challenges in data quality and quantity when such data sets are gathered. We discuss our own experiences when collecting data using wearable sensors for early detection of migraine attacks, but the same lessons learned can be generalized to other studies utilizing wearables for recognition medical symptoms and users’ everyday behaviour

Early detection of migraine attacks based on wearable sensors:experiences of data collection using Empatica E4

Abstract The migraine is a chronic, incapacitating neurovascular disorder, characterized by attacks of severe headache and autonomic nervous system dysfunction, concerning 15% of people in developed countries. It is one of the most understated and incapacitating diseases in the world and costing yearly 111 Billion Euros in Europe only. In our study, we discarded the mechanisms affecting to the migraine but instead the focus was on early detection of the attacks by using human measured biosignals. By using a single, easy and comfortable wearable sensor device the aim was to develop a model that assists individuals to take their medication on time and hence help to avoid the pain in migraine. In this paper, a preliminary study concept is presented as well as the experiences of the data collection using Empatica E4 device is carried out. The experiences are introduced from the point of view of the researchers themselves but also the volunteers actually using the device answered to a short survey about the usability issues as well as gave opinions of the future migraine detection device itself