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Living with Parkinson’s Disease introduces a range of significant challenges into one’s daily life. While medical interventions exist to overcome some of these challenges, patient self-care techniques often form an essential complement to the treatments recommended by medical doctors. Knowledge on these self-care techniques often originates from those living with Parkinson’s themselves or their close caregivers, as they have the knowledge and experience required to assess self-care techniques. This so-called ‘patient knowledge’ is usually exchanged in peer meetings or discussion forums. Although vital to the Parkinson’s Disease community, this information is often difficult to access due to its unstructured format and the difficulty of navigating through online forums. We present an online tool that allows for contributing, assessing, and finally discovering Parkinson’s Disease self-care techniques. The custom discovery tool was populated with self-care knowledge by over 300 people with Parkinson’s and dozens of their carers, spanning areas such as daily well-being and using assistive equipment. Then, we invited patients to explore the discover features in a smaller scale trial. While well-received, our deployment highlighted several challenges that we further discuss in this paper. Overall, our study contributes to crowdsourced digital health solutions and provides both design and research implications to this challenging domain with a vulnerable user group.

A new generation of wearable devices now enable end-users to keep track of their sleep patterns. This paper reports on a longitudinal study of 82 participants who used a state-of-the-art sleep tracking ring for an average of 65 days. We conducted interviews and questionnaires to understand changes to their lifestyle, their perceptions of the tracked information and sleep, and the overall experience of using an unobtrusive sleep tracking device. Our results indicate that such a device is suitable for long-term sleep tracking and helpful in identifying detrimental lifestyle elements that hinder sleep quality. However, tracking one’s sleep can also introduce stress or physical discomfort, potentially leading to adverse outcomes. We discuss these findings in light of related work and highlight the near-term research directions that the rapid commoditisation of sleep tracking technology enables.

Sleep tracking has been rapidly developing alongside wearable technologies and digital trackers are increasingly being used in research, replacing diaries and other more laborious methods. In this work, we describe the user expectations and experiences of four different sleep tracking devices used simultaneously during week-long field deployment. The sensor-based data collection was supplemented with qualitative data from a 2-week long daily questionnaire period which overlapped with device usage for a period of 1 week. We compare the sleep data on each of the tracking nights between all four devices, and showcase that while each device has been validated with the polysomnography (PSG) gold standard, the devices show highly varying results in everyday use. Differences between devices for measuring sleep duration or sleep stages on a single night can be up to an average of 1 h 36 min. Study participants provided their expectations and experiences with the devices, and provided qualitative insights into their usage throughout the daily questionnaires. The participants assessed each device according to ease of use, functionality and reliability, and comfortability and effect on sleep disturbances. We conclude the work with lessons learned and recommendations for researchers who wish to conduct field studies using digital sleep trackers, and how to mitigate potential challenges and problems that might arise regarding data validity and technical issues.

Smartphones are an integral part of our daily life, bringing both positive and negative impacts with them. Recent studies suggest that extensive and untimely smartphone usage directly affects circadian rhythm, i.e. alertness-sleepiness cycle. In this paper, we analyse sleep quality data collected through a wearable ring together with the smartphone interaction in bed just before falling asleep. First, we show that the sleep-tracking devices measuring sleep quality and quantity are also feasible to be used for researching circadian cycles. Second, we analyse the statistical relationships between in-bed smartphone interaction and different sleep metrics, out of which some are more prominent for future use and some present interesting negative results. Third, we present three baseline prediction models to predict sleep quality and circadian cycle based on smartphone app usage, with accuracy ranging from 31% to 67%.

Parkinson’s disease (PD) is an incurable neurodegenerative disorder. It is estimated that more than 9 million individuals globally suffered from PD in 2020, and the number of patients is predicted to exceed 12 million by 2040. PD is the fastest growing neurological disorder, with the ageing population being a fundamental driver. PD motor and non-motor symptoms have a significant impact on patients’ life. From the patient’s perspective, the three most prevalent symptoms are tremors, slowness, and stiffness in the early stages, and varying medication effect (“wearing-off”) in the later stages. Self-care is an essential part of living with a chronic condition. Self-care refers to all actions that aim to minimise the disease’s impact on daily life. Self-care activities can be categorised into maintenance, monitoring, and management. The objectives of this thesis were to create new applications for supporting daily life with PD, i.e. enhancing self-care on all three categories. First, we implemented a mobile application, STOP, to enable patients’ self-assessment and aid in medication adherence. STOP is evaluated with PD patients. The STOP application provides a medication journal to support medication-timely adherence. We demonstrate that accelerometer data from STOP are useful for tremor detection. In relation to this, we introduce the Tremor Intensity Parameter (TIP), calculated with the metrics characterising the accelerometer signal in the time-frequency domain. We digitised the traditional spiral drawing used in the assessment of PD and extended it with a square drawing task. We found the drawing performance differed with PD patients and age matching controls. We found PD patients receptive to digital tools — such as STOP — to track their medication intake and symptoms. The possibility of sharing the data with medical personnel to improve and assist their own care would motivate the use of digital tools. In future, we assume the use of digital monitoring to, Tiivistelmä Parkinsonin tauti on parantumaton hermostoa rappeuttava sairaus. Vuonna 2020 maailmanlaajuisesti arvioitiin olevan yli 9 miljoonaa Parkinsonia sairastavaa, ja määrän ennustetaan ylittävän 12 miljoonaa vuoteen 2040 mennessä. Väestön ikääntymisen myötä Parkinsonin taudista on tullut nopeimmin yleistyvä neurologinen sairaus. Taudin motoriset ja ei-motorisiset oireet vaikuttavat merkittävästi potilaan elämänlaatuun. Taudin alkuvaiheessa kolme yleisintä oiretta ovat vapina, hitaus ja jäykkyys, ja myöhemmissä vaiheissa vaihtelu lääkevasteessa. Itsehoito on tärkeä osa elämää kroonisen taudin kanssa. Toimia, joilla yritetään vähentää sairauden vaikutuksia päivittäiseen elämään, kutsutaan itsehoitokeinoiksi. Keskitason teoria kroonisen sairauden itsehoidosta jakaa itsehoidon kolmeen osa-alueeseen: ylläpitoon, seurantaan ja hallintaan. Tämän väitöskirjan tavoitteena oli löytää uusia sovelluksia tukemaan Parkinsonin potilaiden arkea, eli tehostaa itsehoitoa näillä kolmella osa-alueella. Kehitimme mobiilisovelluksen STOP oireiden itsearviointiin ja lääkityksen aikataulun seurantaan. Sovellus evaluoitiin Parkinsonin potilaiden käytössä. Osoitimme, että kiihtyvyysanturin dataa voidaan käyttää vapinan havainnointiin. Määrittelimme aika- ja taajuustason tunnuslukujen avulla laskettavan Tremor Intensity Parameter (TIP) -parametrin kuvaamaan vapinan voimakkuutta. Teimme digitaalisen version perinteisestä spiraalin piirtotehtävästä, jota käytetään Parkinsonin taudin arvioinnissa. Sovellus tarjoaa myös neliön piirtotehtävän. Parkinsonin potilaiden ja vastaavan ikäisten verrokkien suoritus piirtotehtävissä eroaa. Tutkimuksessa saatiin selville, että Parkinsonia sairastavat ovat valmiita käyttämään digitaalisia työkaluja, kuten STOP, lääkityksen ja oireiden seurantaan, ja ovat halukkaita jakamaan kerätyt tiedot hoitohenkilöstön kanssa hoidon laadun parantamiseksi. Uskomme, että tulevaisuudessa Parkinsonia sairastavat käyttäisivät enemmän digitaalisia seurantatyökaluja, jos ho

The connection between stress and smartphone usage behavior has been investigated extensively. While the prediction results using machine learning are encouraging, the challenge of how to cope with data loss remains. Addressing this problem, we propose an Intelligent Recommender System for logging stress based on adding a subjective user data-based validation to predictions made by intelligent algorithms. In a user study involving 731 daily stress self-reports from 30 participants we found discrepancies between subjective and smartphone usage data, i.e. battery, call information, or network usage. Despite the good prediction accuracy of 65% using a Random Forest classifier, combining both information would be beneficial for avoiding data and improving prediction accuracy. For realizing such a system (i.e., a mobile application), we propose three design recommendations, based on the capabilities of frequently used machine learning classifiers, enabling users to annotate their daily stress levels with a predict-and-validate methodology.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. It is commonly accepted that improving medication adherence alleviates symptoms and maintains motor capabilities. Not following the medication regimen (e.g., skipping or over-medicating) may worsen side-effects, which mislead clinicians and patients. We developed and evaluated a mobile application, STOP, for screening the PD symptoms and medication intake. It contains a game for tracking the PD symptoms, and a medication journal for recording medical intake and adherence. We conducted a 1-month long real-world deployment with 13 PD patients from two countries. We found that the application medication adherence tracking provides non-bias information, and users are receptive to share such data with their care and medical personnel.

The geology of Uganda, spanning more than 3 000 m.y., comprises Archaean lithospheric fragments welded together, inersected or surrounded by Proteroziuc fold belts related to the Eburnian, Grenvillean and Pan-African orogenic cycles at 2.20-1.85 Ga, 1.10-0.95 Ga and 0.75-0.50 Ga respectively. In places post-orogenic molasse-type platform deposits have been preserved, including post-Pan-African Karoo basins and the two branches of the Neogene East Africa rift system. Archaean fragments making up the proto-Congo craton include the Lake Victoria granite-greenstone terrain, the West Tanzanian granitic-gneissic-migmatitic fragment and the Mesoarchaean nucleus of the West Nile block unconformably overlain by mafic volcanic-dominated lithologies and accreted with Neoarchaean rocks., The geology of Uganda, spanning more than 3 000 m.y., comprises Archaean lithospheric fragments welded together, inersected or surrounded by Proteroziuc fold belts related to the Eburnian, Grenvillean and Pan-African orogenic cycles at 2.20-1.85 Ga, 1.10-0.95 Ga and 0.75-0.50 Ga respectively. In places post-orogenic molasse-type platform deposits have been preserved, including post-Pan-African Karoo basins and the two branches of the Neogene East Africa rift system. Archaean fragments making up the proto-Congo craton include the Lake Victoria granite-greenstone terrain, the West Tanzanian granitic-gneissic-migmatitic fragment and the Mesoarchaean nucleus of the West Nile block unconformably overlain by mafic volcanic-dominated lithologies and accreted with Neoarchaean rocks.