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Cognitive decline is the deterioration of one or more cognitive functions such as attention, memory, or processing speed. Inherent to aging, most people will encounter some form of cognitive decline during their lifetime while retaining the ability to perform instrumental activities of daily living. However, beyond this age-related cognitive decline due to aging, some people will experience pathological cognitive decline: an abnormal rate of cognitive impairment due to neurological diseases such as dementia or due to physical trauma. In contrast to age-related cognitive decline, this pathological cognitive decline hinders activities of daily living. In between the stages of age-related and pathological cognitive decline, is the stage of Mild Cognitive Impairment (MCI), which is characterized by a slight yet noticeable decline in cognition. Nevertheless, instrumental activities of daily living remain mostly intact in this stage. While the diagnosis of MCI is not always followed by a diagnosis of dementia, people diagnosed with MCI have a markedly higher chance of progressing to dementia. As such, early case-finding of MCI and timely adjusting the diagnosis is crucial to ensure apt medical support. To aid this cause and to better understand the dwindling of cognition, this dissertation sets out to explore the possibilities of using digital card games to assess differences in cognitive performance due to cognitive aging and MCI.In particular, the use of digital biomarkers, i.e., user-generated physiological and behavioral data collected through digital devices, is investigated. Embedded into day-to-day interactions, these digital biomarkers can be used to support diagnosis without interfering with the person's daily routine. In addition, as they are high-resolution in nature, they allow for making informed inferences of neuropsychological processes previously unavailable to psychologists.In this doctoral dissertation, two different aspects of cognitive decline are measured using different digital card games. First, digital biomarker caused by cognitive aging are assessed using the card game FreeCell. To this end, a generic image processing toolkit was built to extract digital biomarkers from the Microsoft Solitaire Collection. Using this toolkit, data was captured from three different age categories. Machine learning models trained on this data showed promise in classifying the younger and older age categories but lacked in classifying games played by the middle-aged category.Second, digital biomarker differences caused by MCI are assessed using the card game Klondike Solitaire. For this part, an Android application was custom-built to capture digital biomarkers while leaving gameplay untouched. Candidate digital biomarkers were identified in collaboration with 11 experts in cognitive decline. Next, gameplay data was captured from both healthy older adults and older adults diagnosed with MCI. A generalized linear mixed model analysis was conducted to investigate differences between healthy older adults and older adults living with MCI. The results of this analysis suggest it is possible to discriminate healthy participants from participants diagnosed with MCI at a group level. In addition, machine learning models were trained to discern games played by older adults with MCI. These models show promise on an individual level and are successful in discerning healthy older adults from adults living with MCI. While exploratory in nature, the results indicate similar psychometric properties as commonly used screening tests.In sum, these findings suggest that commercial off-the-shelf card games, not built for the purpose of measuring cognition, can be used to capture digital biomarkers of cognitive performance sensitive to the cognitive decline due to aging and MCI.

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Glaucoma is the leading cause of irreversible blindness worldwide, affecting about 64 million people. A large proportion of glaucoma cases worldwide are undiagnosed or sub-optimally managed. More than half of people living with the disease, in low-income countries, are unaware of the condition until it progresses to an advanced stage, resulting in visual impairment. Since adequate treatment significantly slows or halts progression of the disease, early detection of cases is considered important. Visual field test is one of the major tests for screening and diagnosis of glaucoma. The test assesses central and peripheral vision of each eye separately in order to detect vision loss, which, in case of glaucoma, gradually progresses from the periphery to the center. The test is mostly performed with standard automated perimetry equipment which is expensive and not easily portable. For people living in rural areas of low-income countries with limited access to ophthalmic care, glaucoma screening and diagnosis testing through visual field test is near inexistent. If an affordable alternative to the standard automated perimetry(SAP) equipment would become available, it is likely that more glaucoma cases could be detected, especially in rural areas where the burden of the disease is most significant.This doctoral dissertation describes the design, development and assessment of a smartphone based low-cost and portable glaucoma visual field screening platform. This doctoral dissertation aims to explore the feasibility of a low-cost and portable visual field screening platform for the early detection of glaucoma, using a smartphone and a virtual reality(VR) headset. To address this aim, the following research questions will be answered.RQ1: What are the needs and preferences of ophthalmic professionals and patients in rural areas of developing countries towards a visual field screening platform using smartphone and VR headset?RQ2: How do we design and develop a mobile app for visual field screening tailored to the needs of ophthalmic professionals and patients from rural areas?RQ3: What are ophthalmic professionals' use and acceptance of a glaucoma screening platform using smartphone and VR headset?RQ4: What is the accuracy of a glaucoma screening platform using smartphone and VR headset compared to a gold standard perimetry equipment?RQ5: What is the sensitivity and specificity of a glaucoma screening platform using smartphone and VR headset?An adopted version of the user-centered design (UCD) process was followed to address most of the research questions. First, a user and task analysis is conducted in Southwest Ethiopia to identify requirements for a visual field screening application. Multiple contextual requirements were identified during this step using field observations, semi-structured interviews with ophthalmic professionals and patients. Next, design parameters and devices (smartphone and VR headset) were selected based on the requirements identified. Next, a prototype screening Android app, glaucoma easy screener(GES), is developed. Following this, the screening app was assessed for usability and acceptability among ophthalmic professionals in Southwest Ethiopia. OPs perceived GES as easy-to-use, enabling the screening of glaucoma screening tests, especially during outreach to rural areas. Even older patients from rural areas, with limited familiarity with technology, were able to perform the test with GES, and valued a 'technical' assessment. Next, clinical accuracy of GES was evaluated by comparing its results against gold standard SAP equipment. There was good level of agreement (above 85%) based on results of 36 eyes. Finally, an assessment of sensitivity and specificity was conducted by testing 40 glaucomatous eyes and 20 normal eyes with the screening platform. Receiver Operator Characteristic (ROC) was used to determine an optimal cut-off points that yields the best combination of sensitivity and specificity. A cut-off value of 3 missed point yielded an acceptable trade-off between sensitivity (90%) and specificity (85%).These findings suggest that low-cost and portable glaucoma visual field screening using smartphone and VR headsets is feasible.

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Given the ageing population worldwide, researchers are looking for alternatives to support older adults to age in place, i.e., live at home for as long as possible. One solution to empower older adults in maintaining a healthy lifestyle is through Self-Management Health Systems (SMHS). In this manuscript, SMHS are defined as integrated systems that combine data logging via multiple sensors and/or self-reports with data visualisation and possibly risk assessment and decision support. On the one hand, SMHS may include commercial off-the-shelf monitoring devices such as activity trackers that measure and motivate to reach daily step goals, active minutes, or sleep goals. On the other hand, SMHS may include medical-grade applications that monitor vital physiological parameters, e.g., blood pressure or oxygen saturation, and that share this health information with healthcare professionals and relatives.Notwithstanding that SMHS are heralded as essential in a digital health care revolution that empowers older adults and mitigates the challenges of a greying population; several research studies show a limited adoption of health-related ICT among older adults and question its feasibility and desirability. Hence, the aim of this manuscript is to study how to design SMHS for older adults, including the oldest old (>80 years) so that they are ready to accept and use them, and are motivated to maintain a healthy lifestyle. To address this objective, the following research questions will be answered.RQ1. What are older adults' requirements for using a self-management health system?RQ2. What are the main drivers for older adults to adopt a self-management healthsystem to support ageing in place?RQ3. Can motivational design techniques help older adults to engage in using a self-management health system?RQ4. How to perform a human-centred design process with older adults?Within this research study, a Human-Centred Design (HCD) process was conducted.This HCD process is an iterative process, cycling through the subsequent phases of inspiration, ideation, implementation, and evaluation. During this process, essential stakeholders, here older adults, were involved from start to finish. During the entire HCD process, spanning four years, 81 older adults with ages ranging from 65 to 97 (median=83) participated in one or more different phases.In the first phase, i.e., the inspiration phase, the context of use and corresponding requirements for older adults to use an SMHS were investigated by means of focusgroup discussions. In the next phase, i.e., the ideation phase, designs and prototypes were generated and then again evaluated. Next, the implementation phase followed, in which the SMHS application was developed. Three different sensors, i.e., an activity monitor, blood pressure monitor, and sleep monitor, were integrated into the SMHS. Data from these sensors were gathered in collaboration with the company BeWell Innovations. Additionally, self-reports were integrated via three validated questionnaires that measure quality of life, cognitive and physical functioning. Data was captured and visualised to the older adult via a tablet application. After finalising the development of the SMHS, an adoption study was performed to better understand older adults' uses and attitudes, guided by the Unified Theory of Use and Acceptanceof Technology (UTAUT). While results showed positive scores for perceived ease-of-use, perceived usefulness, social support and adequate infrastructure, nevertheless, the behavioural intention of participants to adopt the SMHS was low. In the next iteration, the preference for motivational design techniques was investigated through semi-structured interviews. These findings showed, again, that motivational design techniques are no panacea and are preferred the most by those already motivated to follow a healthy lifestyle. Finally, performing research studies with older adults was not always straightforward. Both during the recruitment and the actual research activities, methodological challenges were encountered. Based on the experiences of conducting user research with 81 older adults over four years, we end with lessons learned for future researchers who want to conduct HCD processes with older adults, including the oldest old.It is our aim to help future designers of SMHS for healthy ageing; this doctoral study contributes to the field of Human-Computer Interaction (HCI) by defining requirements for older adults to welcome SMHS, by evaluating whether they are ready to adopt these systems, by investigating preferences towards Motivational Design Techniques(MDT) and finally, by summarising this into guidelines. In addition, lessons learned on performing an HCD process with this older population are shared and also summarised in several guidelines. Although the findings in this manuscript paint a nuanced picture ofolder adults' readiness to embrace SMHS, there is no doubt that self-care technologies will play an important role in the future. Ultimately, this manuscript aspires to generate insights to guide designers of future SMHS that will be embraced by and truly empower older adults.

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