75 research outputs found
Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps
For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions
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Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry
Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to usersâ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.âs model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited âfluid contextual reasoningâ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups
Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning
Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technolo- gies such as blood glucose meters have long been an essen- tial part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cog- nitive tasks. However, evidence from the first of two stud- ies we present suggests that in demanding and complex situations, some individuals approach disease management in distinctive ways that do not seem to fit well within existing models. This finding motivated, and helped frame our second study, a survey (n=192) to investigate these behaviors in more detail. On the basis of the resulting analysis, we posit Fluid Contextual Reasoning to explain how some people with diabetes respond to particular situations, and discuss how an extended framework might help inform the design of user interfaces for diabetes management
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Children's perspectives on pain-logging: Insights from a Co-Design Approach
Pain is an essential indicator of health and guides clinical treatments. Logging pain is important in supporting this. However, there is little research into pre-adolescent children's pain logging tools. Utilising the Bluebells method to engage children as co-designers, we gathered children's perspectives on pain-logging tools; in the first workshop by using tangible design approaches to support creative thinking, and in the second workshop by discussing developed prototypes based on the children's designs. Our findings highlight design concepts that the research team â despite many years of pain-related research â had not considered in the context of paediatric logging, namely a) prioritizing children's privacy in social settings while using pain-logging tools; b) emphasizing personalization to boost engagement; and c) logging general well-being of children alongside pain intensity to collect more insightful data. These findings thus demonstrate the value of co-designing pain-logging technologies with children
Keratin gene expression profiles after digit amputation in C57BL/6 vs. regenerative MRL mice imply an early regenerative keratinocyte activated-like state
Mouse strains C57BL/6 (B6) and MRL were studied by whole mouse genome chip microarray analyses of RNA isolated from amputation sites at different times pre-and postamputation at the midsecond phalange of the middle digit. Many keratin genes were highly differentially expressed. All keratin genes were placed into three temporal response classes determined by injury/preinjury ratios. One class, containing only Krt6 and Krt16, were uniquely expressed relative to the other two classes and exhibited different temporal responses in MRL vs. B6. Immunohistochemical staining for Krt6 and Krt16 in tissue sections, including normal digit, flank skin, and small intestine, and from normal and injured ear pinna tissue exhibited staining differences in B6 (low) and MRL (high) that were consistent with the microarray results. Krt10 staining showed no injury-induced differences, consistent with microarray expression. We analyzed Krt6 and Krt16 gene association networks and observed in uninjured tissue several genes with higher expression levels in MRL, but not B6, that were associated with the keratinocyte activated state: Krt6, Krt16, S100a8, S100a9, and Il1b; these data suggest that keratinocytes in the MRL strain, but not in B6, are in an activated state prior to wounding. These expression levels decreased in MRL at all times postwounding but rose in the B6, peaking at day 3. Other keratins significantly expressed in the normal basal keratinocyte state showed no significant strain differences. These data suggest that normal MRL skin is in a keratinocyte activated state, which may provide it with superior responses to wounding. Ă© 2013 the American Physiological Society
Co-designing opportunities for human-centred machine learning in supporting type 1 diabetes decision-making
Type 1 Diabetes (T1D) self-management requires hundreds of daily decisions. Diabetes technologies that use machine learning have significant potential to simplify this process and provide better decision support, but often rely on cumbersome data logging and cognitively demanding reflection on collected data. We set out to use co-design to identify opportunities for machine learning to support diabetes self-management in everyday settings. However, over nine months of interviews and design workshops with 15 people with T1D, we had to re-assess our assumptions about user needs. Our participants reported confidence in their personal knowledge and rejected machine learning based decision support when coping with routine situations, but highlighted the need for technological support in the context of unfamiliar or unexpected situations (holidays, illness, etc.). However, these are the situations where prior data are often lacking and drawing data-driven conclusions is challenging. Reflecting this challenge, we provide suggestions on how machine learning and other artificial intelligence approaches, e.g., expert systems, could enable decision-making support in both routine and unexpected situations
Leptomeningeal carcinomatosis as the primary presentation of relapse in breast cancer
Leptomeningeal metastasis (LM) is an uncommon presentation of relapse in breast cancer, which is associated with poor clinical outcomes and poor prognosis. Notably, LM most commonly occurs in breast cancer. The aim of the present review was to investigate the occurrence of LM as the primary presentation of relapse following remission in breast cancer patients and to determine whether specific histological subtypes are predisposed to meningeal metastases. In addition, the present review evaluated whether patients presenting with LM as the primary site of relapse exhibit differences in survival when compared with patients exhibiting metastasis to other sites. Cross-sectional studies have demonstrated that LM is commonly associated with other sites of distant metastasis including lung, liver and bone metastases. The histological breast cancer subtype most commonly associated with LM was invasive lobular carcinoma, while triple-negative breast cancer patients appear to be predisposed to the development of LM when considering the overall prevalence of histological breast cancer subtypes. At present, data regarding LM as the primary site of relapse are limited due to its rarity as the first site of metastasis in breast cancer. Case-controlled studies are required to investigate the incidence of LM as the primary site of recurrence in breast cancer patients as this would enable treatment standardization and identification of prognostic factors for improved survival
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Being cut off from social identity resources has shaped loneliness during the coronavirus pandemic: A longitudinal interview study with medically vulnerable older adults from the United Kingdom
Loneliness is a pernicious problem in older adulthood, associated with physical decline and isolation from valued social groups. However, the longâterm evolving experiences of ageing, identity and loneliness have yet to be elucidated. We use a Qualitative Longitudinal Research interview approach with nine vulnerable older adults (Agemean = 79.4 years), in which five participants were interviewed twice between 2019 and 2020, and four participants were interviewed at threeâtime points from 2019 to 2021. This study aims to understand the unfolding experiences of ageing, social identity and loneliness during a prolonged period of social isolation during the Coronavirus pandemic. A theoretically guided thematic analysis highlights that participants initially experience âCategorisation as Vulnerable and Loss of Agencyâ and âShrinking Social Worldsâ, leading to âUndermining of Reciprocal Supportâ and âFears of Persistent Lonelinessâ. Findings suggest that interventions to ameliorate loneliness among older adults would benefit from addressing ageâbased stereotypes and emphasising the value of reciprocal contributions that older adults can make to their networks, as well as scaffolding and enhancing social identification with new groups. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement
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Digital Intervention in Loneliness in Older Adults: Qualitative Analysis of User Studies
Background: Loneliness is a significant well-being issue that affects older adults. Existing, commonly used social connection platforms do not contain facilities to break the cognitive cycle of loneliness, and loneliness interventions implemented without due processes could have detrimental effects on well-being. There is also a lack of digital technology designed with older adults.Objective:We aimed to iteratively design a user-centered smartphone app that can address loneliness in older adults. The aim of this study was to investigate the loneliness-related psychological processes that our conceptual smartphone app promotes. We also identified the emergent needs and concerns that older adults raised regarding the potential benefits and detriments of the app.Methods: We used technology probes to elicit older adults' reflections on the concept of using the app in 2 studies as follows: concept focus groups (n=33) and concept interviews (n=10). We then conducted a prototype trial with 1 week of use and follow-up interviews (n=12).Results: Thematic analysis explored the experiences and emergent challenges of our app through the design process. This led to the development of 4 themes as follows occurring in all 3 qualitative data sets: reflection on a digital social map is reassuring; app features encourage socializing; the risk of compounding loneliness; and individuals feel more control with mutual, socially beneficial activities.Conclusions: Smartphone apps have the potential to increase older adults' awareness of the richness of their social connections, which may support loneliness reduction. Our qualitative approach to app design enabled the inclusion of older adults' experiences in technology design. Thus, we conclude that the older adults in our study most desired functionalities that can support mutual activities and maintain or find new connections rather than enable them to share an emotional state. They were wary of the app replacing their preferred in-person social interaction. Participants also raised concerns about making the user aware of the lack of support in their social network and wanted specific means of addressing their needs. Further user-centered design work could identify how the app can support mutual activities and socializing
Development and refinement of proxy-climate indicators from peats
Peat, especially from acidic mires (bogs), is a natural archive of past environmental change. Reconstructions of past climate from bogs commenced in the 19th Century through examination of visible peat stratigraphy, and later formed the basis for a postglacial climatic scheme widely used in Northwest Europe. Nevertheless, misconceptions as to how bogs grow led to a 50-year lacuna in peat-climate study, before the concept of "cyclic regeneration" in bogs was refuted. In recent decades, research using proxyclimate indicators from bogs has burgeoned. A range of proxies for past hydrological change has been developed, as well as use of pollen, bog oaks and pines and other data to reconstruct past temperatures. Most of this proxy-climate research has been carried out in Northern Europe, but peat-based research in parts of Asia and North America has increased, particularly during the last decade, while research has also been conducted in Australia, New Zealand and South America. This paper reviews developments in proxy-climate reconstructions from peatlands; chronicles use of a range of palaeo-proxies such as visible peat stratigraphy, plant macrofossils, peat humification, testate amoebae and non-pollen palynomorphs; and explains the use of wiggle-match radiocarbon dating and relationship to climate shifts. It details other techniques being used increasingly, such as biomarkers, stable-isotopes, inorganic geochemistry and estimation of dust flux; and points to new proxies under development. Although explicit protocols have been developed recently for research on ombrotrophic mires, it must be recognised that not all proxies and techniques have universal applicability, owing to differences in species assemblages, mire formation, topographic controls, and geochemical characteristics
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