Continuing the quality improvement of an electronic personal health record and interactive website for people with diabetes in Scotland (My Diabetes My Way).

AIMS: eHealth applications have the potential to enable patients to take more control over managing their own health, helping to delay and prevent complications. My Diabetes My Way (MDMW) is an electronic personal health record/educational platform available to people with diabetes in Scotland. This study aims to assess user experience with respect to demographic subgroups, examine effectiveness of previous improvements made to the platform and inform its ongoing development. METHODS: All active MDMW users (22,665) were invited to take part in a questionnaire combining Likert scale and free-response items relating to system utility. Likert responses were used to generate a 'utility score'. This was used in regression analyses to determine predictors of system utility scoring. Free-response answers were analysed thematically and themes were generated. RESULTS: A total of 4713 (21%) MDMW users responded to the questionnaire. Most agreed that MDMW helps them to track changes over time, prepare for face-to-face consultations, remember information discussed in consultations and reduced the need to contact their general practitioner. Free-response answers showed that users valued earlier enhancements made to the site (e.g. linking Fitbit data), and highlighted areas needing further improvement. Evidence of the 'digital divide' was seen in respondent demographics, and some users mentioned 'lack of digital skills' as a barrier to engaging with the platform. CONCLUSIONS: User experience of MDMW was positive. Users agreed with statements that MDMW facilitates diabetes self management. Several areas of potential improvement were identified, including linking more wearable device data, and assisting/directing users to gain the digital skills required to engage fully with MDMW.

User-Centered Design of A Novel Risk Prediction Behavior Change Tool Augmented With an Artificial Intelligence Engine (MyDiabetesIQ): A Sociotechnical Systems Approach.

BACKGROUND: Diabetes and its complications account for 10% of annual health care spending in the United Kingdom. Digital health care interventions (DHIs) can provide scalable care, fostering diabetes self-management and reducing the risk of complications. Tailorability (providing personalized interventions) and usability are key to DHI engagement/effectiveness. User-centered design of DHIs (aligning features to end users' needs) can generate more usable interventions, avoiding unintended consequences and improving user engagement. OBJECTIVE: MyDiabetesIQ (MDIQ) is an artificial intelligence engine intended to predict users' diabetes complications risk. It will underpin a user interface in which users will alter lifestyle parameters to see the impact on their future risks. MDIQ will link to an existing DHI, My Diabetes My Way (MDMW). We describe the user-centered design of the user interface of MDIQ as informed by human factors engineering. METHODS: Current users of MDMW were invited to take part in focus groups to gather their insights about users being shown their likelihood of developing diabetes-related complications and any risks they perceived from using MDIQ. Findings from focus groups informed the development of a prototype MDIQ interface, which was then user-tested through the "think aloud" method, in which users speak aloud about their thoughts/impressions while performing prescribed tasks. Focus group and think aloud transcripts were analyzed thematically, using a combination of inductive and deductive analysis. For think aloud data, a sociotechnical model was used as a framework for thematic analysis. RESULTS: Focus group participants (n=8) felt that some users could become anxious when shown their future complications risks. They highlighted the importance of easy navigation, jargon avoidance, and the use of positive/encouraging language. User testing of the prototype site through think aloud sessions (n=7) highlighted several usability issues. Issues included confusing visual cues and confusion over whether user-updated information fed back to health care teams. Some issues could be compounded for users with limited digital skills. Results from the focus groups and think aloud workshops were used in the development of a live MDIQ platform. CONCLUSIONS: Acting on the input of end users at each iterative stage of a digital tool's development can help to prioritize users throughout the design process, ensuring the alignment of DHI features with user needs. The use of the sociotechnical framework encouraged the consideration of interactions between different sociotechnical dimensions in finding solutions to issues, for example, avoiding the exclusion of users with limited digital skills. Based on user feedback, the tool could scaffold good goal setting, allowing users to balance their palatable future complications risk against acceptable lifestyle changes. Optimal control of diabetes relies heavily on self-management. Tools such as MDMW/ MDIQ can offer personalized support for self-management alongside access to users' electronic health records, potentially helping to delay or reduce long-term complications, thereby providing significant reductions in health care costs.

Multicentre service evaluation of presentation of newly diagnosed cancers and type 1 diabetes in children in the UK during the COVID-19 pandemic.

Background: The COVID-19 pandemic led to changes in patterns of presentation to emergency departments. Child health professionals were concerned that this could contribute to the delayed diagnosis of life-threatening conditions, including childhood cancer (CC) and type 1 diabetes (T1DM). Our multicentre, UK-based service evaluation assessed diagnostic intervals and disease severity for these conditions. Methods: We collected presentation route, timing and disease severity for children with newly diagnosed CC in three principal treatment centres and T1DM in four centres between 1 January and 31 July 2020 and the corresponding period in 2019. Total diagnostic interval (TDI), patient interval (PI), system interval (SI) and disease severity across different time periods were compared. Results: For CCs and T1DM, the route to diagnosis and severity of illness at presentation were unchanged across all time periods. Diagnostic intervals for CCs during lockdown were comparable to that in 2019 (TDI 4.6, PI 1.1 and SI 2.1 weeks), except for an increased PI in January-March 2020 (median 2.7 weeks). Diagnostic intervals for T1DM during lockdown were similar to that in 2019 (TDI 16 vs 15 and PI 14 vs 14 days), except for an increased PI in January-March 2020 (median 21 days). Conclusions: There is no evidence of diagnostic delay or increased illness severity for CC or T1DM, during the first phase of the pandemic across the participating centres. This provides reassuring data for children and families with these life-changing conditions.

A Longitudinal Perspective on User Uptake of an Electronic Personal Health Record for Diabetes, With Respect To Patient Demographics.

INTRODUCTION: The growing prevalence of diabetes has increased the need for scalable technologies to improve outcomes. My Diabetes My Way (MDMW) is an electronic personal health record (ePHR) available to all people with diabetes in Scotland since 2010, associated with improved clinical outcomes among users. MDMW pulls data from a national clinician-facing informatics platform and provides self-management and educational information. This study aims to describe MDMW user demographics through time with respect to the national diabetes population, with a view to addressing potential health inequalities. METHODS: Aggregate data were obtained retrospectively from the MDMW database and annual Scottish Diabetes Survey (SDS) from 2010 to 2020. Variables included diabetes type, sex, age, socioeconomic status, ethnicity, and glycemic control. Prevalence of MDMW uptake was calculated using corresponding SDS data as denominators. Comparisons between years and demographic sub-groups were made using Chi- Squared tests. RESULTS: Overall uptake of MDMW has steadily increased since implementation. By 2020, of all people with T1D or T2D in Scotland, 13% were fully enrolled to MDMW (39,881/312,326). There was proportionately greater numbers of users in younger, more affluent demographic groups (with a clear social gradient) with better glycemic control. As uptake has increased through time, so too has the observed gaps between different demographic sub-groups. CONCLUSIONS: The large number of MDMW users is encouraging, but remains a minority of people with diabetes in Scotland. There is a risk that innovations like MDMW can widen health inequalities and it is incumbent upon healthcare providers to identify strategies to prevent this.

Viral Etiology and the Impact of Codetection in Young Children Presenting With Influenza-Like Illness.

BACKGROUND: Children with acute respiratory tract infection (ARTI) frequently exhibit virus-virus codetection, yet the clinical significance of ARTI remains contentious. Using data from a prospective cohort of children with influenza-like illness, we examined the virology of ARTI and determined the clinical impact of virus-virus codetection. METHODS: Children aged 6 to 59 months who presented to a tertiary pediatric hospital between influenza seasons 2008 and 2012 with fever and acute respiratory symptoms were enrolled, and nasal samples were collected. Respiratory viruses were identified by culture and polymerase chain reaction. We compared demographics, presenting symptoms, and clinical outcomes of children with a single-virus infection and those in whom 2 or more viruses were detected (virus-virus codetection). We used logistic regression models and estimated marginal means to calculate the adjusted odds ratios and probabilities of symptom presentation, prescription of antibiotics, and hospitalization. RESULTS: Of 2356 children, a virus was detected in 1630 (69.2%) of them; rhinovirus (40.8%), influenza (29.5%), and respiratory syncytial virus (26.4%) were detected most commonly. Two or more viruses were detected in 25% of these children. After we adjusted for demographic factors, children with virus-virus codetection had greater odds of presenting with cough (adjusted odds ratio [aOR], 1.9; 95% confidence interval [CI], 1.2-3.1) and rhinorrhea (aOR, 1.8; 95% CI, 1.1-2.9) than those with a single-virus infection, although both symptoms were common. Children with influenza and respiratory syncytial virus combined had the highest probability of hospitalization (55%; 95% CI, 35%-73%), which was significantly greater than for those with influenza infection alone (22%; 95% CI, 16%-29%). CONCLUSIONS: Overall, virus-virus codetection has limited impact on clinical severity among children with influenza-like illness. However, infection with specific pathogen pairs might be associated with more severe outcomes. Routine diagnostics to identify specific viruses should be restricted to common pathogens.

Clinical Predictors of Influenza in Young Children: The Limitations of "Influenza-Like Illness".

BACKGROUND: Influenza-like illness (ILI) definitions have been infrequently studied in young children. Despite this, clinical definitions of ILI play an important role in influenza surveillance. This study aims to identify clinical predictors of influenza infection in children ≤5 years old from which age-specific ILI definitions are then constructed. METHODS: Children aged 6-59 months with a history of fever and acute respiratory symptoms were recruited in the Western Australia Influenza Vaccine Effectiveness (WAIVE) Study. Clinical data and per-nasal specimens were obtained from all children. Logistic regression identified significant predictors of influenza infection. Different ILI definitions were compared for diagnostic accuracy. RESULTS: Children were recruited from 2 winter influenza seasons (2008-2009; n = 944). Of 919 eligible children, 179 (19.5%) had laboratory-confirmed influenza infection. Predictors of infection included increasing age, lack of influenza vaccination, lower birth weight, fever, cough, and absence of wheeze. An ILI definition comprising fever ≥38°C, cough, and no wheeze had 58% sensitivity (95% confidence interval [CI], 50-66), 60% specificity (95% CI, 56-64), 26% positive predictive value (95% CI, 21-31), and 86% negative predictive value (95% CI, 82-89). The addition of other symptoms or higher fever thresholds to ILI definition had little impact. The Centers for Disease Control and Prevention definition of ILI (presence of fever [≥37.8°C] and cough and/or sore throat) was sensitive (92%; 95% CI, 86-95), yet lacked specificity (10%; 95% CI, 8-13) in this population. CONCLUSIONS: Influenza-like illness is a poor predictor of laboratory-confirmed influenza infection in young children but can be improved using age-specific data. Incorporating age-specific ILI definitions and/or diagnostic testing into influenza surveillance systems will improve the accuracy of epidemiological data.