How Patient Work Changes Over Time for People With Multimorbid Type 2 Diabetes: Qualitative Study.

BACKGROUND: The experiences of patients change throughout their illness trajectory and differ according to their medical history, but digital support tools are often designed for one specific moment in time and do not change with the patient as their health state changes. This presents a fragmented support pattern where patients have to move from one app to another as they move between health states, and some subpopulations of patients do not have their needs addressed at all. OBJECTIVE: This study aims to investigate how patient work evolves over time for those living with type 2 diabetes mellitus and chronic multimorbidity, and explore the implications for digital support system design. METHODS: In total, 26 patients with type 2 diabetes mellitus and chronic multimorbidity were recruited. Each interview was conducted twice, and interviews were transcribed and analyzed according to the Chronic Illness Trajectory Model. RESULTS: Four unique illness trajectories were identified with different patient work goals and needs: living with stable chronic conditions involves patients seeking to make patient work as routinized and invisible as possible; dealing with cycles of acute or crisis episodes included heavily multimorbid patients who sought support with therapy adherence; responding to unstable changes described patients currently experiencing rapid health changes and increasing patient work intensity; and coming back from crisis focused on patients coping with a loss of normalcy. CONCLUSIONS: Patient work changes over time based on the experiences of the individual, and its timing and trajectory need to be considered when designing digital support interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-022163.

Patient Work and Their Contexts: Scoping Review.

BACKGROUND: Having patients self-manage their health conditions is a widely promoted concept, but many patients struggle to practice it effectively. Moreover, few studies have analyzed the nature of work required from patients and how such work fits into the context of their daily life. OBJECTIVE: This study aimed to review the characteristics of patient work in adult patients. Patient work refers to tasks that health conditions impose on patients (eg, taking medications) within a system of contextual factors. METHODS: A systematic scoping review was conducted using narrative synthesis. Data were extracted from PubMed, Excerpta Medica database (EMBASE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and PsycINFO, including studies from August 2013 to August 2018. The included studies focused on adult patients and assessed one or more of the following: (1) physical health-related tasks, (2) cognitive health-related tasks, or (3) contextual factors affecting these tasks. Tasks were categorized according to the themes that emerged: (1) if the task is always visible to others or can be cognitive, (2) if the task must be conducted collaboratively or can be conducted alone, and (3) if the task was done with the purpose of creating resources. Contextual factors were grouped according to the level at which they exert influence (micro, meso, or macro) and where they fit in the patient work system (the macroergonomic layer of physical, social, and organizational factors; the mesoergonomic layer of household and community; and the microergonomic triad of person-task-tools). RESULTS: In total, 67 publications were included, with 58 original research articles and 9 review articles. A variety of patient work tasks were observed, ranging from physical and tangible tasks (such as taking medications and visiting health care professionals) to psychological and social tasks (such as creating coping strategies). Patient work was affected by a range of contextual factors on the micro, meso, or macro levels. Our results indicate that most patient work was done alone, in private, and often imposing cognitive burden with low amounts of support. CONCLUSIONS: This review sought to provide insight into the work burden of health management from a patient perspective and how patient context influences such work. For many patients, health-related work is ever present, invisible, and overwhelming. When researchers and clinicians design and implement patient-facing interventions, it is important to understand how the extra work impacts one's internal state and coping strategy, how such work fits into daily routines, and if these changes could be maintained in the long term.

Therapeutic Effect of Argatroban During Cardiopulmonary Resuscitation and Streptokinase During Extracorporeal Cardiopulmonary Resuscitation in a Porcine Model of Prolonged Cardiac Arrest.

Prolonged cardiac arrest (CA) causes microvascular thrombosis which is a potential barrier to organ reperfusion during extracorporeal cardiopulmonary resuscitation (ECPR). The aim of this study was to test the hypothesis that early intra-arrest anticoagulation during cardiopulmonary resuscitation (CPR) and thrombolytic therapy during ECPR improve recovery of brain and heart function in a porcine model of prolonged out-of-hospital CA. DESIGN: Randomized interventional trial. SETTING: University laboratory. SUBJECTS: Swine. INTERVENTIONS: In a blinded study, 48 swine were subjected to 8 minutes of ventricular fibrillation CA followed by 30 minutes of goal-directed CPR and 8 hours of ECPR. Animals were randomized into four groups (n = 12) and given either placebo (P) or argatroban (ARG; 350 mg/kg) at minute 12 of CA and either placebo (P) or streptokinase (STK, 1.5 MU) at the onset of ECPR. MEASUREMENTS AND MAIN RESULTS: Primary outcomes included recovery of cardiac function measured by cardiac resuscitability score (CRS: range 0-6) and recovery of brain function measured by the recovery of somatosensory-evoked potential (SSEP) cortical response amplitude. There were no significant differences in recovery of cardiac function as measured by CRS between groups (p = 0.16): P + P 2.3 (1.0); ARG + P = 3.4 (2.1); P + STK = 1.6 (2.0); ARG + STK = 2.9 (2.1). There were no significant differences in the maximum recovery of SSEP cortical response relative to baseline between groups (p = 0.73): P + P = 23% (13%); ARG + P = 20% (13%); P + STK = 25% (14%); ARG + STK = 26% (13%). Histologic analysis demonstrated reduced myocardial necrosis and neurodegeneration in the ARG + STK group relative to the P + P group. CONCLUSIONS: In this swine model of prolonged CA treated with ECPR, early intra-arrest anticoagulation during goal-directed CPR and thrombolytic therapy during ECPR did not improve initial recovery of heart and brain function but did reduce histologic evidence of ischemic injury. The impact of this therapeutic strategy on the long-term recovery of cardiovascular and neurological function requires further investigation.

Patient Work Personas of Type 2 Diabetes-A Data-Driven Approach to Persona Development and Validation.

Introduction: Many have argued that a "one-size-fits-all" approach to designing digital health is not optimal and that personalisation is essential to achieve targeted outcomes. Yet, most digital health practitioners struggle to identify which design aspect require personalisation. Personas are commonly used to communicate patient needs in consumer-oriented digital health design, however there is often a lack of reproducible clarity on development process and few attempts to assess their accuracy against the targeted population. In this study, we present a transparent approach to designing and validating personas, as well as identifying aspects of "patient work," defined as the combined total of work tasks required to manage one's health and the contextual factors influencing such tasks, that are sensitive to an individual's context and may require personalisation. Methods: A data-driven approach was used to develop and validate personas for people with Type 2 diabetes mellitus (T2DM), focusing on patient work. Eight different personas of T2DM patient work were constructed based physical activity, dietary control and contextual influences of 26 elderly Australian participants (median age = 72 years) via wearable camera footage, interviews, and self-reported diaries. These personas were validated for accuracy and perceived usefulness for design, both by the original participants and a younger (median age bracket = 45-54 years) independent online cohort f 131 T2DM patients from the United Kingdom and the United States. Results: Both the original participants and the independent online cohort reported the personas to be accurate representations of their patient work routines. For the independent online cohort, 74% (97/131) indicated personas stratified to their levels of exercise and diet control were similar to their patient work routines. Findings from both cohorts highlight aspects that may require personalisation include daily routine, use of time, and social context. Conclusion: Personas made for a specific purpose can be very accurate if developed from real-life data. Our personas retained their accuracy even when tested against an independent cohort, demonstrating their generalisability. Our data-driven approach clarified the often non-transparent process of persona development and validation, suggesting it is possible to systematically identify whether persona components are accurate or. and which aspects require more personalisation and tailoring.

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.

OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically. MATERIALS AND METHODS: We propose a deep learning approach to classify activities of patient work collected from wearable cameras, thereby studying self-management routines more effectively. Twenty-six people with type 2 diabetes and comorbidities wore a wearable camera for a day, generating more than 400 h of video across 12 daily activities. To classify these video images, a weighted ensemble network that combines Linear Discriminant Analysis, Deep Convolutional Neural Networks, and Object Detection algorithms is developed. Performance of our model is assessed using Top-1 and Top-5 metrics, compared against manual classification conducted by 2 independent researchers. RESULTS: Across 12 daily activities, our model achieved on average the best Top-1 and Top-5 scores of 81.9 and 86.8, respectively. Our model also outperformed other non-ensemble techniques in terms of Top-1 and Top-5 scores for most activity classes, demonstrating the superiority of leveraging weighted ensemble techniques. CONCLUSIONS: Deep learning can be used to automatically classify daily activities of patient work collected from wearable cameras with high levels of accuracy. Using wearable cameras and a deep learning approach can offer an alternative approach to investigate patient work, one not subjected to biases commonly associated with self-report methods.

Consumer workarounds during the COVID-19 pandemic: analysis and technology implications using the SAMR framework.

OBJECTIVE: To understand the nature of health consumer self-management workarounds during the COVID-19 pandemic; to classify these workarounds using the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework; and to see how digital tools had assisted these workarounds. MATERIALS AND METHODS: We assessed 15 self-managing elderly patients with Type 2 diabetes, multiple chronic comorbidities, and low digital literacy. Interviews were conducted during COVID-19 lockdowns in May-June 2020 and participants were asked about how their self-management had differed from before. Each instance of change in self-management were identified as consumer workarounds and were classified using the SAMR framework to assess the extent of change. We also identified instances where digital technology assisted with workarounds. RESULTS: Consumer workarounds in all SAMR levels were observed. Substitution, describing change in work quality or how basic information was communicated, was easy to make and involved digital tools that replaced face-to-face communications, such as the telephone. Augmentation, describing changes in task mechanisms that enhanced functional value, did not include any digital tools. Modification, which significantly altered task content and context, involved more complicated changes such as making video calls. Redefinition workarounds created tasks not previously required, such as using Google Home to remotely babysit grandchildren, had transformed daily routines. DISCUSSION AND CONCLUSION: Health consumer workarounds need further investigation as health consumers also use workarounds to bypass barriers during self-management. The SAMR framework had classified the health consumer workarounds during COVID, but the framework needs further refinement to include more aspects of workarounds.