Kunneman, Marleen

Objective Digital health applications (health apps) are increasingly integrated in clinical care. Yet the information describing these apps varies across curated app repositories supporting prescription. We aimed to identify the information items used to describe health apps in repositories; and to quantify the completeness of publicly displayed app-entry information across repositories. Methods We conducted a cross-sectional study of seven national or international curated health app repositories: mHealthBelgium, DiGA, AppThera, ORCHA, GGD AppStore, MIND, and AppGuide. In January 2025, we randomly selected 140 health apps, with 20 apps per repository. For each app, one reviewer extracted the publicly available information displayed in the repository entry and inductively identified the information items reported (i.e., atomic information elements used to describe apps). These information items were then grouped into categories by an expert committee. We calculated, for each repository, the median number of items reported across apps, overall and by category. Results Health apps were described by 32 information items grouped into 10 categories: general information, regulatory information, compatibility, costs, indication, functions, evidence, user experience, data governance and security, and change history. The median number of items reported in repositories ranged from a median of 9/32 items, IQR 9 to 11 in MIND to 27/32, IQR 26 to 28 in DiGA. Only, three repositories (AppThera, DiGA and AppGuide) systematically reported information on the evidence supporting the apps for all listed apps. Conclusions Curated health app repositories differed in the information items presented to clinicians and patients. Incomplete reporting of clinically relevant information, particularly evidence and data governance, may limit informed app selection and prescription. Minimum reporting standards are needed to enable safe prescription and reliable integration of health apps into clinical practice.

Abstract Aim This study aims to assess the treatment burden and health‐related quality of life (HRQoL) in young adults with type 1 diabetes (T1D). Methods Online survey on experienced treatment burden (Treatment Burden Questionnaire, TBQ, 0–150, higher scores indicate higher burden) and HRQoL (Type 1 Diabetes and Life questionnaire for ages 18–25 and 26–45, T1DAL, 0–100, higher scores indicate better HRQoL) among Dutch young adults with T1D (aged 18–30). Results In total, 294 young adults with T1D completed the survey. Mean TBQ score was 59.5 (SD = 24.9). Regular medical care reminding participants of their health problems scored highest. Mean T1DAL scores were 55.9 (SD = 12.9) for ages 18–25, and 62.1 (SD = 13.2) for ages 26–30. Lowest scores were in the subcategory ‘Emotional experiences and daily activities’ (18–25: median = 41.7, IQR = 24.9; 26–30: median = 47.8, IQR = 22.5). TBQ was strongly negatively correlated with T1DAL ( r = −0.69, 95% CI [−0.74, −0.62], p < 0.001). Characteristics associated with higher treatment burden and lower HRQoL included female gender, lower time in range and dissatisfaction with diabetes management approach. Conclusion Young adults with T1D report considerable treatment burden and moderate HRQoL, suggesting a high impact of diabetes care on their lives. Treatment burden is particularly affected by regular medical care activities, whereas diabetes affects the daily functioning and emotional experiences domains of HRQoL. Further research is needed to understand how young adults make care fit and to improve care for these patients, resulting in minimally disruptive care plans. While cautiously interpreted, this may be especially relevant for women and those with adverse clinical outcomes.

Abstract Aim This study aimed to identify what young adults with type 1 diabetes (T1D) do to make diabetes care fit in their lives and the impact of diabetes and diabetes care on living. Methods Dutch young adults with T1D (18–30 years old) submitted photographed real‐life situations of efforts to make care fit and of the impact of care on their lives. Participants organised their photos in themes, which guided the focus group discussions. We added a reflective questionnaire, semi‐structured interview and iterative validation to identify participant‐defined themes and summarise the data. Results Participants ( N = 18) submitted 240 photographs in total, showing a broad range of situations and emotions. Participants identified 16 themes, grouped into four overarching categories describing their experiences with diabetes: (1) My diabetes: glucose levels, workload, 24/7 present; (2) My life: flow of (daily) life, special and irregular circumstances, life changes, body and health; (3) Support: devices and technology, social network, clinical (diabetes) care; (4) Mental aspects: emotional processes, perspective, being a patient. In the overlap of My diabetes and My life, they identified eating and counting carbohydrates, activity and exercise, recreational substances. Conclusion Young adults with T1D face the complex challenge of fitting their care into their ever‐changing lives. While support systems, such as devices, healthcare professionals and social networks can help, they can also create burdens. Participants emphasised the importance of mental health in their lives with T1D. This study highlights the need for diabetes care that acknowledges the emotional, social and practical realities of young adults' lives.

Importance Digital health technologies (DHTs) aiming to monitor, treat, and manage diseases can be prescribed for patients with multimorbidity; yet most DHTs are designed for individual conditions or problems, while approximately half of patients with chronic conditions have multiple chronic conditions. Objectives To identify DHTs approved by the US Food and Drug Administration (FDA) or listed in the Organisation for the Review of Care and Health Apps (ORCHA) library and prescribable for a hypothetical patient with 5 conditions and to model the number of DHTs this patient should be prescribed to receive benefits health professionals considered important. Evidence Review The FDA databases (Premarket Notification 510(k), Premarket Approval, and De Novo) and the ORCHA App Library from National Health Service Somerset were systematically searched for DHTs registered or updated between January 1, 2019, and December 31, 2022, that could be prescribed to a hypothetical woman with 5 chronic conditions (type 2 diabetes, hypertension, chronic obstructive pulmonary disease, osteoporosis, and osteoarthritis). After abstracting each DHT’s elementary functions (ie, simple and delineated features to monitor, treat, and/or manage conditions), an assessment was undertaken to determine the fewest DHTs this hypothetical patient should be prescribed to receive benefit from digital functions health professionals considered important. Findings A total of 148 DHTs were identified (68 [46%] from FDA databases), of which 96 (65%) involved devices and 52 (35%) were standalone health apps. Only 5 DHTs (3.4%) were intended for 2 or more conditions. DHTs offered 140 elementary functions, ranging from recording, tracking, or visualizing health parameters to providing information to digital therapeutics with just-in-time interventions. The hypothetical patient would need to be prescribed up to 13 apps and 7 devices (a blood pressure monitor, a smartwatch, a pulse oximeter, a connected weight scale, a sensor-attached inhaler to monitor adherence, a lung function monitor, and a blood glucose sensor) to receive benefits from 28 functions at least 3 of 5 health professionals considered important. Conclusions and Relevance This systematic review found that almost all prescribable DHTs were developed for a single condition or problem. Thus, patients with multiple chronic conditions would have to routinize many DHTs concurrently in daily life to benefit from digital functions health professionals considered important.

Background The occurrence of shared decision making (SDM) in daily practice remains limited. Various patient characteristics have been suggested to potentially influence the extent to which clinicians involve patients in SDM. Objective To assess associations between patient characteristics and the extent to which clinicians involve patients in SDM. Methods We conducted a secondary analysis of data pooled from 10 studies comparing the care of adult patients with (intervention) or without (control) a within-encounter SDM conversation tool. We included studies with audio(-visual) recordings of clinical encounters in which decisions about starting or reconsidering treatment were discussed. Main Measures In the original studies, the Observing Patient Involvement in Decision Making 12-items (OPTION 12 item ) scale was used to code the extent to which clinicians involved patients in SDM in clinical encounters. We conducted multivariable analyses with patient characteristics (age, gender, race, education, marital status, number of daily medications, general health status, health literacy) as independent variables and OPTION 12 as a dependent variable. Results We included data from 1,614 patients. The between-arm difference in OPTION 12 scores was 7.7 of 100 points ( P < 0.001). We found no association between any patient characteristics and the OPTION 12 score except for education level ( p = 0.030), an association that was very small (2.8 points between the least and most educated), contributed mostly by, and only significant in, control arms (6.5 points). Subanalyses of a stroke prevention trial showed a positive association between age and OPTION 12 score ( P = 0.033). Conclusions Most characteristics showed no association with the extent to which clinicians involved patients in SDM. Without an SDM conversation tool, clinicians devoted more efforts to involve patients with higher education, a difference not observed when the tool was used. Highlights Most sociodemographic patient characteristics show no association with the extent to which clinicians involve patients in shared decision making. Clinicians devoted less effort to involve patients with lower education, a difference that was not observed when a shared decision-making conversation tool was used. SDM conversation tools can be useful for clinicians to better involve patients and ensure patients get involved equally regardless of educational background.

Introduction: Shared decision-making (SDM) about anticoagulant treatment in patients with atrial fibrillation (AF) is widely recommended but its effectiveness is unclear. We assessed the extent to which using an SDM tool promotes high-quality SDM. Hypothesis: SDM will improve communication quality, knowledge, and decisional conflict and will translate to improved medication adherence and outcomes. Methods: In this multicentre encounter-randomized trial, we included patients with nonvalvular AF considering starting/reviewing anticoagulation, and their clinicians. We compared usual care with or without ANTICOAGULATION CHOICE, an SDM conversation tool for use during the clinical encounter that presents individualized risk estimates and compares anticoagulation options across patient important issues. Results: We enrolled and video-recorded 922 patient-clinician encounters. So far, our data have shown that participants in both arms reported near-optimal communication quality, knowledge, and decisional conflict (JAMA IM-2020). Patients in intervention-arm estimated their stroke risks more accurately and were more involved in decision-making (JAMA IM-2020), clinicians were more satisfied (JAMA IM-2020), and cost conversation were more common (JAMA NO-2021). Use of the intervention had no effect on treatment decisions, encounter duration (JAMA IM-2020), treatment adherence, clinical outcomes (JAHA-epub) or patient-perceived sense of the care plan (PEC-epub). New analyses have shown that compared to eligible white participants, black participants more frequently did not enrol to the study. Enrolment of Black, indigenous, and people of colour benefited most form including and prioritizing practices most likely to care for them. We found no evidence of contamination (tool use, functional, or learned) between study arms. Conclusions: Use of an SDM tool improved some measures of SDM quality, without affecting treatment decisions and their sensibility, encounter duration, patient adherence, or clinical outcomes. Our results should calibrate expectations as to what could be accomplished by implementing SDM tools about anticoagulation in the care of patients with AF

Abstract Background/Aims In shared decision-making conversations, patients with or at risk of osteoporosis and their clinicians collaborate to decide which drug treatments fit best for each person. The Perceptions and Practicalities Approach (PAPA) specifies three core considerations of drug uptake and commitment: 1) patient’s perceived personal need for treatment (necessity beliefs). 2) patient concerns about the potential adverse consequences of drugs (e.g. side effects). 3) practical difficulties (e.g. limitations in capacity and resources). This study aims to explore how discussions about perceptions and practicalities of osteoporosis drug treatment arise and evolve in the context of clinical consultations about osteoporosis. Methods Secondary analysis of 38 video-recorded primary care consultations from a Mayo Clinic trial evaluating the Osteoporosis Choice Decision Aid. Video-recorded consultations included ‘usual care’ (n = 24) and decision aid (n = 14). Patient participants were aged 50 and over with osteopenia or osteoporosis not already receiving drug treatment. A quantitative coding scheme, informed by the PAPA, was developed to extract: i) how clinicians framed the ‘problem’ of osteoporosis, including: bone density results, fracture risk, physical impact, social impact, or psychological impact of fracture. ii) whether clinicians asked about the patients’ views of osteoporosis, patients own bone health, and the necessity, concerns, or practical issues associated with osteoporosis drugs. iii) whether clinicians personalised discussions about osteoporosis drugs to patients perceived: importance or need, side effects or harms, or practical issues. Two independent study team members dual extracted data, with a third member resolving discrepancies. Extracted data was summarised using descriptive statistics. Results Commonly, clinicians framed osteoporosis using the patient’s bone density results (n = 38,100%) and fracture risk (n = 30,79%). In contrast, few clinicians acknowledged the potential physical impacts of fracture (n = 5,13%), with no clinicians discussing the possible psychological or social impacts. Clinicians asked 24 questions to elicit patient views in 16 of the 38 encounters (42%, range 0-4 questions per consult). The 24 questions concerned patient views about osteoporosis (n = 1,4%), their bone health (n = 5,21%), or the necessity (n = 1,4%), concerns (n = 2,8%), or practical issues associated with osteoporosis drugs (n = 2,8%). Importantly, 14 consultations (37%) contained no questions to elicit patient views. Across the 38 consultations, clinicians personalised information about the patient’s need for osteoporosis drugs in 15 consultations (39%), with less personalisation about the side effects/harms (n = 10,26%), or practical issues (n = 6,16%) associated with osteoporosis drugs. Conclusion Clinical consultations rarely considered key personal determinants of osteoporosis drug uptake and commitment. Even when clinicians used an osteoporosis decision aid, few discussions specifically elicited or addressed patients’ personal treatment necessity beliefs, concerns and practical difficulties. Additionally, clinicians seldom acknowledged the potential physical and psychosocial impacts of osteoporosis. The findings demonstrate the need for multicomponent interventions, incorporating the PAPA-based approach, to facilitate shared decision-making that takes account of patient perceptions and practicalities. Disclosure A.W. Hawarden: Grants/research support; AH is funded by a Versus Arthritis Clinical Doctoral Fellowship (grant reference 227260). L. Bullock: None. M. León-García: Grants/research support; ML-G had financial support from the Spanish Health Institute Carlos III (ISCIII) co-funded by the European Regional Development Fund (Grant number: F18/00014).. S.A. Hartasanchez: None. I. Hargraves: None. R. Horne: None. A. Maraboto: None. M. Kunneman: Grants/research support; MK had financial support from the Dutch Research Council (NWO) and The Netherlands Organisation for Health Research and Development (ZonMw) (#016.196.138). C. Jinks: Grants/research support; CJ is part funded by NIHR Applied Research Collaboration (ARC) West Midlands. Z. Paskins: Grants/research support; ZP is funded by the National Institute for Health Research (NIHR) [Clinician Scientist Award (CS-2018-18-ST2-010)/NIHR Academy].

AbstractIntroductionFor too many people, their care plans are designed without fully accounting for who they are, the lives they live, what matters to them or what they aspire to achieve. We aimed to summarize instruments capable of measuring dimensions of patient–clinician collaboration to make care fit.MethodsWe systematically searched several databases (Medline, Embase, Cochrane, Scopus and Web of Science) from inception to September 2021 for studies using quantitative measures to assess, evaluate or rate the work of making care fit by any participant in real‐life clinical encounters. Eligibility was assessed in duplicate. After extracting all items from relevant instruments, we coded them deductively on dimensions relevant to making care fit (as presented in a recent Making Care Fit Manifesto), and inductively on the main action described.ResultsWe included 189 papers, mostly from North America (N = 83, 44%) and in the context of primary care (N = 54, 29%). Half of the papers (N = 88, 47%) were published in the last 5 years. We found 1243 relevant items to assess efforts of making care fit, included within 151 instruments. Most items related to the dimensions ‘Patient‐clinician collaboration: content’ (N = 396, 32%) and ‘Patient‐clinician collaboration: manner’ (N = 382, 31%) and the least related to ‘Ongoing and iterative process’ (N = 22, 2%) and in ‘Minimally disruptive of patient lives’ (N = 29, 2%). The items referred to 27 specific actions. Most items referred to ‘Informing’ (N = 308, 25%) and ‘Exploring’ (N = 93, 8%), the fewest items referred to ‘Following up’, ‘Comforting’ and ‘Praising’ (each N = 3, 0.2%).DiscussionMeasures of the work that patients and clinicians do together to make care fit focus heavily on the content of their collaborations, particularly on exchanging information. Other dimensions and actions previously identified as crucial to making care fit are assessed infrequently or not at all. The breadth of extant measures of making care fit and the lack of appropriate measures of this key construct limit both the assessment and the successful implementation of efforts to improve patient care.Patient ContributionPatients and caregivers from the ‘Making care fit Collaborative’ were involved in drafting the dimensions relevant to patient–clinician collaboration.