Diabetes is an increasingly common, long-term condition, requiring 24/7 self-care and constituting one of the greatest health challenges of our time. As with all ‘wicked problems’, a one-size-fits-all approach to care is doomed to fail. In 2020, we welcomed the first international consensus report on precision diabetes medicine, which included a section on patient-centred mental health and quality of life outcomes.1 This included the recommendation that, ‘in the setting of precision diabetes medicine, providers should assess symptoms of diabetes distress, depression, anxiety, disordered eating and cognitive capacities using appropriate standardized and validated tools at the initial visit, at periodic intervals and when there is a change in disease, treatment or life circumstance (..), information that, when combined with other data, are likely to improve the precision of clinical decision making’.1 In 2023, the Precision Medicine in Diabetes Initiative (PMDI) published the second international consensus report, on gaps and opportunities for the clinical translation of precision diabetes medicine.2 This report focused on results ‘from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2)’, to inform the translation of precision medicine research into practice.2 Regrettably, the second consensus omits any such recommendation or discussion of mental health issues. Furthermore, among the ‘key sources of heterogeneity in diabetes’, only ‘behaviour’ was included, while among the ‘pillars of precision medicine’, only ‘lifestyle interventions’ were included.2 The first consensus called for ‘a rigorous review elucidating effective precision medicine strategies, areas of promise and notable gaps across …[diabetes]… to inform an evidence-based road map to optimize the integration of precision medicine into the global response to the diabetes crisis’.1 Of the 15 new systematic reviews conducted to inform the second consensus report, none includes the psychosocial aspects of diabetes.1 Yet, there is a robust evidence base demonstrating the crucial role of psychosocial factors for people living with, or at risk of, diabetes; and this evidence has only strengthened since the first consensus. For example, a recent umbrella review of 25 systematic reviews of longitudinal studies concluded that common mental disorders, such as depression, anxiety disorders, sleep disorders and schizophrenia, are associated with increased risks for developing type 2 diabetes.3 Various psychotropic medications can increase weight, and people living with mental disorders often face additional challenges, such as high stress, lowered self-esteem, lack of energy, as well as socioeconomic disadvantage, all of which may compromise health and healthy behaviours, and need to be considered when managing risk for type 2 diabetes.3 Furthermore, in 2020, a special issue of Diabetic Medicine, commemorating the 25th anniversary of the PsychoSocial Aspects of Diabetes (PSAD) study group, included 14 commissioned reviews of behavioural, psychological and social aspects of diabetes.4 These included diabetes and depression,5 diabetes distress,6 fear of hypoglycaemia,7 disordered eating,8 and disordered sleep,9 other reviews focused on psychological factors related to the use of medications and diabetes technologies, motivation for self-care, importance of social support, the quality of the patient-clinician communication and the impact of diabetes and its management on quality of life.4 These reviews summarized the state-of-the-science regarding the inseparable role of psychology in diabetes, including several effective (and cost-effective) interventions based on psychological and behavioural science, none of which are mentioned in the second international consensus report.1 The systematic removal of essential psychosocial factors from a report focused on the ‘gaps and opportunities for the clinical translation’ of precision diabetes medicine, without any clarification, appears to be a step backwards, creating rather than recognising a gap. Given that approximately one in two people will experience mental health problems at some point in their life, and the crucial role that psychology plays in all self-management behaviours and clinician-patient communications, how can any of the four pillars—prevention, diagnosis, treatment or prognosis—be considered precise without recognizing these issues? The PMDI did not consider these omissions among the potential liabilities of a precision medicine approach. The PMDI statement is also out-of-step with other international consensus reports, which recognize the essential role of psychology in diabetes care, such as that published by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) focused on the management of type 1 diabetes in adults.10 Section 10 describes psychosocial care, providing an overview of psychological comorbidities that can have a negative impact on diabetes outcomes, and explaining how monitoring of these problems should be integrated in diabetes care.10 Consistent with the studies described above, the ADA/EASD consensus statement not only discusses depression, anxiety, anorexia nervosa, bulimia nervosa, binge eating and intentional insulin omission for weight loss, but also different forms of diabetes-specific emotional distress, such as feeling powerless and overwhelmed by the daily self-care demands, fear of hypoglycaemia, worries about complications, a lack of social support or feeling ‘policed’ by family, friends or co-workers.10 Moreover, the ADA/EASD consensus statement explains how validated questionnaires can be used to ‘flag’ these psychological problems that may require psychological support. It is also emphasized that ‘members of the team have a responsibility for providing psychosocial care as an integral component of diabetes care. Preferably, the diabetes care team should include a mental health professional (psychiatrist, psychologist and/or social worker) to advise the team and consult with people with diabetes in need of psychosocial support’.10 Effective psychological therapies are available, including (online) cognitive behavioural therapy (CBT), mindfulness and interpersonal therapies.10 Thus, it is our consensus that psychosocial factors not only affect risks for and the course of diabetes, but that mental health is as important a goal of precision diabetes medicine as physical health. Nearly every mental disorder has a higher prevalence among people with diabetes. Thus, we contend that precision diabetes medicine must also entail precision mental health care. We therefore encourage the PMDI to incorporate phenotypic psychosocial factors into the next revision of the international consensus report, and everyone to recognise that precision diabetes medicine must include precision mental health care.

The aim of this work was to examine the impact of hypoglycaemia on daily functioning among adults with type 1 diabetes or insulin-treated type 2 diabetes, using the novel Hypo-METRICS app. For 70 consecutive days, 594 adults (type 1 diabetes, n=274; type 2 diabetes, n=320) completed brief morning and evening Hypo-METRICS ‘check-ins’ about their experienced hypoglycaemia and daily functioning. Participants wore a blinded glucose sensor (i.e. data unavailable to the participants) for the study duration. Days and nights with or without person-reported hypoglycaemia (PRH) and/or sensor-detected hypoglycaemia (SDH) were compared using multilevel regression models. Participants submitted a mean ± SD of 86.3±12.5

Introduction: Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo-METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1 and insulin-treated type 2 diabetes in real-time using ecological momentary assessment (EMA). To help assess its utility, we aimed to determine Hypo-METRICS app completion rates and factors associated with completion. Methods: Adults with diabetes recruited into the Hypo-METRICS study were given validated patient-reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo-METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics. Results: In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45-66) years; diabetes duration 19 (11-27) years; HbA1c 57 (51-65) mmol/mol), median(IQR) overall app completion rate was 91 (84-96)%, ranging from 90 (81-96)%, 89 (80-94)% and 94(87-97)% for morning, afternoon and evening check-ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion. Discussion: High app completion across all app domains and participant characteristics indicates the Hypo-METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real-time.

Abstract Background Management of type 1 diabetes (T1D) requires the use of insulin, which can cause hypoglycaemia (low blood glucose levels). While most hypoglycaemic episodes can be self-treated, all episodes can be sudden, inconvenient, challenging to prevent or manage, unpleasant and/or cause unwanted attention or embarrassment. Severe hypoglycaemic episodes, requiring assistance from others for recovery, are rare but potentially dangerous. Repeated exposure to hypoglycaemia can reduce classic warning symptoms (‘awareness’), thereby increasing risk of severe episodes. Thus, fear of hypoglycaemia is common among adults with T1D and can have a negative impact on how they manage their diabetes, as well as on daily functioning, well-being and quality of life. While advances in glycaemic technologies and group-based psycho-educational programmes can reduce fear, frequency and impact of hypoglycaemia, they are not universally or freely available, nor do they fully resolve problematic hypoglycaemia or associated worries. This study aims to determine the effectiveness of a fully online, self-directed, scalable, psycho-educational intervention for reducing fear of hypoglycaemia: the Hypoglycaemia Prevention, Awareness of Symptoms, and Treatment (HypoPAST) programme. Methods A 24-week, two-arm, parallel-group, hybrid type 1 randomised controlled trial, conducted remotely (online and telephone). Australian adults (≥ 18 years) with self-reported T1D and fear of hypoglycaemia will be recruited, and allocated at random (1:1) to HypoPAST or control (usual care). The primary outcome is the between-group difference in fear of hypoglycaemia (assessed using HFS-II Worry score) at 24 weeks. A sample size of N = 196 is required to detect a 9-point difference, with 90% power and allowing for 30% attrition. Multiple secondary outcomes include self-reported psychological, behavioural, biomedical, health economic, and process evaluation data. Data will be collected at baseline, 12 and 24 weeks using online surveys, 2-week ecological momentary assessments, website analytics and semi-structured interviews. Discussion This study will provide evidence regarding the effectiveness, cost-effectiveness and acceptability of a novel, online psycho-educational programme: HypoPAST. Due to the fully online format, HypoPAST is expected to provide an inexpensive, convenient, accessible and scalable solution for reducing fear of hypoglycaemia among adults with T1D. Trial registration Australian and New Zealand Clinical Trials Registry (ANZCTR): ACTRN12623000894695 (21 August 2023).

Objective: Use of continuous glucose monitoring (CGM) has led to greater detection of hypoglycemia, the clinical significance of this is not fully understood. The HypoMETRICS study was designed to investigate the rates and duration of sensor-detected hypoglycemia (SDH) and their relationship with person-reported hypoglycemia (PRH) in people living with type 1 (T1D) and insulin-treated type 2 diabetes (T2D) with prior experience of hypoglycemia. Research Design and Methods: We recruited 276 participants with T1D and 321 with T2D who wore a blinded CGM and recorded PRH in the Hypo-METRICS app over 10-weeks. Rates of SDH <70mg/dl, SDH <54 mg/dl and PRH were expressed as median episodes/week. Episodes of SDH were matched to episodes of PRH that occurred within 1 hour. Results: Median [Interquartile range] rates of hypoglycemia were significantly higher in T1D vs. T2D; for SDH <70 mg/dl (6.5 [3.8-10.4] vs 2.1 [0.8-4.0]), SDH <54 mg/dl (1.2 [0.4-2.5] vs 0.2 [0.0-0.5]) and PRH (3.9 [2.4-5.9] vs 1.1 [0.5-2.0]). Overall, 65% of SDH <70 mg/dl was not associated with PRH, and 43% of PRH had no associated SDH. The median proportion of SDH associated with PRH in T1D was higher for SDH <70 mg/dl (40% vs 22%) and <54 mg/dl (47% vs 25%) than T2D. Conclusion: The novel findings that at least half of CGM hypoglycemia is asymptomatic, even below 54 mg/dl, and many reported symptomatic hypoglycemia episodes happen above 70 mg/dl. In the clinical and research setting these episodes cannot be used interchangeably, and both need to be recorded and addressed.

Introduction: This study examined associations between hypoglycemia awareness status and hypoglycemia symptoms reported in real-time using the novel Hypoglycaemia-MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone application (app) among adults with insulin-treated type 1 (T1D) or type 2 diabetes (T2D). Methods: Adults who experienced at least one hypoglycemic episode in the previous 3 months were recruited to the Hypo-METRICS study. They prospectively reported hypoglycemia episodes using the app for 10 weeks. Any of eight hypoglycemia symptoms were considered present if intensity was rated between "A little bit" to "Very much" and absent if rated "Not at all." Associations between hypoglycemia awareness (as defined by Gold score) and hypoglycemia symptoms were modeled using mixed-effects binary logistic regression, adjusting for glucose monitoring method and diabetes duration. Results: Of 531 participants (48% T1D, 52% T2D), 45% were women, 91% white, and 59% used Flash or continuous glucose monitoring. Impaired awareness of hypoglycemia (IAH) was associated with lower odds of reporting autonomic symptoms than normal awareness of hypoglycemia (NAH) (T1D odds ratio [OR] 0.43 [95% confidence interval {CI} 0.25-0.73], P = 0.002); T2D OR 0.51 [95% CI 0.26-0.99], P = 0.048), with no differences in neuroglycopenic symptoms. In T1D, relative to NAH, IAH was associated with higher odds of reporting autonomic symptoms at a glucose concentration <54 than >70 mg/dL (OR 2.18 [95% CI 1.21-3.94], P = 0.010). Conclusion: The Hypo-METRICS app is sensitive to differences in hypoglycemia symptoms according to hypoglycemia awareness in both diabetes types. Given its high ecological validity and low recall bias, the app may be a useful tool in research and clinical settings. The clinical trial registration number is NCT04304963.

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Aims: To assess and compare the psychometric properties and acceptability of four diabetes-specific quality of life (QoL) scales among adults with Type 2 diabetes (T2D). Methods: Adults (>= 18 years) with T2D living in the United Kingdom (n = 1465) or Australia (n = 248) completed a cross-sectional, online survey including the following: ADDQoL, DCP, DIDP and Diabetes QoL-Q (presented in randomised order), followed by rating scales to assess clarity, relevance, ease of completion, length and comprehensiveness of each scale. Demographic, clinical and psychosocial characteristics were collected. Acceptability (scale completeness and user ratings), response patterns, structure (exploratory and confirmatory factor analyses) and validity (convergent, confirmatory, divergent and known-groups) were examined. Data were analysed by country to assess cross-country reproducibility. Results: High completion rates (>= 89%) and positive user ratings were observed across scales indicating broad acceptability. The DIDP was the strongest performing scale: highest completion rate (97%), user ratings (>= 84% positive) and most satisfactory psychometric properties (highest variance explained, consistent factor loadings >0.5 on all items and most permissible model fit parameters). Scale-level floor effects may suggest domain omissions for the brief DIDP. Conclusions: The current study provides novel insights into the acceptability, validity and reliability of diabetes-specific QoL measures for adults with T2D. Consistent with the published Type 1 diabetes cohort findings, the DIDP is recommended as a brief, acceptable and psychometrically sound measure. However, selection needs to be considered in the context of the specific research or clinical aims and further evidence (e.g. responsiveness) may be required before it can be recommended for use in trials or prospective studies.

We aimed to compare weekly rates of sensor detected hypoglycemia (SDH), person reported hypoglycemia (PRH) and psychological outcomes in people with type 1 (T1D) and insulin treated type 2 diabetes (T2D) using routine continuous glucose monitoring (CGM) or capillary blood glucose monitoring (BGM) in the prospective observational Hypo-METRICS study. Participants (T1D/CBG=67, T1D/CGM=210, T2D/CBG=192, T2D/CGM=133, 55% men, median (IQR) age 56 (45-66) years) completed person reported outcomes measures, wore a blinded study CGM and reported PRH in real time on the Hypo-METRICS app for 10 weeks. We used the Wilcoxon rank sum test. Median weekly PRH rates were higher in CGM vs BGM users in both T1D (4.5 (2.4-6.3) vs 2.6 (1-3.9) Z=-5.1, p<0.005) and T2D (1.7 (0.6-2.3) vs 1.0 (0.2-1.2), Z=-5.8 p<0.005). There were no differences in SDH rates at 70 or 54mg/dl for CGM or BGM users with T1D or T2D (Table 1). Diabetes distress (Problem Areas In Diabetes) was higher in CGM vs BGM users with T1D (25 (10-36) vs 19 (5-26), Z=-3.2, p=0.008), but not T2D. There were no significant differences between CGM and BGM users in Hypoglycaemia Fear Survey Behaviour or Worry subscales, depression (PHQ-9) or anxiety (GAD-7) in either group. Despite similar SDH rates, T1D CGM users have higher PRH and diabetes distress scores; this may reflect populations with greater access to CGM. Further research is needed to investigate these findings. Disclosure N.Zaremba: None. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. E.Renard: Consultant; Abbott Diabetes, Dexcom, Inc., AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, Insulet Corporation, MannKind Corporation, Novo Nordisk, Sanofi, Roche Diabetes Care. S.R.Heller: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk, Medtronic. J.Speight: Research Support; Sanofi, Medtronic, Abbott Diabetes, Lilly, Novo Nordisk A/S, Speaker's Bureau; Sanofi. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. P.Divilly: None. G.Martine-edith: Other Relationship; Novo Nordisk A/S. Z.Mahmoudi: Employee; Novo Nordisk. U.Soeholm: Employee; Novo Nordisk A/S. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

Introduction The aim of this study was to determine the acceptability and psychometric properties of the Hypo-METRICS (Hypoglycemia MEasurement, ThResholds and ImpaCtS) application (app): a novel tool designed to assess the direct impact of symptomatic and asymptomatic hypoglycemia on daily functioning in people with insulin-treated diabetes. Materials and methods 100 adults with type 1 diabetes mellitus (T1DM, n = 64) or insulin-treated type 2 diabetes mellitus (T2DM, n = 36) completed three daily ‘check-ins’ (morning, afternoon and evening) via the Hypo-METRICs app across 10 weeks, to respond to 29 unique questions about their subjective daily functioning. Questions addressed sleep quality, energy level, mood, affect, cognitive functioning, fear of hypoglycemia and hyperglycemia, social functioning, and work/productivity. Completion rates, structural validity, internal consistency, and test-retest reliability were explored. App responses were correlated with validated person-reported outcome measures to investigate convergent (r s >±0.3) and divergent (r s <±0.3) validity. Results Participants’ mean±SD age was 54±16 years, diabetes duration was 23±13 years, and most recent HbA1c was 56.6±9.8 mmol/mol. Participants submitted mean±SD 191±16 out of 210 possible ‘check-ins’ (91%). Structural validity was confirmed with multi-level confirmatory factor analysis showing good model fit on the adjusted model (Comparative Fit Index >0.95, Root-Mean-Square Error of Approximation <0.06, Standardized Root-Mean-square Residual<0.08). Scales had satisfactory internal consistency (all ω≥0.5), and high test-retest reliability (r s ≥0.7). Convergent and divergent validity were demonstrated for most scales. Conclusion High completion rates and satisfactory psychometric properties demonstrated that the Hypo-METRICS app is acceptable to adults with T1DM and T2DM, and a reliable and valid tool to explore the daily impact of hypoglycemia.

Consensus defines nocturnal hypoglycemia as occurring between 00:00 and 06:00 hrs. However, this may introduce bias, as for many, these hours only partially match real sleeping hours. Therefore, we compared rates of hypoglycemia while asleep (defined by Fitbit Charge 4) to rates of conventionally-defined nocturnal hypoglycemia. Hypo-METRICS study participants wore a Fitbit charge 4 and blinded FreeStyle Libre glucose monitor for up to 70 days. Every hypoglycemia episode (≤70mg/dL or ≤54mg/dL for ≥15 min) was classified as ‘asleep’ or ‘awake’ based on Fitbit sleep intervals and ‘nocturnal’ (00:00-06:00 hrs) or ‘day-time’. We compared weekly sensor-detected hypoglycemia (SDH) rates whilst asleep to nocturnal SDH rates using a paired sample Wilcoxon test. We included 542 participants (269 with type 1 diabetes, median (IQR) age 56 (44-66) years, HbA1c 7.3%(6.7-7.9), with 69 (65-70) nights with sleep data/participant). Sleep duration was 6.1 (5.2-6.8) hours. Median weekly rates of SDH while asleep were higher than rates of nocturnal SDH both ≤70mg/dL (1.3 (0.6-2.5) vs 0.9 (0.4-1.8), p<0.001) and ≤54mg/dL (0.4 (0.2-0.9) vs 0.3 (0.1-0.7), p<0.001). Stratifying by type of diabetes did not change the direction or magnitude of the differences in SDH rates. Using clock time to estimate nocturnal hypoglycemia underestimates hypoglycemia whilst asleep by up to a third. Using activity trackers such as Fitbit to identify sleep offers a novel way to estimate hypoglycemia whilst asleep to better reflect lived experience of nocturnal hypoglycemia. Disclosure G.Martine-edith: Other Relationship; Novo Nordisk A/S. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. E.Renard: Consultant; Abbott Diabetes, Dexcom, Inc., AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, Insulet Corporation, MannKind Corporation, Novo Nordisk, Sanofi, Roche Diabetes Care. S.R.Heller: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk, Medtronic. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. Hypo-resolve consortium: n/a. P.Divilly: None. N.Zaremba: None. U.Soeholm: Employee; Novo Nordisk A/S. A.Kingsnorth: None. Z.Mahmoudi: Employee; Novo Nordisk. M.Gomes: Employee; Novo Nordisk A/S. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

The multicentre prospective observational Hypo-METRICS study assessed rates of sensor detected hypoglycemia (SDH) and person reported hypoglycemia-PRH (symptoms resolved by carbohydrates or measured glucose <72 mg/dl), in people with type 1 (T1D) and insulin treated type 2 (T2D) diabetes. We report overall rates of hypoglycemia and the proportion and rates of undetected SDH. All 602 participants (277 T1D vs 325 T2D) had ≥1 PRH in the 3 months prior to the study. They wore a blinded continuous glucose monitor and recorded PRH on the Hypo-METRICS smartphone app for 10 weeks. SDH was defined by ATTD consensus. PRH without SDH ± 1 hour was consider undetected. The median (IQR) weekly rate of PRH was 3.4 (1.9-5.3) vs 0.8 (0.3-1.7) episodes/week in T1D vs T2D respectively (p<0.01), SDH <70mg/dl was 6.7 (3.9-10.8) vs 2.1 (0.9-4.3) episodes/week respectively (p<0.01) and SDH <54mg/dl was 1.3 (0.5-2.8) vs 0.3 (0-0.6) episodes/weeks respectively (p<0.01). The proportion of undetected episodes at SDH <70mg/dl was higher in T2D, 68 vs 78% (p<0.01) and at SDH <54mg/dl, 62 vs 82% (p<0.01). The weekly rates of undetected episodes were higher in T1D for SDH <70mg/dl (4.6 vs 1.8 episodes/week, p<0.01) and for SDH <54mg/dl (0.8 vs 0.2 episodes /week, p<0.01). Baseline characteristics are in table 1. Most sensor hypoglycemia goes undetected by people with diabetes. Further work will assess the impact of undetected episodes. Disclosure P.Divilly: None. S.R.Heller: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk, Medtronic. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. Hypo-resolve consortium: n/a. G.Martine-edith: Other Relationship; Novo Nordisk A/S. Z.Mahmoudi: Employee; Novo Nordisk. N.Zaremba: None. U.Soeholm: Employee; Novo Nordisk A/S. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. E.Renard: Consultant; Abbott Diabetes, Dexcom, Inc., AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, Insulet Corporation, MannKind Corporation, Novo Nordisk, Sanofi, Roche Diabetes Care. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

Many episodes of hypoglycemia defined by the ATTD consensus are asymptomatic. We investigated if optimizing the glucose threshold and duration for sensor detected hypoglycemia (SDH) improves precision and sensitivity for identifying symptomatic hypoglycaemia from continuous glucose monitoring (CGM) data. We analysed 10 weeks of blinded CGM (Libre 2) and FitBit data from 435 participants [217 type 1, 218 type 2] with intact hypoglycemic awareness (Gold score <4). They self-reported symptomatic hypoglycemia on the Hypo-METRICS smartphone app. We used particle Markov chain Monte Carlo optimisation to generate the threshold and duration of SDH that maximizes detection of symptomatic hypoglycemia across our population and assessed impact of sleep status and diabetes type. The optimized threshold and duration were 70mg/dl for 27 min, with 2% gain of precision but 8% loss of sensitivity vs level 1 SDH (70mg/dl >15 min). Both level 1 SDH and our optimized definition had 3-fold increased sensitivity vs level 2 SDH (54mg/dl >15 min), with minimal loss of precision (Table). Our data validate ATTD level 1 hypoglycemia. Overall precision for detection of symptomatic hypoglycemia remains low despite optimization. Disclosure P.Divilly: None. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. Hypo-resolve consortium: n/a. Z.Mahmoudi: Employee; Novo Nordisk. G.Martine-edith: Other Relationship; Novo Nordisk A/S. D.Boiroux: Employee; Novo Nordisk A/S. N.Zaremba: None. U.Soeholm: Employee; Novo Nordisk A/S. M.Gomes: Employee; Novo Nordisk A/S. A.A.Vaag: None. B.E.De galan: Research Support; Novo Nordisk. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

People with type 1 diabetes have a higher risk for cardiovascular disease (CVD). Reduced heart rate variability (HRV) is a clinical marker for CVD. In this observational study using continuous HRV measurement across 26 days, we investigated whether psychological stressors (diabetes distress, depressive symptoms) and glycaemic parameters (hypo‐ and hyperglycaemic exposure, glycaemic variability and HbA1c) are associated with lower HRV in people with type 1 diabetes.

AIMS:To determine the frequency, severity, burden, and utility of hypoglycaemia symptoms among adults with type 1 diabetes (T1D) and impaired awareness of hypoglycaemia (IAH) at baseline and week 24 following the HypoCOMPaSS awareness restoration intervention. METHODS:Adults (N = 96) with T1D (duration: 29 ± 12 years; 64% women) and IAH completed the Hypoglycaemia Burden Questionnaire (HypoB-Q), assessing experience of 20 pre-specified hypoglycaemia symptoms, at baseline and week 24. RESULTS:At baseline, 93 (97%) participants experienced at least one symptom (mean ± SD 10.6 ± 4.6 symptoms). The proportion recognising each specific symptom ranged from 15% to 83%. At 24 weeks, symptom severity and burden appear reduced, and utility increased. CONCLUSIONS:Adults with T1D and IAH experience a range of hypoglycaemia symptoms. Perceptions of symptom burden or utility are malleable. Although larger scale studies are needed to confirm, these findings suggest that changing the salience of the symptomatic response may be more important in recovering protection from hypoglycaemia through regained awareness than intensifying symptom frequency or severity.

BACKGROUND:The Hypoglycaemia - MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone app was developed to investigate the impact of hypoglycemia on daily functioning in adults with type 1 diabetes mellitus or insulin-treated type 2 diabetes mellitus. The app uses ecological momentary assessments, thereby minimizing recall bias and maximizing ecological validity. It was used in the Hypo-METRICS study, a European multicenter observational study wherein participants wore a blinded continuous glucose monitoring device and completed the app assessments 3 times daily for 70 days. OBJECTIVE:The 3 aims of the study were to explore the content validity of the app, the acceptability and feasibility of using the app for the duration of the Hypo-METRICS study, and suggestions for future versions of the app. METHODS:Participants who had completed the 70-day Hypo-METRICS study in the United Kingdom were invited to participate in a brief web-based survey and an interview (approximately 1h) to explore their experiences with the app during the Hypo-METRICS study. Thematic analysis of the qualitative data was conducted using both deductive and inductive methods. RESULTS:A total of 18 adults with diabetes (type 1 diabetes: n=10, 56%; 5/10, 50% female; mean age 47, SD 16 years; type 2 diabetes: n=8, 44%; 2/8, 25% female; mean age 61, SD 9 years) filled out the survey and were interviewed. In exploring content validity, participants overall described the Hypo-METRICS app as relevant, understandable, and comprehensive. In total, 3 themes were derived: hypoglycemia symptoms and experiences are idiosyncratic; it was easy to select ratings on the app, but day-to-day changes were perceived as minimal; and instructions could be improved. Participants offered suggestions for changes or additional questions and functions that could increase engagement and improve content (such as providing more examples with the questions). In exploring acceptability and feasibility, 5 themes were derived: helping science and people with diabetes; easy to fit in, but more flexibility wanted; hypoglycemia delaying responses and increasing completion time; design, functionality, and customizability of the app; and limited change in awareness of symptoms and impact. Participants described using the app as a positive experience overall and as having a possible, although limited, intervention effect in terms of both hypoglycemia awareness and personal impact. CONCLUSIONS:The Hypo-METRICS app shows promise as a new research tool to assess the impact of hypoglycemia on an individual's daily functioning. Despite suggested improvements, participants' responses indicated that the app has satisfactory content validity, overall fits in with everyday life, and is suitable for a 10-week research study. Although developed for research purposes, real-time assessments may have clinical value for monitoring and reviewing hypoglycemia symptom awareness and personal impact.

Current definitions of sensor detected hypoglycemia (SDH) give high levels of asymptomatic hypoglycemia on continuous glucose monitoring (CGM). We defined individually optimised thresholds and durations for sensor detected hypoglycemia (SDH) that better identify symptomatic hypoglycemia experienced by people with insulin treated diabetes. We analysed 10 weeks of blinded CGM (Libre 2) and FitBit data from 435 participants [217 type 1, 218 type 2]. They self reported symptomatic hypoglycemia on the Hypo-METRICS smartphone app. We used particle Markov chain Monte Carlo optimization to generate the threshold and duration of SDH that maximizes detection of symptomatic hypoglycemia for each individual (and by Fitbit sleep status). When using individual definitions of SDH, precision for detection of symptomatic hypoglycemia increased by 23% with 2% loss of sensitivity vs the consensus definition of Level 1 SDH (70mg/dl > 15 mins). Precision and sensitivity increased by 41% and 20% respectively vs level 2 SDH (54 mg/dl > 15 mins). Increased precision, with minimal change of sensitivity, were greatest during sleep, and irrespective of diabetes types and sleep status (Table). Optimizing SDH definitions at the individual level and by sleep status could minimize false alarms without increasing missed clinically important hypoglycemia episodes. Disclosure Z.Mahmoudi: Employee; Novo Nordisk. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. E.Renard: Consultant; Abbott Diabetes, Dexcom, Inc., AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, Insulet Corporation, MannKind Corporation, Novo Nordisk, Sanofi, Roche Diabetes Care. S.R.Heller: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk, Medtronic. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. Hypo-resolve consortium: n/a. P.Divilly: None. G.Martine-edith: Other Relationship; Novo Nordisk A/S. D.Boiroux: Employee; Novo Nordisk A/S. N.Zaremba: None. U.Soeholm: Employee; Novo Nordisk A/S. M.Gomes: Employee; Novo Nordisk A/S. A.A.Vaag: None. J.Speight: Research Support; Sanofi, Medtronic, Abbott Diabetes, Lilly, Novo Nordisk A/S, Speaker's Bureau; Sanofi. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

IntroductionHypoglycaemia is a frequent adverse event and major barrier for achieving optimal blood glucose levels in people with type 1 or type 2 diabetes using insulin. The Hypo-RESOLVE (Hypoglycaemia—Redefining SOLutions for better liVEs) consortium aims to further our understanding of the day-to-day impact of hypoglycaemia. The Hypo-METRICS (Hypoglycaemia—MEasurement, ThResholds and ImpaCtS) application (app) is a novel app for smartphones. This app is developed as part of the Hypo-RESOLVE project, using ecological momentary assessment methods that will minimise recall bias and allow for robust investigation of the day-to-day impact of hypoglycaemia. In this paper, the development and planned psychometric analyses of the app are described.Methods and analysisThe three phases of development of the Hypo-METRICS app are: (1) establish a working group—comprising diabetologists, psychologists and people with diabetes—to define the problem and identify relevant areas of daily functioning; (2) develop app items, with user-testing, and implement into the app platform; and (3) plan a large-scale, multicountry study including interviews with users and psychometric validation. The app includes 7 modules (29 unique items) assessing: self-report of hypoglycaemic episodes (during the day and night, respectively), sleep quality, well-being/cognitive function, social interactions, fear of hypoglycaemia/hyperglycaemia and work/productivity. The app is designed for use within three fixed time intervals per day (morning, afternoon and evening). The first version was released mid-2020 for use (in conjunction with continuous glucose monitoring and activity tracking) in the Hypo-METRICS study; an international observational longitudinal study. As part of this study, semistructured user-experience interviews and psychometric analyses will be conducted.Ethics and disseminationUse of the novel Hypo-METRICS app in a multicountry clinical study has received ethical approval in each of the five countries involved (Oxford B Research Ethics Committee, CMO Region Arnhem-Nijmegen, Ethikkommission der Medizinischen Universität Graz, Videnskabsetisk Komite for Region Hovedstaden and the Comite Die Protection Des Personnes SUD Mediterranne IV). The results from the study will be published in peer review journals and presented at national and international conferences.Trial registration numberNCT04304963.