Lipid levels in a cohort of healthy Danish schoolchildren ages 5 to 17 years.

It is internationally recognized to use clinical decision limits (CDL) when interpreting the lipid levels in both adults and children, even though the evidence for children is scarce. The purpose of this study is to describe how lipid levels progress in healthy Danish children ages 5 to 17 years. This study is based on the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-study DK) consisting of 1456 observations of schoolchildren aged 5 to 17 years. Participants have been tested for blood levels of total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and remnant cholesterol levels are calculated. Finally, sex-specific percentile reference curves are presented. Percentile reference curves stratified by sex were generated for all cholesterols and showed that the total cholesterol level peaks at 4.32 mmol/l in 10-year-old boys and 4.46 mmol/l in nine-year-old girls. HDL levels in boys peak at 1.72 mmol/l in nine-year-old boys. HDL levels in girls and LDL levels in both sexes are nearly constant. Triglycerides kept rising to the age of 17 years in both sexes and remnant cholesterol decreased from age 5 to 17 years in both sexes. BMI z-score adjustment revealed no significant association with total cholesterol in both sexes but a significant association between HDL, LDL, triglycerides, and remnant cholesterol. This study is the first to generate percentile reference curves for blood levels of total cholesterol, LDL, HDL, triglycerides, and remnant cholesterol in a cohort of healthy Danish children aged 5 to 17 years.

Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study.

STUDY OBJECTIVES: Nighttime smartphone use is an increasing public health concern. We investigated whether nighttime smartphone use is associated with general health and primary healthcare utilization. METHODS: Four thousand five hundred and twenty individuals (age 35.6 ±â€…9.7 years, 35% male) provided self-reported information on smartphone use frequency, symptoms of depression, and general health (one-item perceived health and cross-symptom composite score). A subset of the study sample (n = 3221) tracked their nighttime smartphone use. Primary healthcare utilization, i.e. the number of weeks in which at least one service from the patient's general practitioner (GP) was billed in 2020, was extracted from Danish population registries. Statistical analysis comprised logistic and multiple linear regression, controlling for sociodemographics. RESULTS: Three hundred and nineteen individuals (7%) reported using their smartphone almost every night or more. More frequent self-reported nighttime smartphone use was associated with poor general health across all measures. Using the smartphone almost every night or more was associated with 2.8 [95% CI: 1.9, 4.1] fold higher odds of reporting poor health and with an average of 1.4 [95% CI: 0.7, 2.1] additional GP utilizations per year compared to no use. Associations were also found for the cross-symptom composite score across all symptoms. Further adjustment for symptoms of depression attenuated some associations. Smartphone use towards the end of the sleep period (sleep-offset use) was associated with poorer self-reported general health, but not with healthcare utilization. CONCLUSIONS: Nighttime smartphone use frequency is associated with poor general health and healthcare utilization. Further studies should investigate the underlying causal structure and nighttime smartphone use as a transdiagnostic intervention target.

Self-reported and tracked nighttime smartphone use and their association with overweight and cardiometabolic risk markers.

Nighttime smartphone use is associated with sleep problems, which in turn have a bidirectional association with overweight. We aim to investigate whether nighttime smartphone use and sleep are related to overweight and metabolic dysfunction in adult populations. We used data from three population samples (aged 16-89) from the SmartSleep Study, which included survey data (N = 29,838), high-resolution tracking data (N = 3446), follow-up data (N = 1768), and cardiometabolic risk markers (N = 242). Frequent self-reported nighttime smartphone use was associated with 51% higher odds (95% CI: 1.32; 1.70) of overweight compared with no use. Tracked nighttime smartphone use was also associated with overweight. Similar results were found for obesity as an outcome. No consistent associations were found between nighttime smartphone use and cardiometabolic risk markers in a small subsample of healthy young women. Poor sleep quality (vs. good sleep quality) was associated with overweight (OR = 1.19, 85% CI: 1.10; 1.28). Overall, frequent nighttime smartphone use was consistently associated with overweight and a higher BMI across diverse population samples. The bidirectional interplay between nighttime smartphone use, sleep, and overweight may create a vicious circle of metabolic dysfunction over time. Therefore, nighttime smartphone use may be a potential target point for public health interventions to reduce overweight at the population level.

Application of life course trajectory methods to public health data: A comparison of sequence analysis and group-based multi-trajectory modeling for modelling childhood adversity trajectories.

There is increasing awareness of the importance of modelling life course trajectories to unravel how social, economic and health factors relate to health over time. Different methods have been developed and applied in public health to classify individuals into groups based on characteristics of their life course. However, the application and results of different methods are rarely compared. We compared the application and results of two methods to classify life course trajectories of individuals, i.e. sequence analysis and group-based multi-trajectory modeling (GBTM), using public health data. We used high-resolution Danish nationwide register data on 926,160 individuals born between 1987 and 2001, including information on the yearly occurrence of 7 childhood adversities in 2 dimensions (i.e. family poverty and family dynamics). We constructed childhood adversity trajectories from 0 to 15 years by applying (1) sequence analysis using optimal matching and cluster analysis using Ward's method and (2) GBTM using logistic and zero-inflated Poisson regressions. We identified 2 to 8 cluster solutions using both methods and determined the optimal solution for both methods. Both methods generated a low adversity, a poverty, and a consistent or high adversity cluster. The 5-cluster solution using sequence analysis additionally included a household psychiatric illness and a late adversity cluster. The 4-group solution using GBTM additionally included a moderate adversity cluster. Compared with the solution obtained through sequence analysis, the solution obtained through GBTM contained fewer individuals in the low adversity cluster and more in the other clusters. We find that the two methods generate qualitatively similar solutions, but the quantitative distributions of children over the groups are different. The method of choice depends on the type of data available and the research question of interest. We provide a comprehensive overview of important considerations and benefits and drawbacks of both methods.

Cohort profile: The SmartSleep Study, Denmark, combining evidence from survey, clinical and tracking data.

PURPOSE: The SmartSleep Study is established to comprehensively assess the impact of night-time smartphone use on sleep patterns and health. An innovative combination of large-scale repeated survey information, high-resolution sensor-driven smartphone data, in-depth clinical examination and registry linkage allows for detailed investigations into multisystem physiological dysregulation and long-term health consequences associated with night-time smartphone use and sleep impairment. PARTICIPANTS: The SmartSleep Study consists of three interconnected data samples, which combined include 30 673 individuals with information on smartphone use, sleep and health. Subsamples of the study population also include high-resolution tracking data (n=5927) collected via a customised app and deep clinical phenotypical data (n=245). A total of 7208 participants are followed in nationwide health registries with full data coverage and long-term follow-up. FINDINGS TO DATE: We highlight previous findings on the relation between smartphone use and sleep in the SmartSleep Study, and we evaluate the interventional potential of the citizen science approach used in one of the data samples. We also present new results from an analysis in which we use 803 000 data points from the high-resolution tracking data to identify clusters of temporal trajectories of night-time smartphone use that characterise distinct use patterns. Based on these objective tracking data, we characterise four clusters of night-time smartphone use. FUTURE PLANS: The unprecedented size and coverage of the SmartSleep Study allow for a comprehensive documentation of smartphone activity during the entire sleep span. The study has been expanded by linkage to nationwide registers, which allow for further investigations into the long-term health and social consequences of night-time smartphone use. We also plan new rounds of data collection in the coming years.

Nighttime smartphone use, sleep quality, and mental health: investigating a complex relationship.

STUDY OBJECTIVES: This study investigated the complex relationship between nighttime smartphone use, sleep, and mental health among adult populations in Denmark. METHODS: Data from three interconnected samples (aged 16-89 years) from the SmartSleep Study included 5798 individuals with survey and register data; 4239 individuals also provided high-resolution smartphone tracking data. Logistic regression models and causal discovery algorithms, which suggest possible causal pathways consistent with the underlying data structure, were used to infer the relationship between self-reported and tracked nighttime smartphone use, self-reported sleep quality, mental health indicators, and register-based psychotropic medication use. RESULTS: Frequent self-reported nighttime smartphone use was associated with high perceived stress (OR: 2.24, 95% CI = 1.42 to 3.55) and severe depressive symptoms (OR: 2.96, 95% CI = 2.04 to 4.28). We found no clear associations between tracked nighttime smartphone use and mental health outcomes, except for the cluster that used their smartphones repeatedly during the sleep period, which was associated with severe depressive symptoms (OR = 1.69, 95% CI = 1.24 to 2.31). Poor sleep quality (vs. good sleep quality) was associated with high perceived stress (OR = 5.07, 95% CI = 3.72 to 6.90), severe depressive symptoms (OR = 9.67, 95% CI = 7.09 to 13.19), and psychotropic medication use (OR = 2.13, 95% CI = 1.36 to 3.35). The causal discovery models suggest that nighttime smartphone use affects mental health through both problematic smartphone use and poor sleep quality. CONCLUSION: The complex relationship between nighttime smartphone use, sleep, and poor mental health may create a vicious circle over time, and nighttime smartphone use may constitute a potential leverage point for public health interventions aimed at improving sleep and mental health.

A Novel Approach to Predicting Early Pregnancy Outcomes Dynamically in a Prospective Cohort Using Repeated Ultrasound and Serum Biomarkers.

This study aimed to develop a dynamic model for predicting outcome during the first trimester of pregnancy using baseline demographic data and serially collected blood samples and transvaginal sonographies. A prospective cohort of 203 unselected women with an assumed healthy pregnancy of < 8 weeks' gestation was followed fortnightly from 4-14 weeks' gestation until either miscarriage or confirmed first trimester viability. The main outcome was development of a model to predict outcome from gestational age-dependent hazard ratios using both baseline and updated serial data from each visit. Secondary outcomes were descriptions of risk factors for miscarriage. The results showed that 18% of the women experienced miscarriages. A fetal heart rate detected before 8 weeks' gestation indicated a 90% (95% CI 85-95%) chance of subsequent delivery. Maternal age (≥ 35 years), insufficient crown-rump-length (CRL) and mean gestational sac diameter (MSD) development, and presence of bleeding increased the risk of miscarriage. Serum biomarkers, including hCG, progesterone, and estradiol, were found to impact the risk of miscarriage with estradiol as the most important. The best model to predict miscarriage was a combination of maternal age, vaginal bleeding, CRL, and hCG. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. This study suggests that combining maternal age, and evolving data from hCG, estradiol, CRL, and bleeding could be used to predict fetal outcome during the first trimester of pregnancy.Trial registration ClinicalTrials.gov identifier: NCT02761772.

Night-time smartphone use, sleep duration, sleep quality, and menstrual disturbances in young adult women: A population-based study with high-resolution tracking data.

Study Objectives: To explore the relationship among night-time smartphone use, sleep duration, sleep quality, and menstrual disturbances in young adult women. Methods: Women aged 18-40 years were included in the SmartSleep Study in which they objectively tracked their smartphone use via the SmartSleep app between self-reported sleep onset and offset times (n = 764) and responded to a survey (n = 1068), which included background characteristics, sleep duration, sleep quality (Karolinska Sleep Questionnaire), and menstrual characteristics (International Federation of Gynecology and Obstetrics' definitions). Results: The median tracking time was four nights (interquartile range: 2-8). Higher frequency (p = .05) and longer duration (p = .02) of night-time smartphone use were associated with long sleep duration (≥9 h), but not with poor sleep quality or short sleep duration (<7 h). Short sleep duration was associated with menstrual disturbances (OR = 1.84, 95% confidence interval [CI] = 1.09 to 3.04) and irregular menstruation (OR = 2.17, 95% CI = 1.08 to 4.10), and poor sleep quality was associated with menstrual disturbances (OR = 1.43, 95% CI = 1.19 to 1.71), irregular menstruation (OR = 1.34, 95% CI = 1.04 to 1.72), prolonged bleedings (OR = 2.50, 95% CI = 1.44 to 4.43) and short-cycle duration (OR = 1.40, 95% CI = 1.06 to 1.84). Neither duration nor frequency of night-time smartphone use was associated with menstrual disturbances. Conclusions: Night-time smartphone use was associated with longer sleep duration, but not with menstrual disturbances in adult women. Short sleep duration and sleep quality were associated with menstrual disturbances. Further investigation of the effects of night-time smartphone use on sleep and female reproductive function in large prospective studies is needed.

Effects of Two Physical Activity Interventions on Sleep and Sedentary Time in Pregnant Women.

Pregnancy is often associated with poor sleep and high sedentary time (SED). We investigated the effect of physical activity (PA) interventions on sleep and SED in pregnant women. A secondary analysis of a randomized controlled trial (n = 219) explored the effect of structured supervised exercise training (EXE) or motivational counseling on PA (MOT) compared to standard prenatal care (CON) on sleep and SED during pregnancy. Three times during pregnancy, sleep was determined by the Pittsburgh Sleep Quality Index (PSQI) and SED by the Pregnancy Physical Activity Questionnaire (PPAQ). Also, a wrist-worn consumer activity tracker measured sleep and SED continuously. Data from the activity tracker confirmed that sleep time decreases, and SED increases by approx. 30 and 24 min/day, respectively, from baseline (maximum gestational age (GA) week 15) to delivery. Compared to CON, the global PSQI score was better for EXE in GA week 28 (-0.8 [-1.5; -0.1], p = 0.031) and for both EXE and MOT in GA week 34 (-1 [-2; -0.5], p = 0.002; -1 [-2; -0.1], p = 0.026). In GA week 28, SED (h/day) from PPAQ was lower in EXE compared to both CON and MOT (-0.69 [-1; -0.0], p = 0.049; -0.6 [-1.0; -0.02], p = 0.042). In conclusion, PA interventions during pregnancy improved sleep quality and reduced SED.

Having a ball: evaluating scoring streaks and game excitement using in-match trend estimation.

Many popular sports involve matches between two teams or players where each team have the possibility of scoring points throughout the match. While the overall match winner and result is interesting, it conveys little information about the underlying scoring trends throughout the match. Modeling approaches that accommodate a finer granularity of the score difference throughout the match is needed to evaluate in-game strategies, discuss scoring streaks, teams strengths, and other aspects of the game. We propose a latent Gaussian process to model the score difference between two teams and introduce the Trend Direction Index as an easily interpretable probabilistic measure of the current trend in the match as well as a measure of post-game trend evaluation. In addition we propose the Excitement Trend Index-the expected number of monotonicity changes in the running score difference-as a measure of overall game excitement. Our proposed methodology is applied to all 1143 matches from the 2019-2020 National Basketball Association season. We show how the trends can be interpreted in individual games and how the excitement score can be used to cluster teams according to how exciting they are to watch. Supplementary Information: The online version contains supplementary material available at 10.1007/s10182-022-00452-w.

Effects of Structured Supervised Exercise Training or Motivational Counseling on Pregnant Women's Physical Activity Level: FitMum - Randomized Controlled Trial.

BACKGROUND: Physical activity (PA) during pregnancy is an effective and safe way to improve maternal health in uncomplicated pregnancies. However, compliance with PA recommendations remains low among pregnant women. OBJECTIVE: The purpose of this study was to evaluate the effects of offering structured supervised exercise training (EXE) or motivational counseling on PA (MOT) during pregnancy on moderate-to-vigorous intensity physical activity (MVPA) level. Additionally, complementary measures of PA using the Pregnancy Physical Activity Questionnaire (PPAQ) and gold standard doubly labeled water (DLW) technique were investigated. The hypotheses were that both EXE and MOT would increase MVPA in pregnancy compared with standard care (CON) and that EXE would be more effective than MOT. In addition, the association between MVPA and the number of sessions attended was explored. METHODS: A randomized controlled trial included 220 healthy, inactive pregnant women with a median gestational age of 12.9 (IQR 9.4-13.9) weeks. A total of 219 women were randomized to CON (45/219), EXE (87/219), or MOT (87/219). The primary outcome was MVPA (minutes per week) from randomization to the 29th gestational week obtained by a wrist-worn commercial activity tracker (Vivosport, Garmin International). PA was measured by the activity tracker throughout pregnancy, PPAQ, and DLW. The primary outcome analysis was performed as an analysis of covariance model adjusting for baseline PA. RESULTS: The average MVPA (minutes per week) from randomization to the 29th gestational week was 33 (95% CI 18 to 47) in CON, 50 (95% CI 39 to 60) in EXE, and 40 (95% CI 30 to 51) in MOT. When adjusted for baseline MVPA, participants in EXE performed 20 (95% CI 4 to 36) minutes per week more MVPA than participants in CON (P=.02). MOT was not more effective than CON; EXE and MOT also did not differ. MVPA was positively associated with the number of exercise sessions attended in EXE from randomization to delivery (P=.04). Attendance was higher for online (due to COVID-19 restrictions) compared with physical exercise training (P=.03). Adverse events and serious adverse events did not differ between groups. CONCLUSIONS: Offering EXE was more effective than CON to increase MVPA among pregnant women, whereas offering MOT was not. MVPA in the intervention groups did not reach the recommended level in pregnancy. Changing the intervention to online due to COVID-19 restrictions did not affect MVPA level but increased exercise participation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03679130; https://clinicaltrials.gov/ct2/show/NCT03679130. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2020-043671.

The metabolic syndrome is frequent in children and adolescents with type 1 diabetes compared to healthy controls.

OBJECTIVE: There is a rise in overweight and obesity among children and adolescents with type 1 diabetes (T1D) in parallel with the rise in the metabolic syndrome (MetS) among children and adolescents. The aim of the study was to describe the prevalence and characteristics of MetS in children and adolescents with T1D compared to their healthy counterparts. RESEARCH DESIGN AND METHODS: The study includes two Danish cohorts; (i) the Copenhagen cross sectional cohort 2016 of 277 children and adolescents with T1D that attend the pediatric outpatient clinic at a large hospital in greater Copenhagen and (ii) the CHAMPS-study DK which is a population-based cohort study of Danish children and adolescents (control cohort). Participants were categorized to have MetS if at least two of the following criteria were met: (i) systolic and/or diastolic blood pressure ≥ 90th percentile, (ii) waist circumference ≥90th percentile, and (iii) triglyceride ≥90th percentile and/or HDL ≤10th percentile. RESULTS: The prevalence of children with Mets in the T1D cohort was higher than in the control cohort (p = 0.002). Moreover, participants with T1D had MetS at a lower level of BMI (p < 0.001) and waist circumference (p < 0.001) than participants with MetS from the control cohort (z-scores = 0.90 and 1.51). Participants with MetS were younger than the other T1D participants (median 12.8 [9.9,14.8] vs. median 14.6 [11.2,16.9] years, p = 0.006). CONCLUSIONS: Children and adolescents with T1D have an increased risk of MetS compared to healthy controls and clinicians and caretakers should consider early prevention and health promotion strategies.

Reference serum percentile values of adiponectin, leptin, and adiponectin/leptin ratio in healthy Danish children and adolescents.

The adipokines adiponectin and leptin play key roles in human metabolic regulation and have gained great attention as biomarkers for various metabolic pathologies. Though, pediatric reference values are few and needed. This study aims to establish age- and sex-specific adipokine reference percentiles based on healthy Danish school children. Further, it elucidates sex-specific differences in associations between z-scores of examined adipokines and metabolic variables. Serum adiponectin and serum leptin from 853 observations of healthy Danish schoolchildren aged 8-17 years (median 10.0) were quantified by immunoassays. Age- and sex-specific adipokine reference percentiles were calculated cross-sectionally using the LMS method, and adipokine z-scores were calculated from the fitted model. Multiple linear regression models were used to examine sex-specific differences in associations between adipokine z-scores and various metabolic variables. Girls had a higher median value of adiponectin (11.31 vs. 10.65 µg/mL, p < .001) and leptin (2.30 vs. 1.00 ng/mL, p < .001) and a lower median value of adiponectin/leptin ratio (4.64 vs. 10.76, p < .001) compared to boys. Sex-specific differences were found in associations between adiponectin z-score and HDL (p = .010), between leptin z-score and waist circumference z-score (p = .027) and LDL (p = .048), and between adiponectin/leptin ratio z-scores and waist circumference z-score (p = .044) and LDL (p = .040). Reference percentiles of adiponectin, leptin, and adiponectin/leptin ratio are presented in this paper. To our knowledge, this study is the first to demonstrate sex-specific differences in associations between adipokine z-scores and waist circumference z-score and lipids, respectively in healthy children and adolescents.

Nighttime smartphone use and changes in mental health and wellbeing among young adults: a longitudinal study based on high-resolution tracking data.

Frequent nighttime smartphone use can disturb healthy sleep patterns and may adversely affect mental health and wellbeing. This study aims at investigating whether nighttime smartphone use increases the risk of poor mental health, i.e. loneliness, depressive symptoms, perceived stress, and low life satisfaction among young adults. High-dimensional tracking data from the Copenhagen Network Study was used to objectively measure nighttime smartphone activity. We recorded more than 250,000 smartphone activities during self-reported sleep periods among 815 young adults (university students, mean age: 21.6 years, males: 77%) over 16 weekdays period. Mental health was measured at baseline using validated measures, and again at follow-up four months later. Associations between nighttime smartphone use and mental health were evaluated at baseline and at follow-up using multiple linear regression adjusting for potential confounding. Nighttime smartphone use was associated with a slightly higher level of perceived stress and depressive symptoms at baseline. For example, participants having 1-3 nights with smartphone use (out of 16 observed nights) had on average a 0.25 higher score (95%CI:0.08;0.41) on the Perceived stress scale ranging from 0 to 10. These differences were small and could not be replicated at follow-up. Contrary to the prevailing hypothesis, nighttime smartphone use is not strongly related to poor mental health, potentially because smartphone use is also a social phenomenon with associated benefits for mental health.

The circadian rhythm of calcium and bone homeostasis in Maasai.

OBJECTIVES: Ethnic groups differ in prevalence of calcium-related diseases. Differences in the physiology and the endogenous circadian rhythm (CR) of calcium and bone homeostasis may play a role. Thus, we aimed to investigate details of CR pattern in calcium and bone homeostasis in East African Maasai. METHODS: Ten clinically healthy adult Maasai men and women from Tanzania were examined. Blood samples were collected every 2nd hour for 24 h. Serum levels of total calcium, albumin, parathyroid hormone (PTH), 25(OH)D, creatinine, C-terminal telopeptide (CTX), bone-specific alkaline phosphatase (BSAP), procollagen type 1 N-terminal propeptide (P1NP), and osteocalcin were measured. Circadian patterns were derived from graphic curves of medians, and rhythmicity was assessed with Fourier analysis. RESULTS: PTH-levels varied over the 24 h exhibiting a bimodal pattern. Nadir level corresponded to 65% of total 24-h mean. CTX and P1NP showed 24-h variations with a morning nadir and nocturnal peak with nadir levels corresponding to 23% and 79% of the 24-h mean, respectively. Albumin-corrected calcium level was held in a narrow range and alterations were corresponding to alterations in PTH. There was no distinct pattern in 24-h variations of 25(OH)D, creatinine, osteocalcin, or BSAP. CONCLUSIONS: All participants showed pronounced 24-h variations in PTH and bone turnover markers CTX and P1NP. These findings support that Maasai participants included in this study have typical patterns of CR in calcium and bone homeostasis consistent with findings from other ethnic populations.

Scaling up context-tailored clinical guidelines and training to improve childbirth care in urban, low-resource maternity units in Tanzania: A protocol for a stepped-wedged cluster randomized trial with embedded qualitative and economic analyses (The PartoMa Scale-Up Study).

While facility births are increasing in many low-resource settings, quality of care often does not follow suit; maternal and perinatal mortality and morbidity remain unacceptably high. Therefore, realistic, context-tailored clinical support is crucially needed to assist birth attendants in resource-constrained realities to provide best possible evidence-based and respectful care. Our pilot study in Zanzibar suggested that co-created clinical practice guidelines (CPGs) and low-dose, high-frequency training (PartoMa intervention) were associated with improved childbirth care and survival. We now aim to modify, implement, and evaluate this multi-faceted intervention in five high-volume, urban maternity units in Dar es Salaam, Tanzania (approximately 60,000 births annually). This PartoMa Scale-up Study will include four main steps: I. Mixed-methods situational analysis exploring factors affecting care; II. Co-created contextual modifications to the pilot CPGs and training, based on step I; III. Implementation and evaluation of the modified intervention; IV. Development of a framework for co-creation of context-specific CPGs and training, of relevance in comparable fields. The implementation and evaluation design is a theory-based, stepped-wedged cluster-randomised trial with embedded qualitative and economic assessments. Women in active labour and their offspring will be followed until discharge to assess provided and experienced care, intra-hospital perinatal deaths, Apgar scores, and caesarean sections that could potentially be avoided. Birth attendants' perceptions, intervention use and possible associated learning will be analysed. Moreover, as further detailed in the accompanying article, a qualitative in-depth investigation will explore behavioural, biomedical, and structural elements that might interact with non-linear and multiplying effects to shape health providers' clinical practices. Finally, the incremental cost-effectiveness of co-creating and implementing the PartoMa intervention is calculated. Such real-world scale-up of context-tailored CPGs and training within an existing health system may enable a comprehensive understanding of how impact is achieved or not, and how it may be translated between contexts and sustained.Trial registration number: NCT04685668.

Glycemic variability assessed by continuous glucose monitoring in hospitalized patients with community-acquired pneumonia.

BACKGROUND: Glycemic variability (GV) has only been sparsely studied in patients with community-acquired pneumonia (CAP). This study aimed to quantify in-hospital GV in CAP patients, including determining the impact of type 2 diabetes mellitus (T2DM) and glucocorticoid (GC) treatment on GV. METHODS: This is a prospective cohort study of CAP patients (N = 40) with or without T2DM and treated or not with GCs. The primary endpoint was GV measured as glucose standard deviation (SD), coefficient of variation (CV), and postprandial glucose excursions (PPGE) based on continuous glucose monitoring (CGM). Analysis of glucose data was split into daytime and nighttime when possible. RESULTS: Patients included had a mean age of 74 (range 55 to 91) years. SD (95%CI) increased by a factor of 1.93 (1.40 to 2.66) and 2.29 (1.38 to 3.81) in patients with T2DM and not treated with GCs during the daytime and the nighttime, respectively (both P < 0.01), and by a factor of 1.42 (1.04 to 1.97) in patients treated with GCs but without T2DM during the daytime (P = 0.031) compared to patients without T2DM and not treated with GCs. CV (95%CI) increased by 5.1 (0.0 to 10.1) and 8.1 (1.0 to 15.2) percentage points during the daytime and the nighttime, respectively, in patients with T2DM and not treated with GCs compared to patients without T2DM and not treated with GCs (P = 0.046 and P = 0.026, respectively). PPGE (95% CI) increased during lunch by 2.5 (0.7 to 4.3) mmol/L (45 (13 to 77) mg/dL) in patients with T2DM and treated with GCs compared to patients without T2DM and not treated with GCs (P = 0.018). CONCLUSIONS: CAP patients receiving GCs, especially those with T2DM, are at great risk of developing high GV and therefore require clinical attention to mitigate GV. This applies particularly during the daytime. Results support the 1 to 2-h post-lunch screening procedure for glucocorticoid-induced hyperglycemia in patients without diabetes. SD was positively correlated with hospital length of stay.

Telemetric Continuous Glucose Monitoring During the COVID-19 Pandemic in Isolated Hospitalized Patients in Denmark: A Randomized Controlled Exploratory Trial.

Objective: To investigate whether telemetric continuous glucose monitoring (CGM) in hospitalized and isolated patients with diabetes mellitus and coronavirus disease 2019 (COVID-19) is associated with better glycemic outcomes and fewer patient health care worker contacts compared to blood glucose monitoring by traditional point-of-care (POC) glucose testing and to investigate the user aspect of implementing a CGM-system in-hospital. Materials and Methods: A randomized controlled exploratory trial was performed on hospitalized and isolated patients with diabetes and COVID-19 from May 2020 until February 2021 at Nordsjællands Hospital, Denmark. Participants were randomized to nonblinded telemetric CGM (as the only glucose monitoring method) or traditional POC glucose testing + blinded CGM. The primary endpoint was time in range (TIR) based on CGM data in both groups. A questionnaire about the user aspect of the CGM system was answered by health care personnel (HCP). Results: We included 64 participants in the analysis, 31 in the CGM group and 33 in the POC glucose group. TIR median was 46% for the CGM group and 68% for the POC glucose group (P = 0.368). The mean glucose value for the CGM group was 11.1 and 10.8 mmol/L in the POC glucose group (P = 0.372). CGM was associated with fewer POC glucose measurements (P < 0.001). Out of 30 HCPs, 28 preferred telemetric CGM over POC glucose testing. Conclusion: Remote glucose monitoring by CGM did not improve glycemic outcomes compared to traditional POC glucose testing, but was associated with fewer patient-personnel contacts, saving time for HCPs performing diabetes-related tasks. Most HCPs preferred CGM. The study is registered at http://www.clinicaltrials.gov (#NCT04430608).

Revision for periprosthetic joint infection rate stratified by seasonality of operation in a national population of total and unicompartmental knee arthroplasty patients: a register-based analysis.

Aim: The aim of this study was to investigate whether the revision rate for periprosthetic joint infection (PJI) depends on the season of the primary procedure using a national population of knee arthroplasty (KA) patients. Seasonal variation of some surgical procedures has been observed to impact subsequent infection risks, with a higher risk of revision for surgeries performed during summer, but an analysis of PJI rates based on a national arthroplasty register has yet to be completed. We hypothesized that an increased risk of revision due to PJI could be demonstrated in a national population when primary surgery was performed during the summer. Methods: The Danish Knee Arthroplasty Registry (DKR) was used to determine the risk of revision due to PJI within 2 years after primary surgery. All primary KA procedures between 1 January 1997 and 31 December 2014 and revisions until 31 December 2016 were identified. Smoothing spline regression was used to identify possible seasonal pattern effects of the primary procedure on revision risk, and logistic regression was used to calculate risk of infection differences between seasons. Results: A total number of 124 809 primary procedures was registered in the study period. After excluding duplicates and matching primary procedures with the first revisions within 2 years after the primary procedure, 3391 were identified. Of these, 348 cases were recorded with an indication of deep infection requiring revision. Spline regression analyses did not demonstrate any clear seasonal pattern of the primary procedure regarding the risk of revision for infection or any other cause. Logistic regression found a decreased risk of revision for infection when the primary procedure was performed during the summer in the years 1997 to 2005, no influence on the risk of revision for infection in 2005 to 2012, and an increased risk of revision for infection following summer procedures during the years 2013 to 2014. Conclusion: It was not possible to demonstrate a consistent seasonal variation of the risk of revision for PJI following primary KA. This is most likely because the underlying etiologies for PJI are not subject to seasonal variation.

Reproducibility of Glucose Fluctuations Induced by Moderate Intensity Cycling Exercise in Persons with Type 1 Diabetes.

AIMS: The purpose was to assess the reproducibility of glucose changes during three sessions of standardized moderate intensity continuous training of cycling on an individual level in people with type 1 diabetes. METHODS: Twelve adults (six females) with type 1 diabetes performed three test sessions on an ergometer bicycle (30 min, 67% of predicted heart rate) on three different days. The participants were 36.5 (26.6-45.5) (median, IQR) years old, and their HbA1c was 65 ± 15 mmol/mol (mean ± SD). Two hours before the tests, the participants had a standard meal. Interstitial glucose (IG) and capillary glucose (CG) were measured using an iPro2 Medtronic continuous glucose monitor and the Bayer Contour XT-device, respectively. Prior to the test sessions, resting heart rate was measured using a digital blood pressure monitor to estimate the desired intensity of the exercise. RESULTS: The average within-participant relationship between the average slope in glucose during sessions 2 and 1 was in IG -0.29 (95% CI -1.11; 0.58) and in CG -0.04 (-0.68; 0.77). Between sessions 3 and 2, IG is 0.18 (-0.27; 0.64) and in CG 0.13 (-0.25; 0.55). Between sessions 3 and 1, IG was 0.06 (-0.57; 0.71) and in CG 0.06 (-0.39; 0.52). The results indicate low reproducibility at participant levels and remained unchanged after adjustment for baseline glucose values. CONCLUSION: On an individual level, the glucose declines during three standardized sessions of PA were not associated with identical responses of the measured IG and CG levels. An overall anticipated decline of glucose concentrations was found in the moderate intensity cycling sessions. This highlights the importance of regular CG measurements during and after physical activity and awareness towards potential exercise-induced hypoglycemia in persons with type 1 diabetes.