Escherichia coli O157 (O157) in Scotland has been 2.5-fold higher than in England and Wales. Results from national cattle surveys conducted in Scotland and England and Wales in 2014/2015 were combined with data on reported human clinical cases from the same time frame to determine if strain differences in national populations of O157 in cattle could be associated with higher human infection rates in Scotland. Shiga toxin subtype (Stx) and phage type (PT) were examined within and between host (cattle vs human) and nation . For a subset of the strains, whole genome sequencing (WGS) provided further insights into geographical and host association. All three major O157 lineages  and most sub-lineages  were represented in cattle and humans in both nations. While the relative contribution of different reservoir hosts to human infection is unknown, WGS analysis indicated that the majority of O157 diversity in human cases was captured by isolates from cattle. Despite comparable cattle O157 prevalence between nations, strain types were localized. PT21/28  was significantly more prevalent in Scottish cattle . In England and Wales, cattle had a significantly higher association with sub-lineage IIa strains  while humans were significantly more closely associated with sub-lineage IIb . Therefore, cattle farms in Scotland were more likely to harbour Stx2a+O157 strains compared to farms in E and W (P<0.001). There was evidence of limited cattle strain migration between nations and clinical isolates from one nation were more similar to cattle isolates from the same nation, with sub-lineage Ic (mainly PT21/28) exhibiting clear national association and evidence of local transmission in Scotland. While we propose the higher rate of O157 clinical cases in Scotland, compared to England and Wales, is a consequence of the nationally higher level of Stx2a+O157 strains in Scottish cattle, we discuss the multiple additional factors that may also contribute to the different infection rates between these nations.For the last two decades, the human infection frequency of  FASTQ files have been submitted to the National Center for Biotechnology Information (NCBI). All data can be found under Bioproject no. PRJNA315192. Individual accessions and isolate meta-data are provided in a separate file .Escherichia coli O157 infection is higher in Scotland than in England and Wales. E. coli O157 expressing Shiga toxin is a serious threat to human health with strains present in cattle as a major reservoir host. In Scotland we have been gathering data on cattle O157 since 1997. However, this is the first comprehensive snapshot of the strain composition in cattle and humans within Great Britain  from the same time frame. The study has demonstrated the much greater diversity of E. coli O157 present in the surveyed cattle population in England and Wales compared with Scotland with the key finding that Stx2a-encoding PT21/28 strains, known to be associated with serious human disease, are more prevalent in Scottish cattle. We discuss whether this strain localization could be the main reason for the higher incidence of E. coli O157 infections in Scotland. This study has fostered collaboration among University and Public Health researchers working on a ‘One Health’ approach to food safety. The results of the survey represent a major advance in understanding how strain composition in the animal reservoir could impact the prevalence, incidence and burden of disease in humans.Based on yearly reports from public health authorities, it is evident that the rate of clinical Escherichia coli (STEC) is an important cause of foodborne infections  whereas cattle in England and Wales had a significantly higher isolation rate of sub-lineage IIa strains  ] .n=20/26 (76.9%)) than farms in England and Wales [n=3/34 (8.8%)]  ] . The mosstx subtypes were found in the sampled cattle: stx1a, stx2a and stx2c ].Three main toxin nd stx2c . Cattle There were spatial differences in cattle strain composition across Scotland and England and Wales, as highlighted in n=6/9) of these transitions occurred at least 10 years previously and therefore direct inference of strain migration is uncertain. In contrast, there was evidence of recent within-nation transmission of strains between farms with two Scottish sub-lineage Ic strains three SNPs apart, two sub-lineage IIb strains on the English/Welsh borders three SNPs apart and two identical English sub-lineage IIa strains in the North West of England.All three major STEC O157 lineages and 5/6 sub-lineages were represented . For allP<0.001, OR 2.2 (1.5–3.2)] whereas clinical cases from England and Wales were significantly more likely to be from sub-lineage IIb  ] .P<0.001, OR 2.18 (1.47–3.21)] and PT32 . Sub-lineage IIb clinical cases from England and Wales were predominantly PT8  and PT1  ] .P=0.1247) for the time frame of this study (n=82) of Stx2a+ strains were sub-lineage Ic, primarily PT21/28. In England and Wales, Stx2a+strains were distributed across several lineages and sub-lineages  ] . Based o(1.5 %)] , but the(1.5 %)] .Five SNP single linkage clusters (SLCs) have been used operationally in the UK to define isolates that are likely to be epidemiologically linked . From thIn contrast to the observed segregation in diversity of cattle isolates between Scotland and England and Wales, the human clinical isolates showed a greater degree of mixing. Examining location as a discrete trait on the phylogeny identified multiple migration events between locations for all main sub-lineages  . For subP<0.05) but not significantly different for Scottish clinical isolates. Stratified by major sub-lineages, Ic isolates from both England and Wales and Scotland were more likely to be closer to an isolate from the same location than from a different location  and this was also the case for sub-lineage IIc from England and Wales  but not from Scotland . In summary, sub-lineage Ic clinical isolates were more likely to be clustered by location while for for sub-lineage IIc strains this was only the case for England and Wales.The genetic distances across the main sub-lineages  were calculated for the clinical isolates within and between Scotland and England and Wales.  more likely to cluster with Scottish cattle isolates while clinical isolates from England and Wales were more likely to cluster with cattle isolates from England and Wales . This observation was consistent when stratifying by sub-lineage  of reported clinical cases of O157 in Scotland has been 2.5-fold higher than in England and Wales. This study has shown that for the period 2014–2015 Scottish clinical cases consisted of significantly more PT21/28 strains  than England and Wales cases. While there were similar proportions of O157 strains with Stx2a+ associated with human infections in England and Wales compared with Scotland, in Scotland >80 % of the Stx2a+ strains were associated with sub-lineage Ic (PT21/28) as opposed to England and Wales where Stx2a+ strains were associated with various sub-lineages and alternative PTs  [es stx2c . IndividThe significantly higher representation of PT21/28  in Scottish clinical cases is mirrored in Scottish cattle. Despite similar overall prevalence of O157 in cattle between Scotland and England and Wales , ScottisE. coli O157 and may also relate to localization of predating phage populations [E. coli O157, and the wider diversity of bacteria susceptible to T4 and T7 phage needs to be taken into account.It is interesting to note that the main sub-lineages in the UK have opposing phage resistance profiles; in other words, sub-lineage Ic is primarily resistant to T4 phage whereas IIc/b are resistant to T7 phage (and visa versa), at least in relation to sensitivities established within the typing phage panel . It has ulations , even thE. coli O157 diversity in human cases is captured by isolates we have sampled from cattle, indicating that there does not need to be another reservoir maintaining a different set of strain sub-types of threat to human health. However, we appreciate that other ruminants, especially sheep, are also a reservoir for human infection [Our study has focused on cattle as the primary source of O157 infection in the UK, either directly or indirectly. It should be noted that analysis of the WGS data indicates the majority of nfection . Sheep anfection . As suchnfection .Ancestral state reconstruction from WGS data for cattle isolates indicated there was little mixing between Scotland and England and Wales supporting the concept that the subtypes were relatively localized. This localization results in different national distributions of strains in cattle with the potential to cause serious disease and, as proposed, could contribute to the higher frequencies of human O157 infection in Scotland compared to England and Wales. Our WGS analysis also found clear evidence of human cases being more locally derived, and clinical isolates from one nation were more likely to cluster with cattle isolates from that nation. Sub-lineage Ic clinical isolates, which are primarily PT21/28, were the most likely to be restricted to Scotland but this association breaks down when larger outbreaks were analysed. This makes sense in light of the fact that some of the larger outbreaks involve products, such as bagged salads, that have a multi-national distribution.et al. [The simplest proposal for the outbreaks being more likely to be nationally restricted is infection from the local environment or contaminated food that has a local source. The risk of environmental exposure will vary in importance depending on location  and nature of the visit (business vs holiday) with visitors to rural areas at more risk of STEC infection . Compareet al.  found anet al. . Taken tet al. .E. coli O157 strains in the Scottish cattle population combined with more opportunities for local exposure through the environment and local food consumption. Local transmission will vary depending on exposure routes, while people and goods move freely within and between Scotland, England and Wales. Through both travel and food networks, people become exposed to strains that are not endemic to where they usually live which dampens the signal from local infections for human cases.We propose that the higher rate of O157 clinincal cases in Scotland is a result of the nationally high level of Stx2a+ E. coli O157 strains indigenous in UK cattle. While we continue to develop interventions such as vaccines, phage and probiotics and feed approaches that can be used on farm to reduce the threat to human health from all forms of transmission, education and awareness remain the main tools to reduce the risk of human infection.For public health, the safest approach may be to consider all farms a source of O157. It is important to point out that the presence of O157 on a farm is not an indication of poor animal husbandry, as this organism is a ubiquitous commensal in the gastrointestinal tract of cattle. As such, food safety must be controlled by ensuring appropriate transport to the abattoir, good practice at the abattoir, pre- and post-slaughter, and within the subsequent supply chain up to and including the point of food preparation. Although outbreaks associated with consumption of contaminated beef still occur, the frequency has decreased in GB over the last three decades , reflectClick here for additional data file.Click here for additional data file.


Ecological momentary assessments (EMAs) are short, repeated surveys designed to collect information on experiences in real-time, real-life contexts. Embedding periodic bursts of EMAs within cohort studies enables the study of experiences on multiple timescales and could greatly enhance the accuracy of self-reported information. However, the burden on participants may be high and should be minimized to optimize EMA response rates.We aimed to evaluate the effects of study design features on EMA response rates.Embedded within an ongoing cohort study , 3 bursts of EMAs were implemented over a 7-month period (April to October 2021). The response rate  for each burst was examined. Following a low response rate in burst 1, changes were made to the subsequent implementation strategy (SMS text message announcements instead of emails). In addition, 2 consecutive randomized controlled trials were conducted to evaluate the efficacy of 4 different reward structures (with fixed and bonus components) and 2 different schedule lengths (7 or 14 d) on changes to the EMA response rate. Analyses were conducted from 2021 to 2022 using ANOVA and analysis of covariance to examine group differences and mixed models to assess changes across all 3 bursts.F3,380=1.75; P=.16). Changing the schedule length did significantly change the response rate ; the response rate was higher for the longer schedule  than the shorter schedule . The average response rate was higher in burst 2 and burst 3  than in burst 1 , and the difference was statistically significant .Participants (N=384) were university students  in Singapore. Changing the reward structure did not significantly change the response rate  may have contributed to increasing the response rate over time. Changing the available rewards did not lead to a significant difference in the response rate, whereas changing the schedule length did lead to a significant difference in the response rate. Our study provides novel insights on how to implement EMA surveys in ongoing cohort studies. This knowledge is essential for conducting high-quality studies using EMA surveys.ClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/ct2/show/NCT05154227 Ecological momentary assessment (EMA) surveys are a method of capturing self-reported information on experiences in real-time, real-life settings. In EMA studies, participants are prompted to respond to brief sets of questions, often multiple times per day for several days . AlthougThere are 3 key advantages of using EMA over traditional retrospective surveys. First, the recall period is short, typically covering current or very recent experience, thus minimizing recall biases ,17. Secobursts of EMA surveys  coul surveys ,22. Such surveys -25. One Nonresponse to EMA surveys may erode the advantages of this approach. EMA protocols must balance comprehensive coverage of the constructs of interest  against acceptable participant burden ,27. QuesCurrently, little is known about maximizing data completeness in EMA studies. Some studies have found that missing data are related to participant characteristics such as age or personality traits  and to sone-off or single-burst EMA studies. The start date of single-burst studies is often clear—a prespecified date or directly following enrollment into a study or contact with the research team. Conversely, when multiple bursts of EMAs are implemented, the start date may be clear for the first burst of EMA if it coincides with the recruitment date. However, the start date of subsequent bursts may only be communicated electronically  or not at all , and this may have important implications for response rates to the upcoming round. If the communication strategy is not optimum, it follows that participants may not be able to respond to all EMA surveys in all EMA bursts.Furthermore, studies that implement repeated bursts of EMA surveys face unique challenges when compared with This study aims to evaluate progressive changes made to the implementation strategy for bursts of EMA surveys embedded within an ongoing cohort study. Our objectives were as follows:Aim 1: to evaluate whether offering different reward structures for completing EMA surveys would lead to an increase in the response rate relative to the control group.Aim 2: to evaluate whether implementing a 7-day EMA schedule (intervention group) would improve the response rate relative to a 14-day schedule (control group).Secondary aim—aim 3: To compare the overall response rates across 3 bursts of EMA surveys following changes to the EMA implementation protocol.This study evaluated participants’ response rates to the first 3 bursts of EMA surveys nested within an ongoing prospective cohort study, Health@NUS .Full details of Health@NUS are available elsewhere . BrieflyThe schedule for the first burst of Health@NUS EMA surveys was designed based on the available literature ,32, the a priori. The flow of participants through these RCTs is shown in Figure 1.To evaluate these changes, we implemented 2 randomized controlled trials (RCTs) within the ongoing Health@NUS study. The practical nature of these nested RCTs  meant that publishing a protocol before starting the study was not feasible. The outcome variable (response rate) was specified Participants were recruited to Health@NUS via email, campus posters, and word of mouth. To be eligible, participants had to (1) be a full-time student at the NUS, (2) be aged 18 to 26 years, (3) be a citizen or permanent resident of Singapore, and (4) own a smartphone compatible with the study app . Recruitment was ongoing at the time of writing this paper.There were no additional eligibility criteria for this study. A total of 384 students who joined Health@NUS during the first wave of recruitment between October 2020 and March 2021 were included. During study enrollment, participants provided written informed consent to receive short surveys  via the study app (HiSG app). Participants were advised that the EMA surveys were optional but highly encouraged and that they would be compensated for answering them. No specific details of the survey timing, frequency, or the compensation were provided during the consent process.This study was based on the first 3 bursts of EMA surveys, delivered on the following dates:Burst 1: April 24 to May 7, 2021 (baseline)Burst 2: July 19 to August 1, 2021 Burst 3: October 11 to October 24, 2021 The EMA questions asked about movement behaviors, eating behaviors, the context of these behaviors, and emotional states. The content and number of questions in each survey varied , the participants received an email to notify them of the upcoming EMA burst. A second reminder email and one push notification reminder were sent midway through the EMA burst , a group that received 25 HP per completed EMA survey plus bonus HP for completing >50% or 80% of EMA surveys , a group that received 50 HP per completed EMA survey (group C), and a group that received 50 HP per EMA survey plus bonus HP for completing >50% or 80% of EMA surveys group D; .aim 1, reward RCT), participants were randomly allocated on a 1:1:1:1 ratio to group A, B, C, or D before burst 2. An independent allocation officer used a random number generator to determine the allocation sequence. The sequence was integrated into the HiSG app and participants were automatically assigned to their respective group for burst 2 of the EMA surveys.To evaluate whether changing the reward structure led to an increase in the EMA response rate  via the reminder emails and push notifications .aim 1) data indicated no significant between-group difference in the response rate to EMA surveys.The original 14-day schedule was condensed into 7 days, and the overall number of EMA surveys was reduced to 30  notifying them of the upcoming EMA burst plus they received an SMS text message every 3 days to remind them to complete the EMA surveys . Particschedule length RCT). We hypothesized that the condensed 7-day schedule (intervention) would achieve a higher response rate than the original 14-day schedule (control) because some studies have reported declining response rates over time [The second nested RCT aimed to evaluate whether a condensed EMA schedule would achieve a higher response rate ; sex ; ethnicity ; marital status ; monthly household income , refuse to answer, or do not know); whether they are an undergraduate or postgraduate student; and the faculty they are studying in.2=underweight; 18.5-22.9 kg/m2=normal; 23-27.4 kg/m2=overweight; and >27.5 kg/m2=obese).Biometric assessments (height in cm and weight in kg) were taken by trained study personnel. BMI was calculated from height and weight measurements and classification recommendations for Asian populations  were folEMA surveys were administered via the HiSG app, and responses were automatically captured by the app and uploaded to a study server in real time.The primary outcome measure was the response rate  for each burst of EMA surveys.Baseline characteristics were analyzed descriptively. Separate 1-way ANOVAs were used to estimate the effect of changing the reward structure  or the schedule length  on the response rate at burst 2 and burst 3, respectively. A sensitivity analysis was conducted using analysis of covariance to adjust for the response rate at burst 1 .Secondary aim 3 was first analyzed using linear mixed model analysis with restricted maximum likelihood estimation to compare the overall response rate across the 3 bursts of EMA surveys following changes to the EMA implementation protocol . All participants provided written informed consent before commencing the study and consented for their deidentified data to be used for research purposes. For this study, the maximum compensation available to participants ranged from SG $21 (US $15) to SG $34.66 (US $25) depending on group allocation during the reward RCT and on the number of EMA surveys they completed.Between October 2020 and March 2021, 384 participants were recruited and enrolled into this study, and data collection was complete by October 2021.Following burst 1 and before burst 2, the participants were randomized to group A (n=96), group B (n=95), group C (n=96), or group D (n=97). Following burst 2 and before burst 3, participants were randomized to the intervention  or control  group. The study participants were predominantly undergraduate students , female , of Chinese ethnicity , and reported as being single or never married . The mean age of the participants was 23.37 (SD 1.25) years, and most of the participants had a BMI that was classified as either normal weight  or moderately overweight . Details of the participant characteristics at baseline for all participants and for each group allocation in the 2 RCTs are presented in For the reward RCT, group A had a slightly higher proportion of participants with a monthly household income of <SG $2000 . For the schedule length RCT, the 7-day schedule group had a slightly higher proportion of male participants and of participants with a reported monthly household income of <SG $2000 (<US $1466) as compared with the 14-day schedule group. The 7-day schedule group also had fewer participants who reported a BMI in the healthy range as compared with the 14-day schedule group .The first aim was to evaluate whether changing the reward structure for completing EMA surveys would lead to an increase in the response rate.The average response rates for the 4 reward groups at burst 1 and burst 2 are presented in The second aim was to evaluate whether implementing a shortened 7-day EMA schedule would improve the response rate.The average response rates for the 2 schedule length groups are shown in F1,382=6.23; P=.01) and remained so after adjusting for the response rate at burst 1  completed 48.3% (SD 33.2%) of EMA surveys at burst 3 compared with 38.5% (SD 33.4%) in the 7-day group (intervention); this difference was significant . Pairwise comparisons indicated that the difference between burst 1 and burst 2 and burst 1 and burst 3 were both significant (P<.001), whereas the difference between burst 2 and burst 3 was not significant (P=.05).At baseline , the average response rate per participant was 25.8% (SD 30.1%). At burst 2, the average response rate across groups was 46.6% (SD 34.6%) and 41% (SD 33.6%) at burst 3, and this difference was statistically significant .Subgroup analysis with control group participants was used to compare the overall response rates across the 3 bursts of EMA surveys following changes to the EMA implementation protocol. This study experimentally evaluated the effect of rewards and schedule length on EMA response rates within the context of an ongoing study implementing repeated bursts of EMA surveys. Reducing the number of days of EMA surveys led to a significantly lower response rate. However, changing the available rewards did not significantly change the response rate. Overall, for all groups the response rate was lowest at baseline (burst 1) as compared with the subsequent bursts of EMAs and the difference was statistically significant. However, our subgroup analysis that was intended to further explore whether this was because of changes in how participants were notified of the relevant burst of EMA surveys found no significant difference in the response rate at each burst.The response rate to burst 1 was very low, prompting this study, and increased substantially in burst 2 and burst 3 in all groups. It is possible that the initial low response rate was because the upcoming EMA burst was not well communicated to the participants. Future bursts included more frequent communication delivered directly to all participants (via SMS text messages and push notifications). These simple changes may have contributed to the increased response rate. However, we did not experimentally evaluate the effects of these communication changes. Our secondary analysis partially supported this finding, as there were significant differences between the response rate at each burst. However, in the subgroup analysis of the control group participants, the differences were no longer significant.The finding that neither offering greater reward amounts nor reducing the schedule length led to an increase in the response rate is broadly consistent with systematic reviews of factors associated with EMA compliance ,32. HoweRewards were selected as the first intervention target as the rewards available in burst 1 were low compared with other studies; participants could receive a total of approximately US $5 for completing all the EMA surveys. In other studies with a similar number of EMA days (between 10 and 14 d) and surveys (between 35 and 50 surveys), the lowest incentive was approximately US $25 ,46, althIn our study, contrary to our expectations, the burst 2 response rate was significantly higher in the 14-day schedule group than in the shorter 7-day schedule group. It seems intuitive that fewer days of EMA surveys would be less intrusive and therefore preferable to participants, particularly in the context of repeated bursts of EMA surveys. However, our findings indicate the opposite. More research is needed in this area as systematic reviews currently report inconsistent relationships between EMA schedule length and response rate ,32.Although the average response rate in EMA studies has been reported to be >70% ,38, thesThe strengths and limitations of this study should be considered. Our RCTs were embedded within an ongoing cohort study that required participants to fulfill several mandatory requirements , whereas completing the EMA surveys was optional but highly encouraged, and participants may have decided to opt out of this study component. Furthermore, although our sample size was larger than that of many other EMA studies ,38, no aOur pragmatic approach also meant that our secondary aim, to explore temporal changes in the response rate, was exploratory in nature. Future studies specifically designed to experimentally evaluate the effect of altering the announcement and communication strategy for each EMA burst  are needed. Studies evaluating the effects of other temporal variables such as holidays and key periods in the academic calendar  are also needed. As is typical of behavioral research, it was not possible to blind participants to their intervention condition, and we also cannot comment on whether participants received all of the EMA surveys that were sent. Furthermore, in our study, the number of EMA surveys per day varied (3-6/d), which may have lowered the response rate as participants did not know when to expect a survey. Finally, our sample consists of university students who may be especially motivated to engage in health research and therefore may not be representative of the broader population of young adults. The extent to which our findings generalize beyond this group is unclear. However, as young adults are a key population studied in EMA studies ,32,52-55This study is one of the first to experimentally evaluate the effect of incentives and schedule length on EMA response rates. It is also the first study to consider factors related to response in the context of an ongoing prospective cohort study administering repeated bursts of EMA over a 2-year period. By embedding RCTs within an ongoing study, it was possible to rapidly implement and evaluate whether altering the implementation strategy, incentives, or schedule length would increase the response rate. Our study therefore contributes to a small but growing body of literature on how to implement EMA. This knowledge is essential for collecting high-quality EMA data, which has a flow-on effect to the quality of conclusions that can be drawn from these data.


Continuous glucose monitoring (CGM) can improve behavioral and clinical outcomes. The use of CGM in real-world practice appears to be increasing. However, actual prevalence and characteristics of using CGM in real-world practice are unknown.To investigate the prevalence of CGM use by American adults with diabetes mellitus and differences in demographics and health-related quality of life (HRQOL) between users of CGM and self-monitoring of blood glucose (SMBG).This serial cross-sectional study using 2014-2020 Behavioral Risk Factor Surveillance System data included nonpregnant adults with self-reported diabetes using CGM or 4-15 times daily SMBG. Outcomes were prevalence of CGM use, demographics, and the 4-item Centers for Disease Control and Prevention HRQOL (CDC HRQOL-4). Unadjusted analysis was performed using univariable regression, and adjusted analysis was performed using nearest neighbor matching to compare CDC HRQOL-4 between SMBG and CGM groups in SAS Studio version 5.2.P < 0.001), employed , earn at least $75,000/year , have insurance coverage , and report fewer comorbidities . After nearest neighbor matching, diabetes management-related characteristics were shown to have statistically significant differences between CGM and SMBG users including: age at diabetes diagnosis , not seeing a doctor because of cost concern , checking hemoglobin A1c biannually , performing daily foot self-examination , receiving foot examination by a health care professional annually , and receiving a shingles vaccine in the past . CDC HRQOL-4 were shown to be similar between the 2 groups across the 4 domains .Among 12,053 included respondents, 231 (1.9%) reported using CGM, and prevalence increased from 0.4% in 2014 to 4.1% in 2020. Compared with SMBG users, CGM users were more likely to be younger  is a glucose monitoring device to help improve and manage diabetes. CGM use rose from 0.4% to 4.1% from 2014 to 2020. CGM is still used much less than the traditional finger prick method to monitor blood glucose level, called self-monitoring of blood glucose (SMBG). Compared with people using SMBG, people using CGM were shown to be younger, be employed, have higher income, and be more likely to have health insurance. Implications for managed care pharmacyAlthough prevalence of CGM use increased from 2014 to 2020, it remained far less than that for SMBG. Socioeconomic factors associated with CGM use over SMBG included employment, income, and health insurance status. Financial barriers may exist to CGM use that should be addressed to increase the use of CGM for glycemic monitoring.1 Although SMBG provides a snapshot of blood glucose levels at a single point in time and A1c correlates to an average of glycemia over a 3-month period, nonimplantable continuous glucose monitoring (CGM) provides insight into glucose levels around the clock for a period of 7 to 14 days per device. Since the early 2000s, CGM has shown benefits complementary to and beyond the use of conventional methods of glycemic monitoring.2 Literature has shown that CGM is associated with improved diabetes outcomes including reduced A1c by up to 0.9% more than traditional SMBG.5 In addition to clinical outcomes, CGM use has also demonstrated favorable behavioral outcomes, such as reduction in caloric intake and increase in exercise.6Controlling and monitoring glucose levels is imperative in the management of diabetes mellitus. Self-monitoring of blood glucose (SMBG) and hemoglobin A1c (A1c) have historically been the mainstays of monitoring glycemic levels to provide clinical and behavioral feedback to patients to help them achieve better disease management.4 The mean absolute relative difference (MARD) is an indicator to describe CGM device accuracy of the measured glucose level compared with the reference measure, in which a lower MARD indicates higher accuracy and a device with a MARD less than 10% is considered to be a well-performing system.7 In 2013, a study by Luijf and colleagues showed the MARDs of 3 CGM devices to be 16.5%, 16.4%, and 20.5%,3 whereas modern-day CGM devices achieve MARD values as low as 8.8%.4 Improved accuracy of CGM devices allows people with diabetes using these devices to better match their decisions regarding their medication therapy and lifestyle measures to their actual level of glycemic control.8 Parallel to the technological development of yielding more accurate CGM devices, many private and public health insurance plans have increased coverage of CGM for people with diabetes. In 2017, the Centers for Medicare and Medicaid Services proposed coverage of CGM for people with type 1 and type 2 diabetes receiving 3 or more daily injections of insulin or using continuous subcutaneous insulin infusion therapy and performing SMBG at least 4 times daily.9 With the increase in accuracy and accessibility of CGM devices, it is hypothesized that the prevalence of CGM use has increased in recent years. However, because insurance coverage still varies greatly and may have strict eligibility requirements for CGM use, it is also hypothesized that health insurance coverage status may play a role in CGM uptake, and demographic factors may differ between CGM and SMBG users.Over the past decade, CGM device accuracy has improved substantially.6 Positive change in behavior among people with diabetes is key to achieving successful diabetes management. Health-related quality of life (HRQOL) is defined as one’s perceived physical and mental health over time,10 and it has been shown that HRQOL is a powerful predictor of morbidity and mortality in people with diabetes.11 Low HRQOL has been identified as an independent predictor for amputation and death in people with diabetic foot ulcers, and high HRQOL has been associated with providing 10% relative risk reduction in cardiovascular mortality in people with diabetes.13 Therefore, understanding the association with CGM and physical and mental health is important, given the direct relationship to health outcomes and survival in people with diabetes. The wealth of information that CGM provides based on the minute-by-minute serial sequence of glucose level changes may serve as an important profiling tool to achieve individualized care for people with varying eating and exercising habits, and it is hypothesized that the mechanism of positive change in health behavior among people with diabetes using CGM may be associated with higher HRQOL compared with SMBG. Currently, no studies have examined the HRQOL of people with diabetes who are using CGM.As noted above, CGM use has led to improvements in behavioral outcomes.14In this study, we examined the yearly prevalence rate of CGM use over a 7-year timespan from 2014 to 2020 in the United States among a large sample of adults aged 18 years or older using data from the Behavioral Risk Factor Surveillance System (BRFSS). We compared demographic characteristics of survey participants using CGM compared with participants performing SMBG 4-15 times per day. Finally, we assessed HRQOL among CGM and SMBG users by examining the results of the 4-item Centers for Disease Control and Prevention HRQOL (CDC HRQOL-4) questionnaire included within the BRFSS.15 The BRFSS includes self-reported demographic, socioeconomic, and dietary data, as well as that for health-related risk behavior, chronic health conditions, and use of preventive services. CGM use began to be collected during the BRFSS interview in 2014; therefore, we have used the BRFSS dataset starting from 2014 to the most recent year available to the investigators at the point of analysis.15This study received exempt status approval by the local institutional review board. This was a serial cross-sectional study using 2014-2020 BRFSS data, which is a publicly available collection of health-related telephone survey results among community-dwelling adult Americans. The design and sampling method used in the BRFSS is reported and available on the Centers for Disease Control and Prevention website.15 To calculate the adjusted weight, the number of eligible samples was counted for each year, and the sum of the number of eligible samples for the 2014-2020 dataset was calculated. To complete the data combining for 2014 to 2020 datasets, each year’s sample count was then divided by the total sample count and multiplied by the weight of each year to create a new value for adjusted weight.The datafile from each year was downloaded and combined following the weight adjustment calculation published by the BRFSS Data User Guide.9 In July 2021, Medicare announced the removal of frequency of checking SMBG at least 4 times daily to qualify for coverage of a CGM; however, given that the regulatory change happened after the data collection period of 2014 to 2020, we have set the lower threshold of 4 times per day SMBG to correlate to eligibility criteria during the time of data collection in this study.9The samples were eligible to be included in the study based on answering “yes” to “(Ever told) (you had) diabetes?” Samples reporting gestational diabetes, prediabetes, or borderline diabetes were excluded. The BRFSS dataset does not include a question that distinguishes between type 1 and type 2 diabetes mellitus; hence, the grouping of SMBG vs CGM was used for comparison among adults with type 1 or type 2 diabetes that are not gestational diabetes, prediabetes, and borderline diabetes. Next, “About how often do you check your blood for glucose or sugar?” was used to categorize respondents into 1 of 2 groups: (1) CGM group or (2) SMBG group. Respondents were categorized to the CGM group if the answer to the above question was coded as “198” based on interviewer prompt of “If the respondent uses a continuous glucose monitoring system (a sensor inserted under the skin to check glucose levels continuously), fill in ‘98 times per day.’” Respondents were categorized to the SMBG group if the answer to the frequency of checking blood glucose level was between 4 and 15 times per day. Respondents indicating SMBG 1-3 times per day were excluded to assimilate participants who would be more likely to be offered CGM in alignment with Medicare coverage criteria.The prevalence of CGM use was calculated and reported in yearly ratios of the number of CGM users per each year divided by the number of respondents meeting the study eligibility criteria . In addition, the overall prevalence ratio among study participants from 2014 to 2020 was calculated based on the total number of samples included in the CGM group from 2014 to 2020 divided by the total number of samples included in the CGM and SMBG groups from 2014 to 2020. Finally, yearly prevalence ratios of the number of CGM users per the total number of respondents with self-reported diabetes were calculated.The following variables were used to capture respondent characteristics and sociodemographic factors between the CGM and SMBG groups: age (in years), sex , race , marital status , education level , employment status , income status , health plan coverage , comorbidity status , body mass index , and current smoker .The following variables were captured and reported on self-reported diabetes-related disease knowledge and self-care behavior: age when diagnosed with type 1 or type 2 diabetes (in years), taking insulin , having not seen a doctor because of cost concern , seen a health care professional in general at least 1 time in the past 12 months , seen a health care professional for diabetes at least 2 times in the past 12 months , had A1c checked at least 2 times in the past 12 months , performed daily foot self-examination , had foot examination by health care professional at least 1 time in the past year , status of diabetes affecting eyes , had eyes checked by a health care professional at least 1 time in the past year , taking classes in managing diabetes , done any exercise in the past month , and vaccine status for shingles, pneumonia, and influenza .16 The 4 items assessed (1) self-rated health; (2) number of unhealthy days due to physical illness or an injury during the past 30 days; (3) number of unhealthy days due to stress, depression, and problems with emotions during the past 30 days; and (4) number of days with limitation in self-care, work, or recreation due to poor physical or mental health during the past 30 days.The standard CDC HRQOL-4 was used to capture the outcome related to HRQOL. The validity of the CDC HRQOL-4 has been documented in the literature.The nearest neighbor matching estimation for average treatment effects was used to compare CDC HRQOL-4 items between the 2 groups. These factors could be associated with HRQOL levels and may serve as confounders in the relationship between CGM use and HRQOL. The nearest neighbor matching was performed based on factors included in P value less than 0.05.Data combining and analyses were performed using SAS Studio Version 5.2. For any missing variables, multiple imputation was performed to retain the original sample size. All analytical comparisons used survey procedures that accounted for the complex survey design of the BRFSS dataset. That is, the total sample adjusted weight, stratification, and clustering variables were applied to compare demographic and socioeconomic factors between the 2 groups. Regression with complex survey design was used to compare outcomes between the 2 groups. The nearest neighbor matching was performed to find matched comparator arm (SMBG) accounting for 17 demographic characteristics. Matched samples were then compared to assess the average treatment effects using CDC HRQOL-4 responses between the 2 groups accounting for the survey procedures. Significance was set as a 2-tailed test with a Supplementary Table 1, available in online article). Of the 12,053 respondents with self-reported diabetes using CGM or 4-15 times daily SMBG from 2014 to 2020, the prevalence of using CGM was 1.9%, and an increased trend was observed in yearly prevalence rates of CGM use from 0.4% in 2014 to 4.1% in 2020 . The yearly prevalence of diabetes was steady from 13.2% in 2014 to 13.0% in 2020. Of those with self-reported diabetes, 231 respondents reported using CGM , and 11,822 respondents reported frequency of checking blood glucose level 4-15 times daily   (P < 0.001), be employed , earn at least $75,000 in income , have health insurance coverage , and have fewer comorbid conditions such as MI , stroke , arthritis , depressive disorder , and kidney disease .Of the 17 demographic characteristics, 9 parameters were significantly different between groups, meeting the a priori significance level of er, CKD) . CompareP = 0.005), less likely to perform daily foot self-examinations , more likely to have had eyes checked by health care professional in the past 12 months , and more likely to have done any exercise in the past month . After performing the nearest neighbor matching to balance the demographic variables, diabetes-specific characteristics, and management-related behaviors were similar between groups, except answers to the questions on age when first diagnosed with diabetes, have not seen doctor because of cost concern, biannual A1c check, performing daily foot self-examinations, annual foot examination by health care professional, and status of receiving the shingles vaccine in the past. That is, the CGM group was younger in age when first diagnosed with diabetes , less likely to report not seeing doctor because of cost concern , more likely to get biannual A1c screening , less likely to perform daily foot self-examinations , and less likely to get an annual foot examination by a health care professional  and had received the shingles vaccine in the past .Regarding items related to diabetes-specific characteristics and management-related behaviors , the cruP = 0.0011), was associated with fewer unhealthy days in a month due to physical reasons , was associated with fewer unhealthy days in a month due to mental reasons , and was associated with fewer unhealthy days due tocombined physical and mental health reasons   . After t7 Additionally, several new devices were approved by the US Food and Drug Administration (FDA) over the course of the study period.17 In 2015, Dexcom’s G5 CGM device was approved; in 2017, Abbott’s Freestyle Libre was approved; and in 2018, Dexcom’s G6, Abbott’s Freestyle Libre 14 day, Senseonics’ Eversense, and Medtronic’s Guardian Connect were all granted FDA approval.20 The approval of 4 new devices in 2018 may help explain the drastic rise in CGM use in 2019. Although CGM use decreased slightly in 2020 down to 41.3 cases per 1,000 persons, this still reflects a 10-fold increase compared with 2014. The slight decrease in 2020 could be related to the COVID-19 pandemic and fewer health care visits to adjust diabetes management strategies.In this large cross-sectional study, we observed a steady increase in CGM use among people with self-reported diabetes. In 2014, the prevalence of CGM use was 4.1 cases per 1,000 persons with self-reported diabetes using either CGM or 4-15 times daily SMBG. Six years later in 2019, the prevalence of CGM use rose to 45.5 cases per 1,000 persons. This 11-fold increase in CGM use may reflect the start of a shift in patients’, providers’, and payors’ acceptance to use CGM devices for diabetes management because of the improved accuracy and accessibility of these devices over the years, which is consistent with the 3.8-fold rise in use from 2016 to 2017 when the Centers for Medicare and Medicaid Services proposed coverage of CGM.Although CGM use rose during the study period, the total number of people self-reporting CGM use was only 231 counts in a large-scale survey interviewing 12,053 people between 2014 and 2020 with self-reported diabetes meeting eligibility criteria. As such, great opportunity exists to further expand the use of CGM among a large portion of CGM-naive people who may receive benefit from a CGM device, and profiling demographic characteristics that are associated with the likelihood of receiving CGM can help guide patients, providers, and payors to improve use of this technology. To the authors’ knowledge, no studies have been published regarding factors predictive of CGM use in the adult population.In this study, we found that participants with self-reported diabetes using CGM appear to be younger and healthier (younger age at BRFSS survey completion and diabetes diagnosis and fewer comorbidities) while also exhibiting signs of financial stability . Although BRFSS is not able to distinguish between type 1 and type 2 diabetes, younger age at the time of the survey and at diabetes diagnosis, as well as fewer comorbidities, may suggest that CGM users may have been more likely to have type 1 diabetes, rather than type 2. This is important because insurance coverage for CGM devices has historically been better for type 1 diabetes, although eligibility restrictions have been slowly loosening. Younger age could also suggest greater acceptance of technological advances and ease of adaptability with frequently evolving technology. Greater financial stability in CGM users compared with SMBG users may indicate that cost is still a barrier to CGM device use and continued advocacy efforts are needed to provide access to this beneficial technology.21It is interesting to note that, after matching, CGM users were more likely to have A1c checked at least twice per year and receive a shingles vaccine , but they were less likely to perform daily foot self-examinations or have an annual foot examination by a health care professional. Although the purpose of foot examinations is to screen for the diabetes-related complication of neuropathy, these behaviors may be lower in the CGM group because of the lower age and overall healthier status of this group. As noted above, the CGM group may have included more people with type 1 diabetes than the SMBG group, and screening for neuropathy is not indicated until 5 years after diagnosis of type 1 diabetes (as opposed to at the time of diagnosis for type 2 diabetes).HRQOL items favored CGM for all 4 items before nearest neighbor matching but showed no difference after matching . AlthougThe results of this study warrant consideration for several limitations. The cross-sectional nature of the BRFSS data is not able to provide a causal relationship between the examined factors. The interviewer-led self-reported nature of the data collection process may leave room for recall bias or misclassification of the exposure status of CGM. Use of CGM and SMBG is not mutually exclusive monitoring therapy; hence, underreporting of CGM use may have been possible depending on the participants’ perceived notion of the dominant and/or usual way to monitor glucose levels at the time of the interview. Additionally, duration of CGM use and the time elapsed since the last time CGM was used to monitor glucose levels are not captured because of the nature of the survey. Differentiating type 1 and type 2 diabetes was not possible, and there was a lack of clinical outcome, such as A1c level. This information would have helped assess the disease control status and whether participants received CGM as a preventative measure to maintain control of their diabetes or as a last resort to manage uncontrolled diabetes.22 Additionally, the data reviewed from 2014 to 2020 reflects the comprehensive nature of the relevant and current datasets. The BRFSS interview began collecting information about CGM use since 2014, and examining all available data over the 7-year period helped depict relevant and meaningful information about the general patterns of CGM use from the public health point of view.There are several strengths to this study. The BRFSS is a large-scale survey capturing samples from a nationally representative participant pool. The multiple imputation methods with the BRFSS data have shown to improve the national level estimates to increase the generalizability of the findings.The results show that CGM use increased from 2014 to 2020 but remains far less than SMBG use. Economic factors appeared to predict CGM use over SMBG, indicating that cost may still limit access to CGM devices. Use of CGM neither negatively nor positively affected HRQOL compared with SMBG use.


Neurofibromatosis type 1 (NF1) or von Recklinghausen syndrome is an autosomal dominant disorder that affects the multisystem in the body with complex presentation caused by the neurofibromin gene mutation on chromosome 17. These patients tend to develop soft tissue sarcomas more than the general population. Leiomyosarcoma is a malignant soft tissue tumor that may occur in patients with NF1 in rare cases. We present a case of a rare development of leiomyosarcoma in a 45-year-old female patient with a history of NF1. She developed a progressively growing mass in the left axilla associated with numerous neurofibromas and axillary freckling. MRI revealed a heterogeneous large mixed signal intensity mass in the left axilla, and the diagnosis was confirmed through biopsy. Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder affecting people across the world with an incidence of 1/2500 to 1/3000 . LeiomyoA 45-year-old female with NF1 presented with a growing mass associated with mild pain in the left axilla for two months. The mass gradually increased in size during this time with increasing pain and discomfort in the movement of the left upper extremity. On physical examination, she had a subcutaneous soft mass about 6x5 cm in size with ill-defined margins in the left axilla which was mobile, non-tender, and non-translucent. The left upper extremity range of motion was normal but had mild discomfort with movement. She had numerous neurofibromas all over the body and axillary freckling  grade 3 sarcoma with patchy weak positive anti-smooth muscle antibody and H-caldesmon on immunohistochemical analysis, which are in favor of leiomyosarcoma. Moreover, other markers such as desmin, S100, CD117, and DOG1  were found to be negative. She refused any possible surgical resection of the tumor and was referred to the oncology department for possible anthracycline-based chemotherapy but was subsequently lost to follow-up.NF1, an autosomal dominant disorder, is mainly diagnosed by the presence of benign neurofibromas, café au lait spots, lisch nodules of the iris, and the presence of learning disabilities and skeletal abnormalities in some cases patients . The NF1Leiomyosarcoma had rarely been reported in patients suffering from neurofibromatosis with cases reported in the hand, thigh, bone, intracranial pelvis, colon, and bladder with most of them being reported in adolescents. Izzeddin et al. reported a case of primary bone leiomyosarcoma in a 14-year-old kid with plexiform neurofibroma . SimilarThe development of such tumors in NF1 patients needs awareness and attention in dealing with patients with NF1 for early diagnosis and timely management of such complications for better outcomes of patients.NF1 with leiomyosarcoma has rarely been reported in the medical literature. This case can be added to the few reported cases of leiomyosarcoma in a patient with neurofibromatosis. Such cases highlight the possibility of malignant tumors in NF1 patients besides MPNST. The case highlights the importance of surveillance and investigating patients comprehensively for the early diagnosis of such malignant tumors in NF1 for a more satisfactory outcome and timely management.


BackgroundPatients with type 2 Diabetes Mellitus (DM) have a poor quality of life because of various clinical effects. The sleep quality among these patients is affected and they encounter challenges to sleep and wakefulness due to physiological imbalance and co-morbid sleep pathologies. The present study was conducted to ascertain the sleep quality in type 2 DM patients attending a tertiary center in West Bengal.MethodologyIt was a hospital-based cross-sectional study that was conducted among old/follow-up patients who were suffering from DM. The eligible subjects were selected by systematic random sampling and were interviewed using a semi-structured and pre-tested questionnaire. Information pertaining to socio-demographic characteristics and sleep quality was ascertained. The sleep quality was assessed by using the Pittsburgh Sleep Quality Index (PSQI). Data was analyzed using Statistical Package for Social Sciences (SPSS) version 20 and statistical association of different parameters was tested using Chi-Square test.ResultsA total of 192 subjects were selected for study. Maximum subjects were above 50 years. A total of 102 males and 90 females participated in the study. A total of 110 subjects were poor sleepers (i.e. PSQI score ≥5) while 82 were good sleepers(PSQI<5). Poor sleepers were maximum in the 20-35 years age group, more in females and unemployed individuals. Subjects with poor diabetes control, i.e. HbA1C more than 7gm/dl were poor sleepers.ConclusionsA higher proportion of patients with type 2 DM had poor quality of sleep. It is important on the part of the health personnel to attend to the sleep issues and impaired quality of life due to sleep inadequacy in DM patients. Diabetes mellitus (DM) is one of the most chronic medical conditions confronted in clinical practice. Diabetes mellitus has reached epidemic proportions worldwide. The World Health Organization (WHO) has commented, " there is an apparent epidemic of diabetes which is strongly related to lifestyle and economic change”. Globally there were around 415 million people with diabetes in the year 2015; this figure is predicted to rise to 642 million by the year 2040 [The number of people with type 2 diabetes is increasing in every country, and more than 80% of them live in low and middle-income countries such as India, Bangladesh, Bhutan, Pakistan, Sri Lanka, Philippines and Indonesia. India ranks second among the top 10 countries in terms of number of people having diabetes. Presently, 69.2 million have diabetes in India and another 36.5 million people are diagnosed with pre-diabetes, which is a high-risk condition for diabetes and cardiovascular diseases [Patients with DM, have a poor quality of life because of its various clinical effects. The sleep quality among these patients is remarkably marred. Diabetes mellitus patients encounter challenges to their sleep and wakefulness due to physiological imbalance and co-morbid sleep pathologies. Clinical research has shown that up to one-third of patients with DM suffered from concomitant sleep disorders, as compared with 8.2% of controls without DM 2. In yet another study more than half of the patients with type 2 DM have been reported as “poor sleepers”,4. StudiIn India, diabetes is one of the major health concerns with an increasing prevalence due to rapid urbanization and changing lifestyles. Few studies have been conducted in India on the quality of sleep among patients with diabetes mellitus and in the eastern part of India, hardly any studies were conducted. Therefore, the current study was undertaken in the Integrated Diabetes and Gestational Diabetes Clinic (IDGDC) of IQ City Medical College and NM Hospital to ascertain the sleep quality in type 2 DM patients attending this clinic and to determine the factors associated with sleep quality.Study design and participants This was a hospital-based cross-sectional study that was conducted over a period of two months (January to February 2020) at the Integrated Diabetes and Gestational Diabetes Clinic (IDGDC) of IQ City Medical College, Durgapur. The study was approved by the Institute Ethics and Research Committee before its implementation. All old and follow-up patients giving their consent, between the age 20-65 years, and having type 2 diabetes mellitus for more than two years duration were included in our study. We excluded pregnant females, and patients suffering from any acute illness or any known psychiatric problem. Assuming the prevalence of poor sleep quality as 50% among diabetics and 15% as the permissible level of error, the sample size worked out to be 171. Considering the non-response rate of 20%, the sample size was fixed at 205 with the help of open Epi software. Data collection The eligible subjects who presented to the IDGDC were selected by modified systematic random sampling. After obtaining informed consent from the patients a face-to-face interview was done by trained physicians and public health workers to gather information using a pre-designed and pretested questionnaire -10. The MeasuresAge, gender, area of residence, level of education, and current working status were assessed using a standardized questionnaire. Working status was categorized as employed and unemployed. Education was categorized as no formal education, less than ten years of schooling, and more than ten years of schooling. The body mass index (BMI) was calculated by dividing the weight in kilograms and height in meters square.A subject was defined as having diabetes if the diagnosis had been made by a doctor if the subject was receiving anti-diabetes therapy or if two fasting blood samples showed glucose concentration according to the WHO definition of diabetes . DiabeteData analysis The data thus generated was checked for completeness and analyzed using SPSS . Descriptive statistics including frequency, percentage, mean and standard deviation were used to understand the sample distribution of sleep quality and diabetes control. A chi-square test was used to determine the factors associated with sleep.A total of 192 subjects were included in the study. Most of them (124) were above 50 years and their mean age was 52.7±10.3 years. The men outnumbered females (17:15) and 53% of the subjects belonged to urban areas. Two-fifths of the subjects (39.6%) were employed and more than half of the study subjects (52.2%) had received more than 10 years of schooling . The number of poor sleepers were more in females though they were equally distributed among subjects of urban and rural area. 62% of the unemployed subjects were poor sleepers. There was no association between the education, residence, or employment status of the study subjects and their PSQI score  between glycosylated Hemoglobin and PSQI score though 59% of the subjects with HBa1C more than 7gm/dl were poor sleepers. Among 110 poor sleepers, 32 subjects perceived that their sleep was very bad or fairly bad. Poor sleepers were more 74.2%) among study participants with a duration of diabetes of more than 10 years and among individuals with a BMI of more than 25 (75%), though it was found to be statistically insignificant  has commented that diabetes is like an epidemic now. It is strongly associated with changes in the lifestyle of humans. It has been projected that DM will be the seventh leading cause of death . There aThe result of the present study indicated that 57.3% of the subjects were classified as poor sleepers (PSQI> 5). The value obtained was higher compared to other studies conducted across the globe -9 which Our study reported a mean PSQI score of 6.70 ±3.32 which was more than that of a study conducted in Iran (5.5±4.4)  and lessWe obtained a significant association between age and PSQI score Table  but no sOur study showed that subjects with more than 10 years duration of DM had a significant association with PSQI score Table  which isUnlike our study, a significant relationship between HbA1c and PSQI score has been reported . 75% of In the present study, the percentage of poor sleepers was slightly higher, though statistically insignificant, among those treated through insulin injection alone or with OHAs Table . ShamshiIn our study, we found that 160 patients rated their sleep quality as good. However, 50% of these subjects were poor sleepers and this association was statistically significant Table . These pThis study has some strengths and limitations. There are very few studies in India assessing the sleep quality in type 2 DM patients. Hence the results of this study can provide valuable information for clinicians to improve the management of diabetes. In our study, we included 192 subjects from a dedicated center for diabetes. The patients referred to this center might not be a representative sample of the entire population with diabetes. The information on sleep duration and sleep quality is self-reported and subjective. Thus, we cannot compare our results directly with other studies that used polysomnographic measures for sleep disturbance. We did not take into consideration the intake of caffeine, alcohol, other substance abuse, and breathing disorders which might have an effect on sleep quality and pattern. A large-scale multi-centric prospective study including diabetic patients from the general population comparing their sleep patterns with the glycemic index needs to be conducted. A subset analysis of the data thus generated based on various modifiable and non-modifiable risk factors for diabetes and poor sleep patterns will further clear the picture and firmly establish the association between them.In this study, a high proportion i.e. two-thirds of type 2 diabetes mellitus patients were found to have poor quality of sleep. The data supported an association between age and sleep quality. Subjects with prolonged duration of diabetes mellitus had poorer quality of sleep. Thus we can conclude that sleep quality is affected in patients with diabetes mellitus. The diabetes patient may not visit the healthcare provider with issues related to sleep. It is important on the part of the health personnel to attend the sleep issues and impaired quality of life due to sleep inadequacy. This will have an effect on the control of diabetes in addition to the quality of life of these patients. Hence sleep hygiene should be a part of diabetes management.


Is the addition of docetaxel to standard of care (SOC) therapy with radiation and testosterone suppression or radical prostatectomy associated with decreased risk of prostate cancer–specific and all-cause deaths in patients with high-grade, nonmetastatic prostate cancer with low prostate-specific antigen levels?In this meta-analysis of 4 randomized clinical trials (145 participants with prostate cancer), adding docetaxel to SOC treatment in patients who were in otherwise good health was associated with a significant reduction in death due to prostate cancer.The poor prognosis of patients with high-grade prostate cancer and low levels of prostate-specific antigen levels may be improved with the addition of docetaxel to SOC treatment. This meta-analysis evaluates whether adding docetaxel to testosterone suppression plus curative local treatment was associated with decreased mortality among patients with high-grade, localized or locally advanced, nonmetastatic prostate cancer with low serum levels of prostate-specific antigen (PSA). Patients with high-grade prostate cancer with low levels of prostate-specific antigen  are at high risk of mortality, necessitating an improved treatment paradigm.To assess for these patients whether adding docetaxel to standard of care (SOC) treatment is associated with decreased prostate cancer–specific mortality (PCSM) and all-cause mortality (ACM).PubMed search from 2000 to 2022.Five prospective randomized clinical trials (RCTs) performed in the US, France, and the United Kingdom evaluating SOC treatment with radiotherapy and androgen deprivation therapy (ADT) or with radical prostatectomy vs SOC plus docetaxel.Individual data were included from patients with nonmetastatic prostate cancer, a PSA level of less than 4 ng/mL, and a Gleason score of 8 to 10. Patients initiated treatment between February 21, 2006, and December 31, 2015 . Data were analyzed on December 16, 2022.Hazard ratio (HR) of ACM and subdistribution HR (sHR) of PCSM adjusted for performance status , Gleason score (9 or 10 vs 8), tumor category (T3-T4 vs T1-T2 or TX), and duration of ADT (2 years vs 4-6 months).From a cohort of 2184 patients, 145 patients (6.6%) in 4 RCTs were eligible . Thirty-one patients died, and of these deaths, 22 were due to prostate cancer. Performance status was 0 for 139 patients (95.9%) and 1 for 6 patients (4.1%). A reduced but nonsignificant risk of ACM  and PCSM  was associated with patients randomized to SOC plus docetaxel compared with SOC. The risk reduction in ACM  was more pronounced among patients with a performance status of 0 and was significant for PCSM .Adding docetaxel to SOC treatment for patients who are in otherwise good health with a PSA level of less than 4 ng/mL and a Gleason score of 8 to 10 was associated with a significant reduction in PCSM and therefore has the potential to improve prognosis. The totality of evidence for use of radiotherapy and androgen deprivation therapy (ADT) with testosterone suppression alone or radical prostatectomy (RP) in patients with high-risk localized or locally advanced nonmetastatic prostate cancer suggests that treatment intensification with docetaxel does not uniformly improve treatment outcomes in these disease states.Docetaxel has been shown to prolong overall survival (OS) in men with castrate-resistant and castrate-sensitive metastatic prostate cancer.18 a hypothesis-generating postrandomization analysis of OS stratified by PSA level was performed and yielded a hazard ratio (HR) of 0.33 among men with high-grade prostate cancer with low PSA levels when docetaxel was added to testosterone suppression for 6 months and primary radiotherapy. Furthermore, in a prior study of men with intermediate or high-risk nonmetastatic prostate cancer,20 only men with no or minimal comorbidity appeared to benefit from the addition of ADT to radiotherapy with respect to OS; those with moderate to severe comorbidity did not. Therefore, using individual patient data from the available RCTs from the Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) Consortium database, we evaluated whether adding docetaxel to testosterone suppression plus curative local treatment (radiotherapy or RP) can reduce prostate cancer–specific mortality (PCSM) and all-cause mortality (ACM) in patients with localized or locally advanced nonmetastatic prostate cancer who have both a PSA level of less than 4 ng/mL and a biopsy Gleason score of 8 to 10 and are in otherwise good health with performance status defined as 0.In 1 of the 7 RCTs,PRISMA) reporting guideline.This meta-analysis was not registered but is within the ICECaP collaboration with an institutional review board–approved protocol and a charter to regulate all secondary analyses; the statistical analysis plan (outlined below) was prespecified and followed for the current study. We used the checklist specifications for reporting of meta-analyses following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses  with or without docetaxel in the ICECaP database17 where RP was the SOC treatment, patients also received 18 to 24 weeks of ADT. Patients with localized or locally advanced nonmetastatic prostate cancer who had a serum PSA level of less than 4 ng/mL and a biopsy Gleason score of 8 to 10 were further identified, totaling 150. The 5 eligible patients of the 413 enrolled in the Urogenital Tumor Study Group (GETUG)–12 RCT16 were excluded due to the use of estramustine with docetaxel, given that estramustine is no longer used in the treatment of prostate cancer after being shown to significantly increase the risk of thromboembolic events21 and therefore increase ACM. This led to a final study cohort of 145 patients as shown in Treatment consisted of either RP alone or radiotherapy and ADT for 6 months to 2 years in the SOC group. For patients treated with radiotherapy, 3-dimensional conformal radiotherapy was primarily used, and inclusion of the pelvic lymph nodes in the radiation volume was generally performed for patients with Gleason scores of 8 to 10. In the docetaxel group of the RCT,All-cause mortality and PCSM were coprimary end points. All-cause mortality was measured from the date of randomization to death due to any cause, censored at the date of last follow-up in surviving patients. Prostate cancer–specific mortality was defined analogously to ACM, but death not due to prostate cancer was considered a competing risk. Cause of death was based on trial-defined events.22 and cumulative incidence function accounting for competing risks,23 respectively. These distributions were compared across randomized treatment groups using a log-rank24P value for the end point of ACM and Gray test23P value for the end point of PCSM.Data were analyzed on December 16, 2022. The distributions of the patient, cancer, and clinical factors including self-reported race as per institutional guidelines were enumerated according to randomized treatment group (SOC with or without docetaxel). For the purpose of illustration, the unadjusted distributions of ACM and PCSM were calculated by randomized treatment group using the 1 − Kaplan-Meier method25 and a subdistribution HR (sHR) (with its 95% CI) was estimated for PCSM using multivariable Fine and Gray26 competing risk regression, with adjustment for performance status (1 vs 0 [reference]), biopsy Gleason score (9 or 10 vs 8), clinical T category (T3-T4 vs T1-T2 or TX), and duration of ADT (2 years [long-term] vs 4 to 6 months [short-term]). Duration of ADT was added as a covariate to the model given that long-term ADT has been shown to be superior to short-term ADT in the management of high-grade prostate cancer with respect to the end point of OS.27 Performance status of 0  was defined as Eastern Cooperative Oncology Group, Zubrod, or World Health Organization performance status scores of 0. Performance status data were not available for patients enrolled in CALGB (Cancer and Leukemia Group B) 90203,17 and they were assigned a performance status of 0, given that they needed to be considered medically able to undergo an RP. A subgroup analysis was performed in the 139 patients with performance status of 0 to investigate whether the treatment effect of adding docetaxel to SOC treatment on the risk of PCSM and ACM was consistent with the results in the overall cohort. Given that only 6 patients had a performance status of 1, an interaction test between performance status and the docetaxel treatment effect was not possible.To evaluate the treatment effect on the coprimary end points, an HR (with its 95% CI) was estimated for ACM using multivariable Cox proportional hazards regression,14 contributed only 9 patients to the study cohort. However, the study cohort was homogenous, with all patients having a PSA of less than 4 ng/mL and biopsy Gleason score of 8 to 10, and the randomized treatment groups were similar, consisting of SOC plus docetaxel vs SOC. These factors combined should minimize heterogeneity of the treatment effect on the end points of ACM and PCSM. All statistical tests were 2-sided, and the Bonferroni method28 was used for multiplicity adjustment of the 2 comparisons performed  when calculating the unadjusted estimates of PCSM and ACM such that P < .025 (.05/2) was considered statistically significant. Median follow-up and associated IQR were calculated using the reverse Kaplan-Meier method.29 We used Stata/SE, version 16.1 (StataCorp LLC) for all statistical analyses.A test for heterogeneity was not performed given that 1 clinical trialAs shown in During a median follow-up of 7.1  years  among the 145 patients in the study cohort, 31  died; of these deaths, 22  were due to prostate cancer. Among the 139 patients with performance status of 0, 27 died ; of these deaths, 19  were due to prostate cancer.After adjustment for covariates, a nonsignificant reduced risk of ACM  and PCSM  was associated with patients randomized to the SOC plus docetaxel group compared with the SOC group . The risP < .025 was considered statistically significant), the comparisons of the estimates between randomized treatment groups was not significant for ACM (P = .03) and was significant for PCSM (P = .007). Specifically, among the 139 patients with performance status of 0, the unadjusted 8-year ACM estimate  had a nonsignificant reduction in the risk of both PCSM and ACM when docetaxel was added to the SOC therapies of testosterone suppression plus radiotherapy or RP. This observation was also valid but was more pronounced among patients with performance status of 0, for whom the reduction in PCSM risk was significant. The clinical importance of these findings is that in this patient cohort with progression to a castrate-resistant state early in the disease course,16 a sensitivity analysis to assess whether these patients benefited from the addition of docetaxel despite concurrent estramustine use was not possible. Second, while the study cohort included only 6.6% of all patients enrolled in the 4 eligible RCTs, death due to prostate cancer in this patient subgroup was the leading cause of death, accounting for 71.0% of all observed deaths; this finding illustrates the importance of identifying more effective therapy for reducing PCSM that can in turn reduce the risk of ACM. Specifically, by adding docetaxel to the SOC treatment, the absolute PCSM rate decreased more than 2-fold (from 15 to 7) among all patients and nearly 3-fold (from 14 to 5) in patients with a performance status of 0. Third, while the 95% CI included 1.00, numerical reduction occurred in PCSM and ACM among patients who received long-term  compared with short-term  ADT with testosterone suppression therapy. This observation is consistent with findings of a prior RCT27 where long-term and short-term ADT were compared among patients with locally advanced prostate cancer. Given that in the RP RCT,17 men in the docetaxel group also received a short course (18-24 weeks) of ADT, they would have been included in the 4- to 6-month ADT cohort; given the docetaxel use, this could have reduced PSCM and ACM, thereby decreasing power to assess a reduction in PCSM and ACM risk in patients receiving long-term compared with short-term ADT. Therefore, we believe it is prudent to consider long-term ADT as part of the testosterone suppression treatment plan when using radiotherapy as the local treatment in patients with Gleason scores of 8 to 10 and PSA levels of less than 4 ng/mL.Several points require further discussion. First, the performance status was associated with both PCSM and ACM. While the latter can be explained by competing risks in patients with comorbidity, the former can be explained by comorbidity in patients with performance status of 1, which can lead to a decreased ability to tolerate and complete the prescribed course of PC treatment. Moreover, while both PCSM and ACM were reduced among all men in the study cohort, these associations were more pronounced among men in otherwise good health (performance status of 0), highlighting the importance of performance status on outcomes following docetaxel use. Further study is warranted to assess the effect of docetaxel use on PCSM and ACM among patients with performance status of 1, given that only 6 men in the current study had a performance status of 1. In addition, with only 5 eligible patients in the GETUG-12 RCT,30 has shown that among patients treated with primary radiotherapy, adjuvant hormonal therapy using testosterone plus adrenal androgen suppression with abiraterone acetate has been associated with both a metastasis-free survival (MFS) and OS benefit in very high-risk prostate cancer when compared with testosterone suppression alone. Some patients enrolled in that study are included in the current study; namely those with Gleason scores of 8 to 10 and T3 to T4 prostate cancer. Therefore, it would be of interest to know whether the patient subgroup defined by a PSA level of less than 4 ng/mL, Gleason score of 8 to 10, and T category of T3 to T4 benefited from the addition of abiraterone to testosterone suppression therapy and radiotherapy. If true, then 2 options would exist to improve outcomes in this patient population. Further study would then be indicated in this patient cohort to ascertain whether triple therapy with testosterone suppression, abiraterone, and docetaxel is superior with respect to OS to the doublet of testosterone suppression and abiraterone. In addition, several androgen receptor–signaling inhibitors have been shown to prolong MFS in patients with castrate-resistant or ADT-resistant nonmetastatic prostate cancer.33 Multiple investigations studying the treatment effect on OS of adding these agents to testosterone suppression treatment with radiotherapy or RP in men with high-risk prostate cancer are actively recruiting or are in follow-up.35 The treatment effect of these more potent hormonal therapies in patients with high-risk prostate cancer and a PSA level of less than 4 ng/mL and Gleason score of 8 to 10 will be of major interest. If an OS benefit is observed with addition of the more potent hormonal therapy in this patient subgroup, then multiple options for treatment escalation will exist. Further study would be needed to determine the optimal specific combinations and sequencing of these agents for patients with a PSA level of less than 4 ng/mL and Gleason score of 8 to 10 and what effect docetaxel has on OS when added to these agents. It is also possible that a luminal proliferating gene expression profile or other genomic classifiers may be able identify which patients are most likely to benefit from docetaxel.37Finally, a meta-analysis of 2 phase 3 RCTs38 However, MFS was not a primary end point in the RCTs evaluated in the current study.18 As a result, the MFS results may be subject to ascertainment bias and not be reliable, given that guidelines on when to look for metastasis and by what means were not prespecified. Fourth, given only 6 patients with a performance status of 1, an interaction test between performance status and the docetaxel treatment effect was not possible. Therefore, the reductions in PCSM and ACM risk may be similar among patients with performance status of 0 and 1. Finally, the inability to report a measure of heterogeneity because 1 RCT14 only provided 9 patients to the study cohort raises the possibility of overstating the precision of results. This possibility exists because 1 of the 4 RCTs included in the meta-analysis was the source trial18 that generated the hypothesis of potential benefit when adding docetaxel to SOC treatment.Limitations of this study exist. First, 94 of the 145 patients (64.8%) had local treatment with RP, and these patients could represent a distinct subset, for example with less underlying comorbidity, compared with those who underwent radiotherapy. Second, 38 patients (26.2%) were in trials where 2 years of ADT was used. While we adjusted for ADT duration in the model and docetaxel use was associated with a 70% reduction in PCSM risk among patients with performance status of 0, the question remains as to what that result would have been if all patients received 2 years of ADT. Third, with 31 deaths representing only 21.4% of the study cohort, the data are still maturing. In addition, it is unlikely that an RCT addressing this question will be performed in this patient group with a poor prognosis. As a result, the current data are all that are available to address this question. Therefore, as the data mature, more precise estimates of the possible benefit of adding docetaxel to SOC in patients with a performance status of 0 will be needed before routine adoption into clinical practice. Metastasis-free survival has been shown to be a surrogate for overall survival.In this meta-analysis of RCTs, we found that adding docetaxel to SOC treatment in patients with prostate cancer who are in otherwise good health with a PSA level of less than 4 ng/mL and a Gleason score of 8 to 10 was associated with a significant reduction in PCSM and therefore has the potential to improve prognosis. In the future, specific attention should be given to this patient subgroup with poor prognosis when reporting on studies that evaluate the effect of more potent hormonal therapies on OS.


To investigate the feasibility and safety of watershed analysis after target pulmonary vascular occlusion for the wedge resection in patients with non-palpable and non-localizable pure ground-glass nodules during uniport thoracoscopic surgery.A total of 30 patients with pure ground-glass nodules < 1 cm in diameter, localized in the lateral third of the lung parenchyma, were enrolled. Three-dimensional reconstruction of thin-section computed tomography (CT) data was performed using Mimics software before surgery to observe and identify the target pulmonary vessels supplying the lung tissue in the area where the pulmonary nodules were localized and to temporarily block the target pulmonary vessels during surgery. Next, the extent of the watershed was determined with the expansion-collapse method, and finally, wedge resection was performed. After wedge resection of the target lung tissue, the blocked pulmonary vessel was released, thus allowing operators to complete the procedure without damaging pulmonary vessels.None of the patients experienced postoperative complications. The chest CT of all patients was reviewed six months after the operation, revealing no tumor recurrence.Our results suggest that watershed analysis following target pulmonary vascular occlusion for wedge resection in pulmonary pure ground-glass nodules is a safe and feasible approach. The increasing popularity of chest computed tomography (CT) during physical examinations has resulted in an increasing number of detected pulmonary nodules . FleischThe clinical data of 30 patients  with pulmonary pure ground-glass nodules who underwent surgery at our hospital between January 2020 and October 2021 were retrospectively collected. Inclusion criteria were as follows: (1) Any pure ground-glass nodule less than 1 cm in right or left upper lung was found under preoperative chest enhanced CT scan  and 35 HU, respectively.); (2) patients were regularly followed up for observation; with indications for operation identified after preoperative discussion, and wedge resection was proposed; (3) lesions were localized in the lateral third of the lung parenchyma, and were obstructed by the scapula or rib, or close to the great vessels of the heart, and were 7-9.5 mm in diameter. Exclusion criteria were: (1) patients with obvious contraindications to surgery, such as heart and lung function disorders; (2) Patients who are easy to puncture and locate under CT before surgery. Three-dimensional reconstruction with Mimics 21.0 software was used in all patients to determine the location of nodules, including 6 cases in RS1, 4 cases in RS2, 4 cases in RS3, 11 cases in LS1 + 2, and 5 cases in LS3.All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The present study was approved by the Ethics Committee of the Third Affiliated Hospital of Chongqing Medical University. Informed consent was waived by the same committee that approved the study  in the ethical approval and consent section.Mediastinal window enhanced CT data with a slice thickness of 0.625 mm were imported into Materialise Mimics 21.0 software (Belgium) for 3D reconstruction of nodules, trachea, and pulmonary vessels. The choice to import mediastinal window enhanced CT data instead of lung window data is based on its greater suitability for reconstructing pulmonary vessels. In Mimics software, when observing ground glass nodules, we can adjust the imported mediastinal window CT data to lung window data using the contrast adjustment function. The data were in DICOM format, and each 3D reconstruction takes approximately half an hour. During the reconstruction process, great efforts were made to reconstruct the peripheral pulmonary vessels to observe the pulmonary vessels closest to the nodules. The principles for selecting the pulmonary vessels to be blocked included: (1) pulmonary vessels closest to nodules; (2) it was not necessary to select the artery, but as long as it was easy to dissect and block under the uniport thoracoscopy, the vein was also considered as a good choice; (3) based on the size and location of the nodules, as well as the ease of dissection, the location for blocking the pulmonary vessels during surgery was at segment level or sub-segment level  it is a simple, economical method that does not require expensive equipment such as fluorescence thoracoscope and electromagnetic navigation bronchoscope; therefore, it can be applied in any hospital performing thoracoscopic surgery. (2) The proposed method is characteristically a wedge resection but without the need to expose and transect the bronchus, which makes it more simple than segmentectomy. Also, the blocked vessels are released after the operation without causing damage to the vessels, which makes the operation safer, and causes less trauma to the patient. (3) Safe margin is usually the most important issue in pulmonary wedge resection  that ourIn conclusion, watershed analysis following blocking the target pulmonary vessels provides a safe, effective, and economical surgical procedure for the precise wedge resection in pulmonary pure ground-glass nodules, with adequate resection margins. Furthermore, this approach is suitable for patients with pulmonary pure ground-glass nodules non-localizable under CT guidance before the surgery due to obstruction by the scapula or rib or close position to the great vessels of the heart, which makes it worthy of being widely popularized in the clinical application.