The aim of this study was to investigate the impact of bedside discharge education on activity levels and healthcare utilization for patients with acute coronary syndrome (ACS) in the first 30 days post-discharge. Knowledge recall and objective activity and location data were collected by global positioning systems (GPS). Participants were asked to carry the tracking applications (apps) for 30–90 days. Eighteen participants were recruited  61% ST elevation myocardial infarction (STEMI), mean age 55 years, 83% male. Recall of discharge education included knowledge of diagnosis , procedures , and comorbidities . In the first 30 days post-discharge, median steps per day was 2506 (standard deviation (SD) ± 369) steps , 62% visited a general practitioner (GP) 16% attended cardiac rehabilitation, 16% visited a cardiologist, 72% a pharmacist, 27% visited the emergency department for cardiac event, and 61% a pathology service (blood tests). Adherence to using the activity tracking apps was 87%. Managing Big Data from the GPS and physical activity tracking apps was a challenge with over 300,000 lines of raw data cleaned to 90,000 data points for analysis. This study was an example of the application of objective data from the real world to help understand post-ACS discharge patient activity. Rates of access to services in the first 30 days continue to be of concern. There has been considerable interest from hospitals and clinicians to better understand and improve modifiable factors associated with 30-day hospital readmissions ,2,3,4,5.Hospitalized patients commonly become deconditioned during hospitalisation and, as a result, often develop impaired stamina, coordination, and strength, which place them at greater risk for accidents and falls . These lCurrently, there is limited evidence for the effectiveness of hospital discharge education in ACS or its cost effectiveness. Translation of innovative, patient-centred, intervention strategies will contribute to the reduction of 30-day re-hospitalisation rates of patients discharged from the hospital after an ACS event ,7,8.p = 0.049). No difference was reported between the intervention and control for medication adherence (p = 0.881). None of the three studies reported on re-admission, mortality or cost-effectiveness. This limited evidence for practice highlights the need for rigorous evaluations of novel ACS discharge patient education interventions [In 2018, we conducted a systematic review of published randomised controlled trials and found only three studies, with one hundred and seventy-five participants evaluating discharge education strategies for ACS . In thesAcknowledging the limited evidence for discharge education, the National Heart Foundation of Australia provides general recovery advice via its website and printed resources ,11. In tOther forms of post-discharge ACS education such as those presented at cardiac rehabilitation programs are often not accessed until several weeks after the event which may be past the point of buy-in for the patient. Additionally, the current uptake of cardiac rehabilitation (CR) and secondary prevention programs in Australia and around the world is between 20% and 46% ) so manyPhysical activity is a major component in a CR program and is integral to recovery from and ACS event . This isThe use of wearable physical monitoring devices offers significant potential for researchers and clinicians working to promote or measure a patient’s activity levels. Activity tracking devices have the potential to effect physical activity behavior change, make a direct and real-time impact on self-management of physical activity and offer clinicians real world assessments of their patients’ daily activity patterns . WearablFinally, global positioning systems (GPS) devices are now commonly used in society e.g., in high performance sport analysis, driving and even tracking movements of pets. Specialist software is required for the download and analysis of these data. The GPS data maps the distance and locations travelled and these can be matched to service locations. Unlike activity tracking devices, GPS devices do not differentiate travel by walking or vehicle nor educate or foster engagement with the tracking process.Determine the level of enthusiasm (satisfaction) of metropolitan and rural Aboriginal and Torres Strait Islander and non-Aboriginal and Torres Strait Islander cardiac patients to be involved in studies using tracking technology applications (apps).Test the adherence of participants to using GPS and fitness tracking technology.Compare the 30-day and 90-day post-discharge activity levels between those who lived in metropolitan areas with those who lived in rural areas.Compare levels of attendance to cardiac healthcare services between patients who lived in areas with high access (metropolitan) and low access .Evaluate participant recall of discharge education and advice.The purpose of this research was to collect real-world data to determine if attendance to cardiac follow-up services was related to access  in this important discharge period for ACS patients. The aim of this study was to also demonstrate the innovative use of GPS combined with geographic information systems (GIS) to objectively measure outcomes and explore if patients implemented recommended activity and utilisation of healthcare services  in the first 30 days and 90 days after discharge from hospital. The specific objectives of this study were to:To achieve our objectives, we used an observational design with quantitative and digital data collection (GPS and GIS) and qualitative patient-reported experiences (satisfaction survey) and a discharge education recall questionnaire.Participants for the study were recruited as inpatients, between January 2018 and September 2018, from two metropolitan hospitals.Participants were stratified to ensure representation from Aboriginal vs. Non-Aboriginal people and metropolitan and rural populations. Participants were recruited immediately prior to discharge. Aboriginal Liaison nurses assisted with the screening and recruitment of potential Aboriginal and Torres Strait Islander people.Eligibility criteria included: an index admission with a confirmed diagnosis of ACS; greater than 18 years of age, able to give consent; ability to speak, read and understand English; willingness to carry a phone for at least 30 days; and ability and willingness to receive a weekly phone call. Participants were ineligible to participate if they had cognitive impairment, were unable to participate in physical activity; had been admitted previously with ACS or were clinically unstable.Participants were educated on the use of the mobile phone activity tracking apps and requested to carry the phone for at least 30 days and up to 90 days. The phone was pre-installed with intervention software allowing for remote, virtual data collection. Participants were instructed to charge the phone overnight when they were asleep to avoid impacting on activity data collection. Participants were contacted weekly by the respective research nurses to assist with any technology issues.This project was not about developing a mobile phone app. There were an overwhelming number of GPS and activity tracking mobile phone apps on the market at the time of this study. A Google search for GPS tracking apps revealed 7.25 million hits and 14.2 million hits for physical activity tracking apps. In this study there were three important criteria for selection of suitable commercially available mobile apps: (1) data could be collected from remote country locations and the data generated by the app had to be downloadable from a web portal, (2) battery usage, the apps needed to be able to run constantly on the phone for 12–16 h without the need for recharging and (3) low cost or free.An unmodified ZTE 4GX smartphone mobile phone , operating on a commercial satellite network was provided to all participants . The dev) and emailed to the project manager on a weekly basis for entry into the database (Microsoft Excel 2016).Baseline data was collected from review of medical records and included age, postcode, gender, Aboriginal or Torres Strait Islander status, smoking status, household structure, height, weight, medical history, cardiac procedures, cardiac risk factors and comorbidities. Demographic data were collected in hard copy Case Report Forms .Downloaded activity and GPS data was used to measure adherence by evaluating missing data, gaps in recording and periods of non-activity.To compare the 30 to 90-days post-discharge activity levels of metropolitan participants to rural, the Google Fit app was used to collect daily step counts. Activity data were emailed daily to the research team from the respective applications in comma separated values (CSV) format. The daily step data obtained from the Google Fit application were aggregated and mean and median steps per person per day were calculated for the periods of 1–30, 31–60 and 61–90 days (the latter period data were adjusted to reflect those who exited the study prior to 90 days). A total of 151,859 rows of raw data were generated in CSV format from this study.TM software . The FollowMee™ application collected geospatial data (primary measure). Descriptive statistics were generated for the accuracy of the data point and stratified by type (GPS or Network) and a rural or metropolitan classification of the participant. A summary of data points, categorised by time, were produced to give an indication of device usage over the project, as was a summary of data availability based upon accuracy of data point. A total of 153,000 lines of raw data cleaned to 45,577 were generated in CSV format during the study period.Participant home locations were stratified using the accessibility and remoteness index of Australia (ARIA) to determine rurality. Participants were asked to give the address of their local services  at base-line and these were mapped using VERITASThe GPS tracking app was required to have a two-minute ping option , while the activity tracking app needed to include a step counter and measure of distance travelled. The FollowMee™ GPS tracking application featuring a two-minute ping combined with the ability to automatically suspend tracking when the participant was inactive and reactivate upon movement offered both battery efficiency and a high level of location accuracy. Similarly, the GoogleFit™ application offered high accuracy with the required activity tracking features while conserving battery power. The cost of subscriptions for both applications needed to access all the required features, were moderate. The use of these applications facilitated the use of a single device by the participants without the need for manual data download or return of the device streamlined the data collection process significantly. The GPS data mapped the distance and locations travelled by the participants and were matched to service locations. The street addresses were geocoded and based on the accuracy of the data points within each of seven arbitrary buffers zone See  a 50 m bImmediately prior to discharge patients were interviewed by the research nurse who administered (face-to-face) a questionnaire summarising “The 6-Steps to Recovery” . The queGiven this was intended to act as a feasibility study to inform a larger project, the study size was determined primarily by the funding timeframe (12 months). This was broken down into device and application testing which ran concurrently with ethical and governance submission (3 months), recruitment period (4 months) follow-up (up to 90 days post-enrolment) and analysis and manuscript preparation (2 months).If participants adhered to the protocol their data was uploaded from the device. Follow-up was attempted once each week on those who were uncontactable at their allocated appointment time. Those who failed four consecutive follow-up calls were considered lost to follow up.Data management were undertaken in Microsoft Excel 2016. All spatial analyses were carried out in ArcGIS Desktop 10.2.2  Redlands, CA, USA) and statistical analyses were undertaken in IBM SPSS for Windows, Version 22. Summary statistics were used to describe data from on the characteristics of the participants of different groups. Cronbach’s alpha was used to quantify interrater reliability between self-report and medical record data. Subgroups were stratified as (1) rural/remote or metropolitan and (2) self-report or medical record. Missing spatial data was unable to be accounted as it relied upon adherence to the study protocol by participants. A summary of data points stratified by time provides is indicative of device adherence. Data accuracy in meters was collected, as was the source (GPS or Network). Stratification of available data were used to guide the final dataset used for analyses . Statistical tests were not used to compare groups due to low overall participant numbers.Health Research Ethics Committee approval was given from both recruitment sites (HREC/17/RAH/58 and R20170222) with additional Aboriginal Health Research Ethics Committee approval AHREC Protocol #: 04-17-714. Activity reported in this study was strictly confined to physical activity and attendance at cardiac related health services during the 30 to 90-day post-discharge period.During the study period, 238 participants were screened including 18 Aboriginal and Torres Strait Islander people. Reasons for exclusion included: admitted previously with ACS ; people who declined to participate (10%); cognitive impairment (6%); awaiting surgery or review (5%) and other included being enrolled in another study (41%). At completion of the recruitment period, 19 participants had consented including two Aboriginal and Torres Strait Islander people. One of the Aboriginal participants was later lost to follow-up which left only one Aboriginal person participating, consequently this person’s data has not been reported separately . AlthougThe mean age of the participants was 55.8 years. The majority were male (83%)  and lived with a partner or carer (88.9%) . The parAt the recruitment phase enthusiasm was high with only 10% of those approached declining to participate. This participant group reported highly satisfactory experiences with participating in the study. Satisfaction was measured on completion of follow-up at 30 days (87%) and 90 days (90%).Downloaded activity and GPS data was used to measure adherence by evaluating missing data, gaps in recording and periods of non-activity. All except one participant maintained (charged) the phones and ensured data transmission. Consequently, at completion of the study, 18 (94%) participants had provided 30 days of tracking data and 15 (83%) participants had provided 90 days of tracking data.Total physical activity recorded for all participants in the was a median of 2506 (standard deviation (SD) ± 369) steps per day in the first 30 days, slightly higher for metro participants (median 3543 (SD ± 826) steps) and slightly less for rural participants (median 1987 (SD ± 141) steps). Two thousand five hundred steps per day was the equivalent of 20 min of walking or approximately 1.5 km .p < 0.05) by 33% to 1686 (±128) steps per day (metro ↓ 22% 2777 (±177) steps vs. rural and remote ↓ 35% 1290 (±172) steps). There was one rural outlier who completed 10,000 steps per day from seven days post-discharge. One of these daily walks demonstrating the maps produced by the GoogleFit™ app is presented in There was an overall 10% increase in physical activity in the next 30 days, but this was not sustained. By 90-days post-discharge the total physical activity as measured by steps-per-day had significantly decreased  compared to the mean accuracy of satellite-captured data which was within 1832.7 m . For rural participants, the mean accuracy of GPS data points was within 98 m  and for metropolitan participants the mean accuracy of GPS data capture was 33.5 m . Based upon the summary of data availability stratified by the accuracy of the data point, only data points with an accuracy of ≤20 m were retained for the spatial analysis . This was a balance between enough rigor and utilising the data available.Access to cardiac related health services in the first 30 to 90 days post-discharge is presented in To evaluate participant, recall of discharge education and advice the Heart Foundation 6 Steps to Recovery discharge education framework was used. The 6 Steps are: (1) Explain diagnosis and procedures; (2) Highlight risk factors relevant to the patient; (3) Emphasise the importance of cardiac rehabilitation; (4) Promote medication adherence; (5) Educate on the warning signs of heart attack and (6) Encourage follow-up with your doctor  Table 4.(1) Explain diagnosis and procedures: When questioned immediately prior to discharge 100% of the participants could recall their diagnosis, 88% of participants who had a stent indicated they had this procedure and a mean of 24% participants could recall other procedures conducted during the admission.(2) Highlight risk factors relevant to the patient: Ninety percent of participants knew they had a family history of CVD, 100% knew they had diabetes, 60% hypertension and 80% hypercholesterolaemia. Sixty percent recalled being given advice about exercise, diet and follow-up appointments and 70% recalled being given advice regarding medications. Ninety percent of participants recalled the advice they were given about driving. With respect to understanding which risk factors could affect their heart, one participant (5.6%) had knowledge regrading diabetes, hypertension and hypercholesterolemia, three (16.7%) understood about the impact of body weight, four (22.2%) smoking and two indicated they thought stress was a risk factor.(3) Emphasise the importance of cardiac rehabilitation: According to the GPS tracking data, at 30-days post-discharge, twp (16.7%) participants  had attended the location where a cardiac rehabilitation service was located. In the period up to-90 days a total of three (25%) participants  had attended a cardiac rehabilitation service .(4) Promote medication adherence: At the 0–30day timepoint 13 (72.2%) had been tracked to their local pharmacist location at 31–90 days 11 (61.1%) participants had attended.(5) Educate on the warning signs of heart attack: Eighty eight percent of the participants recalled receiving warning signs information contained in the discharge information booklet.(6) Encourage follow-up with your doctor: The purpose of this research was to collect real-world data and determine if access to services had an impact on utilisation in this important discharge period for ACS patients. The aim of this study was also to demonstrate the innovative use of GPS combined with GIS to objectively measure outcomes and explore if patients implemented recommended activity and utilisation of healthcare services  in the first 30 days and 90 days after discharge from hospital.Our recruitment plan was to recruit DiNovo ACS patients at discharge including fifty percent Aboriginal and Torres Strait Islander people and non-indigenous people equally from metro and rural and remote areas. Review of our recruitment data indicated that during the recruitment period sixteen of the eighteen potential Aboriginal people screened had had a previous ACS event and were therefore not eligible to participate.The demographic and clinical characteristics of participants mean age of the participants was 55.8 years, the majority were male, 60% STEMI mean of four risk factors per person (range 2–8) were consistent with other studies reporting the characteristics of Australia ACS patients ,26.At the recruitment phase enthusiasm was related high with only 10% of those approached declining to participate. This group, which was mostly male, reported high levels of satisfaction with participating in the study. Our conclusion to the high levels of enthusiasm was the participation incentive of a free phone. However, this was moderated by adherence to the study protocol. At completion of the study, 18 (95%) of participants had provided 30 days of tracking data and 15 (83%) of participants provided 90 days of tracking data. This rate was high compared to our recent systematic review  that exaSatisfaction with the experience of being involved with this study was high (30 days (87%) and 90 days (90%). Patient-reported measures (PREMS) collect information about the experience in a research study, and the outcomes of research involvement, as described by patients. Interest is now growing in strengthening and coordinating efforts to collect patient-reported information to benefit patients participating in health research .At discharge patients with a diagnosis of ACS are advised to aim to build up to doing at least thirty minutes of moderate-intensity physical activity on most, if not all, days of the week. This can be achieved in three lots of ten minutes each if it’s easier for example ten minutes each of walking, gardening and light housework  in this Access to cardiac related health services in the first 30 to 90 days post-discharge could be described as moderate to low. Attendance at a cardiac rehabilitation program which was sixteen percent compared to national average of twenty to forty percent. Additionally, only sixty percent of participants could recall being told about the importance of adhering to medication and keeping their appointments with doctors.In Australia, approximately 96% of the population live within one-hour drive time of the four basic services to support cardiac recovery and secondary prevention, including 96% of older Australians and 75% of the indigenous population. A study measuring access to CVD secondary prevention health services, demonstrated that most Australians have excellent geographic access to services to support cardiac recovery and secondary prevention. Therefore, it appears that it is not the distance to services that affect attendance. Our geographic lens has identified that more research on socioeconomic, sociological or psychological aspects to attendance is needed .Participant recall of the discharge information was variable. All participants and all participants with diabetes knew their diagnosis. The CVD risk factors were less understood and discharge advice seemed to reflect only high-level recall of the issues immediately and directly affecting participant lifestyles, for example ninety percent (range 88–94%) recalled being told about driving limitations but only sixty percent could remember being told about diet, exercise and follow-up appointments. These results reflect the outcomes reported previously where only one-quarter of all patients admitted with ACS received optimal secondary prevention  . The SNAThe main limitations of this study were sample size and follow-up time frames which limits the generalizability of the results. However, this was a feasibility study to inform a larger project, the study size was determined primarily by the funding timeframe of 12 months.Although it was the intention of this study to recruit fifty percent Aboriginal people due to previous ACS admissions the majority who presented during our limited recruitment period were ineligible. The lesson learned from this was that a longer recruitment period would be required for future studies.TM app.Managing Big Data from the tracking apps was a challenge with thousands of data points delivered daily and a large amount of research funding consumed to manage and prepare these data for analysis. A lesson learned from this experience would be that for future similar studies the budget must include funding for a data manager whose role is to solely tend to Big Data management. Logistical issues also occurred in managing an individual email account for each phone/participant from the GoogleFitSome mobile phones exhibited poor battery life and some participants forgot to charge the phones nightly; this was remedied by the addition of a nightly text reminder service.Weekly follow-up calls were also a workload burden even for this small group. Since the apps were installed on mobile phones, we would recommend text messages communication for future studies.Due to the co-location of many community health services data may have overestimating access to cardiac-related healthcare services.Most data points were captured by GPS. Rural participants had a higher proportion of network-capture than metropolitan participants due to line of sight from their location to the mobile phone tower.GPS tracking devices did not differentiate travel by vehicle or educate or foster engagement with the tracking process.TM and GoogleFitTM) were off the shelf and offered limited customizability; furthermore, the FollowMeeTM app was prone to crashing several times a week for some users.Both apps (FollowMeeFinally, the naming of this study was a lesson learned. The original title given to the study was “The Tracker Study”. Early in the recruitment phase we were informed that a “Tracker” is a euphemism for ankle bracelet home detention devices. After consultation with the Wardliparringga Aboriginal Research Unit the study title was modified.This study was an example of the application of objective data from the real world to help understand post-ACS discharge patient activity. Rates of access to services in the first 30 days continue to be of concern. The outcomes of this exploratory work will provide an understanding of the impact of discharge education after ACS hospitalisation. The preliminary data collected from this study will be used to develop innovative, patient-centred, interventions that can improve outcomes by improving adherence to secondary prevention strategies and reduce 30-day re-hospitalisations and mortality.


Although antenatal betamethasone is recommended worldwide for women at risk of preterm delivery, concerns persist regarding the long-term effects associated with this treatment. Indeed, adverse events, mainly dose-related, have been reported. The current recommended dose of antenatal betamethasone directly derives from sheep experiments performed in the late 60’s and has not been challenged in 45 years. Therefore, randomized trials evaluating novel dose regimens are urgently needed.A randomised, double blind, placebo-controlled, non-inferiority trial will be performed in 37 French level 3 maternity units. Women with a singleton pregnancy at risk of preterm delivery before 32 weeks of gestation having already received a first 11.4 mg injection of betamethasone will be randomised to receive either a second injection of 11.4 mg betamethasone (full dose arm) or placebo  administered intramuscularly 24 h after the first injection. The primary binary outcome will be the occurrence of severe respiratory distress syndrome (RDS), defined as the need for exogenous intra-tracheal surfactant in the first 48 h of life. Considering that 20% of the pregnant women receiving the full dose regimen would have a neonate with severe RDS, 1571 patients in each treatment group are required to show that the half dose regimen is not inferior to the full dose, that is the difference in severe RDS rate do not exceed 4% (corresponding to a Relative Risk of 20%), with a 1-sided 2.5% type-1 error and a 80% power. Interim analyses will be done after every 300 neonates who reach the primary outcome on the basis of intention-to-treat, using a group-sequential non-inferiority design.If the 50% reduced antenatal betamethasone dose is shown to be non-inferior to the full dose to prevent severe RDS associated with preterm birth, then it should be used consistently in women at risk of preterm delivery and would be of great importance to their children.NCT 02897076 (registration date 09/13/2016).ClinicalTrials.gov identifier: The online version of this article (10.1186/s12884-019-2206-x) contains supplementary material, which is available to authorized users. The beneficial pulmonary effects of antenatal corticosteroids (ACS) were originally discovered in sheep in the late 60’s when Liggins noticed the partial aeration of the lungs of preterm lambs born after ACS therapy . In the Despite short-term benefits, concerns persist regarding the long-term effects associated with ACS. Indeed, extensive animal studies have simultaneously revealed the impact of ACS on the programming of many fetal tissues and organs –11. AcroFurthermore, results from animal and human studies have shown that these side-effects could be dose-related. Indeed, studies in sheep , rabbitsClinical trials testing dose regimens other than 12 mg betamethasone twice 24 h apart have never been performed. However, sheep studies indicate that a 50% dose reduction is as effective to induce lung maturation as a full dose , 46. In The primary aim of this non-inferiority 1:1 randomised trial is to determine whether a 50% reduction of the antenatal betamethasone dose given to women at risk of very preterm delivery is not inferior to a full dose to prevent severe RDS associated with preterm birth. We made the alternative hypothesis that the difference in failure rate between the reduced-dose and the full-dose arm does not exceed 4% (corresponding to a Relative Risk of 1.20).The secondary aims are to compare other neonatal complications between the 50% reduced and the full antenatal betamethasone dose regimen.Randomised, multicenter, double blind, placebo-controlled, group-sequential non-inferiority trial. The trial protocol  adhered to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement for protocols of clinical trials .Thirty-seven French level 3 maternity units will be involved  24 h after the first injection of betamethasone.Women of the experimental half dose group will receive a second intramuscular injection of serum saline 24 h after the first injection of betamethasone.Women of both groups justifying a rescue course of ACS are allocated a “rescue course treatment pack” from the same treatment group. All others concomitant care and interventions are permitted during the trial and none are prohibited.The trial information sheet is given to all eligible women after the first injection of betamethasone. They will be counseled by a member of the research team and encouraged to discuss the study with their family in the 24 h interval between the 2 injections and before the written informed signed consent is sought.After obtaining maternal consent, randomisation is performed just before the second injection, using a web-based application and a secured access, in a one-to-one ratio to the full or half dose groups according to a computer-generated list of randomly permuted blocks. The randomisation is stratified by maternity units and gestational age (before or after 28 weeks). The randomisation list is centrally computer-generated and a web-based application ensures proper allocation concealment. The allocation sequence is not available to any member of the research team until the database will be completed and locked. Patients, care providers and outcome assessors are masked to assignment. A study number is allocated to the woman corresponding to a treatment pack, each of which looks identical and contains two opaque study-labeled vials.the DRCD in a situation other than an emergency during the work day and during working hours, addressed to the DRCD’s project referent.the poison centre of Fernand Widal Hospital, in the case of an emergency (see emergency situations requiring unblinding), on weekends, bank holidays, when the DRCD is closed and when unblinding cannot be carried out at the DRCDUnblinding will be requested for any reason considered essential by the investigating doctor by calling upon:Women will be followed from randomisation to delivery.Follow-up visit on Day1, 48 h after birth, Day2 to Day7, and Day28 will be performed by the neonatologists in the neonatology units for preterm neonates and on Day 1 and 48 h after birth by neonatologists in the postpartum units for full term neonates. During these visits, neonatologists will assess vital and ventilation parameters of the newborns, the primary and secondary outcome measures. The last research visit will take place in the postpartum units the day of hospital discharge for full term neonates and in the neonatology units not after 37 weeks of postmenstrual age (PMA) for neonates born preterm. At this visit, secondary outcome measures will be assessed by the neonatologists.Neurodevelopmental assessment of children by certified neuropsychologists is planned at 3 years of age, but is not part of the present protocol.The primary assessment criterion is severe respiratory distress syndrome (RDS), defined as need for exogenous intra-tracheal surfactant within the first 48 h of life. It is considered as a binary endpoint: failure in case of severe RDS, or not failure.Respiratory distress syndrome defined within the first 72 h as the use of continuous positive airway pressure and/or supplemental oxygen during at least 24 h, or the use of mechanical ventilation.Transient tachypnea of the newborn defined as a need for continuous positive airway pressure and/or supplemental oxygen, resolving within 24 h.○ Highest appropriate fractional inspired oxygen (FiO2),○ Maximum appropriate Mean Airway Pressure (MAP),○ Use and duration of mechanical ventilation,○ Use and duration of oxygen therapy,○ Need for oxygen therapy after 36 weeks post conception.Outcomes related to the severity of RDS:○ Neonatal death before discharge*,○ Admission to neonatal intensive care unit,○ Use of inotropic support including dopamine, epinephrine, dobutamine, and norepinephrine,○ Pneumothorax,○ Patent ductus arteriosus requiring either medical or surgical treatment,○ Bronchopulmonary dysplasia at 36 weeks of PMA ,○ NEC and grade according to Bell classification ,*○ IVH and grade according to the Papile classification ,*○ Cystic periventricular leukomalacia,○ Use of postnatal steroids ,○ Retinopathy of prematurity requiring anti-VEGF treatment or laser*,○ Length of hospital stay before the first discharge home,○ Survival without severe RDS, IVH grade 3 and 4, NEC grade ≥ 2, or retinopathy of prematurity treated by anti-VEGF or laser.Outcomes related to betamethasone impact on other prematurity-induced complications:○ Birth weight at birth,○ Head circumference at birth,○ Body length at birth,○ Suspected or confirmed early onset sepsis treated using antibiotics during 7 days,○ Hypoglycaemia requiring oral or IV glucose administration or glucagon within 7 days.Outcomes related to potential adverse events of betamethasoneThe secondary assessment criteria will be measured during the neonatal period until hospital discharge for children born at term and not later than 37 weeks of PMA for the premature babies. They will include:safety outcomes that will be monitored at each interim analysis.Outcomes marked with an asterisk * are the four Follow-up data will be collected by trained clinical research technicians on an electronical case-report-form (eCRF). To avoid women lost to follow-up, they will track women deliveries, especially when taking place outside the investigation centres. eCRFs shall be periodically cross-checked for completeness. A data management plan will be written and follow during all the data management and analysis process.Data will be handled according to the French law. The eCRFs will be hosted by a service provider into a secured electronic system via a web navigator and protected by an individual password for each investigator and clinical research technician. Participant’s identifying information will be replaced by an unrelated sequence of characters to ensure confidentiality. The steering committee will have access to the full trial dataset. The trial database file will be stored for 15 years. The sponsor is the owner of the data.To study the non-inferiority of the 50% reduced betamethasone dose regimen, we will test the alternative hypothesis that the difference in failure rate between the half-dose and the full-dose arm do not exceed 4% (corresponding to a Relative Risk of 1.20). This non-inferiority margin has been obtained through a consensus between the investigators of the GROG, neonatologists and the methodologists of the study, considering that a 4% difference is the smallest value that would be clinically relevant between arms and correspond to the preservation of a 70% of the effects of the full dose betamethasone regimen over placebo. Indeed, in the French Epipage2 study (2011) , 62% of The primary non-inferiority statistical analysis will be performed according to both the intention-to-treat and per protocol principle, as it is recommended for non-inferiority trials . The intn = 3142) and (v) the primary endpoint measured in the first 48 h of life, a sequential data analysis method will be used, allowing to provide stopping rules. Analyses will be done after every 300 neonates who reach the primary outcome on the basis of intention to treat. The trial may be stopped for the following reasons:i.Inferiority of the experimental group on the primary outcome,ii.Non-inferiority of the experimental group on the primary outcome,To address the ethical concerns of (i) studying a high-risk population (pregnant women and preterm neonates), (ii) the potential increased rates of babies with severe RDS due to the reduction of the betamethasone dose, (iii) the anticipated length of time of inclusion (30 months), (iv) the number of infants planned for a fixed analysis  will be estimated in order to detect a potential increasing in the experimental group. Those estimation will be made on the overall population, and by gestational age at birth , as expected prevalence are different from one subgroup to the other.Data analysis and reporting will follow the CONSORT guidelines for non-inferiority randomised controlled trials . The twoThe final primary non-inferiority statistical analysis will be conducted on all the neonates enrolled in the trial, including those who did not participate in the interim analysis. The difference between the failure rates observed in both arms along with its 2-sided confidence interval will be estimated. The final boundary for difference will be compared to the critical value corresponding to the number of women finally included, to claim or not the non-inferiority. A figure showing confidence intervals and the margin of non-inferiority will be used to summarize the result on the primary outcome.The analyses on the other pre-specified secondary outcomes will consist in estimations and comparisons between the two arms. The 95% confidence interval for the difference between arms will be constructed. The χ2 or Fisher’s exact test for categorical variables and Student or Mann-Whitney-Wilcoxon test for continuous variables will be used to compare the full-dose and the half-dose regimens, according to the validity conditions. All these statistical tests will be two-sided and the level of statistical significance will be set at 5% (2-sided).Gestational age at birth Gender of the newbornPlanned subgroup analyses include:The primary analysis, using the confidence interval for the difference between full-dose and half-dose.The following secondary analysis: death, IVH grade 3 and 4, NEC grade ≥ 2, or retinopathy of prematurity treated by anti-VEGF or laser, survival without severe RDS, IVH grade 3 and 4, NEC grade ≥ 2, or retinopathy of prematurity treated by anti-VEGF or laser, using 2-sided tests, as stated above.For those subgroups, we will repeat:For both those analyses, we will use the Holm-Bonferroni method to adjust for multiplicity of analyses .Statistical analyses will be performed with SAS  and R (V 3.4.2) software.A trial steering committee will include the coordinating investigator (TS), the scientific director (OB), the biostatistician (MU), the methodologists (CAi and CAs), and the representatives for the sponsor and for the unit in charge for the data collection and management. They will be responsible for the organization and the coordination of the trial. They will meet on a quarterly basis to review the progress of the trial.The trial safety will be evaluated by an independent Data Safety Monitoring Board (DSMB) at each interim analysis or when additional analyses will be requested by the sponsor or the steering committee. The DSMB will include experts in or representatives of the fields of obstetrics, neonatology, and clinical trials methodology. At the first time they met, the DSMB will validate that the methodology is compatible with the safety of the participants. Prior to each DSMB meeting, they will be provided a complete list of all adverse events, and a statistical report including description of the population and results of the interim analysis as described above. At each meeting, the DSMB may give the advice to temporarily or definitely stop the trial if in their opinion there is an unexpected or unacceptable risk for the women or the newborn, or if the interim analysis suggests the non-inferiority or futility.The statistical plan has been written before the starting of the trial and approved by the steering committee, the sponsor, the French Agence National pour la Sécurité du Médicament (ANSM) and the DSMB. All modifications will be subject to the approval of these entities.The steering committee will determine the plan for dissemination policy. Authorship for manuscripts submitted for publication will follow the criteria defined by the International Committee of Medical Journal Editors.Administration of antenatal betamethasone to women at risk of preterm delivery leads to substantial benefits for babies born preterm. Although this treatment is widely used and recommended worldwide, concerns persist regarding its long term effects because adverse events, mainly dose-related, have been reported. Because the current recommended dose of antenatal betamethasone directly derives from sheep experiments performed in the late 60’s and has not been challenged in 45 years, large randomised trials evaluating novel dose regimens are urgently needed. If a 50% reduced antenatal betamethasone dose is shown non inferior to a full dose to prevent the neonatal complications associated with preterm birth then it should be used consistently in women at risk of preterm delivery and it would be of great importance to their children.Additional file 1:Table 1: List of the participating centers. (DOCX 36 kb)


Transfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place.The objective of this paper is to test the feasibility of a system based on a wearable low-cost sensor to monitor transfer skills in real-world settings.We collected data from 9 WU wearing triaxial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data.Results show a good predictive accuracy of support vector machine classifiers when determining the use of head-hip relationship (75.9%) and smoothness of landing (79.6%) when the starting and ending of the transfer are known. Automatic transfer detection reaches performances that are similar to state of the art in this context . However, we achieve these results using only a single sensor and collecting data in a more ecological manner.The use of a single chest-placed accelerometer shows good predictive accuracy for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous-technology based personalized skill development interventions for WU. However, monitoring transfers still require the use of external inputs or extra sensors to identify the start and end of the transfer, which is needed to perform an accurate evaluation. Globally, there are over 70 million wheelchair users (WU), and there is a growing need for wheelchairs to fill the mobility gap for people who are unable, or struggle, to walk . This isTransfers are necessary for many daily activities and happen on average between 14 and 18 times a day ,3. TransLearning how to transfer correctly is a critical skill for WU. In order to maintain this independence WU must preserve the functioning in their upper limbs. However, due to the exceptionally high loads, and the repetitive nature of wheelchair transfers, WU frequently suffer from pain in the shoulders and wrists . This paWheelchair skills training helps to prevent such injuries by teaching WU correct techniques for everyday activities such as pushing over a variety of surfaces and transferring onto and from many surfaces. Clinicians mostly deliver wheelchair skills training  within rIndeed, there is no routine way for wheelchair transfers to be monitored remotely in everyday life settings. Furthermore, the provision of wheelchair skills training is not universal and can depend on geography , medical diagnosis , and funding . The WebWearable technologies offer the opportunity to provide monitoring and feedback to WU during their daily lives, particularly on activities and techniques which are known to cause injury. Research in this area has focused on automatically detecting different types of activities from one another  ,11. MostThere has only been 1 study which has evaluated the accuracy of classification algorithms for detecting the occurrence of wheelchair transfers, alongside other activities . The resThe primary aim of this study was to develop a strategy to enable the use of a single low-cost wearable sensor to evaluate the quality of wheelchair transfers across 3 common transfer scenarios. Body-worn sensors are often used to detect movement . However, they are rarely used to evaluate the quality of body movement . This isThe study was approved by the Internal Review Board at the Georgia Institute of Technology, United States. Calls for participants were made via a laboratory database, recruitment flyers, social media, and relevant online forums. Interested subjects were screened against the following criteria: (1) between 18-65 years of age, (2) use of a wheelchair as primary means of mobility for at least six months, and (3) ability to perform wheelchair transfers independently. Participants were excluded if they (1) were able to fully stand up when transferring, (2) reported the use of a transfer board when transferring, (3) were currently admitted to a hospital or a rehabilitation facility, and (4) reported having upper extremity pain or any medical condition that was likely to be exacerbated through the study protocol .In this study, we consider the use of 1 accelerometer placed on the chest of the user to measure g-force acceleration. The accelerometer was secured to the upper third of the sternum of the participants using double-sided tape. The chest was chosen as it is the part of the body which dictates a good transfer  and is helpful in detecting the start  and end points of the transfer . Also, the trunk is in motion throughout the wheelchair transfer cycle, whereas the arms are often stationary during key moments in the transfer ,20. Finath order low pass Butterworth filter with a cut off frequency of 10Hz to reduce noise. Two video cameras were used to record participants’ transfers, to label the recorded data for transferring quality performance and to determine exact seat-off (start) and landing time (end). Data processing was carried out on MATLAB R2015b, and the accuracy of various classifiers was calculated using WEKA 3.8 data mining suite.Trunk accelerations were recorded using a single wireless 3-axis accelerometer  sampling at 25Hz. The directions of the acceleration see  were meaFor the data collection, a series of ecologically valid scenarios see  was usedParticipants were asked to perform 2 return transfers  for each scenario using their own wheelchair. Participants were asked to complete the transfers as they normally would in their everyday lives. The order of the 3 scenarios was randomized across participants. Also, between each transfer, the person was asked to move around the room to ensure variability between transfer executions. Participants were asked to rest for a minimum of one minute after each transfer. Additional resting time was granted to participants who requested it in order to avoid fatigue.Accelerometer data were collected continuously for the duration of the experiment while the participant rested and moved between different scenarios. Each participant performed 12 transfers for an average of a forty-minute recording for each participant.Descriptive statistics, were determined for demographic data of participants.Following the method proposed by Hwang et al , the traHead-hip relationship (item 12)Subject moves the head in the opposite direction of the hips to make the transfer easier to performControlled flight (item 11)Transfer is smooth and uses coordinated movementsPerson appears to be safe and able to complete the skill in a controlled mannerSmooth landing (item 14)The landing phase of the transfer is smooth and well controlledFor example, hands are not flying off the support surface, and the subject is sitting safely on the target surfaceOther evaluation items were excluded as they referred to the positioning of the wheelchair rather than the use of specific transferring skills , evaluated static body positioning rather than movement , or were only applicable to transfers performed with the assistance of a caregiver (item 15). Finally, item 7 was omitted as its clinical implications were unclear .In keeping with the guidelines of the TAI 3.0, 2 trained physiotherapists, with at least four years of clinical experience and who were familiar with the use of the TAI, evaluated each transfer identified in the video by assigning a dichotomous score  for each item. Each physiotherapist evaluated the transfers independently, and any disagreements over different scores were resolved through consensus meetings.In order to segment the transfer data from the full accelerometer recording sequence, accurate timestamps for start of lift  and landing (when the buttocks of the subject contact the target surface) were obtained from the annotated videos. The accelerometer data were then partitioned in three time windows as shown by Nawoczenski et al : head-hiFeatures for head-hip relationship and landing phases were selected based on the biomechanics characteristics of wheelchair transfers and confirmed by visual inspection of the data. The rationale for the feature selection of each transfer aspect evaluated is described in the following 3 sections.Head-hip relationship phase±0.75s interval around the marked start lift timestampFlight phase±0.5s interval around the marked timestamps for start lift and landingLanding phase±0.75s interval around the marked landing timestampThe performance of a transfer using a correct head-hip relationship requires the subject to perform a quick forward lean which causes a sharp decrease in the frontal acceleration . A sudden trunk flexion is usually more effective in relieving weight from the buttocks . To gather more momentum, some people may move the trunk slightly backward before bending forward, leading to a greater range of frontal acceleration . The direction along which the trunk moves can be represented by a line connecting the trunk with a point slightly ahead of the tip of the person’s feet. An approximation of the acceleration in that direction can be obtained from the sum of the acceleration values in the vertical direction and the inverse of the acceleration values in the frontal direction .A controlled flight can be described as a smooth transition from starting to target surface, as the body follows a linear path without unexpected deviations. We, therefore, selected representative features according to preexisting literature focusing on measuring smoothness of movements during rehabilitation ,27  combined with a smaller peak in the lateral direction . This would likely be reflected in higher average values of acceleration in the observed window of time . Hard landings will also likely cause large variations in the trunk accelerations, as the trunk moves to regain stability .The correlation-based feature subset selection method explained in Hall and Smith  was usedHead-hip relationship featuresMinimum frontal accelerationRange frontal accelerationMaximum frontal-downward accelerationMinimum frontal jerkSmooth landing featuresMaximum total accelerationRange total accelerationMean total accelerationRoot mean square total accelerationth, 25th, 50th, 75th, and 90th percentiles, (4) interquartile range, (5) range between the 10th and the 90th percentiles, and (6) lag-one correlation of the counts in a period of 10 seconds as a measure of temporal dynamics [Accelerometer data were divided into windows of 25 samples (1s at 25Hz) with a 50% overlap between neighboring windows. All windows were labeled for transfer occurrence according to the timestamps extracted from the videos. From each window, 59 features were extracted according to the procedure illustrated by Garcia-Masso et al . Fourteedynamics ,29. Addidynamics ,30 to cadynamics ,31 and tAlthough wheelchair transfers were only 1 of the activities classified by Garcia-Masso et al  the featAs for the automatic transfer quality evaluation, the correlation-based feature subset selection method described by Hall and Smith  identifiTime domain featuresVariance SD th Percentile 10th Percentile (Z)25th Percentile 50th Percentile 75th Percentile 90Interquartile Range th and 90th percentiles Range between 10Lag-one correlation Wavelet transform featuresst level coefficient (Y)Euclidean norm 1nd level coefficient Euclidean norm 2nd level Approximation coefficient of the 2Only selected features were used to build the automatic transfer detection system. Classification algorithms used for transfer monitoring were the same as the one used for automatic transfer quality evaluation. A leave-one-subject-out cross-validation strategy was to evaluate the performance and generalization of the models. Having the participant wear the accelerometer for the whole duration of the experiment minimized the disruption and resulted in the collection of a more realistic dataset. Accelerometer data were recorded continuously for approximately forty minutes for each participant. However, only 12 transfers lasting for a couple of seconds each were performed within the time frame. This resulted in a severe imbalance See  between Nine manual WU  were recruited for the study. Their mean age was 36.4 years (SD 11.5), mean height was 181.5 cm (SD 13.5), and mean weight was 88.4 kg (SD 17.6). All participants were successfully able to complete the 12 transfers and no missing data were found in the dataset see .After the physiotherapists’ evaluations, the dataset contained the following ratio of good/bad transfer instances for each evaluation item: (1) 59/49 for head-hip relationship, (2) 106/2 for controlled flight, and (3) 61/47 for smooth landing. Due to the unbalanced nature of the dataset for the controlled flight item, the automatic evaluation was not performed.For both evaluation items, all classifiers exhibited similar average accuracies across all participants. For the evaluation of the head-hip relationship item average classifier accuracies across all participants were: (1) 75.9% (SD 13.5%) for SVM, (2) 72.2% (SD 15.6%) for random forest, (3) 75% (SD 13.8%) for Naïve Bayes, and (4) 75.9% (SD 14.1%) for MLR. For the evaluation of the smooth landing item average classifiers accuracies across all participants were: (1) 79.6% (SD 7.4%) for SVM, (2) 73.1% (SD 13.7%) for random forest, (3) 78.7% (SD 7.3%) for Naïve Bayes, and (4) 78.7% (SD 7.3%) for MLR. SVM was found to be the most accurate classifiers across all participants for the evaluation of both head-hip relationship use and smoothness of landing.Accuracy and F1 scores displayed substantial variations across individual participants see  while SVAverage classifiers accuracies for automatic transfer detection were: (1) 86.8% (SD 10.1%) for SVM, (2) 83.2% (SD 10.1%) for random forest, (3) 91.9% (SD 4.9%) for Naïve Bayes, and (4) 87.8% (SD4.9%) for MLR. Overall, Naïve Bayes classifiers obtained higher classification accuracies. Naïve Bayes classifiers displayed a considerably higher relative accuracy for no transfer occurrence instances. On the other hand, MLR classifiers achieved a more balanced relative accuracy between the 2 classes .To our knowledge, this is the first paper that has attempted to use a body-worn accelerometer to both monitor the occurrence of wheelchair transfers and evaluate their quality. Using a single body-worn accelerometer located at the chest, we were able to evaluate 2 important elements of wheelchair transfers technique: head-hip relationship use, and smoothness of landing with a respective accuracy of 75.9% and 79.9 %. These results are comparable to previous studies within the WU population, such as research which classifies wheelchair propulsion ,33. UnfoThe choice of using a single chest-mounted accelerometer for the automatic transfer quality evaluation limited our assessment to 3 items of the TAI. However, this evaluation can have important clinical implications if extended to transfers performed in everyday settings. For example, the use of a head-hip relationship during wheelchair transfers has been shown to reduce muscular activity , shouldeDespite not reaching 100% accuracy, the current study shows the potential of using a single chest-mounted sensor to detect the occurrence of wheelchair transfers. Previous research by Garcia-Masso et al  includedThe placement on the chest also allowed for the quality of movement to be evaluated. However, this sensor alone is not sufficient to measure the exact start and end of a transfer (or other items of TAI). Therefore, future work should investigate the use of an unobtrusive second sensor to aid with accurate detection .It should be noted that, even if the data from the current study are not directly comparable with , our datThe detection of transfers was more successful for some participants than others. The Naïve Bayes classifiers were the most accurate across all participants. However, it was unbalanced and overpredicted the number of transfers when no transfer was present. Despite this the Naïve Bayes classifiers were more robust, ensuring an accuracy of more than 80% for all participants. When the more balanced MLR was used the accuracy for participant 1  and 2  dropped below 70%. It is unclear why these participants were so affected. Future work should look to replicate our work in the wild and with a larger and more heterogeneous sample of WU, which we believe would begin to address the limitations of the current dataset. In fact, despite our efforts, the current set of participants included mainly males with SCI. Although the imbalance of genders and medical conditions among participants are not uncommon in wheelchair studies , it can Overall, the use of machine learning techniques to automatically detect and evaluate wheelchair transfers shows good potential for future clinical and well-being applications. A wearable system would allow people to self-monitor their transfers and seek additional medical help as and when required. Also, the system could be used to provide feedback to WU, helping them to identify potential weaknesses and providing suggestions for improvements. If paired with data concerning, for example—the environment, the emotional state of the WU, and time of day—a more complex picture of wheelchair transfers can be built, and better feedback and support mechanisms put in place. Therefore, the larger aim of our project is to develop a wearable system capable of continuously tracking and giving real-time feedback to WU on their transfer ability as they go about their life. Future developments should look into the possibility to combine the chest accelerometer with a portable surface electromyography system placed on the arm, as this could allow for a complete picture of the transfer skills to be captured. This information could then be used to provide more detailed feedback to the WU to help them train and practice the movement in real-life contexts.Finally, the ability to easily map transfer difficulties in the built environment could also allow WU to share their experiences and provide information about accessibility standards of various establishments . This could also be extended to lower- and middle-income countries, where the majority of disabled people live, who frequently do not have access to rehabilitation programs .In this study, we investigated the use of a single chest-mounted accelerometer to monitor the occurrence of wheelchair transfers and evaluate their quality under three ecological settings. Using features extracted from the accelerometer we were able to improve the accuracy of detection of transfers for the ubiquitous computing literature in this area while also detecting key elements of the quality of movement at performance levels observed for other aspects of the movements. Results from this study open new possibilities for unobtrusive monitoring and evaluation of the performance of wheelchair transfers in the real world that could lead to important applications for wheelchair transfers training, upper limb injury prevention, and improved accessibility.