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Background: Mobile health (mHealth) has been hailed as a potential gamechanger for non-communicable disease (NCD) management, especially in low- and middle-income countries (LMIC). Individual studies illustrate barriers to implementation and scale-up, but an overview of implementation issues for NCD mHealth interventions in LMIC is lacking. This paper explores implementation issues from two perspectives: information in published papers and field-based knowledge by people working in this field. Methods: Through a scoping review publications on mHealth interventions for NCDs in LMIC were identified and assessed with the WHO mHealth Evidence Reporting and Assessment (mERA) tool. A two-stage web-based survey on implementation barriers was performed within a NCD research network and through two online platforms on mHealth targeting researchers and implementors. Results: 16 studies were included in the scoping review. Short Message Service (SMS) messaging was the main implementation tool. Most studies focused on patient-centered outcomes. Most studies did not report on process measures and on contextual conditions influencing implementation decisions. Few publications reported on implementation barriers. The websurvey included twelve projects and the responses revealed additional information, especially on practical barriers related to the patients’ characteristics, low demand, technical requirements, integration with health services and with the wider context. Many interventions used low-cost software and devices with limited capacity that not allowed linkage with routine data or patient records, which incurred fragmented delivery and increased workload. Conclusion: Text messaging is a dominant mHealth tool for patient-directed of quality improvement interventions in LMIC. Publications report little on implementation barriers, while a questionnaire among implementors reveals significant barriers and strategies to address them. This information is relevant for decisions on scale-up of mHealth in the domain of NCD. Further knowledge should be gathered on implementation issues, and the conditions that allow universal coverage.

BackgroundAt least half of all adult women will experience infective cystitis (urinary tract infection: UTI) at least once in their life and many suffer from repeated episodes. Recurrent urinary tract infection (rUTI) in adult women is usually treated with long-term, low-dose antibiotics and current national and international guidelines recommend this as the ‘gold standard’ preventative treatment. Although they are reasonably effective, long-term antibiotics can result in bacteria becoming resistant not only to the prescribed antibiotic but to other antimicrobial agents. The problem of antimicrobial resistance is recognised as a global threat and the recent drive for antibiotic stewardship has emphasised the need for careful consideration prior to prescribing antibiotics. This has led clinicians and patients alike to explore potential non-antibiotic options for recurrent UTI prevention.Design /methodsThis is a multicentre, pragmatic, patient-randomised, non-inferiority trial comparing a non-antibiotic preventative treatment for rUTI in women, methenamine hippurate, against the current standard of daily low-dose antibiotics. Women who require preventative treatment for rUTI are the target population. This group is comprised of those with a diagnosis of rUTI, defined as three episodes in 1 year or two episodes in 6 months, and those with a single severe infection requiring hospitalisation. Participants will be recruited from secondary care urology / urogynaecology departments in the UK following referral with rUTI. Participants will be followed up during a 12-month period of treatment and in the subsequent 6 months following completion of the prophylactic medication. Outcomes will be assessed from patient recorded symptoms, quality of life questionnaires and microbiological examination of urine and perineal swabs. The primary outcome is the incidence of symptomatic antibiotic-treated UTI self-reported by participants during the 12-month period of preventative treatment. Health economic outcomes will also be assessed to define the cost-effectiveness of both treatments. A qualitative study will be conducted in the first 8 months of the trial to explore with participants/non-participants’ and recruiting clinicians’ views on trial processes and identify potential barriers to recruitment, reasons for participating and non-participation and for dropping out of the study.DiscussionThe study was commissioned and funded by the National Institute for Health Research (NIHR) and approved under the Medicines and Healthcare products Regulatory Agency (MHRA) notification scheme as a ‘Type A’ study.Trial registrationInternational Standard Randomised Controlled Trial Number (ISRCTN), registry number: ISRCTN70219762. Registered on 31 May 2016.Electronic supplementary materialThe online version of this article (10.1186/s13063-018-2998-4) contains supplementary material, which is available to authorized users.

Introduction: With the rise in the use of ureteroscopy and laser stone lithotripsy (URSL), a proportionate increase in the risk of post-procedural urosepsis has also been observed. The aims of our paper were to analyse the predictors for severe urosepsis using a machine learning model (ML) in patients that needed intensive care unit (ICU) admission and to make comparisons with a matched cohort. Methods: A retrospective study was conducted across nine high-volume endourology European centres for all patients who underwent URSL and subsequently needed ICU admission for urosepsis (Group A). This was matched by patients with URSL without urosepsis (Group B). Statistical analysis was performed with ‘R statistical software’ using the ‘randomforests’ package. The data were segregated at random into a 70% training set and a 30% test set using the ‘sample’ command. A random forests ML model was then built with n = 300 trees, with the test set used for internal validation. Diagnostic accuracy statistics were generated using the ‘caret’ package. Results: A total of 114 patients were included (57 in each group) with a mean age of 60 ± 16 years and a male:female ratio of 1:1.19. The ML model correctly predicted risk of sepsis in 14/17 (82%) cases (Group A) and predicted those without urosepsis for 12/15 (80%) controls (Group B), whilst overall it also discriminated between the two groups predicting both those with and without sepsis. Our model accuracy was 81.3% (95%, CI: 63.7–92.8%), sensitivity = 0.80, specificity = 0.82 and area under the curve = 0.89. Predictive values most commonly accounting for nodal points in the trees were a large proximal stone location, long stent time, large stone size and long operative time. Conclusion: Urosepsis after endourological procedures remains one of the main reasons for ICU admission. Risk factors for urosepsis are reasonably accurately predicted by our innovative ML model. Focusing on these risk factors can allow one to create predictive strategies to minimise post-operative morbidity.