Prevalence and factors associated with high-risk gastric contents in women admitted to the maternity unit for childbirth: a prospective multicentre cohort study.

BACKGROUND: This multicentre prospective observational study sought to determine the prevalence and the factors associated with high-risk gastric contents in women admitted to the maternity unit for childbirth, and to identify the clinical situations in which ultrasound assessment of gastric contents would be most helpful (i.e. when the prevalence of high-risk gastric contents is close to 50%). METHODS: Ultrasound assessments of gastric contents were performed within the first hour after admission to the maternity unit. The prevalence of high-risk gastric contents was calculated and variables associated with high-risk gastric contents were identified using logistic regression analyses. RESULTS: A total of 1003 parturients were analysed. The prevalence of high-risk gastric contents was 70% (379/544; 95% confidence interval: 66-74%) in women admitted in spontaneous labour and 65% (646/1003; 95% confidence interval: 61-67%) in the whole cohort. Lower gestational age, increased fasting duration for solids, and elective Caesarean delivery were independently associated with reduced likelihood of high-risk gastric contents. In women admitted in spontaneous labour and in the whole cohort, the prevalence of high-risk gastric contents ranged from 85% to 86% for fasting duration for solids <6 h, 63%-68% for fasting 6-8 h, 54%-55% for fasting 8-12 h, and 47%-51% for fasting ≥12 h. CONCLUSIONS: Around two-thirds of parturients had high-risk gastric contents within the first hour after admission to the maternity unit. Our results suggest that gastric emptying for solids continues in labouring women, and that gastric ultrasound would be most helpful when fasting duration is ≥8 h.

Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement.

BACKGROUND: Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user's journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches. OBJECTIVE: This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance. METHODS: We searched the PubMed database for publications indexed under the Medical Subject Headings terms "Patient Acceptance of Health Care" and "Mobile Applications." We included publications that (1) contained at least one of the terms "acceptability," "acceptance," "adoption," "accept," or "adopt"; and (2) defined the term. The final corpus included 68 relevant studies. RESULTS: Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools. CONCLUSIONS: Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

Integration of a smartwatch within an internet-delivered intervention for depression: Protocol for a feasibility randomized controlled trial on acceptance.

BACKGROUND: Mood tracking is commonly employed within a range of mental health interventions. Physical activity and sleep are also important for contextualizing mood data but can be difficult to track manually and rely on retrospective recall. Smartwatches could enhance self-monitoring by addressing difficulties in recall of sleep and physical activity and reducing the burden on patients in terms of remembering to track and the effort of tracking. This feasibility study will explore the acceptance of a smartwatch app for self-monitoring of mood, sleep, and physical activity, in an internet-based cognitive-behavioral therapy (iCBT) for depression offered in a routine care setting. METHODS: Seventy participants will be randomly allocated to (i) iCBT intervention plus smartwatch app or (ii) iCBT intervention alone. Patient acceptance will be measured longitudinally using a theory-based acceptance questionnaire to understand and compare the evolution of acceptance of the technology-delivered self-report in the two groups. A post-treatment interview will explore participants subjective experience of using the smartwatch. Engagement with the intervention, including self-report, and clinical outcomes, will be measured across both groups to assess for any differences. IMPLICATIONS: This is the first study investigating the evolution of patient acceptance of smartwatch self-report in an iCBT delivered intervention in a clinical sample. Through an engaging and convenient means of capturing ecologically valid mood data, the study has the potential to show that smartwatches are an acceptable means for patient self-monitoring within iCBT interventions for depression and support potential use-cases for smartwatches in the context of mental health interventions in general. Prospectively registered at ClinicalTrials.gov (NCT04568317).