Background: For most women, participation in decision-making during maternity care has a positive impact on their childbirth experiences. Shared decision-making (SDM) is widely advocated as a way to support people in their healthcare choices. The aim of this study was to identify quality criteria and professional competencies for applying shared decision-making in maternity care. We focused on decision-making in everyday maternity care practice for healthy women. Methods: An international three-round web-based Delphi study was conducted. The Delphi panel included international experts in SDM and in maternity care: mostly midwives, and additionally obstetricians, educators, researchers, policy makers and representatives of care users. Round 1 contained open-ended questions to explore relevant ingredients for SDM in maternity care and to identify the competencies needed for this. In rounds 2 and 3, experts rated statements on quality criteria and competencies on a 1 to 7 Likert-scale. A priori, positive consensus was defined as 70% or more of the experts scoring ≥6 (70% panel agreement). Results: Consensus was reached on 45 quality criteria statements and 4 competency statements. SDM in maternity care is a dynamic process that starts in antenatal care and ends after birth. Experts agreed that the regular visits during pregnancy offer opportunities to build a relationship, anticipate situations and revisit complex decisions. Professionals need to prepare women antenatally for unexpected, urgent decisions in birth and revisit these decisions postnatally. Open and respectful communication between women and care professionals is essential; information needs to be accurate, evidence-based and understandable to women. Experts were divided about the contribution of professional advice in shared decision-making and about the partner’s role. Conclusions: SDM in maternity care is a dynamic process that takes into consideration women’s individual needs and the context of the pregnancy or birth. The identified ingredients for good quality SDM will help practitioners to apply SDM in practice and educators to prepare (future) professionals for SDM, contributing to women’s positive birth experience and satisfaction with care.
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Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.
A transition of today’s energy system towards renewableresources, requires solutions to match renewable energy generationwith demand over time. These solutions include smartgrids, demand-side management and energy storage. Energycan be stored during moments of overproduction of renewableenergy and used from the storage during moments ofinsufficient production. Allocation in real time of generatedenergy towards controlled appliances or storage chargers, isdone by a smart control system which makes decisions basedon predictions (of upcoming generation and demand) andinformation of the actual condition of storages.
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