Falls are the leading cause of injury-related mortality and hospitalization among adults aged greater than or equal to 65 years. An important modifiable fall-risk factor is use of fall-risk ...increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully. The ADFICE_IT trial evaluates the combined use of a clinical decision support system (CDSS) and a patient portal for optimizing the deprescribing of FRIDs in older fallers. The intervention aims to optimize and enhance shared decision making (SDM) and consequently prevent injurious falls and reduce healthcare-related costs. A multicenter, cluster-randomized controlled trial with process evaluation will be conducted among hospitals in the Netherlands. We aim to include 856 individuals aged greater than or equal to 65 years that visit the falls clinic due to a fall. The intervention comprises the combined use of a CDSS and a patient portal. The CDSS provides guideline-based advice with regard to deprescribing and an individual fall-risk estimation, as calculated by an embedded prediction model. The patient portal provides educational information and a summary of the patient's consultation. Hospitals in the control arm will provide care-as-usual. Fall-calendars will be used for measuring the time to first injurious fall (primary outcome) and secondary fall outcomes during one year. Other measurements will be conducted at baseline, 3, 6, and 12 months and include quality of life, cost-effectiveness, feasibility, and shared decision-making measures. Data will be analyzed according to the intention-to-treat principle. Difference in time to injurious fall between the intervention and control group will be analyzed using multilevel Cox regression. The findings of this study will add valuable insights about how digital health informatics tools that target physicians and older adults can optimize deprescribing and support SDM. We expect the CDSS and patient portal to aid in deprescribing of FRIDs, resulting in a reduction in falls and related injuries.
The current study investigates 1) whether using a recycling app can stimulate recycling behavior and 2) which behavioral model best explains whether people use a recycling app (comparing the Theory ...of Planned Behavior and the Technology Acceptance Model). A within-subjects experiment was conducted (N = 118) in which a baseline week of recycling was compared with an intervention week (in which participants used a recycling app). Experience sampling methodology (ESM) was used to assess daily recycling behavior. The results showed that using a recycling app increased recycling behavior. Furthermore, the results showed that both TPB and TAM were suitable to explain recycling app use intentions, but not actual app use. This research provides practical implications by adding insights on how to stimulate app use, and ultimately, recycling behavior. Furthermore, the findings have important theoretical implications because they enhance our understanding of app use for environmental behavior change.
•Using a recycling app increases recycling behavior.•Using a recycling app once already increased recycling knowledge.•Both the TAM and TPB were suitable to explain recycling app use intentions, but not actual app use.•To stimulate app use, it is important that people perceive the app as useful and have a positive attitude towards the app.
