Journal of medical Internet research
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J. Med. Internet Res. · Oct 2018
Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study.
There are documented differences in access to health care across the United States. Previous research indicates that Web-based data regarding patient experiences and opinions of health care are available from Twitter. Sentiment analyses of Twitter data can be used to examine differences in patient views of health care across the United States. ⋯ This study presents methodologies for a deeper understanding of Web-based discussion related to patient experience across space and time and demonstrates how Twitter can provide a unique and unsolicited perspective from users on the health care they receive in the United States.
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J. Med. Internet Res. · Oct 2018
Randomized Controlled TrialCombining Technology and Research to Prevent Scald Injuries (the Cool Runnings Intervention): Randomized Controlled Trial.
New technologies, internet accessibility, social media, and increased smartphone ownership provide new opportunities for health researchers to communicate and engage target audiences. An innovative burn prevention intervention was developed using these channels. ⋯ Despite substantial loss to follow-up, this RCT demonstrates the Cool Runnings app was an effective intervention for improving knowledge about risks of hot beverage scalds and burn first aid in mothers of young children. The benefits of combining gamification elements in the intervention were also highlighted. Given the low cost and large reach of smartphone apps to deliver content to and engage with targeted populations, the results from this RCT provide important information on how smartphone apps can be used for widespread injury prevention campaigns and public health campaigns generally.
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J. Med. Internet Res. · Oct 2018
Multicenter StudyEffectiveness of the Malnutrition eLearning Course for Global Capacity Building in the Management of Malnutrition: Cross-Country Interrupted Time-Series Study.
Scaling up improved management of severe acute malnutrition has been identified as the nutrition intervention with the largest potential to reduce child mortality, but lack of operational capacity at all levels of the health system constrains scale-up. We therefore developed an interactive malnutrition eLearning course that is accessible at scale to build capacity of the health sector workforce to manage severely malnourished children according to the guidelines of the World Health Organization. ⋯ The malnutrition eLearning course improved knowledge, understanding, and skills of health professionals in the diagnosis and management of children with severe acute malnutrition, and changes in clinical practice and confidence were reported following the completion of the course.
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J. Med. Internet Res. · Oct 2018
Randomized Controlled TrialComplaint-Directed Mini-Interventions for Depressive Symptoms: A Health Economic Evaluation of Unguided Web-Based Self-Help Interventions Based on a Randomized Controlled Trial.
Depression prevention and early intervention have become a top priority in the Netherlands, but with considerable room for improvement. To address this, Web-based complaint-directed mini-interventions (CDMIs) were developed. These brief and low-threshold interventions focus on psychological stress, sleep problems, and worry, because these complaints are highly prevalent, are demonstrably associated with depression, and have substantial economic impact. ⋯ Brief and low-threshold Web-based, unguided, self-help CDMIs have the potential to be a cost-effective addition to usual care for adults with mild to moderate depressive symptoms. The CDMIs improved health status, while reducing participant health care costs, and hence dominated the care-as-usual control condition. As intervention costs were relatively low, and the internet is readily available in the Western world, we believe CDMIs can be easily implemented on a large scale.
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J. Med. Internet Res. · Sep 2018
Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data.
Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of chronic obstructive pulmonary disease (COPD) exacerbations, with a view to instituting timely treatment. However, algorithms to identify exacerbations result in frequent false-positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving prediction quality. ⋯ Early detection and management of COPD remains an important goal given its huge personal and economic costs. Machine learning approaches, which can be tailored to an individual's baseline profile and can learn from experience of the individual patient, are superior to existing predictive algorithms and show promise in achieving this goal.