Purpose This study aimed to examine the effects of a multi-component mobile health intervention(wearable,apps,and social media)on cancer survivors’(CS')physical activity(PA),quality of life,and PA determinants co...Purpose This study aimed to examine the effects of a multi-component mobile health intervention(wearable,apps,and social media)on cancer survivors’(CS')physical activity(PA),quality of life,and PA determinants compared to exercise prescription only,social media only,and attention control conditions.Methods A total of 126 CS(age=60.37±7.41 years,mean±SD)were recruited from the United States.The study duration was 6 months and participants were randomly placed into 4 groups.All participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily PA.They(1)received previously established weekly personalized exercise prescriptions via email,(2)received weekly Facebook health education and interacted with one another,(3)received both Conditions 1 and 2,or(4)were part of the control condition,meaning they adopted usual care.CS PA daily steps,quality of life(i.e.,physical health and mental health),and PA determinants(e.g.,self-efficacy,social support)were measured at baseline,3 months,and 6 months.Results The final sample size included 123 CS.The results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention(95%confidence interval(95%CI):368–2951;p<0.05).Similarly,those in the multi-component condition had significantly greater increased physical health than the control condition(95%CI:–0.41 to–0.01;p<0.05)over time.In addition,the social media condition had significantly greater increased perceived social support than the control condition(95%CI:0.01–0.93;p<0.05).No other significant differences on outcomes were identified.Conclusion The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical health.Also,offering social media intervention has the potential to improve CS perceived social support.展开更多
Background.There is growing evidence that social and behavioral determinants of health(SBDH)play a substantial effect in a wide range of health outcomes.Electronic health records(EHRs)have been widely employed to cond...Background.There is growing evidence that social and behavioral determinants of health(SBDH)play a substantial effect in a wide range of health outcomes.Electronic health records(EHRs)have been widely employed to conduct observational studies in the age of artificial intelligence(AI).However,there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods.A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published.Relevance was determined by screening and evaluating the articles.Based on selected relevant studies,a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results.Our synthesis was driven by an analysis of SBDH categories,the relationship between SBDH and healthcare-related statuses,natural language processing(NLP)approaches for extracting SBDH from clinical notes,and predictive models using SBDH for health outcomes.Discussion.The associations between SBDH and health outcomes are complicated and diverse;several pathways may be involved.Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data,efficiently unlocks unstructured data,and aids in the resolution of unstructured data-related issues.Conclusion.Despite known associations between SBDH and diseases,SBDH factors are rarely investigated as interventions to improve patient outcomes.Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness,ultimately promoting health and health equity.展开更多
基金funded by College of Education and Human Development Acceleration Research Award at the University of Minnesota Twin Cities,USA
文摘Purpose This study aimed to examine the effects of a multi-component mobile health intervention(wearable,apps,and social media)on cancer survivors’(CS')physical activity(PA),quality of life,and PA determinants compared to exercise prescription only,social media only,and attention control conditions.Methods A total of 126 CS(age=60.37±7.41 years,mean±SD)were recruited from the United States.The study duration was 6 months and participants were randomly placed into 4 groups.All participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily PA.They(1)received previously established weekly personalized exercise prescriptions via email,(2)received weekly Facebook health education and interacted with one another,(3)received both Conditions 1 and 2,or(4)were part of the control condition,meaning they adopted usual care.CS PA daily steps,quality of life(i.e.,physical health and mental health),and PA determinants(e.g.,self-efficacy,social support)were measured at baseline,3 months,and 6 months.Results The final sample size included 123 CS.The results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention(95%confidence interval(95%CI):368–2951;p<0.05).Similarly,those in the multi-component condition had significantly greater increased physical health than the control condition(95%CI:–0.41 to–0.01;p<0.05)over time.In addition,the social media condition had significantly greater increased perceived social support than the control condition(95%CI:0.01–0.93;p<0.05).No other significant differences on outcomes were identified.Conclusion The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical health.Also,offering social media intervention has the potential to improve CS perceived social support.
基金RZ was partially supported by the National Institutions of Health’s National Center for Complementary&Integrative Health(NCCIH)the Office of Dietary Supplements(ODS)and National Institute on Aging(NIA)grant number R01AT009457(PI:Zhang).
文摘Background.There is growing evidence that social and behavioral determinants of health(SBDH)play a substantial effect in a wide range of health outcomes.Electronic health records(EHRs)have been widely employed to conduct observational studies in the age of artificial intelligence(AI).However,there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods.A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published.Relevance was determined by screening and evaluating the articles.Based on selected relevant studies,a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results.Our synthesis was driven by an analysis of SBDH categories,the relationship between SBDH and healthcare-related statuses,natural language processing(NLP)approaches for extracting SBDH from clinical notes,and predictive models using SBDH for health outcomes.Discussion.The associations between SBDH and health outcomes are complicated and diverse;several pathways may be involved.Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data,efficiently unlocks unstructured data,and aids in the resolution of unstructured data-related issues.Conclusion.Despite known associations between SBDH and diseases,SBDH factors are rarely investigated as interventions to improve patient outcomes.Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness,ultimately promoting health and health equity.
