Focus: Addiction: Relapse Prevention and the Five Rules of Recovery

There are four main ideas in relapse prevention. First, relapse is a gradual process with distinct stages. The goal of treatment is to help individuals recognize the early stages, in which the chances of success are greatest. Second, recovery is a process of personal growth with developmental milestones. Each stage of recovery has its own risks of relapse. Third, the main tools of relapse prevention are cognitive therapy and mind-body relaxation, which are used to develop healthy coping skills. Fourth, most relapses can be explained in terms of a few basic rules. Educating clients in these rules can help them focus on what is important: 1) change your life (recovery involves creating a new life where it is easier to not use); 2) be completely honest; 3) ask for help; 4) practice self-care; and 5) don’t bend the rules.     

Orem自理模式在TURBT术后自我护理中的应用

目的：探讨Orem自理模式在经尿道膀胱肿瘤电切患者护理中的应用,以Orem自护理论作为指导,对膀胱肿瘤患者电切术后实行护理干预,观察Orem自理模式对膀胱肿瘤电切术后患者生活质量及术后复发率的影响。方法：选择2008年10月至2012年6月我科经尿道膀胱肿瘤电切术患者160例随机分为两组,即对照组和实验组,各80例。其中对照组采用术后常规护理,实验组将Orem自理模式贯穿于常规护理的全过程。比较两组患者术后下床活动时间、住院时间、患者对相关知识的掌握情况及患者满意度。结果：实验组患者术后下床活动时间、住院时间、患者对疾病的认知度、患者满意度均明显高于对照组（P〈0．05）。结论iOrem自理模式应用于经尿道膀胱肿瘤电切术后患者的护理,训练患者及家属自我护理技术和能力,不仅能调动和激发经尿道膀胱肿瘤电切患者主观能动性,变“替代护理”为“自我护理”,而且能减少并发症的发生,提高患者的自理能力及生存质量,初步实现了对经尿道膀胱肿瘤电切患者教育的个体化、系统性、连贯性和重复性。     

授权教育联合Orem自理模式对提高UC患者自我效能效果显著

为探讨授权教育联合奥瑞姆自理模式(Orem self-care model)对溃疡性结肠炎(ulcerative colitis, UC)患者自我效能及生活质量的影响,本研究选取2015年1月至2017年4月98例我院UC患者,采用随机数字表法分为对照组(n=49)与研究组(n=49)。对照组采取常规健康教育,研究组采取授权教育联合奥瑞姆自理模式。入院时及干预后统计两组自我效能评分(IBD-SES)、生活质量分值(SF-36)、抑郁及焦虑分值(SDS,SAS)、治疗依从性、夜间排便次数。研究发现,干预后两组IBD-SES分值较干预前增高,且研究组较对照组高(p<0.05);干预后两组SF-36分值相比较于干预前均有所增高,且对照组较研究组低(p<0.05);干预后两组SDS及SAS分值较干预前降低,且研究组较对照组低(p<0.05);研究组治疗依从性(95.92%)较对照组(79.59%)高(p<0.05);干预后两组夜间排便次数较干预前减少,且研究组较对照组少(p<0.05)。本研究初步说明,联合采用授权教育及奥瑞姆自理模式对溃疡性结肠炎患者实施干预效果显著,可提高其自我效能及生活质量,缓解负性情绪状态,改善治疗依从性,并减少夜间排便次数。     

不同护理模式对维持性血液透析患者透析依从性与生活质量的影响

目的探讨不同护理模式对维持性血液透析患者透析治疗依从性与生活质量的影响。方法将行维持性血液透析的患者80例,根据护理模式的不同分为对照组与研究组各40例,对照组采用常规护理模式,研究组采用协同护理模式,两组均干预4个月,并对两组患者干预前后进行透析依从性量表、简明健康调查表(SF-36)、护理满意度进行问卷调查,分析干预前后两组患者透析依从性、生活质量变化及对护理满意度情况。结果与干预前比较,干预后研究组患者在透析依从性评分、SF-36评分及护理满意度方面均明显升高,差异有统计学意义(均P0.01);而且与对照组比较差异亦有显著统计学意义(均P0.01)。结论协同护理模式可显著提高维持性血液透析患者的血透治疗依从性及生活质量,提高了护理满意度,值得临床推广应用。     

Proof-of-Concept Study of Using Supervised Machine Learning Algorithms to Predict Self-Care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy

ObjectiveUsing supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within 6 months.MethodsThis was a single-center retrospective chart review of 100 adult type 1 diabetes mellitus patients on insulin pump therapy (≥6 months). Three SMLAs were deployed: multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included area under the curve-Receiver of characteristics for discrimination and Brier scores for calibration.ResultsVariables predictive of adherence with IPSMB criteria were baseline hemoglobin A1c, continuous glucose monitoring, and sex. The models had comparable discriminatory power (LR = 0.74; RF = 0.74; k-NN = 0.72), with the RF model showing better calibration (Brier = 0.151). Predictors of the good glycemic response included baseline hemoglobin A1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR = 0.81, RF = 0.80, k-NN = 0.78) but the RF model being better calibrated (Brier = 0.099).ConclusionThese proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within 6 months. Subject to further study, nonlinear prediction models may perform better.     

A Roadmap to an Equitable Digital Diabetes Ecosystem

ObjectivesDiabetes management presents a substantial burden to individuals living with the condition and their families, health care professionals, and health care systems. Although an increasing number of digital tools are available to assist with tasks such as blood glucose monitoring and insulin dose calculation, multiple persistent barriers continue to prevent their optimal use.MethodsAs a guide to creating an equitable connected digital diabetes ecosystem, we propose a roadmap with key milestones that need to be achieved along the way.ResultsDuring the Coronavirus 2019 pandemic, there was an increased use of digital tools to support diabetes care, but at the same time, the pandemic also highlighted problems of inequities in access to and use of these same technologies. Based on these observations, a connected diabetes ecosystem should incorporate and optimize the use of existing treatments and technologies, integrate tasks such as glucose monitoring, data analysis, and insulin dose calculations, and lead to improved and equitable health outcomes.ConclusionsDevelopment of this ecosystem will require overcoming multiple obstacles, including interoperability and data security concerns. However, an integrated system would optimize existing devices, technologies, and treatments to improve outcomes.