The impact of self-monitoring of blood glucose on a behavioral weight loss intervention for patients with type 2 diabetes

Transforming AADE7 for Use in an Evaluation Framework for Health Information Technology in Diabetes Mellitus

There is no validated framework to evaluate health information technology (HIT) for diabetes self-management education and support (DSMES). AADE7 Self-Care Behaviors is a patient-centered DSMES designed by the American Association of Diabetes Educators (AADE). We developed a codebook based on the AADE7 Self-Care Behaviors principles as an evaluation framework. In this commentary, we demonstrate the real-life applications of this codebook through three diabetes research studies. The first study analyzed features of mobile diabetes applications. The second study evaluated provider documentation patterns in electronic health records (EHRs) to deliver ongoing patient-centered DSMES. The third study analyzed feedback messages from diabetes apps. We found that this codebook, based on AADE7, can be instrumental as a framework for research, as well as real-life use in HIT for DSMES principles.

Psychological Predictors of COVID-19 Prevention Behavior in Hungarian Women Across Different Generations

BACKGROUND

Age related differences were found in prevention behavior, showing that older individuals tend to be the most proactive. The aim of the study was the identification of psychological predictors on COVID-19 prevention behavior in women, across four generations. In addition, the predictive role of the psychological variables was explored through the lens of negative and positive information processing perspective on total and domain-specific COVID-19 prevention behavior.

METHODS

A cross-sectional research was conducted. The sample included 834 Hungarian speaking women. The assessed variables were: COVID-19 knowledge, risk perception, COVID-19 health anxiety, negative automatic thoughts, psychological flexibility, and four domains of COVID-19 prevention behavior (social distancing, general hygiene, information seeking, health behavior). A three-level hierarchical linear regression analysis was conducted to investigate the predictors of preventive behavior in each generation.

RESULTS

A diversity across generations was found. In case of baby boomer generation, the final model explained 32.4% of the variance for total prevention behavior [F(14,215) = 8.847, p < 0.001], and only perceived risk made a significant contribution. For Gen X the final model accounted for 21.1% of variance of total prevention behavior [F(14,341) = 7.788, p < 0.001], marital status, perceived risk, COVID-19 health anxiety, and negative automatic thoughts made significant contributions. In case of Gen Y the final model accounted for 6.2% of variance on total prevention behavior [F(14,147) = 1.761, p = 0.05], only perceived risk had a contribution to the final model. For Gen Z the final model accounted for 23.4% of variance on total preventive behavior [F(13,71) = 2.979, p = 0.002], and only psychological flexibility made a contribution to the model. The results on the distinct domains of COVID-19 prevention behavior emphasized details in the dissimilarity among generations.

CONCLUSION

The role of generational identity on COVID-19 prevention behavior is relevant. The coexistence of negative and positive information processing may have its beneficial role in certain areas of prevention.

Technology-Assisted Self-Monitoring of Lifestyle Behaviors and Health Indicators in Diabetes: Qualitative Study

BACKGROUND

Self-monitoring is key to successful behavior change in diabetes and obesity, and the use of traditional paper-based methods of self-monitoring may be time-consuming and burdensome.

OBJECTIVE

This study aimed to explore participant experiences while using technology-assisted self-monitoring of lifestyle behaviors and health indicators among overweight or obese adults with type 2 diabetes.

METHODS

Qualitative data collected from the intervention group of a 6-month, three-arm (control, paper diary, and technology-assisted self-monitoring groups) randomized clinical trial were analyzed. Study participants in the intervention group monitored their diet, exercise, and weight using the LoseIt! app, and their blood glucose levels using a glucometer and the Diabetes Connect app. Semistructured group discussions were conducted at 6 weeks (n=10) from the initiation of the behavioral lifestyle intervention and again at 6 months (n=9). All group interviews were audiotaped and transcribed verbatim. Using a combination of thematic and comparative analysis approaches, two trained professionals coded the transcriptions independently and then discussed and concluded common themes for the 6-week and 6-month discussions separately.

RESULTS

The sample (n=10), which primarily involved African American participants (n=7) and female participants (n=8), had a mean age of 59.4 years. The following eight themes emerged: (1) perceived benefits of technology-assisted self-monitoring; (2) perceived ease of use (eg, barriers: technical difficulties and lack of self-discipline; facilitators: help from family, friends, and the program); (3) use of technology-assisted self-monitoring; (4) facilitators of engaging in healthy lifestyle behaviors (eg, visualization and awareness of calorie input/expenditure); (5) positive lifestyle change; (6) barriers of engaging in healthy lifestyle behaviors (eg, event influence); (7) learning curve; and (8) monitored data sharing. The first six of these themes were shared between the 6-week and 6-month timepoints, but the codes within these themes were not all the same and differed slightly between the two timepoints. These differences provide insights into the evolution of participant thoughts and perceptions on using technology for self-monitoring and subsequent behavioral lifestyle changes while participating in lifestyle interventions. The findings from the 6-week and 6-month data helped to paint a picture of participant comfort and the integration of technology and knowledge overtime, and clarified participant attitudes, difficulties, behavioral processes, and modifications, as well as health indicators that were experienced throughout the study.

CONCLUSIONS

Although there were some barriers, participants were able to identify various individual and external facilitators to adjust to and engage in technology-assisted self-monitoring, and it was concluded that the technology-assisted self-monitoring approach was beneficial, safe, and feasible to use for positive lifestyle change. These patient perspectives need to be considered in future research studies when investigating the effectiveness of using technology-assisted self-monitoring, as well as in clinical practice when recommending technology-assisted self-monitoring of lifestyle behaviors and health indicators to improve health outcomes.

Binge Eating Disorder in Patients with Type 2 Diabetes: Diagnostic and Management Challenges

Type 2 diabetes mellitus (T2DM) is associated with an increased risk of disordered eating behaviors including binge eating disorder (BED). Comorbid BED in patients with T2DM has been associated with adverse clinical outcomes such as higher body mass index (BMI) and depressive symptoms. Identifying and addressing this disorder in patients with T2DM is a significant challenge for health-care providers. The purpose of this narrative review is to discuss current perspectives on BED in the context of T2DM with implications for screening and management of these highly comorbid conditions. BED continues to be underrecognized and underdiagnosed. However, there are established tools that providers can use to screen for BED such as the SCOFF Questionnaire and Questionnaire on Eating and Weight Patterns-5. There are several effective treatments for BED including cognitive behavioral therapy, interpersonal therapy, and lisdexamfetamine dimesylate. However, few studies have examined the effects of these treatments in patients with co-morbid T2DM and BED.

Perspectives of patients with non-insulin-treated type 2 diabetes on self-monitoring of blood glucose: A qualitative study

AIMS AND OBJECTIVES

To explore experiences of self-monitoring of blood glucose among patients with non-insulin-treated type 2 diabetes.

BACKGROUND

Self-monitoring of blood glucose is essential to diabetes care and facilitates glycaemic control. Patients' perspectives of self-monitoring of blood glucose have seldom been discussed in the literature, and engagement in self-monitoring of blood glucose is consistently low.

DESIGN

The descriptive phenomenological method was used.

METHODS

Purposive sampling was conducted to recruit participants from the endocrinology departments of medical institutions in Taiwan based on the following criteria: (i) having a medical diagnosis of type 2 diabetes, (ii) not being treated with insulin, (iii) having engaged in self-monitoring of blood glucose at least once within the preceding 6 months, (iv) being at least 20 years old and (v) not having any major mental or cognitive disorders. Data were collected in outpatient consultation rooms, the participants' homes and other settings where the participants felt secure and comfortable. In-depth interviews were conducted to collect data from 16 patients with diabetes.

RESULTS

The participants perceived that lifestyle affected blood glucose levels and did not know how to handle high or low blood glucose levels. Their willingness to continue self-monitoring of blood glucose depended on whether healthcare professionals checked or discussed their blood glucose levels with them.

CONCLUSIONS

The patients' knowledge regarding blood glucose variation and healthcare professionals' attitudes affected the patients' self-monitoring of blood glucose behaviours. The empirical findings illustrated self-monitoring of blood glucose experiences and recommended that healthcare professionals' closely attend to patients' requirements and responses to diabetes and incorporate the self-monitoring of blood glucose into therapy plans.

RELEVANCE TO CLINICAL PRACTICE

Healthcare professionals should reinforce patients' knowledge on appropriate responses to high and low blood glucose levels, intervene appropriately, discuss self-monitoring of blood glucose results with patients and track these results.

An Overview of Factors Associated with Adherence to Lifestyle Modification Programs for Weight Management in Adults

This review aims to provide an overview of the factors associated with adherence reported in existing literature on lifestyle modification programs for weight management among the adult population. An electronic search was performed using PubMed, Medline, PsycINFO and PsycARTICLE to identify studies that examined the factors of adherence to lifestyle modification programs with explicit definition of adherence indicators. We identified 19 studies published between 2004 and 2016. The most commonly used indicator of adherence was attrition, followed by attendance, self-monitoring and dietary adherence. A broad array of factors has been studied but only few studies exploring each factor. Limited evidence suggested older age, higher education, healthier eating and physical activity behaviours, higher stage of change at baseline and higher initial weight loss may predict better adherence. On the other hand, having depression, stress, strong body shape concern, more previous weight loss attempts and being unemployed may predict poor adherence. Inconsistent findings were obtained for self-efficacy, motivation and male gender. This review highlights the need for more rigorous studies to enhance our knowledge on factors related to adherence. Identification of the factors of adherence could provide important implication for program improvement, ultimately improving the effectiveness and the cost-effectiveness of lifestyle modification program.

Weight loss intervention adherence and factors promoting adherence: a meta-analysis

BACKGROUND

Adhering to weight loss interventions is difficult for many people. The majority of those who are overweight or obese and attempt to lose weight are simply not successful. The objectives of this study were 1) to quantify overall adherence rates for various weight loss interventions and 2) to provide pooled estimates for factors associated with improved adherence to weight loss interventions.

METHODS

We performed a systematic literature review and meta-analysis of all studies published between January 2004 and August 2015 that reviewed weight loss intervention adherence.

RESULTS

After applying inclusion and exclusion criteria and checking the methodological quality, 27 studies were included in the meta-analysis. The overall adherence rate was 60.5% (95% confidence interval [CI] 53.6-67.2). The following three main variables were found to impact adherence: 1) supervised attendance programs had higher adherence rates than those with no supervision (rate ratio [RR] 1.65; 95% CI 1.54-1.77); 2) interventions that offered social support had higher adherence than those without social support (RR 1.29; 95% CI 1.24-1.34); and 3) dietary intervention alone had higher adherence than exercise programs alone (RR 1.27; 95% CI 1.19-1.35).

CONCLUSION

A substantial proportion of people do not adhere to weight loss interventions. Programs supervising attendance, offering social support, and focusing on dietary modification have better adherence than interventions not supervising attendance, not offering social support, and focusing exclusively on exercise.

Effectiveness of personalised support for self-management in primary care: a cluster randomised controlled trial

BACKGROUND

Self-management support is an important component of the clinical management of many chronic conditions. The validated Self-Management Screening questionnaire (SeMaS) assesses individual characteristics that influence a patient's ability to self-manage.

AIM

To assess the effect of providing personalised self-management support in clinical practice on patients' activation and health-related behaviours.

DESIGN AND SETTING

A cluster randomised controlled trial was conducted in 15 primary care group practices in the south of the Netherlands.

METHOD

After attending a dedicated self-management support training session, practice nurses in the intervention arm discussed the results of SeMaS with the patient at baseline, and tailored the self-management support. Participants completed a 13-item Patient Activation Measure (PAM-13) and validated lifestyle questionnaires at baseline and after 6 months. Data, including individual care plans, referrals to self-management interventions, self-monitoring, and healthcare use, were extracted from patients' medical records. Multilevel multiple regression was used to assess the effect on outcomes.

RESULTS

The PAM-13 score did not differ significantly between the control (n = 348) and intervention (n = 296) arms at 6 months. In the intervention arm, 29.4% of the patients performed self-monitoring, versus 15.2% in the control arm (effect size r = 0.9, P = 0.01). In the per protocol analysis (control n = 348; intervention n = 136), the effect of the intervention was significant on the number of individual care plans (effect size r = 1.3, P = 0.04) and on self-monitoring (effect size r = 1.0, P = 0.01).

CONCLUSION

This study showed that discussing SeMaS and offering tailored support did not affect patient activation or lifestyle, but did stimulate patients to self-monitor and use individual care plans.

Laboratory medicine and mobile health technologies at crossroads: Perspectives for the management of chronic diseases

Management of chronic diseases represents a leading health and economic issue worldwide. Biomarkers are critical for the diagnosis and management of both communicable and non-communicable chronic diseases, and mobile health (mHealth) technologies are about to change the "game" with regard to the management of patients with such chronic diseases. The development of efficient, accurate and interactive solutions that integrate biomarkers and mHealth opens new perspectives for caregivers for the management of chronic illness.

Clinical Outcomes Associated With a Collaborative Pharmacist-Endocrinologist Diabetes Intense Medical Management "Tune Up" Clinic in Complex Patients

BACKGROUND

No previous studies exist examining the impact of a short-term pharmacist-endocrinologist collaborative practice model on glycemic control in complex patients.

OBJECTIVE

Evaluate outcomes associated with a PharmD-Endocrinologist Diabetes Intense Medical Management (DIMM) "tune up" clinic for complex patients.

METHODS

A retrospective cohort study of 99 patients referred to DIMM clinic versus a comparator group of 56 primary care provider (PCP) patients meeting the same criteria (adult type 2 diabetes patients, glycosylated hemoglobin [A1C] ≥ 8%, follow-up visit within 6 months) in a Veterans Affairs Medical Center. DIMM clinic used a short-term model that coupled personalized clinical care with real-time, patient-specific diabetes education during two to four 60-minute visits over 6 months. PCP patients received usual care. Primary outcome was mean A1C change after 6 months. Secondary measures included fasting blood glucose, lipids, blood pressure, weight, body mass index, and percentage of patients meeting goals.

RESULTS

Patients in each group had an average of 8 and were taking 12 to 14 medications daily. Mean A1C (%) improvement in DIMM group was significantly greater at 6 months (-2.4 [SD = 2.1] vs -0.8 [SD = 1.7]; P < 0.001), than PCP group. Percentage meeting A1C goal levels (<7%, <8%, and <9%) was significantly greater at 3 and 6 months compared with baseline in the DIMM group (P < 0.001) versus (only <8%) at 3 and 6 months compared with baseline in PCP group.

CONCLUSIONS

The DIMM clinic "tune up" model demonstrates a successful collaborative practice which helped complex diabetes patients achieve glycemic control in a 6-month period.

Patient monitoring in mobile health: opportunities and challenges

BACKGROUND

In most countries chronic diseases lead to high health care costs and reduced productivity of people in society. The best way to reduce costs of health sector and increase the empowerment of people is prevention of chronic diseases and appropriate health activities management through monitoring of patients. To enjoy the full benefits of E-health, making use of methods and modern technologies is very important.

METHODS

This literature review articles were searched with keywords like Patient monitoring, Mobile Health, and Chronic Disease in Science Direct, Google Scholar and Pub Med databases without regard to the year of publications.

RESULTS

Applying remote medical diagnosis and monitoring system based on mobile health systems can help significantly to reduce health care costs, correct performance management particularly in chronic disease management. Also some challenges are in patient monitoring in general and specific aspects like threats to confidentiality and privacy, technology acceptance in general and lack of system interoperability with electronic health records and other IT tools, decrease in face to face communication between doctor and patient, sudden interruptions of telecommunication networks, and device and sensor type in specific aspect.

CONCLUSIONS

It is obvious identifying the opportunities and challenges of mobile technology and reducing barriers, strengthening the positive points will have a significant role in the appropriate planning and promoting the achievements of the health care systems based on mobile and helps to design a roadmap for improvement of mobile health.

Using the Common Sense Model of Self-Regulation to Review the Effects of Self-Monitoring of Blood Glucose on Glycemic Control for Non–Insulin-Treated Adults With Type 2 Diabetes

Purpose

This systematic review examined the relationship between self-monitoring of blood glucose (SMBG) and glycemic control in patients with type 2 diabetes. The Common Sense Model of Self-Regulation (CSM) served as a theoretical framework for examining how, when (mediators), and for whom (moderators) SMBG improved glycemic control.

Data Sources

Five databases were searched: Medline, PsychInfo, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Cumulative Index to Nursing & Allied Health Literature.

Study Selection

Included studies had cross-sectional, longitudinal, or randomized controlled trial designs; were published between 2007 and 2011; and included patients with type 2 diabetes at least some of whom were not taking insulin; 1318 studies were screened, 119 were reviewed in detail, and 26 were included.

Data Extraction

Data were collected on the relationship between SMBG and glycemic control, study design, mediators, moderators, participant characteristics, the CSM, and limitations.

Data Synthesis

Twenty-six studies met criteria for inclusion: 11 cross-sectional, 4 longitudinal, and 11 randomized controlled trials. The results of the cross-sectional studies were inconclusive. Results from the longitudinal studies and randomized control trials suggested that SMBG may improve glycemic control. The few studies investigating mediators or moderators reported mixed results. Few studies effectively measured the CSM.

Conclusion

Data suggested that SMBG may help improve glycemic control. Future trials must be designed to test hypotheses and improve our understanding of when, how, and for whom SMBG can enhance glycemic control. Rigorously controlled repetitions of current 2-arm trials will yield little new knowledge of theoretical or practical value.