Barriers and Facilitators to the Implementation of a Mobile Insulin Titration Intervention for Patients With Uncontrolled Diabetes: A Qualitative Analysis

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance

BACKGROUND

Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.

OBJECTIVE

To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.

METHODS

The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.

RESULTS

In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.

CONCLUSIONS

Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.

A scoping review of applications of the Consolidated Framework for Implementation Research (CFIR) to telehealth service implementation initiatives

BACKGROUND

The Consolidated Framework for Implementation Research (CFIR), introduced in 2009, has the potential to provide a comprehensive understanding of the determinants of implementation-effectiveness of health service innovations. Although the CFIR has been increasingly used in recent years to examine factors influencing telehealth implementation, no comprehensive reviews currently exist on the scope of knowledge gained exclusively from applications of the CFIR to telehealth implementation initiatives. This review sought to address this gap.

METHODS

PRISMA-ScR criteria were used to inform a scoping review of the literature. Five academic databases (PUBMED, PROQUEST, SCIDIRECT, CINAHL, and WoS) were searched for eligible sources of evidence from 01.01.2010 through 12.31.2021. The initial search yielded a total of 18,388 records, of which, 64 peer-reviewed articles met the inclusion criteria for the review. Included articles were reviewed in full to extract data, and data collected were synthesized to address the review questions.

RESULTS

Most included articles were published during or after 2020 (64%), and a majority (77%) were qualitative or mixed-method studies seeking to understand barriers or facilitators to telehealth implementation using the CFIR. There were few comparative- or implementation-effectiveness studies containing outcome measures (5%). The database search however, revealed a growing number of protocols for implementation-effectiveness studies published since 2020. Most articles (91%) reported the CFIR Inner Setting domain (e.g., leadership engagement) to have a predominant influence over telehealth implementation success. By comparison, few articles (14%) reported the CFIR Outer Setting domain (e.g., telehealth policies) to have notable influence. While more (63%) telehealth initiatives were focused on specialty (vs primary) care, a vast majority (78%) were focused on clinical practice over medical education, healthcare administration, or population health.

CONCLUSIONS

Organized provider groups have historically paid considerable attention to advocating for telehealth policy (Outer Setting) reform. However, results suggest that for effective telehealth implementation, provider groups need to refocus their efforts on educating individual providers on the complex inter-relationships between Inner Setting constructs and telehealth implementation-effectiveness. On a separate note, the growth in implementation-effectiveness study protocols since 2020, suggests that additional outcome measures may soon be available, to provide a more nuanced understanding of the determinants of effective telehealth implementation based on the CFIR domains and constructs.

Exploring determinants and strategies for implementing self-management support text messaging interventions in safety net clinics

Background

Text message-delivered interventions for chronic disease self-management have potential to reduce health disparities, yet limited research has explored implementing these interventions into clinical care. We partnered with safety net clinics to evaluate a texting intervention for type 2 diabetes called REACH (Rapid Encouragement/Education And Communications for Health) in a randomized controlled trial. Following evaluation, we explored potential implementation determinants and recommended implementation strategies.

Methods

We interviewed clinic staff (n = 14) and a subset of intervention participants (n = 36) to ask about REACH's implementation potential. Using the Consolidated Framework for Implementation Research (CFIR) as an organizing framework, we coded transcripts and used thematic analysis to derive implementation barriers and facilitators. We integrated the CFIR-ERIC (Expert Recommendations for Implementing Change) Matching Tool, interview feedback, and the literature to recommend implementation strategies.

Results

Implementation facilitators included low complexity, strong evidence and quality, available clinic resources, the need for a program to support diabetes self-management, and strong fit between REACH and both the clinics' existing workflows and patients' needs and resources. The barriers included REACH only being available in English, a lack of interoperability with electronic health record systems, patients' concerns about diabetes stigma, limited funding, and high staff turnover. Categories of recommended implementation strategies included training and education, offering flexibility and adaptation, evaluating key processes, and securing funding.

Conclusion

Text message-delivered interventions have strong potential for integration in low-resource settings as a supplement to care. Pursuing implementation can ensure patients benefit from these innovations and help close the research to practice gap.

A scoping review of implementation of health-focused interventions in vulnerable populations

Vulnerable populations face significant challenges in getting the healthcare they need. A growing body of implementation science literature has examined factors, including facilitators and barriers, relevant to accessing healthcare in these populations. The purpose of this scoping review was to identify themes relevant for improving implementation of healthcare practices and programs for vulnerable populations. This scoping review relied on the methodological framework set forth by Arksey and O’Malley, and the Consolidated Framework for Implementation Research (CFIR) to evaluate and structure our findings. A framework analytic approach was used to code studies. Of the five CFIR Domains, the Inner Setting and Outer Setting were the most frequently examined in the 81 studies included. Themes that were pertinent to each domain are as follows—Inner Setting: organizational culture, leadership engagement, and integration of the intervention; Outer Setting: networks, external policies, and patients’ needs and resources; Characteristics of the Individual: knowledge and beliefs about the intervention, self-efficacy, as well as stigma (i.e., other attributes); Intervention Characteristics: complexities with staffing, cost, and adaptations; and Process: staff and patient engagement, planning, and ongoing reflection and evaluation. Key themes, including barriers and facilitators, are highlighted here as relevant to implementation of practices for vulnerable populations. These findings can inform tailoring of implementation strategies and health policies for vulnerable populations, thereby supporting more equitable healthcare.

Monday-focused tailored rapid interactive mobile messaging for weight management 2 (MTRIMM2): results from a randomized controlled trial

BACKGROUND

Text-messaging interventions can reach many individuals across a range of socioeconomic groups, at a low cost. Few randomized controlled trials (RCTs) of text-messaging weight loss interventions have been conducted in United States.

METHODS

From September of 2016 to September of 2018, we conducted a two-parallel group, superiority, RCT of a 16-week text-messaging, weight loss intervention in Baltimore, Maryland, in overweight and obese adults younger than 71, who were able to receive text-messages. Our objective was to assess the effect of receiving the message content only (in printed documents distributed at baseline and week 8), versus receiving messages via short messaging service (SMS) on weight loss (primary outcome), body mass index, perceived exercise benefits and barriers, self-efficacy, and physical activity (PA). The random allocation sequence was equally balanced intervention groups by gender and age groups. Participants were randomized after the baseline assessment. Then, participants and most study staff were unblinded. Follow-up assessments were conducted at 8-, 16-, and 42-week post randomization. We performed intention-to-treat analysis using mixed linear regression models.

RESULTS

Of the 155 adults randomized (printed messages =77, SMS =78), 87.1% were women, 53.5% were African Americans, and 93.5% non-Hispanic. Participants who completed at least one follow-up assessment were included in regression analyses (n=145, printed messages =74, SMS =71). Compared to baseline, at the 42-week assessment, the average percent weight loss was 1.23 for the SMS group (P=0.006) and 0.86 for the printed messages group (P=0.047). Both groups experienced small reductions in weight (printed messages: -0.96 kg, P=0.022; SMS: -1.19 kg, P=0.006), BMI (printed messages: -0.32, P=0.035; SMS: -0.52, P=0.002), and percent energy from fat consumption (printed messages: -1.43, P=0.021; SMS: -2.14, P≤0.001). No statistically significant between groups differences were detected for any of the study outcomes. SMS response rates were not statistically significantly associated with study outcomes. No adverse events were reported.

CONCLUSIONS

A semi-tailored SMS weight loss intervention among overweight and obese adults was not statistically superior in efficacy to paper-based messaging.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04506996.

Challenges of conducting a remote behavioral weight loss study: Lessons learned and a practical guide

OBJECTIVES

To describe challenges and lessons learned in conducting a remote behavioral weight loss trial.

METHODS

The Personal Diet Study is an ongoing randomized clinical trial which aims to compare two mobile health (mHealth) weight loss approaches, standardized diet vs. personalized feedback, on glycemic response. Over a six-month period, participants attended dietitian-led group meetings via remote videoconferencing and were encouraged to self-monitor dietary intake using a smartphone app. Descriptive statistics were used to report adherence to counseling sessions and self-monitoring. Challenges were tracked during weekly project meetings.

RESULTS

Challenges in connecting to and engaging in the videoconferencing sessions were noted. To address these issues, we provided a step-by-step user manual and video tutorials regarding use of WebEx, encouraged alternative means to join sessions, and sent reminder emails/texts about the WebEx sessions and asking participants to join sessions early. Self-monitoring app-related issue included inability to find specific foods in the app database. To overcome this, the study team incorporated commonly consumed foods as "favorites" in the app database, provided a manual and video tutorials regarding use of the app and checked the self-monitoring app dashboard weekly to identify nonadherent participants and intervened as appropriate. Among 135 participants included in the analysis, the median attendance rate for the 14 remote sessions was 85.7% (IQR: 64.3%-92.9%).

CONCLUSIONS

Experience and lessons shared in this report may provide critical and timely guidance to other behavioral researchers and interventionists seeking to adapt behavioral counseling programs for remote delivery in the age of COVID-19.

Pediatric trauma telemedicine in a rural state: Lessons learned from a 1-year experience

BACKGROUND

Previous research from our center has shown that 27% of the pediatric trauma transfers from referring facilities are potentially preventable. Our hospital is the only level 1 pediatric trauma center (PTC) in our state, and we are developing a pediatric trauma telehealth network to help keep certain injured children closer to home. We instituted a pediatric trauma telehealth program with a partnering community-based hospital in our state and aim to report our experience over the first year.

METHODS

All pediatric trauma patients that presented to our partnering hospital from January 2019 to February 2020 were reviewed. Disposition was: a) telehealth consultation, b) admission to the children's unit without a telehealth consultation per our head trauma protocol, or c) transfer without telehealth consultation. Data on demographics, hospital course, and disposition were collected via chart review.

RESULTS

Eight patients underwent telehealth consults and another 8 patients were admitted to the partnering hospital's children's unit based on the head trauma protocol without a telehealth consult. Patient's ages ranged from 7 months to 15 years. Of the patients that underwent telehealth consult, 7 presented with a head injury and 1 presented with a rib fracture/small pneumothorax. The patient with a pneumothorax was observed for 6 h and discharged home after a repeat chest x-ray was stable. All 15 patients with head injuries were observed and discharged from either the emergency department or children's unit after passing concussion testing. No patients required transfer to our PTC after observation, and none were readmitted. Fifty-six patients were transferred without telehealth consultation, and 3 of these patients could potentially have avoided transfer with a telehealth consultation.

CONCLUSIONS

Telehealth in pediatric trauma can be a safe mechanism for preventing the transfer of patients that can be safely observed at a partnering hospital. From a facility that transfers an average of 30 trauma patients per year to our hospital, this program prevented 16 such transfers. Development of a head trauma protocol in collaboration with a pediatric neurosurgeon leads to an unexpected number of patients being admitted to the partnering hospital for observation without utilization of a telehealth consultation.

TYPE OF STUDY

Retrospective study.

LEVEL OF EVIDENCE

III.

Efficacy and Safety of a Decision Support Intervention for Basal Insulin Self-Titration Assisted by the Nurse in Outpatients with T2DM: A Randomized Controlled Trial

OBJECTIVE

The main aim of this study was to evaluate a combined fasting blood glucose based dosage self-titration setting and decision supported telephone coaching intervention on glycemic control and diabetes self-management skills, compared to the conventional care.

METHODS

A 12-week, single-blinded, randomized controlled trial was conducted on adults with type 2 diabetes (T2DM) primarily treated with basal insulin. After randomization, the intervention group (IG, n = 426) received a basal insulin self-titration decision support intervention administered by the Diabetes Specialty Nurses while the control group (CG, n = 423) received conventional care for 12 weeks, both included five telephone interviews. The primary efficacy endpoint was the effect of intervention on glycemic control, measured as the change in glycated hemoglobin (HbA1c) from baseline to Week 12 (after intervention) compared to the control group. Other endpoints included comparisons of the effects of intervention on fasting plasma glucose (FPG), postprandial plasma glucose (PPG), body weight, Michigan diabetes knowledge test (MDKT), diabetes empowerment scale-short Form (DES-DSF), and summary of diabetes self-care activities (SDSCA). Changes in the primary and secondary outcomes were compared using the t-test for continuous variables with a normal distribution and χ ²-test for categorical variables.

RESULTS

The IG showed more improvements on mean HbA1c, compared to the CG (-2.8% vs -1.8%), so did the FPG, PPG, MDKT, DES-DSF and SDSCA (all P<0.01) after the 12-week follow up. Though the final mean insulin dose in the IG was higher than the CG at the end of the study (0.32 U/kg vs 0.28 U/kg), the changes of body weight were similar between the two groups (0.46kg vs 0.40kg, P=0.246), and the proportion of patients with hypoglycemia events during the whole trial were similar (20.65% vs 17.73%, P=0.279).

CONCLUSION

Decision supporting of basal insulin glargine self-titration assisted by Diabetes Specialty Nurses is effective and safe in patients with T2DM. Decision supported telephone coaching intervention offers ongoing encouragement, guidance, and determination of relevant sources of decisional conflict, facilitating adjusting the insulin dose.

Implementation of Technology-Delivered Diabetes Self-care Interventions in Clinical Care: a Narrative Review

PURPOSE OF REVIEW

Evidence is growing for the positive effects of technology-delivered diabetes self-care interventions on behavioral and clinical outcomes. However, our understanding of how to effectively implement these interventions into routine clinical practice is limited. This article provides an overview of the methods and results of studies examining the implementation of technology-delivered diabetes self-care interventions into clinical care. We focus specifically on patient-facing behavioral interventions delivered with technology (e.g., text messaging, apps, websites).

RECENT FINDINGS

Eleven articles were included in the review. Most studies (n = 9) examined barriers and facilitators to implementation, while about half (n = 5) integrated the intervention into clinical care and evaluated implementation and/or effectiveness. Only six studies applied a theory or framework. The most common determinants of implementation were time constraints for clinic staff, familiarity with technology, knowledge of the intervention, and perceived value. We found substantial variation in implementation outcomes, including which were reported, how they were assessed, and the results. In the four studies that evaluated effectiveness, hemoglobin A1c improved. Successful implementation of technology-delivered interventions has the potential to transform healthcare delivery and improve diabetes health on a population level. Promising strategies to address common determinants of implementation include appointing a clinic champion, developing staff training and educational materials, and adapting intervention processes to the clinic context. Future research should evaluate these implementation strategies to understand when and how they impact outcomes. Frameworks such as Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) can help ensure outcomes are systematically reported and allow for comparison across studies.

Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study

BACKGROUND

Tablet and smartphone ownership have increased among US adults over the past decade. However, the degree to which people use mobile devices to help them make medical decisions remains unclear.

OBJECTIVE

The objective of this study is to explore factors associated with self-reported use of tablets or smartphones to support medical decision making in a nationally representative sample of US adults.

METHODS

Cross-sectional data from participants in the 2018 Health Information National Trends Survey (HINTS 5, Cycle 2) were evaluated. There were 3504 responses in the full HINTS 5 Cycle 2 data set; 2321 remained after eliminating respondents who did not have complete data for all the variables of interest. The primary outcome was use of a tablet or smartphone to help make a decision about how to treat an illness or condition. Sociodemographic factors including gender, race/ethnicity, and education were evaluated. Additionally, mobile health (mHealth)- and electronic health (eHealth)-related factors were evaluated including (1) the presence of health and wellness apps on a tablet or smartphone, (2) use of electronic devices other than tablets and smartphones to monitor health (eg, Fitbit, blood glucose monitor, and blood pressure monitor), and (3) whether people shared health information from an electronic monitoring device or smartphone with a health professional within the last 12 months. Descriptive and inferential statistics were conducted using SAS version 9.4. Weighted population estimates and standard errors, univariate odds ratios, and 95% CIs were calculated, comparing respondents who used tablets or smartphones to help make medical decisions (n=944) with those who did not (n=1377), separately for each factor. Factors of interest with a P value of <.10 were included in a subsequent multivariable logistic regression model.

RESULTS

Compared with women, men had lower odds of reporting that a tablet or smartphone helped them make a medical decision. Respondents aged 75 and older also had lower odds of using a tablet or smartphone compared with younger respondents aged 18-34. By contrast, those who had health and wellness apps on tablets or smartphones, used other electronic devices to monitor health, and shared information from devices or smartphones with health care professionals had higher odds of reporting that tablets or smartphones helped them make a medical decision, compared with those who did not.

CONCLUSIONS

A limitation of this research is that information was not available regarding the specific health condition for which a tablet or smartphone helped people make a decision or the type of decision made (eg, surgery, medication changes). In US adults, mHealth and eHealth use, and also certain sociodemographic factors are associated with using tablets or smartphones to support medical decision making. Findings from this study may inform future mHealth and other digital health interventions designed to support medical decision making.