Using a nurse-driven and home-based telehealth intervention to improve insulin therapy for people with type 2 diabetes in primary care: a feasibility study

Piotie, Patrick Ngassa; Wood, Paola; Muchiri, Jane Wanjiku; Webb, Elizabeth M.; Rheeder, Paul

doi:10.1080/16089677.2022.2074122

Cited by 3 publications

(26 citation statements)

References 44 publications

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“…Twenty-one of identified dose guidance methods were developed for titration of glargine (30,32,34,(36)(37)(38)(39)41,42,44,45,47,49,51,(53)(54)(55)(56)58,61,62) , three for detemir (40,48,52) , five for degludec (8,10,31,43,46) , one for icodec (50) , and one for glargine and detemir (59) . Four studies did not specify insulin further than it was basal insulin analogs (33,35,57,60) .…”

Section: Characteristics Of the Dose Guidance Methodsmentioning

confidence: 99%

“…Approximately 70% of the studies were in an outpatient clinic. The remaining studies were in primary care (34,35,42,51,52,61) or did not specify the setting (8,10,36,50,62) .…”

Section: Characteristics Of the Dose Guidance Methodsmentioning

confidence: 99%

“…Telehealth solutions covered telemonitoring solutions with titration across a digital platform (30,45,54,57,59,60) and combined with home visits (35) , or self-titration decision support (33,39,44,47,55) . In contrast to studies addressing paper-based algorithms, the organizational setup was altered in these studies.…”

Section: Categorization Of the Dose Guidance Methodsmentioning

confidence: 99%

“…The most significant difference was whether participants were insulin naïve at start-of-trial. Study population in 60% of the studies were insulin naïve (8,10,31,34,35,37,(39)(40)(41)45,46,(48)(49)(50)(51)53,(56)(57)(58)61,62) . In 14% of studies, the population continued basal insulin treatment initiated before the study (30,32,33,36,43) , and 26% of studies included a study population of both insulin naïve and continuers (38,42,44,47,52,54,55,59,60) .…”

Section: Participant Characteristicsmentioning

confidence: 99%

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

Thomsen

Hangaard

Kronborg

et al. 2022

J Diabetes Sci Technol

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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.

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Section: Characteristics Of the Dose Guidance Methodsmentioning

confidence: 99%

“…Approximately 70% of the studies were in an outpatient clinic. The remaining studies were in primary care (34,35,42,51,52,61) or did not specify the setting (8,10,36,50,62) .…”

Section: Characteristics Of the Dose Guidance Methodsmentioning

confidence: 99%

Section: Categorization Of the Dose Guidance Methodsmentioning

confidence: 99%

Section: Participant Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

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

Thomsen

Hangaard

Kronborg

et al. 2022

J Diabetes Sci Technol

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“…The findings from the pilot study are published elsewhere. 15 After the pilot, the researchers conducted a Strengths, Weakness, Opportunities and Threat (SWOT) analysis to identify the strengths (S) and weaknesses (W) of the intervention, as well as opportunities (O) to be exploited and threats (T) that must be avoided to ensure the success of the TIP during large-scale implementation. A SWOT analysis is an effective planning tool to identify the intrinsic (S and W) and extrinsic (O and T) factors that need to be considered to achieve success.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting the implementation of a complex health intervention to improve insulin management in primary care: A SWOT analysis

Piotie

Filmalter

Mohlala

et al. 2022

Afr. j. prim. health care fam. med.

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Background: In South Africa, initiating and managing insulin in primary care for people living with type 2 diabetes (PLWD) is a major challenge. To address these challenges, a multidisciplinary team from the University of Pretoria (South Africa) developed the Tshwane Insulin project (TIP) intervention.Aim: To determine internal and external factors, either facilitators or barriers, that could influence the implementation of the TIP intervention and propose strategies to ensure sustainability.Setting: Tshwane District, Gauteng province, South Africa.Methods: We used the SWOT framework to qualitatively analyse the strengths, weaknesses, opportunities, and threats influencing the implementation of the TIP intervention. Four field researchers and three managers from the TIP team participated in an online group discussion. We also conducted semi-structured interviews with healthcare providers (HCPs) (seven nurses, five doctors) and patients with type 2 diabetes (n = 13).Results: Regardless of the identified weaknesses, the TIP intervention was accepted by PLWD and HCPs. Participants identified strengths including app-enabled insulin initiation and titration, pro-active patient follow-up, patient empowerment and provision of glucose monitoring devices. Participants viewed insulin resistance and the attitudes of HCPs as potential threats. Participants suggested that weaknesses and threats could be mitigated by translating education material into local languages and using the lived experiences of insulin-treated patients to address insulin resistance. The procurement of glucose monitoring devices by national authorities would promote the sustainability of the intervention.Conclusion: Our findings may help decision-makers and health researchers to improve insulin management for PLWD in resource-constrained settings by using telehealth interventions.

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Nurse and midwife involvement in task‐sharing and telehealth service delivery models in primary care: A scoping review

Moulton,

Botfield,

Subasinghe

et al. 2024

Journal of Clinical Nursing

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AimTo synthesise and map current evidence on nurse and midwife involvement in task‐sharing service delivery, including both face‐to‐face and telehealth models, in primary care.DesignThis scoping review was informed by the Joanna Briggs Institute (JBI) Methodology for Scoping Reviews.Data Source/Review MethodsFive databases (Ovid MEDLINE, Embase, PubMed, CINAHL and Cochrane Library) were searched from inception to 16 January 2024, and articles were screened for inclusion in Covidence by three authors. Findings were mapped according to the research questions and review outcomes such as characteristics of models, health and economic outcomes, and the feasibility and acceptability of nurse‐led models.ResultsOne hundred peer‐reviewed articles (as 99 studies) were deemed eligible for inclusion. Task‐sharing models existed for a range of conditions, particularly diabetes and hypertension. Nurse‐led models allowed nurses to work to the extent of their practice scope, were acceptable to patients and providers, and improved health outcomes. Models can be cost‐effective, and increase system efficiencies with supportive training, clinical set‐up and regulatory systems. Some limitations to telehealth models are described, including technological issues, time burden and concerns around accessibility for patients with lower technological literacy.ConclusionNurse‐led models can improve health, economic and service delivery outcomes in primary care and are acceptable to patients and providers. Appropriate training, funding and regulatory systems are essential for task‐sharing models with nurses to be feasible and effective.ImpactNurse‐led models are one strategy to improve health equity and access; however, there is a scarcity of literature on what these models look like and how they work in the primary care setting. Evidence suggests these models can also improve health outcomes, are perceived to be feasible and acceptable, and can be cost‐effective. Increased utilisation of nurse‐led models should be considered to address health system challenges and improve access to essential primary healthcare services globally.Reporting MethodThis review is reported against the PRISMA‐ScR criteria.Patient or Public ContributionNo patient or public contribution.Protocol registrationThe study protocol is published in BJGP Open (Moulton et al., 2022).

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Using a nurse-driven and home-based telehealth intervention to improve insulin therapy for people with type 2 diabetes in primary care: a feasibility study

Cited by 3 publications

References 44 publications

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

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

Factors affecting the implementation of a complex health intervention to improve insulin management in primary care: A SWOT analysis

Nurse and midwife involvement in task‐sharing and telehealth service delivery models in primary care: A scoping review

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