Analysis of Effectiveness and Psychological Techniques Implemented in mHealth Solutions for Middle-Aged and Elderly Adults with Type 2 Diabetes: A Narrative Review of the Literature

Improving the management of type 2 diabetes in China using a multifaceted digital health intervention in primary health care: the SMARTDiabetes cluster randomised controlled trial

Background

There is limited evidence, mainly from high-income countries, that digital health interventions improve type 2 diabetes (T2DM) care. Large-scale implementation studies are lacking.

Methods

A multifaceted digital health intervention comprising: (1) a self-management application ('app') for patients and lay 'family health promotors' (FHPs); and (2) clinical decision support for primary care doctors was evaluated in an open-label, parallel, cluster randomized controlled trial in 80 communities (serviced by a primary care facility for >1000 residents) in Hebei Province, China. People >40 years with T2DM and a glycated haemoglobin (HbA1c) ≥7% were recruited (∼25/community). After baseline assessment, community clusters were randomly assigned to intervention or control groups (1:1) via a web-based system, stratified by locality (rural/urban). Control arm clusters received usual care without access to the digital health application or family health promoters. The primary outcome was at the participant level defined as the proportion with ≥2 "ABC" risk factor targets achieved (HbA1c < 7.0%, blood pressure < 140/80 mmHg and LDL-cholesterol < 2.6 mmol/L) at 24 months.

Findings

A total of 2072 people were recruited from the 80 community clusters (40 urban and 40 rural), with 1872 (90.3%) assessed at 24 months. In the intervention arm, patients used FHPs for support more in rural than urban communities (252 (48.6%) rural vs 92 (21.5%) urban, p < 0.0001). The mean monthly proportion of active app users was 46.4% (SD 7.8%) with no significant difference between urban and rural usage rates. The intervention was associated with improved ABC control rates (339 [35.9%] intervention vs 276 [29.9%] usual care; RR 1.20, 95% CI 1.02-1.40; p = 0.025), with significant heterogeneity by geography (rural 220 [42.6%] vs 158 [31.0%]; urban 119 [27.9%] vs 118 [28.6%]; p = 0.022 for interaction). Risk factor reductions were mainly driven by improved glycaemic control (mean HbA1C difference -0.33%, 95% CI -0.48 to -0.17; p = 0.00025 and mean fasting plasma glucose difference -0.58 mmol, 95% CI -0.89 to -0.27; p = 0.00013). There were no changes in blood pressure and LDL-cholesterol levels.

Interpretation

A multifaceted digital health intervention improved T2DM risk factor control rates, particularly in rural communities where there may be stronger relationships between patients and doctors and greater family member support.

Funding

National Health and Medical Research CouncilGlobal Alliance for Chronic Diseases (ID 1094712).

Introducing a Remote Patient Monitoring Usability Impact Model to Overcome Challenges

Telehealth and remote patient monitoring (RPM), in particular, have been through a massive surge of adoption since 2020. This initiative has proven potential for the patient and the healthcare provider in areas such as reductions in the cost of care. While home-use medical devices or wearables have been shown to be beneficial, a literature review illustrates challenges with the data generated, driven by limited device usability. This could lead to inaccurate data when an exam is completed without clinical supervision, with the consequence that incorrect data lead to improper treatment. Upon further analysis of the existing literature, the RPM Usability Impact model is introduced. The goal is to guide researchers and device manufacturers to increase the usability of wearable and home-use medical devices in the future. The importance of this model is highlighted when the user-centered design process is integrated, which is needed to develop these types of devices to provide the proper user experience.

Cognitive Profile and Cardiovascular Risk Factors in Older Adults with Mild Cognitive Impairment

The prevalence of cardiovascular risk factors (CVRFs) in the older adults population and their specific impact on their cognitive profiles still requires further research. For this purpose, a cross-sectional study was carried out to describe the presence of CVRFs and their association with cognitive performance in a sample of older adults (65-85 years old) with Mild Cognitive Impairment (MCI). Participants (n = 185) were divided into three groups concerning their cardiovascular risk level determined by the presence of different CVRFs, including Type 2 Diabetes (T2D), dyslipidemia, hypertension, and obesity. The primary outcome measures were the participant's scores in the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Sociodemographic, clinical, and psychosocial data were collected. Non-parametrical statistical analyses and effect sizes were calculated. Findings revealed that a greater presence of CVRFs was not associated with a worse overall cognitive performance. High-risk patients were more likely to have significantly worse performance in the attentional domain compared to medium-risk (p = 0.029, r = 0.42) and compared to low-risk (p = 0.041, r = 0.35), specifically in the digits repetition subtest (p = 0.042). T2D alone was the CVRF associated with cognitive differences (p = 0.037, r = 0.32), possibly mediated by the duration of the condition. Consequently, a higher presence of CVRFs did not lead to a worse overall cognitive performance. However, high-risk individuals were more likely to experience cognitive impairment, particularly in the attentional domain. T2D played a significant role in these cognitive profile differences, possibly influenced by its duration.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.

Impact of digital services on healthcare and social welfare: An umbrella review

BACKGROUND

Digital services can be effective and cost-efficient options for treating non-communicable diseases, but generalizability is limited due to heterogeneous treatment effects. This umbrella review aims to evaluate the impact of digital services on population health, costs, and patient and healthcare professional satisfaction, and to identify facilitators and barriers to using digital services in healthcare and social welfare.

METHODS

The protocol of the study was registered on the 4th of September 2022 to the International Prospective Register of Systematic Reviews, PROSPERO (CRD42022355635). The review was performed using the Centre for Reviews and Dissemination, Cochrane, Ovid Medline, Scopus, and Web of Science in June 2022. The methodological quality of the included reviews was assessed. The impact of digital services was categorized as no evidence, no dominance, and mixed and positive effect. Inductive content analysis was used to identify facilitators and barriers.

RESULTS

A total of 66 studies were included in the review, 64 % of which were evaluated as high quality. Studies on the impact of digital services in social welfare were not identified. Sixty-five percent of reviews evaluated the impact of digital services on population health with mixed effects; 21 % were on costs with mixed effects; 27 % were on patient satisfaction with positive effects; and 7.6 % were on healthcare professionals' satisfaction with mixed effects. Various features, allocation, end-user support, organized services, and service development facilitated the use of digital services. Correspondingly, barriers were related to service limitations, digital competency, funding- and service strategies, resources and change management.

CONCLUSIONS

Compared to usual care, digital services had a mixed impact on population health and costs with high satisfaction in patients. Mixed healthcare professionals' satisfaction was associated with the use of digital services, and it was less studied. To ensure successful implementation and sustainability of digital services, attention must be paid to address barriers and supporting facilitators at all levels.

DiabeText, a mobile health intervention to support medication taking and healthy lifestyle in adults with type 2 diabetes: Study protocol for a randomized controlled trial

AIM

To evaluate the effectiveness of DiabeText, a low-intensity, multifaceted, mobile health (mHealth) intervention to support medication taking and lifestyle change targeted to people with type 2 diabetes (T2D).

DESIGN

Phase III, 12-months, two-arm (1:1 allocation ratio), randomized parallel-group trial.

METHODS

We will recruit 740 adults with glycated hemoglobin (A1c) >8% (>64 mmol/mol) and with at least one prescription of a non-insulin antidiabetic drug. They will be allocated to a control (usual care) group or an intervention (DiabeText messaging intervention) group. The primary outcome measure will be A1c at 12 months follow-up. Secondary outcomes will include medication possession ratio and behavioral and psychological outcomes.

DISCUSSION

Recent trials suggest that digital health interventions can effectively support diabetes self-management improving T2D control and reducing important T2D complications. In Spain this type of interventions is understudied.

IMPACT

This trial will strengthen the evidence base of the impact of mHealth interventions to support diabetes self-management. If effective, DiabeText may offer a low-cost and highly scalable strategy to improve health at the population level in a sustainable way.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT05006872; Official Title: Supporting People with Type 2 Diabetes in Effective Use of their Medicine Through a System Comprising Mobile Health Technology Integrated with Clinical Care.