Using Mobile Health to Improve Social Support for Low-Income Latino Patients with Diabetes: A Mixed-Methods Analysis of the Feasibility Trial of TExT-MED + FANS

Burner, Elizabeth; Lam, Chun Nok; DeRoss, Rebecca; Kagawa‐Singer, Marjorie; Menchine, Michael; Arora, Sanjay

doi:10.1089/dia.2017.0198

Cited by 46 publications

(84 citation statements)

References 44 publications

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“…The Alive-PD intervention promoted social support by creating virtual teams using a participant messaging system and the option to share content on social media [21]. Gamification methods include healthyliving challenges [22,31], team competitions, a points system with monetary rewards [21], trivia questions [22] and quizzes [21,27]. Engagement may also be promoted with enhanced media, including video messages [27,32] and voice recognition [33].…”

Section: Patient Engagementmentioning

confidence: 99%

Digital health technology and mobile devices for the management of diabetes mellitus: state of the art

2019

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Diabetes mellitus is a disease that can be difficult to manage and requires high levels of health literacy and numeracy, selfmonitoring and frequent contact with clinicians. If not optimally controlled, diabetes can lead to kidney failure, blindness and cardiovascular complications, which, in turn, contribute to increasing healthcare costs. Although not yet widely used, mobile health (mHealth) tools have enhanced diabetes management and prevention and are likely to play an increasing role with the growth of smartphone ownership and medical device innovations. Recent mHealth interventions targeting type 1 and type 2 diabetes are diverse in their goals and components, and include insulin management applications, wearable blood glucose meters, automated text messages, health diaries and virtual health coaching. In this paper, we review the modalities and components of various impactful interventions for insulin management, diabetes education, self-management and prevention. More work is needed to investigate how individual demographic, socioeconomic, behavioural and clinical characteristics contribute to patient engagement and the efficacy of mHealth tools for diabetes.

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Section: Patient Engagementmentioning

confidence: 99%

Digital health technology and mobile devices for the management of diabetes mellitus: state of the art

2019

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“…Beneficial effects of technology use on self-management and health outcomes were typically reported in studies that addressed non-professional social support by the patient's personal networks [37,38]. The use of social media, online communities, or mobile health (mHealth) has enhanced or improved psychological empowerment [39,40], self-care information and knowledge [38,41], blood glucose levels (HbA1c), as well as glucose self-monitoring and physical activity [3,42], and other health aspects [43][44][45]. The inclusion of family members or spouses in technological diabetes management systems was found to improve their understanding of self-management requirements and affected the communication about diabetes and the support of the patient positively [46].…”

Section: Social Support and Technology-supported Diabetes Selfmanagementmentioning

confidence: 99%

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

Brew‐Sam

Chib

Rossmann

2020

BMC Med Inform Decis Mak

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Background: Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as a means for delivering social support, this paper argues that social support has an impact on the use of diabetes mHealth apps. Specifically, we postulate differences between the impact of healthcare professional versus nonprofessional (family/friends) support on mobile app use for diabetes self-management. Methods: This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N = 21, Study 1) and an online survey (N = 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus nonprofessionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decisionmaking, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory. Results: The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on their medical specialty. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the physician specialty significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). Additionally, specialist care positively related to a higher chance of shared decision-making and better physicianpatient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use. Conclusion: The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literature. In particular, the use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and may decrease when such support is high (e.g., no perceived need to use technology).

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“…The patients with cardiometabolic disease studied reported that lifestyle-targeting text messaging approaches were feasible and acceptable, and that the text messages were easy to understand (88%) and were sent at an Connected Health Interventions to Cardiovascular Disease Prevention Agher et al 549 appropriate frequency (71%), in appropriate language (88%). Most of the studies used "smartphone health applications (connect)" [8][9][10][11][12]15,16,19,20,22,[24][25][26][27] . "Focus group" (do) and "Education" (read) were used together in two articles.…”

Section: Connected Health Interventionmentioning

confidence: 99%

Influence of Connected Health Interventions for Adherence to Cardiovascular Disease Prevention: A Scoping Review

et al. 2020

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Background Recent health care developments include connected health interventions to improve chronic disease management and/or promote actions reducing aggravating risk factors for conditions such as cardiovascular diseases. Adherence is one of the main challenges for ensuring the correct use of connected health interventions over time. Objective This scoping review deals with the connected health interventions used in interventional studies, describing the ways in which these interventions and their functions effectively help patients to deal with cardiovascular risk factors over time, in their own environments. The objective is to acquire knowledge and highlight current trends in this field, which is currently both productive and immature. Methods A structured literature review was constructed from Medline-indexed journals in PubMed. We established inclusion criteria relating to three dimensions (cardiovascular risk factors, connected health interventions, and level of adherence). Our initial search yielded 98 articles; 78 were retained after screening on the basis of title and abstract, 49 articles underwent full-text screening, and 24 were finally retained for the analysis, according to preestablished inclusion criteria. We excluded studies of invasive interventions and studies not dealing with digital health. We extracted a description of the connected health interventions from data for the population or end users. Results We performed a synthetic analysis of outcomes, based on the distribution of bibliometrics, and identified several connected health interventions and main characteristics affecting adherence. Our analysis focused on three types of user action: to read, to do, and to connect. Finally, we extracted current trends in characteristics: connect, adherence, and influence. Conclusion Connected health interventions for prevention are unlikely to affect outcomes significantly unless other characteristics and user preferences are considered. Future studies should aim to determine which connected health design combinations are the most effective for supporting long-term changes in behavior and for preventing cardiovascular disease risks.

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Using Mobile Health to Improve Social Support for Low-Income Latino Patients with Diabetes: A Mixed-Methods Analysis of the Feasibility Trial of TExT-MED + FANS

Abstract: mHealth is a feasible, acceptable, and promising avenue to improve social support and diabetes outcomes.

Cited by 46 publications

References 44 publications

Digital health technology and mobile devices for the management of diabetes mellitus: state of the art

Digital health technology and mobile devices for the management of diabetes mellitus: state of the art

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

Influence of Connected Health Interventions for Adherence to Cardiovascular Disease Prevention: A Scoping Review

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