Older Adult Self-Efficacy Study of Mobile Phone Diabetes Management

Quinn, Charlene C.; Khokhar, Bilal; Weed, Kelly; Barr, Erik; Gruber‐Baldini, Ann L.

doi:10.1089/dia.2014.0341

Cited by 52 publications

(86 citation statements)

References 49 publications

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“…This is similar to previous literature findings [11]. Despite the increased research activities, only 3 studies reviewed here explicitly targeted older adults, revealing a lack of gerontological smart device-based studies [50,54,74]. This is a lost opportunity as more and more older adults are displaying interest in using smartphones and tablets for obtaining health information [78].…”

Section: Discussionsupporting

confidence: 84%

“…The second most frequently used strategy was automated feedback [37,43,48,49,51,54,56,59,63,66,68-71,76]. The nature and extent of the automated feedback varied between studies.…”

Section: Resultsmentioning

confidence: 99%

“…Quality of life was assessed in 9 studies, and 6 assessment instruments were used [28,37,42,47,49,54,59,66,68]. Only the 36-Item Short Form Health Survey (SF-36) and the EuroQoL Five Dimensions Questionnaire (EQ-5D) were each used in 2 studies (Table 7).…”

Section: Resultsmentioning

confidence: 99%

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Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Kim¹,

Lee²

2017

JMIR Mhealth Uhealth

191

127

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BackgroundThe emergence of smartphones and tablets featuring vastly advancing functionalities (eg, sensors, computing power, interactivity) has transformed the way mHealth interventions support chronic disease management for older adults. Baby boomers have begun to widely adopt smart devices and have expressed their desire to incorporate technologies into their chronic care. Although smart devices are actively used in research, little is known about the extent, characteristics, and range of smart device-based interventions.ObjectiveWe conducted a scoping review to (1) understand the nature, extent, and range of smart device-based research activities, (2) identify the limitations of the current research and knowledge gap, and (3) recommend future research directions.MethodsWe used the Arksey and O’Malley framework to conduct a scoping review. We identified relevant studies from MEDLINE, Embase, CINAHL, and Web of Science databases using search terms related to mobile health, chronic disease, and older adults. Selected studies used smart devices, sampled older adults, and were published in 2010 or after. The exclusion criteria were sole reliance on text messaging (short message service, SMS) or interactive voice response, validation of an electronic version of a questionnaire, postoperative monitoring, and evaluation of usability. We reviewed references. We charted quantitative data and analyzed qualitative studies using thematic synthesis. To collate and summarize the data, we used the chronic care model.ResultsA total of 51 articles met the eligibility criteria. Research activity increased steeply in 2014 (17/51, 33%) and preexperimental design predominated (16/50, 32%). Diabetes (16/46, 35%) and heart failure management (9/46, 20%) were most frequently studied. We identified diversity and heterogeneity in the collection of biometrics and patient-reported outcome measures within and between chronic diseases. Across studies, we found 8 self-management supporting strategies and 4 distinct communication channels for supporting the decision-making process. In particular, self-monitoring (38/40, 95%), automated feedback (15/40, 38%), and patient education (13/40, 38%) were commonly used as self-management support strategies. Of the 23 studies that implemented decision support strategies, clinical decision making was delegated to patients in 10 studies (43%). The impact on patient outcomes was consistent with studies that used cellular phones. Patients with heart failure and asthma reported improved quality of life. Qualitative analysis yielded 2 themes of facilitating technology adoption for older adults and 3 themes of barriers.ConclusionsLimitations of current research included a lack of gerontological focus, dominance of preexperimental design, narrow research scope, inadequate support for participants, and insufficient evidence for clinical outcome. Recommendations for future research include generating evidence for smart device-based programs, using patient-generated data for advanced data mining techniques, vali...

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Section: Discussionsupporting

confidence: 84%

“…The second most frequently used strategy was automated feedback [37,43,48,49,51,54,56,59,63,66,68-71,76]. The nature and extent of the automated feedback varied between studies.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Kim¹,

Lee²

2017

JMIR Mhealth Uhealth

191

127

View full text Add to dashboard Cite

show abstract

“…These studies were published between January 2010 and August 2015, and they evaluated a total of 8 mobile medical app products: Blue Star 8,9 Diabeo, 10–12 Diabetes Diary, 13 DID, 14,15 Accu-Chek Aviva Expert, 16,17 Diabetes Insulin Guidance Systems, 18 FreeStyle InsulLinx, 19,20 and Telcare. 21 Six apps (Dexcom Share, Glooko, Accu-Chek Connect, Dario, Livongo, and Telcare) have received FDA clearance for use but do not have studies on efficacy and safety in peer-reviewed literature.…”

Section: Introductionmentioning

confidence: 99%

Evidence-based Mobile Medical Applications in Diabetes

Drincic

Prahalad

Greenwood

et al. 2016

Endocrinology and Metabolism Clinics of North America

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“…The rise in ownership, affordability and functionality of mobile devices has created further opportunities for interventions that can be applied to older adults [18]. For example, existing interventions have used mobile phones and wearables such as smartwatches to deliver reminders and provide suggestions for activities, and to measure and provide feedback on physical activity [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Application of the behaviour change wheel framework to the development of interventions within the City4Age project

Direito

Michie

Lefevre

et al. 2017

2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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Abstract-The probability of an intervention being effective is likely increased if it is designed following a behavioural analysis and with the aid of evidence-based intervention frameworks. For example, the Behaviour Change Wheel (BCW) framework and its associated COM-B model of behaviour have been used successfully as a starting point for designing behaviour change interventions. However, the BCW framework can also be applied at a later stage in the design process, such as when an intervention has been designed but before it is deployed. Here we describe the application of the BCW framework and COM-B model to evaluate and refine already designed interventions. We use a multidisciplinary multi-site project ('City4Age') as a case study. The project aims to promote independent living of older adults through the deployment of interventions using wearable and environment-based technology. We conducted face-to-face interviews with site representatives to identify the target behaviours, perceived barriers and facilitators, intervention functions, and modes of delivery for each planned intervention. Additionally, literature reviews were conducted to identify evidence-based facilitators and barriers for each targeted behaviour. Subsequently, we 1) compared the intervention functions proposed by the project-sites with those most likely to be effective according to the BCW; and 2) assessed the congruency of the barriers and facilitators identified by the pilot-sites with those identified in the literature. For five planned interventions across two project-sites (Birmingham and Singapore), two had intervention functions unlikely to be effective according to the BCW. The two planned interventions to promote social engagement did not address barriers or facilitators evident in the literature, indicating they required refinement. Applying the BCW framework allowed to identify which interventions needed refining. It also helped in providing specific guidance in our recommendations for improvements prior to deployment.

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Older Adult Self-Efficacy Study of Mobile Phone Diabetes Management

Cited by 52 publications

References 49 publications

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Evidence-based Mobile Medical Applications in Diabetes

Application of the behaviour change wheel framework to the development of interventions within the City4Age project

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