Dataset Information

Acceptability of an mHealth App Intervention for Persons With Type 2 Diabetes and its Associations With Initial Self-Management: Randomized Controlled Trial.

ABSTRACT: Mobile health interventions are increasingly used in health care. The level of acceptability may indicate whether and how such digital solutions will be used.This study aimed to explore associations between the level of acceptability of a mobile diabetes app and initial ability of self-management for patients with type 2 diabetes.Participants with type 2 diabetes were recruited from primary health care settings to a 3-armed randomized controlled trial in the Norwegian study in the RENEWING HEALTH project. At the 1-year follow-up, 75 out of 101 participants from the intervention groups completed an acceptability questionnaire (The Service User Technology Acceptability Questionnaire). In the randomized controlled trial, the 2 intervention groups (n=101 in total) received a mobile phone with a diabetes diary app, and one of the groups received additional health counseling given by telephone calls from a diabetes specialist nurse (n=50). At baseline, we collected clinical variables from medical records, whereas demographic data and self-management (The Health Education Impact Questionnaire) measures were self-reported. Log data from the use of the app by self-monitoring were registered continuously. Associations between initial ability to self-manage at baseline and acceptability of the diabetes diary app after 1 year were analyzed using linear regression.We found statistically significant associations between 5 of the 8 self-management domains and perceived benefit, one of the acceptability factors. However, when adjusting for age, gender, and frequency of use, only 1 domain, skill and technique acquisition, remained independently associated with perceived benefit. Frequency of use of the app was the factor that revealed the strongest association with the acceptability domain perceived benefit.Our findings indicate that persons with diabetes may accept the app, despite its perceived benefit being associated with only one of the 8 domains of their initial level of self-management.ClinicalTrials.gov NCT01315756; https://clinicaltrials.gov/show/NCT01315756 (Archived by WebCite at http://www.webcitation.org/6z46qPhWl).

SUBMITTER: Torbjornsen A

PROVIDER: S-EPMC5987047 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Acceptability of an mHealth App Intervention for Persons With Type 2 Diabetes and its Associations With Initial Self-Management: Randomized Controlled Trial.

Torbjørnsen Astrid A Småstuen Milada Cvancarova MC Jenum Anne Karen AK Årsand Eirik E Ribu Lis L

JMIR mHealth and uHealth 20180521 5

<h4>Background</h4>Mobile health interventions are increasingly used in health care. The level of acceptability may indicate whether and how such digital solutions will be used.<h4>Objective</h4>This study aimed to explore associations between the level of acceptability of a mobile diabetes app and initial ability of self-management for patients with type 2 diabetes.<h4>Methods</h4>Participants with type 2 diabetes were recruited from primary health care settings to a 3-armed randomized controll ...[more]

PMID: 29784635

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Project description:BACKGROUND:Patients with diabetes and poorly controlled hypertension are at increased risk for adverse renal and cardiovascular outcomes. Identifying these patients early and addressing modifiable risk factors is central to delaying renal complications such as diabetic kidney disease. Mobile health (mHealth), a relatively inexpensive and easily scalable technology, can facilitate patient-centered care and promote engagement in self-management, particularly for patients of lower socioeconomic status. Thus, mHealth may be a cost-effective way to deliver self-management education and support. OBJECTIVE:This feasibility study aimed to build a population management program by identifying patients with diabetes and poorly controlled hypertension who were at risk for adverse renal outcomes and evaluate a multifactorial intervention to address medication self-management. We recruited patients from a federally qualified health center (FQHC) in an underserved, diverse county in the southeastern United States. METHODS:Patients were identified via electronic health record. Inclusion criteria were age between 18 and 75 years, diagnosis of type 2 diabetes, poorly controlled hypertension over the last 12 months (mean clinic systolic blood pressure [SBP] ?140 mm Hg and/or diastolic blood pressure [DBP] ?90 mm Hg), access to a mobile phone, and ability to receive text messages and emails. The intervention consisted of monthly telephone calls for 6 months by a case manager and weekly, one-way informational text messages. Engagement was defined as the number of phone calls completed during the intervention; individuals who completed 4 or more calls were considered engaged. The primary outcome was change in SBP at the conclusion of the intervention. RESULTS:Of the 141 patients enrolled, 84.0% (118/141) of patients completed 1 or more phone calls and had follow-up SBP measurements for analysis. These patients were on average 56.9 years of age, predominately female (73/118, 61.9%), and nonwhite by self-report (103/118, 87.3%). The proportion of participants with poor baseline SBP control (50/118, 42.4%) did not change significantly at study completion (53/118, 44.9%) (P=.64). Participants who completed 4 or more phone calls (98/118, 83.1%) did not experience a statistically significant decrease in SBP when compared to those who completed fewer calls. CONCLUSION:We did not reduce uncontrolled hypertension even among the more highly engaged. However, 83% of a predominately minority and low-income population completed at least 67% of the multimodal mHealth intervention. Findings suggest that combining an automated electronic health record system to identify at-risk patients with a tailored mHealth protocol can provide education to this population. While this intervention was insufficient to effect behavioral change resulting in better hypertension control, it does suggest that this FQHC population will engage in low-cost population health applications with a potentially promising impact. TRIAL REGISTRATION:ClinicalTrials.gov NCT02418091; https://clinicaltrials.gov/ct2/show/NCT02418091 (Archived by WebCite at http://www.webcitation.org/76RBvacVU).

Project description:BackgroundOver the past decade, there has been a significant increase in research on the use of mobile health (mHealth) apps as disease management tools. However, very few apps are currently available for prostate cancer (PCa) patient management, and the available apps do not combine the needs of physicians with the requirements of patients. This study aimed to describe the development of a mHealth application for PCa survivors called RyPros, which includes dynamic visualization, intelligent reminders, and instant messaging to support decision-making regarding treatment and follow-up and test the initial accessibility and acceptability application.MethodsThe application was developed through a three-step procedure: logical structure design, application programming, and testing. Dynamic visualization, intelligent reminders, and instant messaging were the core functions of RyPros. Twenty-eight participants who had PCa were enrolled in four weeks of follow-up using the RyPros App. We initially evaluated participants' acceptance of RyPros based on their use of the app (login data, questionnaire completion) and a satisfaction survey.ResultsWe successfully designed and tested the application. A total of 32 participants were enrolled, of whom 28 completed the 4-week follow-up, yielding a participation rate of 87.5%. Each participant logged on an average of 2.82 times and achieved an average of 0.89 questionnaires per week over the four weeks. Most participants (64%) liked the app, and most participants (71%) were satisfied, giving the RyPros app a rating of 4 or 5. More than half of the participants (61%) intended to use the RyPros app regularly, and the majority of participants agreed that the three core functionalities of RyPros were helpful (20/28, 71% for instant messaging; 16/28, 57% for visualization; and 18/28, 64% for reminders and assessments).ConclusionsThe mHealth application we developed for PCa survivor management provided dynamic visualization, reminders, assessments, and instant messaging to support decision-making based on multidisciplinary collaboration. PCa survivors showed high acceptance of the RyPros app.

Project description:BackgroundAlthough mobile health (mHealth) interventions can help improve outcomes among patients with chronic lower back pain (CLBP), many available mHealth apps offer content that is not evidence based. Limbr was designed to enhance self-management of CLBP by packaging self-directed rehabilitation tutorial videos, visual self-report tools, remote health coach support, and activity tracking into a suite of mobile phone apps, including Your Activities of Daily Living, an image-based tool for quantifying pain-related disability.ObjectiveThe aim is to (1) describe patient engagement with the Limbr program, (2) describe patient-perceived utility of the Limbr program, and (3) assess the validity of the Your Activities of Daily Living module for quantifying functional status among patients with CLBP.MethodsThis was a single-arm trial utilizing a convenience sample of 93 adult patients with discogenic back pain who visited a single physiatrist from January 2016 to February 2017. Eligible patients were enrolled in 3-month physical therapy program and received the Limbr mobile phone app suite for iOS or Android. The program included three daily visual self-reports to assess pain, activity level, and medication/coping mechanisms; rehabilitation video tutorials; passive activity-level measurement; and chat-based health coaching. Patient characteristics, patient engagement, and perceived utility were analyzed descriptively. Associations between participant characteristics and program interaction were analyzed using multiple linear regression. Associations between Your Activities of Daily Living and Oswestry Disability Index (ODI) assessments were examined using Pearson correlation and hierarchical linear modeling.ResultsA total of 93 participants were enrolled; of these, 35 (38%) completed the program (age: mean 46, SD 16 years; female: 22/35, 63%). More than half of completers finished assessments at least every 3 days and 70% (19/27) used the rehabilitation component at least once a week. Among respondents to a Web-based feedback survey, 76% (16/21) found the daily notifications helped them remember to complete their exercises, 81% (17/21) found the system easy to use, and 62% (13/21) rated their overall experience good or excellent. Baseline Your Activities of Daily Living score was a significant predictor of baseline ODI score, with ODI increasing by 0.30 units for every 1-unit increase in Your Activities of Daily Living (P<.001). Similarly, hierarchical linear modeling analysis indicated that Your Activities of Daily Living daily assessment scores were significant predictors of ODI scores over the course of the study (P=.01).ConclusionsEngagement among participants who completed the Limbr program was high, and program utility was rated positively by most respondents. Your Activities of Daily Living was significantly associated with ODI scores, supporting the validity of this novel tool. Future studies should assess the effect of Limbr on clinical outcomes, evaluate its use among a wider patient sample, and explore strategies for reducing attrition.Trial registrationClinicalTrials.gov NCT03040310; https://clinicaltrials.gov/ct2/show/NCT03040310 (Archived by WebCite at http://www.webcitation.org/722mEvAiv).

Project description:Background Cancer pain remains highly prevalent and persistent throughout survivorship, and it is crucial to investigate the potential of leveraging the advanced features of mobile health (mHealth) apps to empower individuals to self-manage their pain.Objective This review aims to comprehensively understand the acceptability, users' experiences, and effectiveness of mHealth apps in supporting cancer pain self-management.Methods We conducted an integrative review following Souza and Whittemore and Knafl's 6 review processes. Literature was searched in PubMed, Scopus, CINAHL Plus with Full Text, PsycINFO, and Embase, from 2013 to 2023. Keywords including "cancer patients," "pain," "self-management," "mHealth applications," and relevant synonyms were used in the search. The Johns Hopkins research evidence appraisal tool was used to evaluate the quality of eligible studies. A narrative synthesis was conducted to analyze the extracted data.Results A total of 20 studies were included, with the overall quality rated as high (n=15) to good (n=5). Using mHealth apps to monitor and manage pain was acceptable for most patients with cancer. The internal consistency of the mHealth in measuring pain was 0.96. The reported daily assessment or engagement rate ranged from 61.9% to 76.8%. All mHealth apps were designed for multimodal interventions. Participants generally had positive experiences using pain apps, rating them as enjoyable and user-friendly. In addition, 6 studies reported significant improvements in health outcomes, including enhancement in pain remission (severity and intensity), medication adherence, and a reduced frequency of breakthrough pain. The most frequently highlighted roles of mHealth apps included pain monitoring, tracking, reminders, education facilitation, and support coordination.Conclusions mHealth apps are effective and acceptable in supporting pain self-management. They offer a promising multi-model approach for patients to monitor, track, and manage their pain. These findings provide evidence-based insights for leveraging mHealth apps to support cancer pain self-management. More high-quality studies are needed to examine the effectiveness of digital technology-based interventions for cancer pain self-management and to identify the facilitators and barriers to their implementation in real-world practice.

Project description:BackgroundAlthough the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.ObjectiveThis study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.MethodsThis research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.ResultsIn total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.ConclusionsSelf-management mHealth apps may offer an essential means of managing users' health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

Project description:ObjectivesThe HEAR-aware project targets adults ≥50 years who were recently diagnosed with hearing loss and declined hearing aids, but were open for support via a smartphone app on different target behaviors (TBs). The HEAR-aware app, based on Ecological Momentary Assessment and Ecological Momentary Intervention (EMA, EMI), contains educational materials ("snippets") tailored partly to the user's experienced listening situations. The app aims to increase adults' TB-specific readiness to take action on hearing problems. The present study focused on examining feasibility regarding three novel aspects: (1) the app's acceptability, mainly regarding its EMA and EMI elements (compliance, usability, usefulness, satisfaction), (2) psychometric properties of 10 new TB-specific stages-of-change (SoC) measures (test-retest reliability, construct validity), and (3) the potential of tailoring snippets on a person's SoC.DesignA nonrandomized intervention study including four measurements with 2-week intervals (T0-T3). (1) The intervention period lasted 4 weeks. App usage data were collected throughout (T1-T3). Usability, usefulness, and satisfaction were measured at T3 (n = 26). (2) Reliability concerned T0 and T1 data, in between which no intervention occurred. Intraclass correlation coefficients (ICCs) were calculated (n = 29). Construct validity was examined by calculating correlations between the different TB-specific scales (at T0), and also between each of them and self-reported hearing disability (n = 29). (3) Person-tailoring by SoC was examined using T0 and T1 data. Linear mixed models were applied to test whether users rated snippets corresponding to their SoC as more interesting and useful than noncorresponding snippets (n = 25).Results(1) The percentage of participants that complied with the intended usage varied across the five predefined compliance criteria (lowest: 8%; highest: 85%). Median snippet satisfaction scores were reasonably positive (3.5 to 4.0 of 5). Usability was good (System Usability Score, mean = 72.4, SD = 14.3) and usefulness satisfactory (Intrinsic Motivation Inventory, mean = 4.4, SD = 1.4), but showed large variance. (2) The 10 TB-specific scales showed fair-to-excellent reliabilities (range ICCs = 0.51 to 0.80). Correlations between the TB-specific scales ranged between -0.17 ( p > 0.05) and 0.74 ( p < 0.001), supporting only partly overlap between their underlying constructs. Only the correlation between TB-specific readiness for hearing aid uptake and self-reported hearing disability was significant. (3) Correspondence of a snippet's SoC with the person's SoC significantly related to "interesting" ratings ( p = 0.006). Unexpectedly, for snippets with a lower SoC than the participant's, further deviation of the snippet's SoC from the participant's SoC, increased the participant's interest in the snippet. The relationship with "usefulness" was borderline significant.Conclusions(1) Overall usability, usefulness, and satisfaction scores indicated sufficient app acceptability. The high variance and fairly low compliance showed room for improving the app's EMA/EMI parts for part of the participants. (2) The 10 new TB-specific SoC measures showed sufficient reliability, supporting that they measured different types of readiness to take action on hearing problems (construct validity). (3) The unexpected findings regarding tailoring educational app materials to individuals' SoC deserve further study.

Project description:BackgroundMultiple sclerosis (MS) is one of the most common neurological disorders worldwide, and self-management is considered an essential dimension in its control. This study aimed to develop an evidence-based mobile application for MS self-management and evaluate it.MethodsThis study was undertaken in three phases: content preparation, design, and evaluation. In the content preparation phase, the researchers extracted MS self-management needs based on related guidelines and guides, existing apps on the self-management of MS, and the field experts' views and confirmation. The design phase was conducted in five steps: defining app functionalities, depicting the wireframe, preparing the media, coding the app, and testing the app's performance. The app was developed using the Android Studio environment and Java programming language for the Android operating system. The performance of the developed app was tested separately in several turns, and existing defects were corrected in each turn. Finally, after using the app for three weeks, the app was evaluated for its short-term impact on MS management and user-friendliness using a researcher-constructed questionnaire from participants' (N = 20) perspectives.ResultsThe IDoThis app is an offline app for people with MS that includes five main modules: three modules for training or informing users about different aspects of MS, one module for monitoring the user's MS condition, and a reporting module. In the initial evaluation of the app, 75% (n = 15) of participants mentioned that using this app improved MS self-management status at intermediate and higher levels, but 25% (n = 5) of the participants mentioned that the effect of using the app on the self-management tasks was low or was very low. The majority of users rated the user-friendliness of the app as high. The users found the sections "exercises in MS" and "monitoring of MS status" beneficial to their self-management. Still, the fatigue and sleep management sections are needed to meet users' expectations.ConclusionUsing IDoThis app as a self-management tool for individuals with MS appears feasible, that can meet the need for a free and accessible self-management tool for individuals with MS. Future directions should consider the users' fatigue and sleep management expectations.

Dataset Information

Acceptability of an mHealth App Intervention for Persons With Type 2 Diabetes and its Associations With Initial Self-Management: Randomized Controlled Trial.

Publications

Acceptability of an mHealth App Intervention for Persons With Type 2 Diabetes and its Associations With Initial Self-Management: Randomized Controlled Trial.

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