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A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review.

ABSTRACT: BACKGROUND:There is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions. OBJECTIVE:This study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations. METHODS:We conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables. RESULTS:A total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes. CONCLUSIONS:Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being.

SUBMITTER: Pham Q

PROVIDER: S-EPMC6356188 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review.

Pham Quynh Q Graham Gary G Carrion Carme C Morita Plinio P PP Seto Emily E Stinson Jennifer N JN Cafazzo Joseph A JA

JMIR mHealth and uHealth 20190118 1

<h4>Background</h4>There is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions.<h4>Objective</h4>This study aimed to consolidate how analytic indicators of engagem ...[more]

PMID: 30664463

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Project description:BACKGROUND:Mobile health (mHealth) apps for pediatric chronic conditions are growing in availability and challenge investigators to conduct rigorous evaluations that keep pace with mHealth innovation. Traditional research methods are poorly suited to operationalize the agile, iterative trials required to evidence and optimize these digitally mediated interventions. OBJECTIVE:We sought to contribute a resource to support the quantification, analysis, and visualization of analytic indicators of effective engagement with mHealth apps for chronic conditions. METHODS:We applied user-centered design methods to design and develop an Analytics Platform to Evaluate Effective Engagement (APEEE) with consumer mHealth apps for chronic conditions and implemented the platform to analyze both retrospective and prospective data generated from a smartphone-based pain self-management app called iCanCope for young people with chronic pain. RESULTS:Through APEEE, we were able to automate the process of defining, operationalizing, and evaluating effective engagement with iCanCope. Configuring the platform to integrate with the app was feasible and provided investigators with a resource to consolidate, analyze, and visualize engagement data generated by participants in real time. Preliminary efforts to evaluate APEEE showed that investigators perceived the platform to be an acceptable evaluative resource and were satisfied with its design, functionality, and performance. Investigators saw potential in APEEE to accelerate and augment evidence generation and expressed enthusiasm for adopting the platform to support their evaluative practice once fully implemented. CONCLUSIONS:Dynamic, real-time analytic platforms may provide investigators with a powerful means to characterize the breadth and depth of mHealth app engagement required to achieve intended health outcomes. Successful implementation of APEEE into evaluative practice may contribute to the realization of effective and evidence-based mHealth care.

Project description:BackgroundPersuasion knowledge, commonly referred to as advertising literacy, is a cognitive dimension that embraces recognition of advertising, its source and audience, and understanding of advertisers' persuasive and selling intents as well as tactics. There is little understanding of users' awareness of organizations that develop or sponsor mobile health (mHealth) apps, especially in light of personal data privacy. Persuasion knowledge or recognition of a supporting organization's presence, characteristics, competencies, intents, and persuasion tactics are crucial to investigate because app users have the right to know about entities that support apps and make informed decisions about app usage. The abundance of free consumer mHealth apps, especially those in the area of fitness, often makes it difficult for users to identify apps' dual purposes, which may be related to not only helping the public manage health but also promoting the supporting organization itself and collecting users' information for further consumer targeting by third parties.ObjectiveThis study aims to investigate smartphone users' awareness of mHealth apps' affiliations with 3 different types of supporting organizations (commercial, government, and nonprofit); differences in users' persuasion knowledge and mHealth app quality and credibility evaluations related to each of the 3 organization types; and users' coping mechanisms for dealing with personal information management within consumer mHealth apps.MethodsIn-depth semistructured interviews were conducted with 25 smartphone users from a local community in midwestern United States. Interviews were thematically analyzed using inductive and deductive approaches.ResultsParticipants indicated that their awareness of and interest in mHealth app-supporting organizations were secondary to the app's health management functions. After being probed, participants showed a high level of persuasion knowledge regarding the types of app-supporting organizations and their promotional intents. They thought that commercial companies sponsored mHealth apps mostly as entertainment tools, whereas noncommercial entities sponsored mHealth apps for users' education. They assigned self-promotional motives to commercial organizations; however, they associated commercial mHealth apps with good quality and functioning. Noncommercial entities were perceived as more credible. Participants were concerned about losing control over personal information within mHealth apps supported by different organizations. They used alternative digital identities to protect themselves from privacy invasion and advertising spam. They were willing to trade some personal information for high-quality commercial mHealth apps. There was a sense of fatalism in discussing privacy risks linked to mHealth app usage, and some participants did not perceive the risks to be serious.ConclusionsThe discussion of and recommendations for the safe and ethical use of mHealth apps associated with organizations' promotional strategies and personal data protection are provided to ensure users' awareness of and enhanced control over digitalized personal information flows. The theoretical implications are discussed in the context of the Persuasion Knowledge Model and dual-processing theories.

Project description:BackgroundUsability is a key factor affecting the acceptance of mobile health applications (mHealth apps) for elderly individuals, but traditional usability evaluation methods may not be suitable for use in this population because of aging barriers. The objectives of this study were to identify, explore, and summarize the current state of the literature on the usability evaluation of mHealth apps for older adults and to incorporate these methods into the appropriate evaluation stage.MethodsElectronic searches were conducted in 10 databases. Inclusion criteria were articles focused on the usability evaluation of mHealth apps designed for older adults. The included studies were classified according to the mHealth app usability evaluation framework, and the suitability of evaluation methods for use among the elderly was analyzed.ResultsNinety-six articles met the inclusion criteria. Research activity increased steeply after 2013 (n = 92). Satisfaction (n = 74) and learnability (n = 60) were the most frequently evaluated critical measures, while memorability (n = 13) was the least evaluated. The ratios of satisfaction, learnability, operability, and understandability measures were significantly related to the different stages of evaluation (P < 0.05). The methods used for usability evaluation were questionnaire (n = 68), interview (n = 36), concurrent thinking aloud (n = 25), performance metrics (n = 25), behavioral observation log (n = 14), screen recording (n = 3), eye tracking (n = 1), retrospective thinking aloud (n = 1), and feedback log (n = 1). Thirty-two studies developed their own evaluation tool to assess unique design features for elderly individuals.ConclusionIn the past five years, the number of studies in the field of usability evaluation of mHealth apps for the elderly has increased rapidly. The mHealth apps are often used as an auxiliary means of self-management to help the elderly manage their wellness and disease. According to the three stages of the mHealth app usability evaluation framework, the critical measures and evaluation methods are inconsistent. Future research should focus on selecting specific critical measures relevant to aging characteristics and adapting usability evaluation methods to elderly individuals by improving traditional tools, introducing automated evaluation tools and optimizing evaluation processes.

Project description:BackgroundAccording to the World Health Organization, the worldwide prevalence of diabetes mellitus (DM) is increasing dramatically and DM comprises a large part of the global burden of disease. At the same time, the ongoing digitalization that is occurring in society today offers novel possibilities to deal with this challenge, such as the creation of mobile health (mHealth) apps. However, while a great variety of DM-specific mHealth apps exist, the evidence in terms of their clinical effectiveness is still limited.ObjectiveThe objective of this review was to evaluate the clinical effectiveness of mHealth apps in DM management by analyzing health-related outcomes in patients diagnosed with type 1 DM (T1DM), type 2 DM (T2DM), and gestational DM.MethodsA scoping review was performed. A systematic literature search was conducted in MEDLINE (PubMed), Cochrane Library, EMBASE, CINAHL, and Web of Science Core Collection databases for studies published between January 2008 and October 2020. The studies were categorized by outcomes and type of DM. In addition, we carried out a meta-analysis to determine the impact of DM-specific mHealth apps on the management of glycated hemoglobin (HbA1c).ResultsIn total, 27 studies comprising 2887 patients were included. We analyzed 19 randomized controlled trials, 1 randomized crossover trial, 1 exploratory study, 1 observational study, and 5 pre-post design studies. Overall, there was a clear improvement in HbA1c values in patients diagnosed with T1DM and T2DM. In addition, positive tendencies toward improved self-care and self-efficacy as a result of mHealth app use were found. The meta-analysis revealed an effect size, compared with usual care, of a mean difference of -0.54% (95% CI -0.8 to -0.28) for T2DM and -0.63% (95% CI -0.93 to -0.32) for T1DM.ConclusionsDM-specific mHealth apps improved the glycemic control by significantly reducing HbA1c values in patients with T1DM and T2DM patients. In general, mHealth apps effectively enhanced DM management. However, further research in terms of clinical effectiveness needs to be done in greater detail.

Project description:BackgroundCancer is increasingly being treated as a chronic disease rather than an acute one-time illness. Additionally, oral anticancer therapies, as opposed to intravenous chemotherapy, are now available for an increasing number of cancer indications. Mobile health (mHealth) apps for use on mobile devices (eg, smartphones or tablets) are designed to help patients with medication adherence, symptom tracking, and disease management. Several previous literature reviews have been conducted regarding mHealth apps for cancer. However, these studies did not address patient preferences for the features of cancer mHealth apps.ObjectiveThe primary aim was to review the scientific literature that describes the features and functions of mHealth apps designed for cancer self-management.MethodsAs the purpose of this review was to explore the depth and breadth of research on mHealth app features for cancer self-management, a scoping review methodology was adopted. Four databases were used for this review: PubMed/MEDLINE, Embase, CINAHL, and PsycINFO. Citation and reference searches were conducted for manuscripts meeting the inclusion criteria. A gray literature search was also conducted. Data extracted from manuscripts included author, title, publication date, study type, sampling type, cancer type, treatment, age of participants, features, availability (free or subscription), design input, and patient preferences. Finally, the features listed for each app were compared, highlighting similarities across platforms as well as features unique to each app.ResultsAfter the removal of duplicates, 522 manuscripts remained for the title and abstract review, with 51 undergoing full-text review. A total of 7 manuscripts (referred to as studies hereafter) were included in the final scoping review. App features described in each study varied from 2 to 11, with a median of 4 features per app. The most reported feature was a symptom or side effect tracker, which was reported in 6 studies. Two apps specified the inclusion of patients and health care providers during the design, while 1 app noted that IT and communications experts provided design input. The utility of the apps for end users was measured in several ways, including acceptability (measuring the end users' experience), usability (assessing the functionality and performance by observing real users completing tasks), or qualitative data (reports from end users collected from interviews or focus groups).ConclusionsThis review explored the literature on cancer mHealth apps. Popular features within these mHealth apps include symptom trackers, cancer education, and medication trackers. However, these apps and features are often developed with little input from patients. Additionally, there is little information regarding patient preferences for the features of existing apps. While the number of cancer-related apps available for download continues to increase, further exploration of patient preferences for app features could result in apps that better meet patient disease self-management needs.

Project description:BACKGROUND:Knowledge translation (KT) is currently endorsed by global health policy actors as a means to improve outcomes by institutionalising evidence-informed policy-making. Organisational knowledge brokers, comprised of researchers, policy-makers and other stakeholders, are increasingly being used to undertake and promote KT at all levels of health policy-making, though few resources exist to guide the evaluation of these efforts. Using a scoping review methodology, we identified, synthesised and assessed indicators that have been used to evaluate KT infrastructure and capacity-building activities in a health policy context in order to inform the evaluation of organisational knowledge brokers. METHODS:A scoping review methodology was used. This included the search of Medline, Global Health and the WHO Library databases for studies regarding the evaluation of KT infrastructure and capacity-building activities between health research and policy, published in English from 2005 to 2016. Data on study characteristics, outputs and outcomes measured, related indicators, mode of verification, duration and/or frequency of collection, indicator methods, KT model, and targeted capacity level were extracted and charted for analysis. RESULTS:A total of 1073 unique articles were obtained and 176 articles were qualified to be screened in full-text; 32 articles were included in the analysis. Of a total 213 indicators extracted, we identified 174 (174/213; 81.7%) indicators to evaluate the KT infrastructure and capacity-building that have been developed using methods beyond expert opinion. Four validated instruments were identified. The 174 indicators are presented in 8 domains based on an adaptation of the domains of the Lavis et al. framework of linking research to action - general climate, production of research, push efforts, pull efforts, exchange efforts, integrated efforts, evaluation and capacity-building. CONCLUSION:This review presents a total of 174 method-based indicators to evaluate KT infrastructure and capacity-building. The presented indicators can be used or adapted globally by organisational knowledge brokers and other stakeholders in their monitoring and evaluation work.

Project description:BackgroundDespite the potential of mobile health (mHealth) interventions to facilitate the early detection of signs of heart failure (HF) decompensation and provide personalized management of symptoms, the outcomes of such interventions in patients with HF have been inconsistent. As engagement with mHealth is required for interventions to be effective, poor patient engagement with mHealth interventions may be associated with mixed evidence. It is crucial to understand how engagement with mHealth interventions is measured in patients with HF, and the effects of engagement on HF outcomes.ObjectiveIn this review, we aimed to describe measures of patient engagement with mHealth interventions and the effects of engagement on HF outcomes.MethodsWe conducted a systematic literature search in 7 databases for relevant studies published in the English language from 2009 to September 2021 and reported the descriptive characteristics of the studies. We used content analysis to identify themes that described patient engagement with mHealth interventions in the qualitative studies included in the review.ResultsWe synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (3239/4771, 67.88%, male), ranging from a sample of 7 to 1571 (median 53.3) patients, followed for a median duration of 90 (IQR 45-180) days. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data in 72% (23/32) of the studies, only qualitatively based on data from semistructured interviews and focus groups in 6% (2/32) of studies, and by a combination of both quantitative and qualitative data in 22% (7/32) of studies. System usage data were evaluated using 6 metrics of engagement: number of physiological parameters transmitted (19/30, 63% studies), number of HF questionnaires completed (2/30, 7% studies), number of log-ins (4/30, 13% studies), number of SMS text message responses (1/30, 3% studies), time spent (5/30, 17% studies), and the number of features accessed and screen viewed (4/30, 13% studies). There was a lack of consistency in how the system usage metrics were reported across studies. In total, 80% of the studies reported only descriptive characteristics of system usage data. The emotional, cognitive, and behavioral domains of patient engagement were identified through qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalization were inconclusive.ConclusionsThe measures of patient engagement with mHealth interventions in patients with HF are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. There is a need for a working group on mHealth that may consolidate the previous operational definitions of patient engagement into an optimal and standardized measure.

Project description:Objective This review aims to identify the mhealth apps delivering early intervention to support parents of children with autism spectrum disorders (ASD). We aim to explore the concept, context and methodology of implementation that is, theoretical framework, feasibility, quality of evidence, for such apps. Background To improve outcomes for children with autism, early intervention has been found to be promising. Parental training, parent psychoeducation and parent-mediated intervention are regarded as the gold standard, to achieve early childhood development goals. Digital health technologies like tele-health, web-based services, have been used to deliver this at a reduced cost. There is little evidence about their use and efficacy in empowering parents of children with ASD. Inclusion criteria The studies reporting the use of mhealth apps to support parents of children with ASD, in community settings, school settings, special schools, clinics, hospitals or child development centres. There will be no exclusion based on region, gender or sociocultural factors. The types of studies included will be quantitative, qualitative, mixed-methods study designs, case reports, grey literature, systematic reviews, clinical trials and studies reporting feasibility of digital mhealth applications. Method Using the NICE Healthcare Databases Advanced Search, we will search the following databases: MEDLINE, PUBMED, CINAHL, EMBASE, PsycINFO, Cochrane Library, EbscoHost, Sabinet, SAGE Journals, Directory of Open Access Journals, BioMed Central, Scopus, ScienceDirect. Furthermore, grey literature will be searched through Google Scholar, ShodhGanga, JSTOR, CORE, EBSCO, DOAJ, BASE. The searches will be limited to the age range of children between 2 and 6 years with ASD, and the date range is from the inception of the database to the current date. The terms for the ASD will be combined with terms for parent, early intervention and digital mhealth to identify eligible studies.

Dataset Information

A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review.

Publications

A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review.

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