MHealth and Engagement Concerning Persons With Chronic Somatic Health Conditions: Integrative Literature Review.
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ABSTRACT:
SUBMITTER: Tuvesson H
PROVIDER: S-EPMC7414402 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
mHealth and Engagement Concerning Persons With Chronic Somatic Health Conditions: Integrative Literature Review.
Tuvesson Hanna H Eriksén Sara S Fagerström Cecilia C
JMIR mHealth and uHealth 20200724 7
<h4>Background</h4>Chronic somatic health conditions are a global public health challenge. Being engaged in one's own health management for such conditions is important, and mobile health (mHealth) solutions are often suggested as key to promoting engagement.<h4>Objective</h4>The aim of this study was to review, critically appraise, and synthesize the available research regarding engagement through mHealth for persons with chronic somatic health conditions.<h4>Methods</h4>An integrative literatu ...[more]
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Project description: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.
| S-EPMC6356188 | biostudies-literature
Project description:BACKGROUND:Chronic conditions are increasingly more common and negatively impact quality of life, disability, morbidity, and mortality. Health coaching has emerged as a possible intervention to help individuals with chronic conditions adopt health supportive behaviors that improve both quality of life and health outcomes. METHODS/DESIGN:We planned a systematic review and meta-analysis of the contemporary health coaching literature published in the last decade to evaluate the effect of health coaching on clinically important, disease-specific, functional, and behavioral outcomes. We will include randomized controlled trials or quasi-experimental studies that compared health coaching to alternative interventions or usual care. To enable adoption of effective interventions, we aim to explore how the effect of intervention is modified by the intervention components, delivering personnel (i.e., health professionals vs trained lay or peer persons), dose, frequency, and setting. Analysis of intervention outcomes will be reported and classified using an existing theoretical framework, the Theory of Patient Capacity, to identify the areas of patients' capacity to access and use healthcare and enact self-care where coaching may be an effective intervention. DISCUSSION:This systematic review and meta-analysis will identify and synthesize evidence to inform the practice of health coaching by providing evidence on components and characteristics of the intervention essential for success in individuals with chronic health conditions. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42016039730.
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Project description:BackgroundResearch focusing on management of foot health has become more evident over the past decade, especially related to chronic conditions such as diabetes. The level of methodological rigour across this body of work however is varied and outputs do not appear to have been developed or translated into clinical practice. The aim of this systematic review was to assess the latest guidelines, standards of care and current recommendations relative to people with chronic conditions to ascertain the level of supporting evidence concerning the management of foot health.MethodsA systematic search of electronic databases (Medline, Embase, Cinahl, Web of Science, SCOPUS and The Cochrane Library) for literature on recommendations for foot health management for people with chronic conditions was performed between 2000 and 2016 using predefined criteria. Data from the included publications was synthesised via template analysis, employing a thematic organisation and structure. The methodological quality of all included publications was appraised using the Appraisal for Research and Evaluation (AGREE II) instrument. A more in-depth analysis was carried out that specifically considered the levels of evidence that underpinned the strength of their recommendations concerning management of foot health.ResultsThe data collected revealed 166 publications in which the majority (102) were guidelines, standards of care or recommendations related to the treatment and management of diabetes. We noted a trend towards a systematic year on year increase in guidelines standards of care or recommendations related to the treatment and management of long term conditions other than diabetes over the past decade. The most common recommendation is for preventive care or assessments (e.g. vascular tests), followed by clinical interventions such as foot orthoses, foot ulcer care and foot health education. Methodological quality was spread across the range of AGREE II scores with 62 publications falling into the category of high quality (scores 6-7). The number of publications providing a recommendation in the context of a narrative but without an indication of the strength or quality of the underlying evidence was high (79 out of 166).ConclusionsIt is clear that evidence needs to be accelerated and in place to support the future of the Podiatry workforce. Whilst high level evidence for podiatry is currently low in quantity, the methodological quality is growing. Where levels of evidence have been given in in high quality guidelines, standards of care or recommendations, they also tend to be strong-moderate quality such that further strategically prioritised research, if performed, is likely to have an important impact in the field.
| S-EPMC5700544 | biostudies-literature
Project description:BackgroundDecision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance.ObjectiveThrough an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality.MethodsA search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases.ResultsAfter analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches.ConclusionsThe main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques.International registered report identifier (irrid)RR2-10.1101/2022.05.31.22275804.
| S-EPMC10646672 | biostudies-literature
Project description:IntroductionPeople diagnosed with anorexia nervosa are offered or compelled to undergo various treatments and therapies. In Sweden, the lack of highly specialised eating disorder services means that people with anorexia nervosa might be cared for in general psychiatric wards. However, there has been limited research attention paid to how this specific context might benefit or harm persons with anorexia nervosa. There is also limited evidence regarding which form of care is most effective in anorexia nervosa and which type of treatment is optimal in general psychiatric inpatient care. Our intention is to highlight personal and professional perspectives of care for patients with anorexia nervosa in this context, with a focus on person-centred care, gender and power. This protocol describes an integrative literature review aiming to synthesise existing knowledge on general psychiatric inpatient care for persons with anorexia nervosa.Methods and analysisThe study will be an integrative literature review following Whittemore and Knafl. We will search the academic databases PubMed, CINAHL and PsycInfo in accordance with a specific search strategy determined in collaboration with a librarian with expertise on systematic reviews. Data will be analysed using a constant comparison method. Persons with their own experience of anorexia nervosa in general psychiatric inpatient care will be involved as coresearchers.Ethics and disseminationAs this is a literature review, formal ethical review is not required. The findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.AmendmentsIf we need to amend this protocol, we will give the date of each amendment, describe the change and give the rationale in this section.Prospero registration numberCRD42023426095.
| S-EPMC10546143 | biostudies-literature
Project description:Behavioral medicine is devoting increasing attention to the topic of participant engagement and its role in effective mobile health (mHealth) behavioral interventions. Several definitions of the term "engagement" have been proposed and discussed, especially in the context of digital health behavioral interventions. We consider that engagement refers to specific interaction and use patterns with the mHealth tools such as smartphone applications for intervention, whereas adherence refers to compliance with the directives of the health intervention, independent of the mHealth tools. Through our analysis of participant interaction and self-reported behavioral data in a college student health study with incentives, we demonstrate an example of measuring "effective engagement" as engagement behaviors that can be linked to the goals of the desired intervention. We demonstrate how clustering of one year of weekly health behavior self-reports generate four interpretable clusters related to participants' adherence to the desired health behaviors: healthy and steady, unhealthy and steady, decliners, and improvers. Based on the intervention goals of this study (health promotion and behavioral change), we show that not all app usage metrics are indicative of the desired outcomes that create effective engagement. As such, mHealth intervention design might consider eliciting not just more engagement or use overall, but rather, effective engagement defined by use patterns related to the desired behavioral outcome.
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Project description:BackgroundEvaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in this context are necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention.ObjectiveThe objectives of this paper are to discuss the utility of the joint modeling approach in the analysis of longitudinal engagement data in mHealth research and to illustrate the application of this approach using data from an mHealth intervention designed to support illness management among people with schizophrenia.MethodsEngagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to a naïve model that does not account for the relationship between dropout and engagement.ResultsThe joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app 1 day more per week was associated with a 23% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model.ConclusionsNaïve longitudinal models that do not account for informative missingness in mHealth data may produce biased results. Joint models provide a way to model intensively collected engagement outcomes while simultaneously accounting for the relationship between engagement and missing data in mHealth intervention research.
| S-EPMC5269557 | biostudies-literature
Project description:BackgroundPatient and family engagement (PFE) has been defined as a partnership between patients, families, and health care providers to achieve positive health care outcomes. There is evidence that PFE is critical to improving outcomes. We sought to systematically identify and map the evidence on PFE strategies for adults with chronic conditions and identify areas needing more research.MethodsWe searched PubMed, CINAHL, EMBASE, and Cochrane, January 2015 to September 2021 for systematic reviews on strategies for engaging patients with chronic conditions and their caregivers. From each review, we abstracted search dates, number and type of studies, populations, interventions, and outcomes. PFE strategies were categorized into direct patient care, health system, and community-policy level strategies. We found few systematic reviews on strategies at the health system, and none at the community-policy level. In view of this, we also searched for original studies that focused on PFE strategies at those two levels and reviewed the PFE strategies used and study findings.ResultsWe found 131 reviews of direct patient care strategies, 5 reviews of health system strategies, and no reviews of community-policy strategies. Four original studies addressed PFE at the health system or community-policy levels. Most direct patient care reviews focused on self-management support (SMS) (n = 85) and shared decision-making (SDM) (n = 43). Forty-nine reviews reported positive effects, 35 reported potential benefits, 37 reported unclear benefits, and 4 reported no benefits. Health system level strategies mainly involved patients and caregivers serving on advisory councils. PFE strategies with the strongest evidence focused on SMS particularly for patients with diabetes. Many SDM reviews reported potential benefits especially for patients with cancer.DiscussionMuch more evidence exists on the effects of direct patient care strategies on PFE than on the effects of health system or community-policy strategies. Most reviews indicated that direct patient care strategies had positive effects or potential benefits. A limitation of this evidence map is that due to its focus on reviews, which were plentiful, it did not capture details of individual interventions. Nevertheless, this evidence map should help to focus attention on gaps that require more research in efforts to improve PFE.
| S-EPMC8898416 | biostudies-literature
Project description:BackgroundAdults with chronic conditions are disproportionately burdened by COVID-19 morbidity and mortality. Although COVID-19 mobile health (mHealth) apps have emerged, research on attitudes toward using COVID-19 mHealth tools among those with chronic conditions is scarce.ObjectiveThis study aimed to examine attitudes toward COVID-19, identify determinants of COVID-19 mHealth tool use across demographic and health-related characteristics, and evaluate associations between chronic health conditions and attitudes toward using COVID-19 mHealth tools (eg, mHealth or web-based methods for tracking COVID-19 exposures, symptoms, and recommendations).MethodsWe used nationally representative data from the COVID-19 Impact Survey collected from April to June 2020 (n=10,760). Primary exposure was a history of chronic conditions, which were defined as self-reported diagnoses of cardiometabolic, respiratory, immune-related, and mental health conditions and overweight/obesity. Primary outcomes were attitudes toward COVID-19 mHealth tools, including the likelihood of using (1) a mobile phone app to track COVID-19 symptoms and receive recommendations; (2) a website to track COVID-19 symptoms, track location, and receive recommendations; and (3) an app using location data to track potential COVID-19 exposure. Outcome response options for COVID-19 mHealth tool use were extremely/very likely, moderately likely, or not too likely/not likely at all. Multinomial logistic regression was used to compare the likelihood of COVID-19 mHealth tool use between people with different chronic health conditions, with not too likely/not likely at all responses used as the reference category for each outcome. We evaluated the determinants of each COVID-19 mHealth intervention using Poisson regression.ResultsOf the 10,760 respondents, 21.8% of respondents were extremely/very likely to use a mobile phone app or a website to track their COVID-19 symptoms and receive recommendations. Additionally, 24.1% of respondents were extremely/very likely to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19. After adjusting for age, race/ethnicity, sex, socioeconomic status, and residence, adults with mental health conditions were the most likely to report being extremely/very or moderately likely to use each mHealth intervention compared to those without such conditions. Adults with respiratory-related chronic diseases were extremely/very (conditional odds ratio 1.16, 95% CI 1.00-1.35) and moderately likely (conditional odds ratio 1.23, 95% CI 1.04-1.45) to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19.ConclusionsOur study demonstrates that attitudes toward using COVID-19 mHealth tools vary widely across modalities (eg, web-based method vs app) and chronic health conditions. These findings may inform the adoption of long-term engagement with COVID-19 apps, which is crucial for determining their potential in reducing disparities in COVID-19 morbidity and mortality among individuals with chronic health conditions.
| S-EPMC7748389 | biostudies-literature
Project description:BackgroundUse of mobile health (mHealth) apps is growing at an exponential rate in the United States and around the world. Mild cognitive impairment (MCI), Alzheimer disease, and related dementias are a global health problem. Numerous mHealth interventions exist for this population, yet the effect of these interventions on health has not been systematically described.ObjectiveThe aim of this study is to catalog the types of health outcomes used to measure effectiveness of mHealth interventions and assess which mHealth interventions have been shown to improve the health of persons with MCI, Alzheimer disease, and dementia.MethodsWe searched 13 databases, including Ovid MEDLINE, PubMed, EMBASE, the full Cochrane Library, CINAHL, PsycINFO, Ei Compendex, IEEE Xplore, Applied Science & Technology Source, Scopus, Web of Science, ClinicalTrials.gov, and Google Scholar from inception through May 2017 for mHealth studies involving persons with cognitive impairment that were evaluated using at least one quantitative health outcome. Proceedings of the Annual ACM Conferences on Human Factors in Computing Systems, the ACM User Interface Software and Technology Symposium, and the IEEE International Symposium on Wearable Computers were searched in the ACM Digital Library from 2012 to 2016. A hand search of JMIR Publications journals was also completed in July 2017.ResultsAfter removal of duplicates, our initial search returned 3955 records. Of these articles, 24 met final inclusion criteria as studies involving mHealth interventions that measured at least one quantitative health outcome for persons with MCI, Alzheimer disease, and dementia. Common quantitative health outcomes included cognition, function, mood, and quality of life. We found that 21.2% (101/476) of the fully reviewed articles were excluded because of a lack of health outcomes. The health outcomes selected were observed to be inconsistent between studies. For those studies with quantitative health outcomes, more than half (58%) reported postintervention improvements in outcomes.ConclusionsResults showed that many mHealth app interventions targeting those with cognitive impairment lack quantitative health outcomes as a part of their evaluation process and that there is a lack of consensus as to which outcomes to use. The majority of mHealth app interventions that incorporated health outcomes into their evaluation noted improvements in the health of persons with MCI, Alzheimer disease, and dementia. However, these studies were of low quality, leading to a grade C level of evidence. Clarification of the benefits of mHealth interventions for people with cognitive impairment requires more randomized controlled trials, larger numbers of participants, and trial designs that minimize bias.Trial registrationPROSPERO Registration: PROSPERO 2016:CRD42016033846; http://www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42016033846 (Archived by WebCite at http://www.webcitation.org/6sjjwnv1M).
| S-EPMC5597798 | biostudies-literature