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TitleMeasuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies.
AuthorsShort, CE; DeSmet, A; Woods, C; Williams, SL; Maher, C; Middelweerd, A; Müller, AM; Wark, PA; Vandelanotte, C; Poppe, L; Hingle, MD; Crutzen, R
JournalJournal of medical Internet research
Publication Date16 Nov 2018
Date Added to PubMed18 Nov 2018
AbstractEngagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.
TitlePromotion of Physical Activity in Older People Using mHealth and eHealth Technologies: Rapid Review of Reviews.
AuthorsMcGarrigle, L; Todd, C
JournalJournal of medical Internet research
Publication Date29 Dec 2020
Date Added to PubMed30 Dec 2020
AbstractOlder people are at increased risk of adverse health events because of reduced physical activity. There is concern that activity levels are further reduced in the context of the COVID-19 pandemic, as many older people are practicing physical and social distancing to minimize transmission. Mobile health (mHealth) and eHealth technologies may offer a means by which older people can engage in physical activity while physically distancing. The objective of this study was to assess the evidence for mHealth or eHealth technology in the promotion of physical activity among older people aged 50 years or older. We conducted a rapid review of reviews using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for systematic reviews published in the English language in 3 electronic databases: MEDLINE, CINAHL Plus, and Scopus. Two reviewers used predefined inclusion criteria to select relevant reviews and extracted data on review characteristics and intervention effectiveness. Two independent raters assessed review quality using the AMSTAR-2 tool. Titles and abstracts (n=472) were screened, and 14 full-text reviews were assessed for eligibility. Initially, we included 5 reviews but excluded 1 from the narrative as it was judged to be of critically low quality. Three reviews concluded that mHealth or eHealth interventions were effective in increasing physical activity. One review found that the evidence was inconclusive. There is low to moderate evidence that interventions delivered via mHealth or eHealth approaches may be effective in increasing physical activity in older adults in the short term. Components of successful interventions include self-monitoring, incorporation of theory and behavior change techniques, and social and professional support.
TitleBehaviour Change and e-Health - Looking Broadly: A Scoping Narrative Review.
AuthorsScott, RE; Mars, M
JournalStudies in health technology and informatics
Publication Date2 Mar 2020
Date Added to PubMed7 Mar 2020
AbstractBehaviour change can refer to any transformation or modification of human behaviour. Within healthcare it refers to a broad range of activities and approaches that focus on the individual, community, or environmental influences on health-related behaviour. For e-Health (or digital health) it refers to behavioural impacts mediated through a specific e-Health intervention. However, there are also other health-related behaviour changes being quietly imposed upon both the populace and the healthcare professions broadly, by use of information and communications technologies for health. To better understand these deliberate or incidental impacts on the behaviour of healthcare consumers and providers alike, a scoping narrative review was performed using peer-reviewed and grey literature resources. Qualitative information was charted from the selected literature. This created an objective analysis of both contemporary and less commonly appreciated aspects of behaviour change in our 'digital' age. Many contemporary examples exist. The Internet and www brought alternate approaches moving from face-to-face or paper-based to websites, electronic diaries, and now mobile phones (particularly smartphones) to personalize health-related behaviour change in a myriad of diseases and conditions. Segments of the population have also exhibited health-related behaviour change through their growing www-based health-information seeking. More recent examples include 'spontaneous telemedicine' where physicians have changed the behaviour of themselves and colleagues through use of Instant Messaging, e.g., WhatsApp. Patients are also changing their behaviour spontaneously through taking and providing 'medical selfies'. However, the recent and rapid growth in accessibility and popularity of social media has markedly impacted behaviour change through the speed with which information can be spread, by both legitimate users and socialbots. Insidious examples include spread of health-related 'misinformation' (e.g., vaginal cleansing,), and now 'disinformation' (e.g., the 'anti-vaccination' movement, now resulting in recurrence of once eradicated diseases). These, and other examples, represent the broader, sometimes incidental, impact of some current e-health approaches on health-related behaviour change and should be identified and acknowledged as such. Doing so may fundamentally change opinion and efforts to redirect elements of behaviour change and aspects of behaviour change theory in unexpected ways.
TitleCharacterizing Active Ingredients of eHealth Interventions Targeting Persons With Poorly Controlled Type 2 Diabetes Mellitus Using the Behavior Change Techniques Taxonomy: Scoping Review.
AuthorsKebede, MM; Liedtke, TP; Möllers, T; Pischke, CR
JournalJournal of medical Internet research
Publication Date12 Oct 2017
Date Added to PubMed14 Oct 2017
AbstractThe behavior change technique taxonomy v1 (BCTTv1; Michie and colleagues, 2013) is a comprehensive tool to characterize active ingredients of interventions and includes 93 labels that are hierarchically clustered into 16 hierarchical clusters. The aim of this study was to identify the active ingredients in electronic health (eHealth) interventions targeting patients with poorly controlled type 2 diabetes mellitus (T2DM) and relevant outcomes. We conducted a scoping review using the BCTTv1. Randomized controlled trials (RCTs), studies with or pre-post-test designs, and quasi-experimental studies examining efficacy and effectiveness of eHealth interventions for disease management or the promotion of relevant health behaviors were identified by searching PubMed, Web of Science, and PsycINFO. Reviewers independently screened titles and abstracts for eligibility using predetermined eligibility criteria. Data were extracted following a data extraction sheet. The BCTTv1 was used to characterize active ingredients of the interventions reported in the included studies. Of the 1404 unique records screened, 32 studies fulfilled the inclusion criteria and reported results on the efficacy and or or effectiveness of interventions. Of the included 32 studies, 18 (56%) were Web-based interventions delivered via personal digital assistant (PDA), tablet, computer, and/or mobile phones; 7 (22%) were telehealth interventions delivered via landline; 6 (19%) made use of text messaging (short service message, SMS); and 1 employed videoconferencing (3%). Of the 16 hierarchical clusters of the BCTTv1, 11 were identified in interventions included in this review. Of the 93 individual behavior change techniques (BCTs), 31 were identified as active ingredients of the interventions. The most common BCTs identified were instruction on how to perform behavior, adding objects to the environment, information about health consequences, self-monitoring of the outcomes and/or and prefers to be explicit to avoid ambiguity. Response: Checked and avoided of a certain behavior Author: Please note that the journal discourages the use of parenthesis to denote either and/or and prefers to be explicit to avoid ambiguity. Response: Checked and avoided "and/or" and prefers to be explicit to avoid ambiguity. Response: Checked and avoided, and feedback on outcomes of behavior. Our results suggest that the majority of BCTs employed in interventions targeting persons with T2DM revolve around the promotion of self-regulatory behavior to manage the disease or to assist patients in performing health behaviors necessary to prevent further complications of the disease. Detailed reporting of the BCTs included in interventions targeting this population may facilitate the replication and further investigation of such interventions.
TitlePhysical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric Analysis.
AuthorsMüller, AM; Maher, CA; Vandelanotte, C; Hingle, M; Middelweerd, A; Lopez, ML; DeSmet, A; Short, CE; Nathan, N; Hutchesson, MJ; Poppe, L; Woods, CB; Williams, SL; Wark, PA
JournalJournal of medical Internet research
Publication Date18 Apr 2018
Date Added to PubMed20 Apr 2018
AbstractElectronic health (eHealth) and mobile health (mHealth) approaches to address low physical activity levels, sedentary behavior, and unhealthy diets have received significant research attention. However, attempts to systematically map the entirety of the research field are lacking. This gap can be filled with a bibliometric study, where publication-specific data such as citations, journals, authors, and keywords are used to provide a systematic overview of a specific field. Such analyses will help researchers better position their work. The objective of this review was to use bibliometric data to provide an overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet. The Web of Science (WoS) Core Collection was searched to retrieve all existing and highly cited (as defined by WoS) physical activity, sedentary behavior, and diet related eHealth and mHealth research papers published in English between January 1, 2000 and December 31, 2016. Retrieved titles were screened for eligibility, using the abstract and full-text where needed. We described publication trends over time, which included journals, authors, and countries of eligible papers, as well as their keywords and subject categories. Citations of eligible papers were compared with those expected based on published data. Additionally, we described highly-cited papers of the field (ie, top ranked 1%). The search identified 4805 hits, of which 1712 (including 42 highly-cited papers) were included in the analyses. Publication output increased on an average of 26% per year since 2000, with 49.00% (839/1712) of papers being published between 2014 and 2016. Overall and throughout the years, eHealth and mHealth papers related to physical activity, sedentary behavior, and diet received more citations than expected compared with papers in the same WoS subject categories. The Journal of Medical Internet Research published most papers in the field (9.58%, 164/1712). Most papers originated from high-income countries (96.90%, 1659/1717), in particular the United States (48.83%, 836/1712). Most papers were trials and studied physical activity. Beginning in 2013, research on Generation 2 technologies (eg, smartphones, wearables) sharply increased, while research on Generation 1 (eg, text messages) technologies increased at a reduced pace. Reviews accounted for 20 of the 42 highly-cited papers (n=19 systematic reviews). Social media, smartphone apps, and wearable activity trackers used to encourage physical activity, less sedentary behavior, and/or healthy eating were the focus of 14 highly-cited papers. This study highlighted the rapid growth of the eHealth and mHealth physical activity, sedentary behavior, and diet research field, emphasized the sizeable contribution of research from high-income countries, and pointed to the increased research interest in Generation 2 technologies. It is expected that the field will grow and diversify further and that reviews and research on most recent technologies will continue to strongly impact the field.
TitleKey facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects - an umbrella review.
AuthorsFiedler, J; Eckert, T; Wunsch, K; Woll, A
JournalBMC public health
Publication Date23 Oct 2020
Date Added to PubMed25 Oct 2020
AbstractElectronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology. PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized. The systematic search revealed 11 systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions. Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions. The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902 .
TitleFactors Determining Patients' Choice Between Mobile Health and Telemedicine: Predictive Analytics Assessment.
AuthorsKhairat, S; Liu, S; Zaman, T; Edson, B; Gianforcaro, R
JournalJMIR mHealth and uHealth
Publication Date8 Jun 2019
Date Added to PubMed15 Jun 2019
AbstractThe solution to the growing problem of rural residents lacking health care access may be found in the use of telemedicine and mobile health (mHealth). Using mHealth or telemedicine allows patients from rural or remote areas to have better access to health care. The objective of this study was to understand factors influencing the choice of communication medium for receiving care, through the analysis of mHealth versus telemedicine encounters with a virtual urgent clinic. We conducted a postdeployment evaluation of a new virtual health care service, Virtual Urgent Clinic, which uses mHealth and telemedicine modalities to provide patient care. We used a multinomial logistic model to test the significance and predictive power of a set of features in determining patients' preferred method of telecare encounters-a nominal outcome variable of two levels (mHealth and telemedicine). Postdeployment, 1403 encounters were recorded, of which 1228 (87.53%) were completed with mHealth and 175 (12.47%) were telemedicine encounters. Patients' sex (P=.004) and setting (P<.001) were the most predictive determinants of their preferred method of telecare delivery, with significantly small P values of less than .01. Pearson chi-square test returned a strong indication of dependency between chief concern and encounter mediums, with an extremely small P<.001. Of the 169 mHealth patients who responded to the survey, 154 (91.1%) were satisfied by their encounter, compared with 31 of 35 (89%) telemedicine patients. We studied factors influencing patients' choice of communication medium, either mHealth or telemedicine, for a virtual care clinic. Sex and geographic location, as well as their chief concern, were strong predictors of patients' choice of communication medium for their urgent care needs. This study suggests providing the option of mHealth or telemedicine to patients, and suggesting which medium would be a better fit for the patient based on their characteristics.
TitleUsing codesign to develop a culturally tailored, behavior change mHealth intervention for indigenous and other priority communities: A case study in New Zealand.
AuthorsVerbiest, MEA; Corrigan, C; Dalhousie, S; Firestone, R; Funaki, T; Goodwin, D; Grey, J; Henry, A; Humphrey, G; Jull, A; Vano, M; Pekepo, C; Morenga, LT; Whittaker, R; Mhurchu, CN
JournalTranslational behavioral medicine
Publication Date16 Jul 2019
Date Added to PubMed6 Nov 2018
AbstractThe obesity rate in New Zealand is one of the highest worldwide (31%), with highest rates among Māori (47%) and Pasifika (67%). Codesign was used to develop a culturally tailored, behavior change mHealth intervention for Māori and Pasifika in New Zealand. The purpose of this article is to provide an overview of the codesign methods and processes and describe how these were used to inform and build a theory-driven approach to the selection of behavioral determinants and change techniques. The codesign approach in this study was based on a partnership between Māori and Pasifika partners and an academic research team. This involved working with communities on opportunity identification, elucidation of needs and desires, knowledge generation, envisaging the mHealth tool, and prototype testing. Models of Māori and Pasifika holistic well-being and health promotion were the basis for identifying key content modules and were applied to relevant determinants of behavior change and theoretically based behavior change techniques from the Theoretical Domains Framework and Behavior Change Taxonomy, respectively. Three key content modules were identified: physical activity, family/whānau [extended family], and healthy eating. Other important themes included mental well-being/stress, connecting, motivation/support, and health literacy. Relevant behavioral determinants were selected, and 17 change techniques were mapped to these determinants. Community partners established that a smartphone app was the optimal vehicle for the intervention. Both Māori and Pasifika versions of the app were developed to ensure features and functionalities were culturally tailored and appealing to users. Codesign enabled and empowered users to tailor the intervention to their cultural needs. By using codesign and applying both ethnic-specific and Western theoretical frameworks of health and behavior change, the mHealth intervention is both evidence based and culturally tailored.
TitleLatent user groups of an eHealth physical activity behaviour change intervention for people interested in reducing their cardiovascular risk.
AuthorsWienert, J; Kuhlmann, T; Storm, V; Reinwand, D; Lippke, S
JournalResearch in sports medicine (Print)
Publication Date1 Dec
Date Added to PubMed27 Jul 2018
AbstractEHealth behaviour change interventions that help participants to adhere to professional physical activity recommendations can help to prevent future events of cardiovascular diseases (CVD). Therefore, identifying user groups of such interventions based on stages of health behaviour change is of great importance to provide tailored content to users instead of one-size-fits-all approaches. Our study used Latent Class Analysis (LCA) to identify underlying classes of users of an eHealth behaviour change intervention based on stages of change and associated variables. We compared participants' self-allocated stage with their latent class stage membership to display the correlation and mean differences between the two approaches. This was done by analysing baseline data of N = 310 people interested in reducing their CVD risk. LCA identified a three-class solution: (non-)intenders (19.4%), non-habituated actors (43.2%) and habituated actors (37.4%). The interrelation between self-allocated and latent class stage membership was moderate (ρ(308) = .49, < .001). Significant mean differences for (non-)intenders and non-habituated actors were found in social-cognitive variables. Results showed that self-allocated stage outcomes represent a pseudo stage model - linear trends can be reported for stage-associated social-cognitive variables. The study provides information on the validity of stage measures, which can inform future interventions.
TitleHealth Behavior Theory to Enhance eHealth Intervention Research in HIV: Rationale and Review.
AuthorsSimoni, JM; Ronen, K; Aunon, FM
JournalCurrent HIV/AIDS reports
Publication Date1 Dec 2018
Date Added to PubMed5 Dec 2018
AbstractOptimal design and evaluation of eHealth interventions requires the specification of behavioral targets and hypothesized mechanisms of action-both of which can be enhanced with the use of established health behavior theories (HBTs). In this paper, we describe the major HBTs and examine their use in studies of eHealth interventions for HIV prevention and treatment and assess the contribution of HBT in developing and evaluating eHealth interventions. Based on our review of the literature, we argue the field can benefit from more systematic selection, application, and reporting of HBT. We highlight theories specifically designed for eHealth and describe ways that HBT can be used by researchers and practitioners to improve the rigor and impact of eHealth interventions for individuals living with or at risk for HIV. This brief overview of HBTs and their application to eHealth intervention in HIV research has underscored the importance of a theoretically intentional approach. The theory should be used to inform the design of the eHealth intervention; the intervention should not determine the theory. A theory-driven iterative model of eHealth intervention development may not only improve our repertoire of effective strategies but also has the potential to expand our theoretical and empirical knowledge of health behavior change.
MNCHFPRHHIV/AIDSMalariaNoncommunicable diseaseCOVID-19Decision-makingEducation & trainingBehavior changeGovernancePrivacy & securityEquityCHWsYouth & adolescentsSystematic reviewsProtocols & research designMedical RecordsLaboratoryPharmacyHuman ResourcesmHealthSMSChatbotsAI