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      The application of mHealth to monitor implementation of best practices to support healthy eating and physical activity in afterschool programs

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          Abstract

          Background:

          Childhood obesity continues to be a global epidemic and many child-based settings (e.g. school, afterschool programs) have great potential to make a positive impact on children’s health behaviors. Innovative and time-sensitive methods of gathering health behavior information for the purpose of evaluation and strategically deploying support are needed in these settings.

          Purpose:

          The aim is to (1) demonstrate the feasibility of mobile health (mHealth) for monitoring implementation of healthy eating and physical activity (HEPA) standards and, (2) illustrate the utility of mHealth for identifying areas where support is needed, within the afterschool setting.

          Methods:

          Site leaders ( N = 175) of afterschool programs (ASPs) were invited to complete an online observation checklist via a mobile web app (Healthy Eating and Physical Activity Mobile, HEPA m) once per week during ASP operating hours. Auto-generated weekly text reminders were sent to site leaders’ mobile devices during spring and fall 2015 and 2016 and spring 2017 school semesters. Data from HEPA m was separated into HEPA variables, and expressed as a percent of checklists where an item was present. A higher percentage for a given item would indicate an afterschool has higher compliance with current HEPA standards.

          Results:

          A total of 141 site leaders of ASPs completed 13,960 HEPA m checklists. The average number of checklists completed per ASP was 43 (range 1–220) for healthy eating and 50 (range 1–230) for physical activity. For healthy eating, the most common challenge for ASPs was ‘Staff educating children about healthy eating’, and for physical activity checklists, ‘Girls only physical activity is provided at ASP’.

          Conclusion:

          HEPA m was widely used and provided valuable information that can be used to strategically deploy HEPA support to ASPs. This study gives confidence to the adoption of mHealth strategies as a means for public health practitioners to monitor compliance of an initiative or intervention.

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          Most cited references18

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          Physical activity levels among children attending after-school programs.

          To describe the physical activity (PA) levels of children attending after-school programs, 2) examine PA levels in specific after-school sessions and activity contexts, and 3) evaluate after-school PA differences in groups defined by sex and weight status. One hundred forty-seven students in grades 3-6 (mean age: 10.1 +/- 0.7, 54.4% male, 16.5 % overweight (OW), 22.8% at-risk for OW) from seven after-school programs in the midwestern United States wore Actigraph GT1M accelerometers for the duration of their attendance to the program. PA was objectively assessed on six occasions during an academic year (three fall and three spring). Stored activity counts were uploaded to a customized data-reduction program to determine minutes of sedentary (SED), light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity. Time spent in each intensity category was calculated for the duration of program attendance, as well as specific after-school sessions (e.g., free play, snack time). On average, participants exhibited 42.6 min of SED, 40.8 min of LPA, 13.4 min of MPA, and 5.3 min of VPA. The average accumulation of MVPA was 20.3 min. Boys exhibited higher levels of MPA, VPA, and MVPA, and lower levels of SED and LPA, than girls. OW and at-risk-for-OW students exhibited significantly less VPA than nonoverweight students, but similar levels of LPA, MPA, and MVPA. MVPA levels were significantly higher during free-play activity sessions than during organized or structured activity sessions. After-school programs seem to be an important contributor to the PA of attending children. Nevertheless, ample room for improvement exists by making better use of existing time devoted to physical activity.
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            End User and Implementer Experiences of mHealth Technologies for Noncommunicable Chronic Disease Management in Young Adults: Systematic Review

            Background Chronic noncommunicable diseases (NCDs) such as asthma, diabetes, cancer, and persistent musculoskeletal pain impose an escalating and unsustainable burden on young people, their families, and society. Exploring how mobile health (mHealth) technologies can support management for young people with NCDs is imperative. Objective The aim of this study was to identify, appraise, and synthesize available qualitative evidence on users’ experiences of mHealth technologies for NCD management in young people. We explored the perspectives of both end users (young people) and implementers (health policy makers, clinicians, and researchers). Methods A systematic review and meta-synthesis of qualitative studies. Eligibility criteria included full reports published in peer-reviewed journals from January 2007 to December 2016, searched across databases including EMBASE, MEDLINE (PubMed), Scopus, and PsycINFO. All qualitative studies that evaluated the use of mHealth technologies to support young people (in the age range of 15-24 years) in managing their chronic NCDs were considered. Two independent reviewers identified eligible reports and conducted critical appraisal (based on the Joanna Briggs Institute Qualitative Assessment and Review Instrument: JBI-QARI). Three reviewers independently, then collaboratively, synthesized and interpreted data through an inductive and iterative process to derive emergent themes across the included data. External validity checking was undertaken by an expert clinical researcher and for relevant content, a health policy expert. Themes were subsequently subjected to a meta-synthesis, with findings compared and contrasted between user groups and policy and practice recommendations derived. Results Twelve studies met our inclusion criteria. Among studies of end users (N=7), mHealth technologies supported the management of young people with diabetes, cancer, and asthma. Implementer studies (N=5) covered the management of cognitive and communicative disabilities, asthma, chronic self-harm, and attention deficit hyperactivity disorder. Quality ratings were higher for implementer compared with end user studies. Both complementary and unique user themes emerged. Themes derived for end users of mHealth included (1) Experiences of functionality that supported self-management, (2) Acceptance (technical usability and feasibility), (3) Importance of codesign, and (4) Perceptions of benefit (self-efficacy and empowerment). For implementers, derived themes included (1) Characteristics that supported self-management (functional, technical, and behavior change); (2) Implementation challenges (systems level, service delivery level, and clinical level); (3) Adoption considerations for specific populations (training end users; specific design requirements); and (4) Codesign and tailoring to facilitate uptake and person-centered care. Conclusions Synthesizing available data revealed both complementary and unique user perspectives on enablers and barriers to designing, developing, and implementing mHealth technologies to support young people’s management of their chronic NCDs. Trial Registration PROSPERO CRD42017056317; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD 42017056317 (Archived by WebCite at http://www.webcitation.org/6vZ5UkKLp)
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              Effectiveness and Appropriateness of mHealth Interventions for Maternal and Child Health: Systematic Review

              Background The application of mobile health (mHealth) technology in reproductive, maternal, newborn, and child health (RMNCH) is increasing worldwide. However, best practice and the most effective mHealth interventions have not been reviewed systematically. Objective A systematic review and meta-analysis of studies of mHealth interventions for RMNCH around the world were conducted to investigate their characteristics as well as the features and effectiveness of mHealth interventions. Methods Studies of mHealth interventions for RMNCH between January 2011 and December 2016 were retrieved from 6 databases (PubMed, EMBASE, Global Health, China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, and Wanfang Data Knowledge Service Medium). Comparable studies were included in a random-effects meta-analysis for both exclusive breastfeeding (EBF) and antenatal checks (ANC). Descriptive analyses were conducted for mHealth studies with a range of study designs. Results Analyses of 245 studies were included, including 51 randomized controlled trials (RCTs). Results showed that there are increasing numbers of studies on mHealth interventions for RMNCH. Although 2 meta-analysis, one with 2 RCTs on EBF (odds ratio [OR] 2.03, 95% CI 1.34-3.08, I2=25%) and the other with 3 RCTs on ANC (OR 1.43, 95% CI 1.13-1.79, I2=78%), showed that mHealth interventions are more effective than usual care, almost half (43%) of RCTs showed negative or unclear results on mHealth interventions. Functions described in mHealth interventions were diverse, and the health stages covered were broad. However, single function or single stage appeared to be dominant among mHealth interventions compared with multiple functions or stages. Conclusions More rigorous evaluations are needed to draw consistent conclusions and to analyze mHealth products with multiple functions, especially those popular in the app markets.
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                Author and article information

                Journal
                Global Health Promotion
                Glob Health Promot
                SAGE Publications
                1757-9759
                1757-9767
                March 2020
                May 29 2018
                March 2020
                : 27
                : 1
                : 33-40
                Affiliations
                [1 ]University of South Carolina, Columbia, SC, USA
                [2 ]Wake Forest School of Medicine, Winston-Salem, NC, USA
                [3 ]Arizona State University, Phoenix, AZ, USA
                [4 ]Washington University in St Louis, St Louis, MO, USA
                Article
                10.1177/1757975918768442
                6e1295de-31e7-4a84-b82a-30ef4e46babd
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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