There are thousands of apps promoting dietary improvement, increased physical activity (PA) and weight management. Despite a growing number of reviews in this area, popular apps have not been comprehensively analysed in terms of features related to engagement, functionality, aesthetics, information quality, and content, including the types of change techniques employed. The databases containing information about all Health and Fitness apps on GP and iTunes (7,954 and 25,491 apps) were downloaded in April 2015. Database filters were applied to select the most popular apps available in both stores. Two researchers screened the descriptions selecting only weight management apps. Features, app quality and content were independently assessed using the Mobile App Rating Scale (MARS) and previously-defined categories of techniques relevant to behaviour change. Inter-coder reliabilities were calculated, and correlations between features explored. Of the 23 popular apps included in the review 16 were free (70 %), 15 (65 %) addressed weight control, diet and PA combined; 19 (83 %) allowed behavioural tracking. On 5-point MARS scales, apps were of average quality (Md = 3.2, IQR = 1.4); “functionality” (Md = 4.0, IQR = 1.1) was the highest and “information quality” (Md = 2.0, IQR = 1.1) was the lowest domain. On average, 10 techniques were identified per app (range: 1–17) and of the 34 categories applied, goal setting and self-monitoring techniques were most frequently identified. App quality was positively correlated with number of techniques included (rho = .58, p < .01) and number of “technical” features (rho = .48, p < .05), which was also associated with the number of techniques included (rho = .61, p < .01). Apps that provided tracking used significantly more techniques than those that did not. Apps with automated tracking scored significantly higher in engagement, aesthetics, and overall MARS scores. Those that used change techniques previously associated with effectiveness (i.e., goal setting, self-monitoring and feedback) also had better “information quality”. Popular apps assessed have overall moderate quality and include behavioural tracking features and a range of change techniques associated with behaviour change. These apps may influence behaviour, although more attention to information quality and evidence-based content are warranted to improve their quality.

Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity. This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS. Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation. In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05). The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.

Background: Digital health interventions (DHIs) are increasingly being adopted globally to address various public health issues. DHIs can be categorized according to four main types of technology: mobile based, web based, telehealth, and electronic health records. In 2006, Norman and Skinner introduced the eHealth literacy model, encompassing six domains of skills and abilities (basic, health, information, scientific, media, and computer) needed to effectively understand, process, and act on health-related information. Little is known about whether these domains are assessed or accounted for in DHIs. Objective: This study aims to explore how DHIs assess and evaluate the eHealth literacy model, describe which health conditions are addressed, and which technologies are used. Methods: We conducted a scoping review of the literature on DHIs, based on randomized controlled trial design and reporting the assessment of any domain of the eHealth literacy model. MEDLINE, CINAHL, Embase, and Cochrane Library were searched. A duplicate selection and data extraction process was performed; we charted the results according to the country of origin, health condition, technology used, and eHealth literacy domain. Results: We identified 131 unique DHIs conducted in 26 different countries between 2001 and 2020. Most DHIs were conducted in English-speaking countries (n=81, 61.8%), delivered via the web (n=68, 51.9%), and addressed issues related to noncommunicable diseases (n=57, 43.5%) or mental health (n=26, 19.8%). None of the interventions assessed all six domains of the eHealth literacy model. Most studies focused on the domain of health literacy (n=96, 73.2%), followed by digital (n=19, 14.5%), basic and media (n=4, 3%), and information and scientific literacy (n=1, 0.7%). Of the 131 studies, 7 (5.3%) studies covered both health and digital literacy. Conclusions: Although many selected DHIs assessed health or digital literacy, no studies comprehensively evaluated all domains of the eHealth literacy model; this evidence might be overlooking important factors that can mediate or moderate the effects of these interventions. Future DHIs should comprehensively assess the eHealth literacy model while developing or evaluating interventions to understand how and why interventions can be effective.

Background: Academics in all disciplines increasingly use social media to share their publications on the internet, reaching out to different audiences. In the last few years, specific indicators of social media impact have been developed (eg, Altmetrics), to complement traditional bibliometric indicators (eg, citation count and h-index). In health research, it is unclear whether social media impact also translates into research impact. Objective: The primary aim of this study was to systematically review the literature on the impact of using social media on the dissemination of health research. The secondary aim was to assess the correlation between Altmetrics and traditional citation-based metrics. Methods: We conducted a systematic review to identify studies that evaluated the use of social media to disseminate research published in health-related journals. We specifically looked at studies that described experimental or correlational studies linking the use of social media with outcomes related to bibliometrics. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using a predefined search strategy (International Prospective Register of Systematic Reviews: CRD42017057709). We conducted independent and duplicate study selection and data extraction. Given the heterogeneity of the included studies, we summarized the findings through a narrative synthesis. Results: Of a total of 18,624 retrieved citations, we included 51 studies: 7 (14%) impact studies (answering the primary aim) and 44 (86%) correlational studies (answering the secondary aim). Impact studies reported mixed results with several limitations, including the use of interventions of inappropriately low intensity and short duration. The majority of correlational studies suggested a positive association between traditional bibliometrics and social media metrics (eg, number of mentions) in health research. Conclusions: We have identified suggestive yet inconclusive evidence on the impact of using social media to increase the number of citations in health research. Further studies with better design are needed to assess the causal link between social media impact and bibliometrics.

Widespread diffusion of mobile phone and Web 2.0 technologies make them potentially useful tools for promoting health and tackling public health issues, such as the increasing prevalence of overweight and obesity. Research in this domain is growing rapidly but, to date, no review has comprehensively and systematically documented how mobile and Web 2.0 technologies are being deployed and evaluated in relation to weight management. To provide an up-to-date, comprehensive map of the literature discussing the use of mobile phone and Web 2.0 apps for influencing behaviors related to weight management (ie, diet, physical activity [PA], weight control, etc). A systematic scoping review of the literature was conducted based on a published protocol (registered at PROSPERO: CRD42014010323). Using a comprehensive search strategy, we searched 16 multidisciplinary electronic databases for original research documents published in English between 2004 and 2014. We used duplicate study selection and data extraction. Using an inductively developed charting tool, selected articles were thematically categorized. We identified 457 articles, mostly published between 2013 and 2014 in 157 different journals and 89 conference proceedings. Articles were categorized around two overarching themes, which described the use of technologies for either (1) promoting behavior change (309/457, 67.6%) or (2) measuring behavior (103/457, 22.5%). The remaining articles were overviews of apps and social media content (33/457, 7.2%) or covered a combination of these three themes (12/457, 2.6%). Within the two main overarching themes, we categorized articles as representing three phases of research development: (1) design and development, (2) feasibility studies, and (3) evaluations. Overall, articles mostly reported on evaluations of technologies for behavior change (211/457, 46.2%). There is an extensive body of research on mobile phone and Web 2.0 technologies for weight management. Research has reported on (1) the development, feasibility, and efficacy of persuasive mobile technologies used in interventions for behavior change (PA and diet) and (2) the design, feasibility, and accuracy of mobile phone apps for behavioral assessment. Further research has focused exclusively on analyses of the content and quality of available apps. Limited evidence exists on the use of social media for behavior change, but a segment of studies deal with content analyses of social media. Future research should analyze mobile phone and Web 2.0 technologies together by combining the evaluation of content and design aspects with usability, feasibility, and efficacy/effectiveness for behavior change, or accuracy/validity for behavior assessment, in order to understand which technological components and features are likely to result in effective interventions.

Purpose: – The purpose of this paper is to investigate the reasons for participating and not participating in an e-health workplace physical activity (PA) intervention.Design/methodology/approach: – Semi-structured interviews and two focus groups were conducted with a purposive sample of employees who enrolled and participated in the intervention and with those who did not complete enrolment, hence did not participate in it. Data were examined using thematic analysis according to the clusters of “reasons for participation” and for “non-participation”.Findings: – Reported reasons for participation included a need to be more active, to increase motivation to engage in PA, and to better manage weight. Employees were attracted by the perceived ease of use of the programme and by the promise of receiving reminders. Many felt encouraged to enrol by managers or peers. Reported reasons for non-participation included lack of time, loss of interest towards the programme, or a lack of reminders to complete enrolment.Practical implications: – Future e-health workplace behavioural interventions should consider focusing on employees’ needs and motivators to behaviour change, provide regular reminders for participants to complete enrolment and ensure that procedures are completed successfully. Barriers to participation could be identified through formative research with the target population and feasibility studies.Originality/value: – This study combines a qualitative analysis of the reasons why some employees decided to enrol in a workplace PA intervention and why some others did not. This study highlights factors to consider when designing, implementing and promoting similar interventions and that could inform strategies to enhance participation in workplace PA interventions.