Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies.

Engagement 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.

Physical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric Analysis.

BACKGROUND: Electronic 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. OBJECTIVE: 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. METHODS: 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%). RESULTS: 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. CONCLUSIONS: 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.

What features do Dutch university students prefer in a smartphone application for promotion of physical activity? A qualitative approach.

BACKGROUND: The transition from adolescence to early adulthood is a critical period in which there is a decline in physical activity (PA). College and university students make up a large segment of this age group. Smartphones may be used to promote and support PA. The purpose of this qualitative study was to explore Dutch students' preferences regarding a PA application (PA app) for smartphones. METHODS: Thirty Dutch students (aged 18-25 years) used a PA app for three weeks and subsequently attended a focus group discussion (k = 5). To streamline the discussion, a discussion guide was developed covering seven main topics, including general app usage, usage and appreciation of the PA app, appreciation of and preferences for its features and the sharing of PA accomplishments through social media. The discussions were audio and video recorded, transcribed and analysed according to conventional content analysis. RESULTS: The participants, aged 21 ± 2 years, were primarily female (67%). Several themes emerged: app usage, technical aspects, PA assessment, coaching aspects and sharing through social media. Participants most often used social networking apps (e.g., Facebook or Twitter), communication apps (e.g., WhatsApp) and content apps (e.g., news reports or weather forecasts). They preferred a simple and structured layout without unnecessary features. Ideally, the PA app should enable users to tailor it to their personal preferences by including the ability to hide features. Participants preferred a companion website for detailed information about their accomplishments and progress, and they liked tracking their workout using GPS. They preferred PA apps that coached and motivated them and provided tailored feedback toward personally set goals. They appreciated PA apps that enabled competition with friends by ranking or earning rewards, but only if the reward system was transparent. They were not willing to share their regular PA accomplishments through social media unless they were exceptionally positive. CONCLUSIONS: Participants prefer PA apps that coach and motivate them, that provide tailored feedback toward personally set goals and that allow competition with friends.

Apps to promote physical activity among adults: a review and content analysis.

BACKGROUND: In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear. METHODS: The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play. RESULTS: On average, the reviewed apps included 5 behavior change techniques (range 2-8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found. CONCLUSIONS: The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used in other types of physical activity promotion interventions.

A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing.

BACKGROUND: A healthy lifestyle, including regular physical activity and a healthy diet, is increasingly part of type 2 diabetes (T2D) management. As many people with T2D have difficulty living and maintaining a healthy lifestyle, there is a need for effective interventions. eHealth interventions that incorporate behavior change theories and tailoring are considered effective tools for supporting a healthy lifestyle. The E-Supporter 1.0 digital coach contains eHealth content for app-based eHealth interventions and offers tailored coaching regarding physical activity and a healthy diet for people with T2D. OBJECTIVE: This study aimed to assess the acceptability of E-Supporter 1.0 and explore its limited efficacy on physical activity, dietary behavior, the phase of behavior change, and self-efficacy levels. METHODS: Over a span of 9 weeks, 20 individuals with T2D received daily motivational messages and weekly feedback derived from behavioral change theories and determinants through E-Supporter 1.0. The acceptability of the intervention was assessed using telephone-conducted, semistructured interviews. The interview transcripts were coded using inductive thematic analysis. The limited efficacy of E-Supporter 1.0 was explored using the Fitbit Charge 2 to monitor step count to assess physical activity and questionnaires to assess dietary behavior (using the Dutch Healthy Diet index), phase of behavior change (using the single-question Self-Assessment Scale Stages of Change), and self-efficacy levels (using the Exercise Self-Efficacy Scale). RESULTS: In total, 5 main themes emerged from the interviews: perceptions regarding remote coaching, perceptions regarding the content, intervention intensity and duration, perceived effectiveness, and overall appreciation. The participants were predominantly positive about E-Supporter 1.0. Overall, they experienced E-Supporter 1.0 as a useful and easy-to-use intervention to support a better lifestyle. Participants expressed a preference for combining E-Supporter with face-to-face guidance from a health care professional. Many participants found the intensity and duration of the intervention to be acceptable, despite the coaching period appearing relatively short to facilitate long-term behavior maintenance. As expected, the degree of tailoring concerning the individual and external factors that influence a healthy lifestyle was perceived as limited. The limited efficacy testing showed a significant improvement in the daily step count (z=-2.040; P=.04) and self-efficacy levels (z=-1.997; P=.046) between baseline and postintervention. Diet was improved through better adherence to Dutch dietary guidelines. No significant improvement was found in the phase of behavior change (P=.17), as most participants were already in the maintenance phase at baseline. CONCLUSIONS: On the basis of this explorative feasibility study, we expect E-Supporter 1.0 to be an acceptable and potentially useful intervention to promote physical activity and a healthy diet in people with T2D. Additional work needs to be done to further tailor the E-Supporter content and evaluate its effects more extensively on lifestyle behaviors.

A Digital Lifestyle Coach (E-Supporter 1.0) to Support People With Type 2 Diabetes: Participatory Development Study.

BACKGROUND: A healthy lifestyle, including regular physical activity and a healthy diet, is becoming increasingly important in the treatment of chronic diseases. eHealth interventions that incorporate behavior change techniques (BCTs) and dynamic tailoring strategies could effectively support a healthy lifestyle. E-Supporter 1.0 is an eCoach designed to support physical activity and a healthy diet in people with type 2 diabetes (T2D). OBJECTIVE: This paper aimed to describe the systematic development of E-Supporter 1.0. METHODS: Our systematic design process consisted of 3 phases. The definition phase included the selection of the target group and formulation of intervention objectives, and the identification of behavioral determinants based on which BCTs were selected to apply in the intervention. In the development phase, intervention content was developed by specifying tailoring variables, intervention options, and decision rules. In the last phase, E-Supporter 1.0 integrated in the Diameter app was evaluated using a usability test in 9 people with T2D to assess intervention usage and acceptability. RESULTS: The main intervention objectives were to stimulate light to moderate-vigorous physical activities or adherence to the Dutch dietary guidelines in people with T2D. The selection of behavioral determinants was informed by the health action process approach and theories explaining behavior maintenance. BCTs were included to address relevant behavioral determinants (eg, action control, self-efficacy, and coping planning). Development of the intervention resulted in 3 types of intervention options, consisting of motivational messages, behavioral feedback, and tailor-made supportive exercises. On the basis of IF-THEN rules, intervention options could be tailored to, among others, type of behavioral goal and (barriers to) goal achievement. Data on these variables could be collected using app data, activity tracker data, and daily ecological momentary assessments. Usability testing revealed that user experiences were predominantly positive, despite some problems in the fixed delivery of content. CONCLUSIONS: The systematic development approach resulted in a theory-based and dynamically tailored eCoach. Future work should focus on expanding intervention content to other chronic diseases and lifestyle behaviors, enhancing the degree of tailoring and evaluating intervention effects on acceptability, use, and cost-effectiveness.

Incentivizing appropriate prescribing in primary care: Development and first results of an electronic health record-based pay-for-performance scheme.

OBJECTIVE: Part of the funding of Dutch General Practitioners (GPs) care is based on pay-for-performance, including an incentive for appropriate prescribing according to guidelines in national formularies. Aim of this paper is to describe the development of an indicator and an infrastructure based on prescription data from GP Electronic Health Records (EHR), to assess the level of adherence to formularies and the effects of the pay-for-performance scheme, thereby assessing the usefulness of the infrastructure and the indicator. METHODS: Adherence to formularies was calculated as the percentage of first prescriptions by the GP for medications that were included in one of the national formularies used by the GP, based on prescription data from EHRs. Adherence scores were collected quarterly for 2018 and 2019 and subsequently sent to health insurance companies for the pay-for-performance scheme. Adherence scores were used to monitor the effect of the pay-for-performance scheme. RESULTS: 75% (2018) and 83% (2019) of all GP practicesparticipated. Adherence to formularies was around 85% or 95%, depending on the formulary used. Adherence improved significantly, especially for practices that scored lowest in 2018. DISCUSSION: We found high levels of adherence to national formularies, with small improvements after one year. The infrastructure will be used to further stimulate formulary-based prescribing by implementing more actionable and relevant indicators on adherence scores for GPs.

The Use and Effects of an App-Based Physical Activity Intervention "Active2Gether" in Young Adults: Quasi-Experimental Trial.

BACKGROUND: Insufficient physical activity (PA) is highly prevalent and associated with adverse health conditions and the risk of noncommunicable diseases. To increase levels of PA, effective interventions to promote PA are needed. Present-day technologies such as smartphones, smartphone apps, and activity trackers offer several possibilities in health promotion. OBJECTIVE: This study aimed to explore the use and short-term effects of an app-based intervention (Active2Gether) to increase the levels of PA in young adults. METHODS: Young adults aged 18-30 years were recruited (N=104) using diverse recruitment strategies. The participants were allocated to the Active2Gether-Full condition (tailored coaching messages, self-monitoring, and social comparison), Active2Gether-Light condition (self-monitoring and social comparison), and the Fitbit-only control condition (self-monitoring). All participants received a Fitbit One activity tracker, which could be synchronized with the intervention apps, to monitor PA behavior. A 12-week quasi-experimental trial was conducted to explore the intervention effects on weekly moderate-to-vigorous PA (MVPA) and relevant behavioral determinants (ie, self-efficacy, outcome expectations, social norm, intentions, satisfaction, perceived barriers, and long-term goals). The ActiGraph wGT3XBT and GT3X+ were used to assess baseline and postintervention follow-up PA. RESULTS: Compared with the Fitbit condition, the Active2Gether-Light condition showed larger effect sizes for minutes of MVPA per day (regression coefficient B=3.1; 95% CI -6.7 to 12.9), and comparatively smaller effect sizes were seen for the Active2Gether-Full condition (B=1.2; 95% CI -8.7 to 11.1). Linear and logistic regression analyses for the intervention effects on the behavioral determinants at postintervention follow-up showed no significant intervention effects of the Active2Gether-Full and Active2Gether-Light conditions. The overall engagement with the Fitbit activity tracker was high (median 88% (74/84) of the days), but lower in the Fitbit condition. Participants in the Active2Gether conditions reported more technical problems than those in the Fitbit condition. CONCLUSIONS: This study showed no statistically significant differences in MVPA or determinants of MVPA after exposure to the Active2Gether-Full condition compared with the Active2Gether-Light or Fitbit condition. This might partly be explained by the small sample size and the low rates of satisfaction in the participants in the two Active2Gether conditions that might be because of the high rates of technical problems.

App-Based Intervention Combining Evidence-Based Behavior Change Techniques With a Model-Based Reasoning System to Promote Physical Activity Among Young Adults (Active2Gether): Descriptive Study of the Development and Content.

BACKGROUND: The Active2Gether intervention is an app-based intervention designed to help and encourage young adults to become and remain physically active by means of personalized, real-time activity tracking and context-specific feedback. OBJECTIVE: The objective of our study was to describe the development and content of the Active2Gether intervention for physical activity promotion. METHODS: A systematic and stepwise approach was used to develop the Active2Gether intervention. This included formulating objectives and a theoretical framework, selecting behavior change techniques, specifying the tailoring, pilot testing, and describing an evaluation protocol. RESULTS: The development of the Active2Gether intervention comprised seven steps: analyzing the (health) problem, developing a program framework, writing (tailored) messages, developing tailoring assessments, developing the Active2Gether intervention, pilot testing, and testing and evaluating the intervention. The primary objective of the intervention was to increase the total time spent in moderate-vigorous physical activity for those who do not meet the Dutch guideline, maintain physical activity levels of those who meet the guideline, or further increase physical activity levels if they so indicated. The theoretical framework is informed by the social cognitive theory, and insights from other theories and evidence were added for specific topics. Development of the intervention content and communication channel resulted in the development of an app that provides highly tailored coaching messages that are framed in an autonomy-supportive style. These coaching messages include behavior change techniques aiming to address relevant behavioral determinants (eg, self-efficacy and outcome expectations) and are partly context specific. A model-based reasoning engine has been developed to tailor the intervention with respect to the type of support provided by the app, send relevant and context-specific messages to the user, and tailor the graphs displayed in the app. For the input of the tailoring, different instruments and sensors are used, such as an activity monitor (Fitbit One), Web-based and mobile questionnaires, and the location services on the user's mobile phone. CONCLUSIONS: The systematic and stepwise approach resulted in an intervention that is based on theory and input from end users. The use of a model-based reasoning system to provide context-specific coaching messages goes beyond many existing eHealth and mHealth interventions.

Do intrapersonal factors mediate the association of social support with physical activity in young women living in socioeconomically disadvantaged neighbourhoods? A longitudinal mediation analysis.

BACKGROUND: Levels of physical activity (PA) decrease when transitioning from adolescence into young adulthood. Evidence suggests that social support and intrapersonal factors (self-efficacy, outcome expectations, PA enjoyment) are associated with PA. The aim of the present study was to explore whether cross-sectional and longitudinal associations of social support from family and friends with leisure-time PA (LTPA) among young women living in disadvantaged areas were mediated by intrapersonal factors (PA enjoyment, outcome expectations, self-efficacy). METHODS: Survey data were collected from 18-30 year-old women living in disadvantaged suburbs of Victoria, Australia as part of the READI study in 2007-2008 (T0, N = 1197), with follow-up data collected in 2010-2011 (T1, N = 357) and 2012-2013 (T2, N = 271). A series of single-mediator models were tested using baseline (T0) and longitudinal data from all three time points with residual change scores for changes between measurements. RESULTS: Cross-sectional analyses showed that social support was associated with LTPA both directly and indirectly, mediated by intrapersonal factors. Each intrapersonal factor explained between 5.9-37.5% of the associations. None of the intrapersonal factors were significant mediators in the longitudinal analyses. CONCLUSIONS: Results from the cross-sectional analyses suggest that the associations of social support from family and from friends with LTPA are mediated by intrapersonal factors (PA enjoyment, outcome expectations and self-efficacy). However, longitudinal analyses did not confirm these findings.

A Validation Study of the Fitbit One in Daily Life Using Different Time Intervals.

PURPOSE: Accelerometer-based wearables can provide the user with real-time feedback through the device's interface and the mobile platforms. Few studies have focused on the minute-by-minute validity of wearables, which is essential for high-quality real-time feedback. This study aims to assess the validity of the Fitbit One compared with the ActiGraph GT3x + for assessing physical activity (i.e., steps, time spent in moderate, vigorous, and moderate-vigorous physical activity) in young adults using traditional time intervals (i.e., days) and smaller time intervals (i.e., minutes and hours). METHODS: Healthy young adults (N = 34) wore the ActiGraph GT3x+ and a Fitbit One for 1 wk. Three aggregation levels were used: minute, hour, and day. Mixed models analyses, intraclass correlation coefficients, Bland-Altman analyses, and absolute error percentage for steps per day were conducted to analyze the validity for steps and minutes spent in moderate, vigorous, and moderate-vigorous physical activity. RESULTS: As compared with ActiGraph (mean = 9 steps per minute, 509 steps per hour and 7636 steps per day), the Fitbit One systematically overestimated physical activity for all aggregation levels: on average 0.82 steps per minute, 45 steps per hour, and 677 steps per day. Strong and significant associations were found between ActiGraph and Fitbit results for steps taken (B = 0.72-0.89). Weaker but statistically significant associations were found for minutes spent in moderate, vigorous, and moderate-vigorous physical activity for all time intervals (B = 0.39-0.57). CONCLUSIONS: Although the Fitbit One overestimates the step activity compared with the ActiGraph, it can be considered a valid device to assess step activity, including for real-time minute-by-minute self-monitoring. However, agreement and correlation between ActiGraph and Fitbit One regarding time spent in moderate, vigorous, and moderate-vigorous physical activity were lower.

Dutch Young Adults Ratings of Behavior Change Techniques Applied in Mobile Phone Apps to Promote Physical Activity: A Cross-Sectional Survey.

BACKGROUND: Interventions delivered through new device technology, including mobile phone apps, appear to be an effective method to reach young adults. Previous research indicates that self-efficacy and social support for physical activity and self-regulation behavior change techniques (BCT), such as goal setting, feedback, and self-monitoring, are important for promoting physical activity; however, little is known about evaluations by the target population of BCTs applied to physical activity apps and whether these preferences are associated with individual personality characteristics. OBJECTIVE: This study aimed to explore young adults' opinions regarding BCTs (including self-regulation techniques) applied in mobile phone physical activity apps, and to examine associations between personality characteristics and ratings of BCTs applied in physical activity apps. METHODS: We conducted a cross-sectional online survey among healthy 18 to 30-year-old adults (N=179). Data on participants' gender, age, height, weight, current education level, living situation, mobile phone use, personality traits, exercise self-efficacy, exercise self-identity, total physical activity level, and whether participants met Dutch physical activity guidelines were collected. Items for rating BCTs applied in physical activity apps were selected from a hierarchical taxonomy for BCTs, and were clustered into three BCT categories according to factor analysis: "goal setting and goal reviewing," "feedback and self-monitoring," and "social support and social comparison." RESULTS: Most participants were female (n=146), highly educated (n=169), physically active, and had high levels of self-efficacy. In general, we observed high ratings of BCTs aimed to increase "goal setting and goal reviewing" and "feedback and self-monitoring," but not for BCTs addressing "social support and social comparison." Only 3 (out of 16 tested) significant associations between personality characteristics and BCTs were observed: "agreeableness" was related to more positive ratings of BCTs addressing "goal setting and goal reviewing" (OR 1.61, 95% CI 1.06-2.41), "neuroticism" was related to BCTs addressing "feedback and self-monitoring" (OR 0.76, 95% CI 0.58-1.00), and "exercise self-efficacy" was related to a high rating of BCTs addressing "feedback and self-monitoring" (OR 1.06, 95% CI 1.02-1.11). No associations were observed between personality characteristics (ie, personality, exercise self-efficacy, exercise self-identity) and participants' ratings of BCTs addressing "social support and social comparison." CONCLUSIONS: Young Dutch physically active adults rate self-regulation techniques as most positive and techniques addressing social support as less positive among mobile phone apps that aim to promote physical activity. Such ratings of BCTs differ according to personality traits and exercise self-efficacy. Future research should focus on which behavior change techniques in app-based interventions are most effective to increase physical activity.

Daily stride rate activity and heart rate response in children with cerebral palsy.

OBJECTIVE: To compare daily stride rate activity, daily exercise intensity, and heart rate intensity of stride rate in children with cerebral palsy with that of typically developing children. METHODS: Forty-three children with cerebral palsy, walking without (Gross Motor Function Classification System (GMFCS) I and II) or with (GMFCS III) an aid and 27 typically developing children (age range 7-14 years) wore a StepWatch™ activity monitor and a heart rate monitor. Time spent and mean heart rate reserve at each stride rate activity level and time spent in each mean heart rate reserve zone was compared. RESULTS: Daily stride rate activity was lower in children with cerebral palsy (39%, 49% and 79% in GMFCS I, II and III, respectively) compared with typically developing children (p < 0.05), while there were no differences in time spent at different mean heart rate reserve zones. Mean heart rate reserve at all stride rate activity levels was not different between typically developing children, GMFCS I and II, while mean heart rate reserve was higher for GFMCS III at stride rates < 30 strides/min (p < 0.05). CONCLUSION: Stride rate activity levels reflect the effort of walking, in children with cerebral palsy who are walking without aids, similar to that of typically developing, whereas children with cerebral palsy using walking aids show higher effort of walking. Despite a lower stride rate activity in cerebral palsy, daily exercise intensity seems comparable, indicating that the StepWatch™ monitor and the heart rate monitor measure different aspects of physical activity.