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Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends.

ABSTRACT: 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.

SUBMITTER: Alshurafa N

PROVIDER: S-EPMC7173729 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends.

Alshurafa Nabil N Jain Jayalakshmi J Alharbi Rawan R Iakovlev Gleb G Spring Bonnie B Pfammatter Angela A

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 20181201 4

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 compli ...[more]

PMID: 32318650

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Project description:BackgroundContemporary mobile health (mHealth) interventions use various behavior change techniques to promote healthier lifestyles. Social comparison is one of the techniques that is consensually agreed to be effective in engaging the general population in mHealth interventions. However, it is unclear how this strategy can be best used to engage preadolescents. Nevertheless, this strategy has great potential for this target audience, as they are particularly developing their social skills.ObjectiveThis study aims to evaluate how social comparison drives preadolescents' engagement with an mHealth app.MethodsWe designed a 12-week crossover experiment in which we studied 3 approaches to implementing behavior change via social comparison. This study was hosted in a school environment to leverage naturally existing social structures among preadolescents. During the experiment, students and teachers used an mHealth tool that awarded points for performing healthy activities. Participants could read their aggregated scores on a leaderboard and compare their performance with others. In particular, these leaderboards were tweaked to implement 3 approaches of the social comparison technique. The first approach focused on intragroup comparison (ie, students and teachers competing against each other to obtain the most points), whereas the other two approaches focused on intergroup comparison (ie, classes of students and their mentoring teachers collaborating to compete against other classes). Additionally, in the third approach, the performance of teachers was highlighted to further increase students' engagement through teachers' natural exemplary function. To obtain our results, we used linear modeling techniques to analyze the dropout rates and engagement levels for the different approaches. In such analyses, we also considered individual participant traits.ResultsOur sample included 313 participants-290 students (92.7%) and 23 teachers (7.3%). It was found that student engagement levels dropped over time and declined during holidays. However, students seemed to monitor the intergroup competitions more closely than the intragroup competitions, as they, on average, checked the mHealth app more often when they were engaged in team-based comparisons. Students, on average, performed the most unique activities when they were engaged in the second intergroup setting, perhaps because their teachers were most active in this setting. Moreover, teachers seemed to play an important role in engaging their students, as their relationship with their students influenced the engagement of the students.ConclusionsWhen using social comparison to engage preadolescents with an mHealth tool, an intergroup setting, rather than an intragroup competition, motivated them to engage with the app but did not necessarily motivate them to perform more activities. It seems that the number of unique activities that preadolescents perform depends on the activeness of a role model. Moreover, this effect is amplified by preadolescents' perceptions of closeness to that role model.

Project description:Animal-associated microbes are highly variable, contributing to a diverse set of symbiont-mediated phenotypes. Given that host and symbiont genotypes, and their interactions, can impact symbiont-based phenotypes across environments, there is potential for extensive variation in fitness outcomes. Pea aphids, Acyrthosiphon pisum, host a diverse assemblage of heritable facultative symbionts (HFS) with characterized roles in host defense. Protective phenotypes have been largely studied as single infections, but pea aphids often carry multiple HFS species, and particular combinations may be enriched or depleted compared to expectations based on chance. Here, we examined the consequences of single infection versus coinfection with two common HFS exhibiting variable enrichment, the antiparasitoid Hamiltonella defensa and the antipathogen Regiella insecticola, across three host genotypes and environments. As expected, single infections with either H. defensa or R. insecticola raised defenses against their respective targets. Single infections with protective H. defensa lowered aphid fitness in the absence of enemy challenge, while R. insecticola was comparatively benign. However, as a coinfection, R. insecticola ameliorated H. defensa infection costs. Coinfected aphids continued to receive antiparasitoid protection from H. defensa, but protection was weakened by R. insecticola in two clones. Notably, H. defensa eliminated survival benefits conferred after pathogen exposure by coinfecting R. insecticola Since pathogen sporulation was suppressed by R. insecticola in coinfected aphids, the poor performance likely stemmed from H. defensa-imposed costs rather than weakened defenses. Our results reveal a complex set of coinfection outcomes which may partially explain natural infection patterns and suggest that symbiont-based phenotypes may not be easily predicted based solely on infection status.IMPORTANCE The hyperdiverse arthropods often harbor maternally transmitted bacteria that protect against natural enemies. In many species, low-diversity communities of heritable symbionts are common, providing opportunities for cooperation and conflict among symbionts, which can impact the defensive services rendered. Using the pea aphid, a model for defensive symbiosis, we show that coinfections with two common defensive symbionts, the antipathogen Regiella and the antiparasite Hamiltonella, produce outcomes that are highly variable compared to single infections, which consistently protect against designated enemies. Compared to single infections, coinfections often reduced defensive services during enemy challenge yet improved aphid fitness in the absence of enemies. Thus, infection with multiple symbionts does not necessarily create generalist aphids with "Swiss army knife" defenses against numerous enemies. Instead, particular combinations of symbionts may be favored for a variety of reasons, including their abilities to lessen the costs of other defensive symbionts when enemies are not present.

Project description:BackgroundChildhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics.ObjectiveThe aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement.MethodsThe Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant's date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models.ResultsThe overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including "app only" users or "both" app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005).ConclusionsThe EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions.

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Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends.

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Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends.

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