Designing in-app messages to nudge behavior change: Lessons learned from a weight management app for young adults

Valle, Carmina G.; Nezami, Brooke T.; Tate, Deborah F.

doi:10.1016/j.obhdp.2020.10.004

Cited by 19 publications

(14 citation statements)

References 30 publications

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“…Second, the delivery of a digital stimulus to engage individuals in a specific task is an important component of digital interventions. These digital stimuli may include “nudges” (Valle et al, 2020; Weintraub et al, 2021), which Thaler and Sunstein (2021) define as a subtle intervention designed to modify people’s behavior “without forbidding any options or significantly changing economic incentives.” Nudges can take various forms, such as text reminders (e.g., to take a walk), graphic warnings (e.g., about the impact of sedentarism), or making a specific option salient (e.g., highlighting the presence of a park nearby). However, digital stimuli used to promote engagement in digital interventions are not limited to nudges.…”

Section: Engagement In Digital Interventionsmentioning

confidence: 99%

Engagement in digital interventions.

Nahum‐Shani¹,

Shaw²,

Carpenter³

et al. 2022

American Psychologist

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The notion of “engagement,” which plays an important role in various domains of psychology, is gaining increased currency as a concept that is critical to the success of digital interventions. However, engagement remains an ill-defined construct, with different fields generating their own domain-specific definitions. Moreover, given that digital interactions in real-world settings are characterized by multiple demands and choice alternatives competing for an individual’s effort and attention, they involve fast and often impulsive decision-making. Prior research seeking to uncover the mechanisms underlying engagement has nonetheless focused mainly on psychological factors and social influences and neglected to account for the role of neural mechanisms that shape individual choices. This article aims to integrate theories and empirical evidence across multiple domains to define engagement and discuss opportunities and challenges to promote effective engagement in digital interventions. We also propose the affect–integration–motivation and attention–context–translation (AIM–ACT) framework, which is based on a neurophysiological account of engagement, to shed new light on how in-the-moment engagement unfolds in response to a digital stimulus. Building on this framework, we provide recommendations for designing strategies to promote engagement in digital interventions and highlight directions for future research.

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Section: Engagement In Digital Interventionsmentioning

confidence: 99%

Engagement in digital interventions.

Nahum‐Shani¹,

Shaw²,

Carpenter³

et al. 2022

American Psychologist

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“…JITAIs typically guide the sequencing and adaptation of digital-intervention components. These interventions are becoming increasingly popular across various domains of psychological sciences, including health (Conroy et al, 2020; Nahum-Shani, Rabbi, et al, 2021), clinical (Comer et al, 2019; Coppersmith, 2022), educational (Cook et al, 2018), and organizational (Valle et al, 2020) psychology. Leveraging powerful digital technologies, a JITAI is a protocol that specifies how rapidly changing information about the individual’s internal state (e.g., mood, substance use) and context (e.g., geographical location, presence of other people) should be used in practice to decide whether and how to deliver intervention content (e.g., feedback, motivational messages, behavioral or cognitive strategies) in real time in the individual’s natural environment (Nahum-Shani et al, 2015, 2018).…”

Section: Jitaismentioning

confidence: 99%

“…The MRT (Qian et al, 2022) is an experimental design to inform the development of JITAIs. MRTs are experiencing rapid uptake in psychological research despite being relatively new (Figueroa et al, 2021; Valle et al, 2020). At each JITAI decision point, participants are randomly assigned among a set of intervention options.…”

Section: The Mrtmentioning

confidence: 99%

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Nahum‐Shani

Dziak

Walton

et al. 2022

Advances in Methods and Practices in Psychological Science

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Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)—a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).

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“…16 out of the 20 studies (80%) to measure engagement included at least one measure of this category. Generally, researchers measured usage in three domains: (1) usage of a mobile application [17,21,28,35,38], (2) participant retention [18,25,30,31],…”

Section: Usagementioning

confidence: 99%

“…In Goldstein et al [21], researchers planned to use the percentage of in-app interventions accessed as an indicator of usage. Valle et al [38] on the other hand, measured usage as the proportion of intervention messages viewed daily. One study planned to measure usage in terms of time since last interaction with the intervention application [28].…”

Section: Usagementioning

confidence: 99%

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Leong¹,

Chakraborty²

2022

Preprint

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Background: Micro-randomised trials (MRTs) have emerged as the gold standard for the development and evaluation of multi-component, adaptive mobile health (mHealth) interventions. However, not much is known about the state of participant engagement measurement in MRTs of mHealth interventions.Objective: In this review, we aimed to quantify the proportion of existing/planned MRTs of mHealth interventions to date that have assessed (or have planned to assess) engagement. For trials that have assessed (or have planned to assess) engagement, we also aimed to investigate how engagement has been operationalized and to identify what kind of factors have been studied as determinants of engagement in MRTs of mHealth interventions.Methods: We conducted a broad search for MRTs of mHealth interventions in 5 databases and manually searched preprint servers and trial registries. Study characteristics of each included evidence source were extracted. We coded and categorized this data to identify how engagement has been operationalized and which determinants, moderators, and control variables have been assessed in existing MRTs.Results: Our database and manual searches yielded 22 eligible evidence sources. Most of these studies were designed to evaluate the effect of intervention components on health outcomes (14/22 studies). The median sample size of the included MRTs was 110.5. At least one measure of engagement was included in 90.91% (20/22 studies) of the included MRTs. Engagement operationalized as usage (16/20; 80%) was most prevalent followed by responsiveness (11/20; 55%), practice (6/20; 30%), and then comprehension (1/20; 5%). Only 6 of the 20 studies to measure engagement (30%) assessed the determinants of engagement in MRTs of mHealth interventions – notification-related variables were the most common determinants of engagement assessed (4/6 studies). Three of these studies also examined the moderators of participant engagement – 2 studies investigated time-related moderators exclusively and 1 study planned to investigate a comprehensive set of physiological and psychosocial moderators in addition to time-related moderators.Conclusions: Although the measurement of participant engagement in MRTs of mHealth interventions is prevalent, there is a need for future trials to strike a balance between measuring engagement as usage/responsiveness and as practice. There is also a need for researchers to address the lack of attention to how engagement is determined and moderated in MRTs of mHealth interventions. We hope that by mapping the state of engagement measurement in existing MRTs of mHealth interventions, this review will encourage researchers to pay more attention to these issues when planning for engagement measurement in future trials.

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Designing in-app messages to nudge behavior change: Lessons learned from a weight management app for young adults

Cited by 19 publications

References 30 publications

Engagement in digital interventions.

Engagement in digital interventions.

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

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