Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial.

Carpenter, Stephanie M.; Yap, Jamie; Patrick, Megan E.; Morrell, Nicole; Dziak, John J.; Almirall, Daniel; Yoon, Carolyn; Nahum‐Shani, Inbal

doi:10.1037/adb0000855

Cited by 11 publications

(10 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unfortunately, we were not able to do so, as some studies and protocols did not clearly operationalize their measurement of engagement in exact terms. Finally, it must be noted that because of the inclusion and exclusion criteria, we were not able to include several well-designed MRTs in this review because they were not strictly evaluations of mHealth interventions—they were designed either to evaluate digital but not mHealth interventions [ 52 , 53 ] or to evaluate engagement strategies only [ 54 - 56 ]. To fully understand the extent of engagement measurement in digital health interventions evaluated by MRTs, we encourage future reviews to broaden their inclusion and exclusion criteria to include these 2 types of evidence sources.…”

Section: Discussionmentioning

confidence: 99%

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Leong¹,

Chakraborty²

2023

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Microrandomized trials (MRTs) have emerged as the gold standard for the development and evaluation of multicomponent, 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 scoping review, we aimed to quantify the proportion of existing or planned MRTs of mHealth interventions to date that have assessed (or have planned to assess) engagement. In addition, for the trials that have explicitly assessed (or have planned to assess) engagement, we aimed to investigate how engagement has been operationalized and to identify the factors that 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 these data to identify how engagement has been operationalized and which determinants, moderators, and covariates have been assessed in existing MRTs. Results Our database and manual search yielded 22 eligible evidence sources. Most of these studies (14/22, 64%) were designed to evaluate the effects of intervention components. The median sample size of the included MRTs was 110.5. At least 1 explicit measure of engagement was included in 91% (20/22) of the included MRTs. We found that objective measures such as system usage data (16/20, 80%) and sensor data (7/20, 35%) are the most common methods of measuring engagement. All studies included at least 1 measure of the physical facet of engagement, but the affective and cognitive facets of engagement have largely been neglected (only measured by 1 study each). Most studies measured engagement with the mHealth intervention (Little e) and not with the health behavior of interest (Big E). Only 6 (30%) of the 20 studies that measured engagement assessed the determinants of engagement in MRTs of mHealth interventions; notification-related variables were the most common determinants of engagement assessed (4/6, 67% studies). Of the 6 studies, 3 (50%) 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 diversify the measurement of engagement. There is also a need for researchers to address the lack of attention to how engagement is determined and moderated. 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Leong¹,

Chakraborty²

2023

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Our results highlight two potential time-varying tailoring variables (i.e., response to previous prompt and average response rate) that can be further studied using micro-randomized trials [MRTs: ( 36 )] to optimize the delivery of just-in-time adaptive interventions ( 4 ) for promoting engagement and preventing disengagement. For example, an MRT could be designed to investigate whether and what type of an intervention should be triggered when an individual does not respond to a given prompt and/or when their response rate falls below a specific threshold [see other examples: ( 11 , 13 , 37 )]. Using mobile health technology, such MRT could evaluate a variety of novel real-time engagement strategies [e.g., delivering summaries of self-reported data; ( 13 )] beyond the incentives used in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…High monetary incentives may also undermine intrinsic motivation which can hinder completion rates in future studies ( 40 ). There is a need to design mobile health studies that identify and address insufficient engagement while minimizing the use of monetary incentives for completing digital self-reports ( 13 , 37 ).…”

Section: Discussionmentioning

confidence: 99%

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies

Sobolev

Anand

Dziak

et al. 2023

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

ObjectiveInsufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies.MethodData from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response.ResultsAlthough prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time.ConclusionOur study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies.

show abstract

“…Unfortunately, we were not able to do so as some studies and protocols did not clearly operationalize their measurement of engagement in exact terms. Lastly, it must be noted that because of our inclusion/exclusion criteria, we were not able to include several, well-designed MRTs in this review because they were not strictly evaluations of mHealth interventionsthey were either designed to evaluate digital but not mHealth interventions [50,51] or to evaluate engagement strategies only [52][53][54]. To fully understand the extent of engagement measurement in digital health interventions evaluated by MRTs, we encourage future reviews to broaden their inclusion/exclusion criteria to admit these two types of evidence source.…”

Section: Limitationsmentioning

confidence: 99%

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Leong¹,

Chakraborty²

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial.

Cited by 11 publications

References 80 publications

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Contact Info

Product

Resources

About