A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings

Yang, Min-Jeong; Sutton, Steven K.; Hernandez, Laura M; Jones, Sarah R.; Wetter, David W.; Kumar, Santosh; Vinci, Christine

doi:10.1016/j.addbeh.2022.107467

Cited by 12 publications

(24 citation statements)

References 47 publications

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“…and for studies involving wearable sensor technology the (3) usage of wearable sensors [19,22,23,26,28,29,36].…”

Section: Usagementioning

confidence: 99%

“…Interestingly, the measurement of engagement as participant retention was unique to studies that were designed to evaluate the effect of intervention components on health outcomes. For engagement as usage of wearable sensors, time spent wearing the sensors was the only indicator of engagement [19,22,23,26,28,29,36]. Wang et al [29] for example, measured the proportion of days in a week that participants wore the study's…”

Section: Usagementioning

confidence: 99%

“…11 out of the 20 studies (55%) to measure engagement included at least one measure of this category. 10 out of 11 studies measured responsiveness in terms of survey completion (completion of single items, logs, diaries, or ecological momentary assessments) [18,19,21,22,25,28,30,31,36,37]. Aguilera et al [18] for example, measured engagement as response rates to daily text messages tasking participants to rate their mood.…”

Section: Responsivenessmentioning

confidence: 99%

“…Engagement as practicethat is, performing a behavior/activity taught, prompted, and/or promoted by the interventionwas measured in six out of the 20 studies (30%) to measure engagement [25,[32][33][34][35][36]. All six studies measured this category of engagement via self-reports: Latham [25] evaluated a sleep intervention designed to improve the regularity of wake times in college students via prompts.…”

Section: Practicementioning

confidence: 99%

“…The Nordby et al [34] study introduced above also included a measure of engagement as the self-reported frequency of practicing the coping strategies taught in the web-based intervention. Yang et al [36] measured engagement as the percentage of prompted strategies self-reported as completed in their evaluation of a JITAI for smoking cessation.…”

Section: Practicementioning

confidence: 99%

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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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“…and for studies involving wearable sensor technology the (3) usage of wearable sensors [19,22,23,26,28,29,36].…”

Section: Usagementioning

confidence: 99%

Section: Usagementioning

confidence: 99%

Section: Responsivenessmentioning

confidence: 99%

Section: Practicementioning

confidence: 99%

Section: Practicementioning

confidence: 99%

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Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Leong¹,

Chakraborty²

2022

Preprint

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Statistical methods for predicting e‐cigarette use events based on beat‐to‐beat interval (BBI) data collected from wearable devices

Yang,

Piper,

Indic

et al. 2024

Statistics in Medicine

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The prevalence of e‐cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de‐noise beat‐to‐beat interval (BBI) data from smartwatches worn by 12 young adult regular e‐cigarette users for 7 days; and (2) summarize the de‐noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de‐noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention.

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Systematic Review of Mindfulness-Based Ecological Momentary Interventions: Synthesizing Current Methods and Identifying Directions for Future Research

Pavlacic,

Hampton,

Young

et al. 2024

J. technol. behav. sci.

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A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings

Cited by 12 publications

References 47 publications

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Statistical methods for predicting e‐cigarette use events based on beat‐to‐beat interval (BBI) data collected from wearable devices

Systematic Review of Mindfulness-Based Ecological Momentary Interventions: Synthesizing Current Methods and Identifying Directions for Future Research

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