Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study

Uncovering the Missing Pieces: Predictors of Nonresponse in a Mobile Experience Sampling Study on Media Effects Among Youth

Mobile Experience Sampling (MES) is a promising tool for understanding youth digital media use and its effects. Unfortunately, the method suffers from high levels of missing data. Depending on whether the data is randomly or non-randomly missing, it can have severe effects on the validity of findings. For this reason, we investigated predictors of non-response in an MES study on displacement effects of digital media use on adolescents' well-being and academic performance (N = 347). Multilevel binary logistic regression identified significant influencing factors of response odds, such as afternoon beeps and being outside. Importantly, adolescents with poorer school grades were more likely to miss beeps. Because this missingness was related to the outcome variable, modern missing data methods such as multiple imputation should be applied before analyzing the data. Understanding the reasons for non-response can be seen as the first step to preventing, minimizing, and handling missing data in MES studies, ultimately ensuring that the collected data is fully utilized to draw accurate conclusions.

Predicting Completion of Ecological Momentary Assessments Among Substance-Using Women Experiencing Intimate Partner Violence

Noncompletion of ecological momentary assessment (EMA) surveys is a common issue and may yield bias in results if not properly handled. Using data observed at scheduled times as well as data retrieved later to fill missing responses, this study aims to investigate predictors of EMA completion, including demographic characteristics, time-related factors, and momentary experiences/behaviors. Data were from a 30-day EMA study including 145 women currently experiencing intimate partner violence (IPV) and using substances. The average rate of EMA completion was initially 51.4% at the scheduled times and increased to 72.6% after incorporating data from later-retrieved surveys. Participants who were younger, had more children, or had lower mean levels of negative affect dysregulation showed lower completion rates. At the momentary survey level, more days into the study and afternoon/evening reports (vs. morning reports) were associated with lower completion; lower levels of negative affect dysregulation, less smoking or alcohol use, and experiencing IPV were linked to lower momentary completion. Implications of the results for handling missing data in EMA are discussed and have important ramifications for future research, practice, and theory.

Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation

BACKGROUND

Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system's decision-making.

OBJECTIVE

This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems.

METHODS

To inform the design of such dynamic adaptation, we analyzed data from the implementation and deployment of a system that incorporates passively sensed data across everyday work devices to send just-in-time stress reduction microinterventions in the workplace to 43 participants during a 4-week deployment. We evaluated 27 trait-based factors (ie, individual characteristics), state-based factors (ie, workplace contextual and behavioral signals and momentary stress), and intervention-related factors (ie, location and function) across 1585 system-initiated interventions. We built logistical regression models to identify the factors contributing to momentary engagement, the choice of interventions, the engagement given an intervention choice, the user rating of interventions engaged, and the stress reduction from the engagement.

RESULTS

We found that women (odds ratio [OR] 0.41, 95% CI 0.21-0.77; P=.03), those with higher neuroticism (OR 0.57, 95% CI 0.39-0.81; P=.01), those with higher cognitive reappraisal skills (OR 0.69, 95% CI 0.52-0.91; P=.04), and those that chose calm interventions (OR 0.43, 95% CI 0.23-0.78; P=.03) were significantly less likely to experience stress reduction, while those with higher agreeableness (OR 1.73, 95% CI 1.10-2.76; P=.06) and those that chose prompt-based (OR 6.65, 95% CI 1.53-36.45; P=.06) or video-based (OR 5.62, 95% CI 1.12-34.10; P=.12) interventions were substantially more likely to experience stress reduction. We also found that work-related contextual signals such as higher meeting counts (OR 0.62, 95% CI 0.49-0.78; P<.001) and higher engagement skewness (OR 0.64, 95% CI 0.51-0.79; P<.001) were associated with a lower likelihood of engagement, indicating that state-based contextual factors such as being in a meeting or the time of the day may matter more for engagement than efficacy. In addition, a just-in-time intervention that was explicitly rescheduled to a later time was more likely to be engaged with (OR 1.77, 95% CI 1.32-2.38; P<.001).

CONCLUSIONS

JITAI systems have the potential to integrate timely support into the workplace. On the basis of our findings, we recommend that individual, contextual, and content-based factors be incorporated into the system for tailoring as well as for monitoring ineffective engagements across subgroups and contexts.

Recruitment and retention of adolescents for an ecological momentary assessment measurement burst mental health study: The MHIM engagement strategy

INTRODUCTION

Recruitment and long-term retention of adolescent participants in longitudinal research are challenging and may be especially so in studies involving remote measurement and biosampling components. The ability to effectively recruit and retain participants can be supported by the use of specific evidence-based engagement strategies that are built in from the earliest stages.

METHODS

Informed by a review of the evidence on effective engagement strategies and consultations with adolescents (via two Young Person Advisory Groups [YPAGs]; ages 11-13 and 14-17), the current protocol describes the planned participant engagement strategy for the Mental Health in the Moment Study: a multimodal measurement burst study of adolescent mental health across ages 11-19.

RESULTS

The protocol incorporates engagement strategies in four key domains: consultations/co-design with the target population, incentives, relationship-building and burden/barrier reduction. In addition to describing general engagement strategies in longitudinal studies, we also discuss specific concerns regarding engagement in data collection methods such as biosampling and ecological momentary assessment where a paucity of evidence exists.

CONCLUSION

Engagement strategies for adolescent mental health studies should be based on existing evidence and consultations with adolescents. We present our approach in developing the planned engagement strategies and also discuss limitations and future directions in engaging adolescents in longitudinal research.

PATIENT OR PUBLIC CONTRIBUTION

The study design for this project places a strong emphasis on the active engagement of adolescents throughout its development. Specifically, the feedback and suggestions provided by the YPAGs have been instrumental in refining our strategies for maximising the recruitment and retention of participants.

The Associations among Perceived Courtesy Stigma, Health and Social Behaviours in Family Members and Friends of People Who Use Substances: An Ecological Momentary Assessment Study

Background: The stigma and discrimination experienced by individuals with an alcohol/substance use disorder often extends to the family members and friends who provide care, which is known as courtesy stigma. This courtesy stigma can lead to isolation, poor mental health and might impact the quality-of-care these individuals provide. The aim of this study was to examine the frequency of experienced courtesy stigma/discrimination in individuals in a family support service for a loved one's substance use, and to examine any cross-sectional associations with changes in mood, health- and social-related outcomes. Methods: Thirty-six individuals (25 female) with a mean age of 51.91 years took part in an ecological momentary assessment study in which the experience of courtesy stigma/discrimination and measures of mood, health (e.g. alcohol use, nicotine use, healthy eating, sleep, physical activity) and social connections were taken 3 times per day for fourteen days. Results: Across 1029 competed assessments (compliance ∼68%), there were 122 (∼11%) reports of courtesy stigma/discrimination. The most common sources of stigma/discrimination were from family members (∼43% of occurrences) and friends (∼31% of occurrences). Experiencing this stigma/discrimination was associated with increases in alcohol and nicotine use, as well as reductions in healthy eating, physical activity, sleep, social connections, and mood. Conclusions: The experience of courtesy stigma/discrimination was common in a sample of individual's who support a loved one with alcohol or substance use disorder. These experiences are associated with changes in health and social behaviors and may lead to a poorer quality of care.

Compliance Trends in a 14-Week Ecological Momentary Assessment Study of Undergraduate Alcohol Drinkers

High compliance is a priority for successful ecological momentary assessment (EMA) research, but meta-analyses of between-study differences show that reasons for missed prompts remain unclear. We examined compliance data from a 14-week, 182-survey EMA study of undergraduate alcohol use to test differences over time and across survey types between participants with better and worse compliance rates, and to evaluate the impact of incentives on ongoing participation. Participants were N = 196 students (65.8% female; Mage = 20.6). Overall compliance was 76.5%, declining gradually from 88.9% to 70% over 14 weeks. Declines were faster in participants with lower overall compliance, but we found no demographic, personality, mental health, or substance use differences between participants with better versus worse compliance rates. Compliance varied by survey type, and unannounced bonus incentives did not impact compliance rates. Participants completed fewer surveys the week after winning a gift card. We offer recommendations for designing future EMA studies.

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors

BACKGROUND

Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data.

METHODS

Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis.

RESULTS

Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00-01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization.

CONCLUSIONS

Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses

BACKGROUND

When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data.

METHODS

A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly.

DISCUSSION

The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices.

TRIAL REGISTRATION

NCT05481138.

Respondent characteristics associated with adherence in a general population ecological momentary assessment study

OBJECTIVES

Ecological momentary assessment (EMA) has seen an explosion in popularity in recent years; however, an improved understanding of how to minimise (selective) non-adherence is needed.

METHODS

We examined a range of respondent characteristics predictors of adherence (defined as the number of EMA surveys completed) in the D2M EMA study. Participants were a sample of n = 255 individuals drawn from the longitudinal z-proso cohort who completed up to 4 EMA surveys per day for a period of 2 weeks.

RESULTS

In unadjusted analyses, lower moral shame, lower self-control, lower levels of self-injury, and higher levels of aggression, tobacco use, psychopathy, and delinquency were associated with lower adherence. In fully adjusted analyses with predictors selected using lasso, only alcohol use was related to adherence: beer and alcopops to higher adherence and spirits to lower adherence.

CONCLUSIONS

These findings provide potential insights into some of the psychological mechanisms that may underlie adherence in EMA. They also point to respondent characteristics for which additional or tailored efforts may be needed to promote adherence.

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2-4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance

The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.

Well Played! Promoting Phonemic Awareness Training Using EdTech-GraphoGame Brazil-During the COVID-19 Pandemic

Early literacy skills such as alphabet knowledge and phonemic awareness are made up the foundation for learning to read. These skills are more effectively taught with explicit instruction starting inpreschool and then continuing during early elementary school years. The COVID19 pandemic school closures severely impacted early literacy development worldwide. Brazil had one of the longest school closure periods, which resulted in several children having no access to any educational activities. Education Technology (EdTech) tools can leverage access to pedagogical materials and remediate the consequences of school closure. We investigated the impact of using an early literacy EdTech, GraphoGame Brazil, to foster learning of early literacy skills during the height of COVID19 school closures, in Brazil. We carried out a quasi-experimental, pretest and posttest study with elementary school students who were taking online classes. Participants were pseudo randomly assigned to (1) an experimental group, who played GraphoGame Brazil, and to (2) an active control group, who played an EdTech that focuses on early numeracy skills. The results show a significant positive training effect on word reading accuracy associated with the use of GraphoGame for the children in the experimental group, relative to the control group. We also found statistically significant negative effect in lowercase naming for the control group. We address the consequences of COVID19 school closures, the promise of EdTech and its limitations, and discuss the issue of fostering successful early literacy instruction in countries that have struggled with teaching children to read even before the pandemic.

Adherence to eHealth-Delivered Exercise in Adults with no Specific Health Conditions: A Scoping Review on a Conceptual Challenge

Adherence has emerged as a focal point and critical determinant of success for physical activity interventions. The term is used for both traditional and digital interventions, and for prescribed and nonprescribed activities. Many other terms for adherence are being used interchangeably, as there is no consensus on its precise conceptualization. This scoping review aimed to advance the definition of adherence to eHealth programs, specifically for the adult population with no specific health conditions. A total of 2983 papers, published between 1 January 2016 and 13 March 2022, were retrieved from different databases (including grey literature). Of those, 13 studies met the eligibility criteria and were included for review. The selected studies used a wide array of technologies and consisted mainly of exercise interventions. Most of the reviewed publications contemplated exercise adherence as a percentage of expected dose. Most (8 out of 13) studies neither assessed nor specified an expected use of the involved technology. Results suggest a need for homogeneity in the conceptualization of adherence to physical activity and exercise, including those interventions delivered digitally.

Contextual Biases in Microinteraction Ecological Momentary Assessment (μEMA) Non-response

Ecological momentary assessment (EMA) is used to gather in-situ self-report on behaviors using mobile devices. Microinteraction EMA (μEMA), is a type of EMA where each survey is only one single question that can be answered with a glanceable microinteraction on a smartwatch. Prior work shows that even when μEMA interrupts far more frequently than smartphone-EMA, μEMA yields higher response rates with lower burden. We examined the contextual biases associated with non-response of μEMA prompts on a smartwatch. Based on prior work on EMA non-response and smartwatch use, we identified 10 potential contextual biases from three categories: temporal (time of the day, parts of waking day, day of the week, and days in study), device use (screen state, charging status, battery mode, and phone usage), and activity (wrist motion and location). We used data from a longitudinal study where 131 participants (Mean age 22.9 years, SD = 3.0) responded to μEMA surveys on a smartwatch for at least six months. Using mixed-effects logistic regression, we found that all temporal, activity/mobility, and device use variables had a statistically significant (p<0.001) association with momentary μEMA non-response. We discuss the implication of these results for future use of context-aware μEMA methodology.