The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations.

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial.

OBJECTIVE: Mobile technologies allow for accessible and cost-effective health monitoring and intervention delivery. Despite these advantages, mobile health (mHealth) engagement is often insufficient. While monetary incentives may increase engagement, they can backfire, dampening intrinsic motivations and undermining intervention scalability. Theories from psychology and behavioral economics suggest useful nonmonetary strategies for promoting engagement; however, examinations of the applicability of these strategies to mHealth engagement are lacking. This proof-of-concept study evaluates the translation of theoretically-grounded engagement strategies into mHealth, by testing their potential utility in promoting daily self-reporting. METHOD: A microrandomized trial (MRT) was conducted with adolescents and emerging adults with past-month substance use. Participants were randomized multiple times daily to receive theoretically-grounded strategies, namely reciprocity (the delivery of inspirational quote prior to self-reporting window) and nonmonetary reinforcers (e.g., the delivery of meme/gif following self-reporting completion) to improve proximal engagement in daily mHealth self-reporting. RESULTS: Daily self-reporting rates (62.3%; n = 68) were slightly lower than prior literature, albeit with much lower financial incentives. The utility of specific strategies was found to depend on contextual factors pertaining to the individual's receptivity and risk for disengagement. For example, the effect of reciprocity significantly varied depending on whether this strategy was employed (vs. not employed) during the weekend. The nonmonetary reinforcement strategy resulted in different outcomes when operationalized in various ways. CONCLUSIONS: While the results support the translation of the reciprocity strategy into this mHealth setting, the translation of nonmonetary reinforcement requires further consideration prior to inclusion in a full scale MRT. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Understanding the Social Determinants of Mental Health of Undergraduate Students in Bangladesh: Interview Study.

BACKGROUND: The undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. OBJECTIVE: This study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia; study the manifestation of these determinants in their day-to-day lives; and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. METHODS: We conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. RESULTS: Our results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). CONCLUSIONS: Although some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world.

Understanding Adolescent and Young Adult 6-Mercaptopurine Adherence and mHealth Engagement During Cancer Treatment: Protocol for Ecological Momentary Assessment.

BACKGROUND: Adolescents and young adults (AYAs) with cancer demonstrate suboptimal oral chemotherapy adherence, increasing their risk of cancer relapse. It is unclear how everyday time-varying contextual factors (eg, mood) affect their adherence, stalling the development of personalized mobile health (mHealth) interventions. Poor engagement is also a challenge across mHealth trials; an effective adherence intervention must be engaging to promote uptake. OBJECTIVE: This protocol aims to determine the temporal associations between daily contextual factors and 6-mercaptopurine (6-MP) adherence and explore the proximal impact of various engagement strategies on ecological momentary assessment survey completion. METHODS: At the Children's Hospital of Philadelphia, AYAs with acute lymphoblastic leukemia or lymphoma who are prescribed prolonged maintenance chemotherapy that includes daily oral 6-MP are eligible, along with their matched caregivers. Participants will use an ecological momentary assessment app called ADAPTS (Adherence Assessments and Personalized Timely Support)-a version of an open-source app that was modified for AYAs with cancer through a user-centered process-and complete surveys in bursts over 6 months. Theory-informed engagement strategies will be microrandomized to estimate the causal effects on proximal survey completion. RESULTS: With funding from the National Cancer Institute and institutional review board approval, of the proposed 30 AYA-caregiver dyads, 60% (18/30) have been enrolled; of the 18 enrolled, 15 (83%) have completed the study so far. CONCLUSIONS: This protocol represents an important first step toward prescreening tailoring variables and engagement components for a just-in-time adaptive intervention designed to promote both 6-MP adherence and mHealth engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32789.

Toward a Just-in-Time Adaptive Intervention to Reduce Emerging Adult Alcohol Use: Testing Approaches for Identifying When to Intervene.

ABBREVIATIONS: JITAI: Just-in-time adaptive intervention; ROC: receiver operating characteristic; AUC: area under the curve; MRT: micro-randomized trial.

Developing an Adaptive Mobile Intervention to Address Risky Substance Use Among Adolescents and Emerging Adults: Usability Study.

BACKGROUND: Substance use among adolescents and emerging adults continues to be an important public health problem associated with morbidity and mortality. Mobile health (mHealth) provides a promising approach to deliver just-in-time adaptive interventions (JITAIs) to prevent escalation of use and substance use-related consequences. OBJECTIVE: This pilot study aims to describe the iterative development and initial feasibility and acceptability testing of an mHealth smartphone app, called MiSARA, designed to reduce escalation in substance use. METHODS: We used social media advertisements to recruit youth (n=39; aged 16-24 years, who screened positive for past-month binge drinking or recreational cannabis use) with a waiver of parental consent. Participants used the MiSARA app for 30 days, with feasibility and acceptability data reported at a 1-month follow-up. We present descriptive data regarding behavior changes over time. RESULTS: The results show that most participants (31/39, 79%) somewhat liked the app at least, with most (29/39, 74%) rating MiSARA as 3 or more stars (out of 5). Almost all participants were comfortable with self-reporting sensitive information within the app (36/39, 92%); however, most participants also desired more interactivity (27/39, 69%). In addition, participants' substance use declined over time, and those reporting using the app more often reported less substance use at the 1-month follow-up than those who reported using the app less often. CONCLUSIONS: The findings suggest that the MiSARA app is a promising platform for JITAI delivery, with future trials needed to optimize the timing and dose of messages and determine efficacy.

Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example.

Long-term engagement with mobile health (mHealth) apps can provide critical data for improving empirical models for real-time health behaviors. To learn how to improve and maintain mHealth engagement, micro-randomized trials (MRTs) can be used to optimize different engagement strategies. In MRTs, participants are sequentially randomized, often hundreds or thousands of times, to different engagement strategies or treatments. The data gathered are then used to decide which treatment is optimal in which context. In this paper, we discuss an example MRT for youth with cancer, where we randomize different engagement strategies to improve self-reports on factors related to medication adherence. MRTs, moreover, can go beyond improving engagement, and we reference other MRTs to address substance abuse, sedentary behavior, and so on.

Artificial intelligence decision-making in mobile health.

It is likely that you or someone you know is affected by a chronic health condition. For example, a staggering six in 10 adults in the USA are currently suffering from a chronic disease (National Center for Chronic Disease Prevention and Health Promotion, 2019). Unfortunately, chronic conditions are not treatable overnight, but they can often be improved by regular incorporation of preventative behaviours (e.g., taking medication, healthy sleeping habits, being physically active, healthy eating, etc.). However, due to the many contingencies that arise in our lives, regular incorporation of healthy behaviours is difficult, and often when we need help in enacting these behaviours, support from clinical professionals is not available.

ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence.

Besides passive sensing, ecological momentary assessments (EMAs) are one of the primary methods to collect in-the-moment data in ubiquitous computing and mobile health. While EMAs have the advantage of low recall bias, a disadvantage is that they frequently interrupt the user and thus long-term adherence is generally poor. In this paper, we propose a less-disruptive self-reporting method, "assisted recall," in which in the evening individuals are asked to answer questions concerning a moment from earlier in the day assisted by contextual information such as location, physical activity, and ambient sounds collected around the moment to be recalled. Such contextual information is automatically collected from phone sensor data, so that self-reporting does not require devices other than a smartphone. We hypothesized that providing assistance based on such automatically collected contextual information would increase recall accuracy (i.e., if recall responses for a moment match the EMA responses at the same moment) as compared to no assistance, and we hypothesized that the overall completion rate of evening recalls (assisted or not) would be higher than for in-the-moment EMAs. We conducted a two-week study (N=54) where participants completed recalls and EMAs each day. We found that providing assistance via contextual information increased recall accuracy by 5.6% (p = 0.032) and the overall recall completion rate was on average 27.8% (p < 0.001) higher than that of EMAs.

Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults.

BACKGROUND: Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution. OBJECTIVE: MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain. METHODS: A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities. RESULTS: In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (ßint=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (ßint=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (ßint=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday. CONCLUSIONS: In the pilot study, MyBehaviorCBP's automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP's efficacy on a larger cohort and over a longer period of time.

Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults.

BACKGROUND: Substance use is an alarming public health issue associated with significant morbidity and mortality. Adolescents and emerging adults are at particularly high risk because substance use typically initiates and peaks during this developmental period. Mobile health apps are a promising data collection and intervention delivery tool for substance-using youth as most teens and young adults own a mobile phone. However, engagement with data collection for most mobile health applications is low, and often, large fractions of users stop providing data after a week of use. OBJECTIVE: Substance Abuse Research Assistant (SARA) is a mobile application to increase or sustain engagement of substance data collection overtime. SARA provides a variety of engagement strategies to incentivize data collection: a virtual aquarium in the app grows with fish and aquatic resources; occasionally, funny or inspirational contents (eg, memes or text messages) are provided to generate positive emotions. We plan to assess the efficacy of SARA's engagement strategies over time by conducting a micro-randomized trial, where the engagement strategies will be sequentially manipulated. METHODS: We aim to recruit participants (aged 14-24 years), who report any binge drinking or marijuana use in the past month. Participants are instructed to use SARA for 1 month. During this period, participants are asked to complete one survey and two active tasks every day between 6 pm and midnight. Through the survey, we assess participants' daily mood, stress levels, loneliness, and hopefulness, while through the active tasks, we measure reaction time and spatial memory. To incentivize and support the data collection, a variety of engagement strategies are used. First, predata collection strategies include the following: (1) at 4 pm, a push notification may be issued with an inspirational message from a contemporary celebrity; or (2) at 6 pm, a push notification may be issued reminding about data collection and incentives. Second, postdata collection strategies include various rewards such as points which can be used to grow a virtual aquarium with fishes and other treasures and modest monetary rewards (up to US $12; US $1 for each 3-day streak); also, participants may receive funny or inspirational content as memes or gifs or visualizations of prior data. During the study, the participants will be randomized every day to receive different engagement strategies. In the primary analysis, we will assess whether issuing 4 pm push-notifications or memes or gifs, respectively, increases self-reporting on the current or the following day. RESULTS: The microrandomized trial started on August 21, 2017 and the trial ended on February 28, 2018. Seventy-three participants were recruited. Data analysis is currently underway. CONCLUSIONS: To the best of our knowledge, SARA is the first mobile phone app that systematically manipulates engagement strategies in order to identify the best sequence of strategies that keep participants engaged in data collection. Once the optimal strategies to collect data are identified, future versions of SARA will use this data to provide just-in-time adaptive interventions to reduce substance use among youth. TRIAL REGISTRATION: ClinicalTrials.gov NCT03255317; https://clinicaltrials.gov/show/NCT03255317 (Archived by WebCite at http://www.webcitation.org/70raGWV0e). REGISTERED REPORT IDENTIFIER: RR1-10.2196/9850.

SARA: A Mobile App to Engage Users in Health Data Collection.

Despite the recent progress in sensor technologies, many relevant health data can be only captured with manual input (e.g., food intake, stress appraisal, subjective emotion, substance use). A common problem of manual logging is that users often disengage within a short time because of high burden. In this work, we propose SARA, a novel app to engage users with ongoing tracking using timely rewards thereby reinforcing users for data input. SARA is developed for adolescents and emerging adults at risk for substance abuse. The rewards in SARA are designed to be developmentally and culturally appropriate to the target demographic and are theoretically grounded in the behavioral science literature. In this paper, we describe SARA and its rewards to increase data collection. We also briefly discuss future plans to evaluate SARA and develop just in time adaptive interventions for engagement and behavior change.

Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain.

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing.

Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults.

BACKGROUND: A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users' behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. OBJECTIVE: MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user's environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. METHODS: MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior's personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. RESULTS: In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior's personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). CONCLUSIONS: MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed. TRIAL REGISTRATION: ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).

Objective measurement of sociability and activity: mobile sensing in the community.

PURPOSE: Automated systems able to infer detailed measures of a person's social interactions and physical activities in their natural environments could lead to better understanding of factors influencing well-being. We assessed the feasibility of a wireless mobile device in measuring sociability and physical activity in older adults, and compared results with those of traditional questionnaires. METHODS: This pilot observational study was conducted among a convenience sample of 8 men and women aged 65 years or older in a continuing care retirement community. Participants wore a waist-mounted device containing sensors that continuously capture data pertaining to behavior and environment (accelerometer, microphone, barometer, and sensors for temperature, humidity, and light). The sensors measured time spent walking level, up or down an elevation, and stationary (sitting or standing), and time spent speaking with 1 or more other people. The participants also completed 4 questionnaires: the 36-Item Short Form Health Survey (SF-36), the Yale Physical Activity Survey (YPAS), the Center for Epidemiologic Studies-Depression (CES-D) scale, and the Friendship Scale. RESULTS: Men spent 21.3% of their time walking and 64.4% stationary. Women spent 20.7% of their time walking and 62.0% stationary. Sensed physical activity was correlated with aggregate YPAS scores (r(2)=0.79, P=.02). Sensed time speaking was positively correlated with the mental component score of the SF-36 (r(2)=0.86, P = .03), and social interaction as assessed with the Friendship Scale (r(2)=0.97, P = .002), and showed a trend toward association with CES-D score (r(2)=-0.75, P = .08). In adjusted models, sensed time speaking was associated with SF-36 mental component score (P = .08), social interaction measured with the Friendship Scale (P = .045), and CES-D score (P=.04). CONCLUSIONS: Mobile sensing of sociability and activity is well correlated with traditional measures and less prone to biases associated with questionnaires that rely on recall. Using mobile devices to collect data from and monitor older adult patients has the potential to improve detection of changes in their health.

Passive and In-situ Assessment of Mental and Physical Well-being using Mobile Sensors.

The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous sensing and inference of social interactions and physical activities.