Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

Efficacy of app-based mobile health interventions for stress management: A systematic review and meta-analysis of self-reported, physiological, and neuroendocrine stress-related outcomes

Stress is a significant mental health concern for the general population, highlighting the need for effective and scalable solutions, such as mobile health (mHealth) app interventions. This systematic review and meta-analysis aimed to investigate the effects of mHealth apps designed primarily to reduce stress and distress in non-clinical and subclinical populations. A comprehensive literature search was conducted up to August 2024, including studies that measured both self-reported and physiological stress outcomes. 80 studies were analyzed. A small but significant effect size (g = 0.33) was found for self-reported stress outcomes, with studies that used specific active controls, operated in naturalistic contexts, and had a low risk of bias showing significantly lower effect sizes. A similarly small effect size was observed for physiological outcomes (g = 0.24). Notably, studies that employed muscle and breathing relaxation, meditation strategies, personalized guidance, experimental usage settings, and measured acute stress responses demonstrated significantly higher effect sizes. Further analysis of specific physiological systems revealed small effect sizes for autonomic (g = 0.32) and cardiac outcomes (g = 0.36). The significant effects observed across both psychological and physiological outcomes support the efficacy and potential of mHealth apps for the self-management of stress responses in the broader population.

One Month Whole Food Plant-Based Nutrition Educational Program Lowers LDL, A1C, and Decreases Inflammatory Markers

Lifestyle-related chronic disease increases in the United States have led to the need for innovative programs targeting dietary choices. Based on growing evidence supporting whole food plant-based (WFPB) nutrition to improve overall health, we devised a one-month WFPB intervention program, Jumpstart Your Health! (JYH), to introduce and encourage adoption of the WFPB dietary lifestyle. This paper investigates its effects on various health indicators associated with cardiovascular and metabolic diseases. Among the total of 150 participants, before and after physical measurements and blood chemistries demonstrate significant (p< 0.05) decreases in weight (-4.2 pounds), cholesterol (-25.3 mg/dl), LDL (-19.0 mg/dl), HDL (-5.6 mg/dl), hemoglobin A1c (-0.2%), and hsCRP (-1.9 mg/L). Among the high-risk participants, we found significant decreases in systolic blood pressure (-10 mmHg), diastolic blood pressure (-8.7 mmHg), weight (-4.3 pounds), cholesterol (-38.8 mg/dl), LDL (-22.7 mg/dl), HDL (-2.8 mg/dl), hemoglobin A1c (-0.2 %), and hsCRP (-2.3 mg/L). We demonstrate that a simple WFPB intervention implemented over one month resulted in significant reductions in physical measurements and blood chemistries that could translate to lowered risk or improvement for obesity, cardiovascular disease, and type-2 diabetes.

Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach

OBJECTIVE

Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context.

METHODS

The year-long micro-randomized trial (MRT) HeartSteps II provides an unprecedented opportunity to investigate DBCI engagement among ethnically diverse participants. We combined data streams from wearable sensors (Fitbit Versa, i.e., walking behavior), the HeartSteps II app (i.e. page views), and ecological momentary assessments (EMAs, i.e. perceived intrinsic and extrinsic motivation) to build the idiographic models. A system identification approach and a fluid analogy model were used to conduct autoregressive with exogenous input (ARX) analyses that tested hypothesized relationships between these variables inspired by Self-Determination Theory (SDT) with DBCI engagement through time.

RESULTS

Data from 11 HeartSteps II participants was used to test aspects of the hypothesized SDT dynamic model. The average age was 46.33 (SD=7.4) years, and the average steps per day at baseline was 5,507 steps (SD=6,239). The hypothesized 5-input SDT-inspired ARX model for app engagement resulted in a 31.75 % weighted RMSEA (31.50 % on validation and 31.91 % on estimation), indicating that the model predicted app page views almost 32 % better relative to the mean of the data. Among Hispanic/Latino participants, the average overall model fit across inventories of the SDT fluid analogy was 34.22 % (SD=10.53) compared to 22.39 % (SD=6.36) among non-Hispanic/Latino Whites, a difference of 11.83 %. Across individuals, the number of daily notification prompts received by the participant was positively associated with increased app page views. The weekend/weekday indicator and perceived daily busyness were also found to be key predictors of the number of daily application page views.

CONCLUSIONS

This novel approach has significant implications for both personalized and adaptive DBCIs by identifying factors that foster or undermine engagement in an individual's respective context. Once identified, these factors can be tailored to promote engagement and support sustained behavior change over time.

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

The efficacy of the TEACH e-Learning course at improving early childhood educators' physical activity and sedentary behaviour self-efficacy, knowledge, intentions, and perceived behavioural control: a randomized controlled trial

BACKGROUND

Early childhood educators play a critical role in promoting physical activity and reducing sedentary time in childcare centres. However, early childhood educators receive limited specialised pre- and in-service learning opportunities relating to these behaviours and may lack the capacity to effectively engage children in healthy movement behaviours. This study aimed to examine the efficacy of an e-Learning course on increasing early childhood educators' physical activity and sedentary behaviour-related capacities.

METHODS

A two-group parallel randomized controlled trial was conducted with early childhood educators in Canada (Mage = 41.78, 97% female). Participants randomized to the intervention group were asked to complete a physical activity and sedentary behaviour e-Learning course within a 4-week period. Participants randomized to the waitlist control condition were assigned to a waitlist to receive the intervention after the testing period. Participants reported on their self-efficacy, knowledge, intentions, and perceived behavioural control relating to physical activity and sedentary behaviours at baseline, post-intervention, and 3 months follow-up. Linear mixed effects models were estimated to determine difference in changes in outcomes from baseline to post-intervention, and follow-up.

RESULTS

A total of 209 early childhood educators participated in the study (intervention n = 98; control n = 111). The TEACH e-Learning course was found to be efficacious at improving all of the examined outcomes, with standardized effect sizes ranging from d = 0.58 to d = 0.65 for self-efficacy outcomes, d = 0.66 to d = 1.20 for knowledge outcomes, d = 0.50 to d = 0.65 for intention outcomes, and d = 0.33 to d = 0.69 for perceived behavioural control outcomes post-intervention. The intervention effects were sustained at follow-up for all outcomes apart from perceived behavioural control to limit screen time. Additionally, the magnitude of the effect for knowledge outcomes decreased at follow-up, with standardized effect sizes ranging from d = 0.49 to d = 0.67.

CONCLUSIONS

The e-Learning course was highly successful at improving early childhood educators' capacity pertaining to physical activity and sedentary behaviours. Providing training content through e-Learning may be an efficacious approach to providing continual professional learning opportunities relating to physical activity and sedentary time to early childhood educators on a large scale.

A Chatbot-Delivered Stress Management Coaching for Students (MISHA App): Pilot Randomized Controlled Trial

BACKGROUND

Globally, students face increasing mental health challenges, including elevated stress levels and declining well-being, leading to academic performance issues and mental health disorders. However, due to stigma and symptom underestimation, students rarely seek effective stress management solutions. Conversational agents in the health sector have shown promise in reducing stress, depression, and anxiety. Nevertheless, research on their effectiveness for students with stress remains limited.

OBJECTIVE

This study aims to develop a conversational agent-delivered stress management coaching intervention for students called MISHA and to evaluate its effectiveness, engagement, and acceptance.

METHODS

In an unblinded randomized controlled trial, Swiss students experiencing stress were recruited on the web. Using a 1:1 randomization ratio, participants (N=140) were allocated to either the intervention or waitlist control group. Treatment effectiveness on changes in the primary outcome, that is, perceived stress, and secondary outcomes, including depression, anxiety, psychosomatic symptoms, and active coping, were self-assessed and evaluated using ANOVA for repeated measure and general estimating equations.

RESULTS

The per-protocol analysis revealed evidence for improvement of stress, depression, and somatic symptoms with medium effect sizes (Cohen d=-0.36 to Cohen d=-0.60), while anxiety and active coping did not change (Cohen d=-0.29 and Cohen d=0.13). In the intention-to-treat analysis, similar results were found, indicating reduced stress (β estimate=-0.13, 95% CI -0.20 to -0.05; P<.001), depressive symptoms (β estimate=-0.23, 95% CI -0.38 to -0.08; P=.003), and psychosomatic symptoms (β estimate=-0.16, 95% CI -0.27 to -0.06; P=.003), while anxiety and active coping did not change. Overall, 60% (42/70) of the participants in the intervention group completed the coaching by completing the postintervention survey. They particularly appreciated the quality, quantity, credibility, and visual representation of information. While individual customization was rated the lowest, the target group fitting was perceived as high.

CONCLUSIONS

Findings indicate that MISHA is feasible, acceptable, and effective in reducing perceived stress among students in Switzerland. Future research is needed with different populations, for example, in students with high stress levels or compared to active controls.

TRIAL REGISTRATION

German Clinical Trials Register DRKS 00030004; https://drks.de/search/en/trial/DRKS00030004.

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies

The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

Attrition in Conversational Agent-Delivered Mental Health Interventions: Systematic Review and Meta-Analysis

BACKGROUND

Conversational agents (CAs) or chatbots are computer programs that mimic human conversation. They have the potential to improve access to mental health interventions through automated, scalable, and personalized delivery of psychotherapeutic content. However, digital health interventions, including those delivered by CAs, often have high attrition rates. Identifying the factors associated with attrition is critical to improving future clinical trials.

OBJECTIVE

This review aims to estimate the overall and differential rates of attrition in CA-delivered mental health interventions (CA interventions), evaluate the impact of study design and intervention-related aspects on attrition, and describe study design features aimed at reducing or mitigating study attrition.

METHODS

We searched PubMed, Embase (Ovid), PsycINFO (Ovid), Cochrane Central Register of Controlled Trials, and Web of Science, and conducted a gray literature search on Google Scholar in June 2022. We included randomized controlled trials that compared CA interventions against control groups and excluded studies that lasted for 1 session only and used Wizard of Oz interventions. We also assessed the risk of bias in the included studies using the Cochrane Risk of Bias Tool 2.0. Random-effects proportional meta-analysis was applied to calculate the pooled dropout rates in the intervention groups. Random-effects meta-analysis was used to compare the attrition rate in the intervention groups with that in the control groups. We used a narrative review to summarize the findings.

RESULTS

The systematic search retrieved 4566 records from peer-reviewed databases and citation searches, of which 41 (0.90%) randomized controlled trials met the inclusion criteria. The meta-analytic overall attrition rate in the intervention group was 21.84% (95% CI 16.74%-27.36%; I2=94%). Short-term studies that lasted ≤8 weeks showed a lower attrition rate (18.05%, 95% CI 9.91%- 27.76%; I2=94.6%) than long-term studies that lasted >8 weeks (26.59%, 95% CI 20.09%-33.63%; I2=93.89%). Intervention group participants were more likely to attrit than control group participants for short-term (log odds ratio 1.22, 95% CI 0.99-1.50; I2=21.89%) and long-term studies (log odds ratio 1.33, 95% CI 1.08-1.65; I2=49.43%). Intervention-related characteristics associated with higher attrition include stand-alone CA interventions without human support, not having a symptom tracker feature, no visual representation of the CA, and comparing CA interventions with waitlist controls. No participant-level factor reliably predicted attrition.

CONCLUSIONS

Our results indicated that approximately one-fifth of the participants will drop out from CA interventions in short-term studies. High heterogeneities made it difficult to generalize the findings. Our results suggested that future CA interventions should adopt a blended design with human support, use symptom tracking, compare CA intervention groups against active controls rather than waitlist controls, and include a visual representation of the CA to reduce the attrition rate.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022341415; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022341415.

Studying missingness in spinal cord injury data: challenges and impact of data imputation

BACKGROUND

In the last decades, medical research fields studying rare conditions such as spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However, most analysis methods rely on complete data. This is particularly troublesome when studying clinical data as they are prone to missingness. Often, researchers mitigate this problem by removing patients with missing data from the analyses. Less commonly, imputation methods to infer likely values are applied.

OBJECTIVE

Our objective was to study how handling missing data influences the results reported, taking the example of SCI registries. We aimed to raise awareness on the effects of missing data and provide guidelines to be applied for future research projects, in SCI research and beyond.

METHODS

Using the Sygen clinical trial data (n = 797), we analyzed the impact of the type of variable in which data is missing, the pattern according to which data is missing, and the imputation strategy (e.g. mean imputation, last observation carried forward, multiple imputation).

RESULTS

Our simulations show that mean imputation may lead to results strongly deviating from the underlying expected results. For repeated measures missing at late stages (> = 6 months after injury in this simulation study), carrying the last observation forward seems the preferable option for the imputation. This simulation study could show that a one-size-fit-all imputation strategy falls short in SCI data sets.

CONCLUSIONS

Data-tailored imputation strategies are required (e.g., characterisation of the missingness pattern, last observation carried forward for repeated measures evolving to a plateau over time). Therefore, systematically reporting the extent, kind and decisions made regarding missing data will be essential to improve the interpretation, transparency, and reproducibility of the research presented.

Advancing methods for the biodemography of aging within social contexts

Several social dimensions including social integration, status, early-life adversity, and their interactions across the life course can predict health, reproduction, and mortality in humans. Accordingly, the social environment plays a fundamental role in the emergence of phenotypes driving the evolution of aging. Recent work placing human social gradients on a biological continuum with other species provides a useful evolutionary context for aging questions, but there is still a need for a unified evolutionary framework linking health and aging within social contexts. Here, we summarize current challenges to understand the role of the social environment in human life courses. Next, we review recent advances in comparative biodemography and propose a biodemographic perspective to address socially driven health phenotype distributions and their evolutionary consequences using a nonhuman primate population. This new comparative approach uses evolutionary demography to address the joint dynamics of populations, social dimensions, phenotypes, and life history parameters. The long-term goal is to advance our understanding of the link between individual social environments, population-level outcomes, and the evolution of aging.

Low-effort internet intervention to reduce students' stress delivered with Meta's Messenger chatbot (Stressbot): A randomized controlled trial

In order to be more accessible and overcome the challenges of low adherence and high dropout, self-guided internet interventions need to seek new delivery formats. In this study, we tested whether a widely-adopted social media app - Meta's (Facebook) Messenger - would be a suitable conveyor of such an internet intervention. Specifically, we verified the efficacy of Stressbot: a Messenger chatbot-delivered intervention focused on enhancing coping self-efficacy to reduce stress and improve quality of life in university students. Participants (N = 372) were randomly assigned to two conditions: (1) an experimental group with access to the Stressbot intervention, and (2) a waitlist control group. Three outcomes, namely coping self-efficacy, stress, and quality of life, were assessed at three time points: a baseline, post-test, and one-month follow-up. Linear Mixed Effects Models were used to analyze the data. At post-test, we found improvements in the Stressbot condition compared to the control condition for stress (d = -0.33) and coping self-efficacy (d = 0.50), but not for quality of life. A sensitivity analysis revealed that the positive short-term intervention effects were robust. At the follow-up, there were no differences between groups, indicating that the intervention was effective only in the short term. In sum, the results suggest that the Messenger app is a viable means to deliver a self-guided internet intervention. However, modifications such as a more engaging design or boosters are required for the effects to persist.

Effects of Participant's Choice of Different Digital Interventions on Outcomes for Binge-Spectrum Eating Disorders: A Pilot Doubly Randomized Preference Trial

It is unclear whether offering individuals a choice between different digital intervention programs affects treatment outcomes. To generate initial insights, we conducted a pilot doubly randomized preference trial to test whether offering individuals with binge-spectrum eating disorder a choice between two digital interventions is causally linked with superior outcomes than random assignment to these interventions. Participants with recurrent binge eating were randomized to either a choice (n = 77) or no-choice (n = 78) group. Those in the choice group could choose one of the two digital programs, while those in the no-choice group were assigned a program at random. The two digital interventions (a broad and a focused program) took 4 weeks to complete, were based on cognitive-behavioral principles and have demonstrated comparable efficacy, but differ in scope, content, and targeted change mechanisms. Most participants (79%) allocated to the choice condition chose the broad program. While both groups experienced improvements in primary (Eating Disorder Examination Questionnaire global scores and number of binge eating episodes over the past month) and secondary outcomes (dietary restraint, body image concerns, etc.), no significant between-group differences were observed. The two groups did not differ on dropout rates, nor on most indices of intervention engagement. Findings provide preliminary insights towards the role of client preferences in digital mental health interventions for eating disorders. Client preferences may not determine outcomes when digital interventions are based on similar underlying principles, although larger trials are needed to confirm this.

Self-compassion and body image in pregnancy and postpartum: A randomized pilot trial of a brief self-compassion meditation intervention

The current study evaluated the efficacy of a three-week self-compassion (SC) meditation intervention in improving body image and SC during pregnancy and postpartum. Participants (n = 71; age = 31.92 ± 3.98 years; white = 61, 85.9%; intervention = 35, 49.3%; pregnant = 33, 46.5%; postpartum = 38, 53.5%) were recruited from a health coaching program and 35 were randomly assigned into a three-week SC meditation intervention while 36 were randomly assigned to a waitlist control condition. Linear regressions using full-information maximum likelihood estimation examined the effect of intervention group on body image and SC outcomes controlling for baseline level of outcome, pregnancy or postpartum status, previous meditation experience, and physical activity. Results indicated women in the intervention group reported significantly reduced body shame and body dissatisfaction and improved body appreciation and self-compassion compared to women in the control group. Implementation of a brief SC meditation intervention during pregnancy and postpartum has potential to improve mental health outcomes related to body image. Future work should replicate this study with a larger, more diverse sample of women.

BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data

In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.

A Randomized Controlled Trial of a Smartphone-Based Well-Being Training in Public School System Employees During the COVID-19 Pandemic

While the extraordinary pressures of the COVID-19 pandemic on student mental health have received considerable attention, less attention has been placed on educator well-being. School system employees play a vital role in society, and teacher levels of well-being are associated with the educational outcomes of young people. We extend extant research on the prevalence and correlates of educator distress during the pandemic by reporting on a pragmatic randomized wait-list controlled trial (N=662; 64% teachers) of an innovative mental health promotion strategy implemented during the pandemic; a free four-week smartphone-based meditation app designed to train key constituents of well-being (Healthy Minds Program; HMP). Following our preregistered analysis plan and consistent with hypotheses, assignment to the HMP predicted significantly larger reductions in psychological distress, our primary outcome, at post-intervention (Cohen's d=-0.52, 95% confidence interval [-0.68, -0.37], p<.001) and at the three-month follow-up (d=-0.33 [-0.48, -0.18], p<.001). Also consistent with hypotheses, we observed similar indications of immediate and sustained benefit following the HMP on all six preregistered secondary outcomes selected to tap skills targeted in the app (e.g., perseverative thinking, social connection, well-being; absolute ds=0.19-0.42, all ps<.031 corrected except mindful action at follow-up). We found no evidence for elevated adverse events and the HMP was equally effective among participants with elevated baseline anxiety and depressive symptoms. These data suggest that the HMP may be an effective and scalable approach to supporting the mental health and well-being of teachers and other school system employees, with implications for employee retention and performance, and student outcomes.

Overall and Differential Attrition in Mindfulness-Based Interventions: A Meta-Analysis

Objectives

Attrition is very common in longitudinal research, including randomized controlled trials (RCTs) testing psychological interventions. Establishing rates and predictors of attrition in mindfulness-based interventions (MBIs) can assist clinical trialists and intervention developers. Differential attrition in RCTs that compared MBIs with structure and intensity matched active control conditions also provides an objective metric of relative treatment acceptability.

Methods

We aimed to evaluate rates and predictors of overall and differential attrition in RCTs of MBIs compared with matched active control conditions. Attrition was operationalized as loss to follow-up at post-test. Six online databases were searched.

Results

Across 114 studies (n = 11,288), weighted mean attrition rate was 19.1% (95% CI [.16, .22]) in MBIs and 18.6% ([.16, .21]) in control conditions. In the primary model, no significant difference was found in attrition between MBIs and controls (i.e., differential attrition; odds ratio [OR] = 1.05, [0.92, 1.19]). However, in sensitivity analyses with trim-and-fill adjustment, without outliers, and when using different estimation methods (Peto and Mantel-Haenszel), MBIs yielded slightly higher attrition (ORs = 1.10 to 1.25, ps < .050). Despite testing numerous moderators of overall and differential attrition, very few significant predictors emerged.

Conclusions

Results support efforts to increase the acceptability of MBIs, active controls, and/or RCTs, and highlight the possibility that for some individuals, MBIs may be less acceptable than alternative interventions. Further research including individual patient data meta-analysis is warranted to identify predictors of attrition and to characterize instances where MBIs may or may not be recommended. Meta-Analysis Review Registration: Open Science Framework (https://osf.io/c3u7a/).

Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials

BACKGROUND

Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).

METHODS

Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.

RESULTS

Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.

CONCLUSION

The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.

TRIAL REGISTRATION

The PRINT trial was prospectively registered at the German Clinical Trials Register (DRKS00014274, date of registration: 12th March 2018).

Can the Academic and Experiential Study of Flourishing Improve Flourishing in College Students? A Multi-University Study

Objectives

Significant concerns have been raised about the "mental health crisis" on college campuses, with attention turning to what colleges can do beyond counseling services to address students' mental health and well-being. We examined whether primarily first-year (89.1%) undergraduate students (n=651) who enrolled in the Art and Science of Human Flourishing (ASHF), a novel academic and experiential for-credit elective course on human flourishing, would demonstrate improved mental health and strengthen skills, perspectives, and behaviors associated with flourishing relative to students who did not enroll in this course.

Methods

In a two-wave, multi-site, propensity-score matched controlled trial (ASHF n=217, Control n=434; N=651), we used hierarchal linear models and false discovery rate corrected doubly robust estimates to evaluate the impact of the ASHF on attention and social-emotional skill development, flourishing perspectives, mental health, health, and risk behavior outcomes.

Results

ASHF participants reported significantly improved mental health (i.e., reduced depression) and flourishing, improvements on multiple attention and social-emotional skills (e.g., attention function, self-compassion), and increases in prosocial attitudes (empathic concern, shared humanity; Cohen's ds= 0.18-0.46) compared to controls. There was no evidence for ASHF course impacts on health or risk behaviors, raising the possibility that these outcomes take more time to change.

Conclusions

This research provides initial evidence that the ASHF course may be a promising curricular approach to reduce and potentially prevent poor mental health while promoting flourishing in college students. Continued research is needed to confirm these conclusions.

Digital phenotyping correlations in larger mental health samples: analysis and replication

BACKGROUND

Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.

AIMS

To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.

METHOD

Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.

RESULTS

Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.

CONCLUSIONS

Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.