Reactivity to smartphone-based ecological momentary assessment of depressive symptoms (MoodMonitor): protocol of a randomised controlled trial

Treating young adult cannabis use disorder with text message-delivered peer network counseling

BACKGROUND

Approximately 16.5% of U.S. young adults have a cannabis use disorder (CUD) and are at risk for negative outcomes. Treatment can reduce cannabis use, but young adults are less likely to seek help than older adults. Peer Network Counseling-txt (PNC-txt) is a brief, text-delivered, Motivational Interviewing-informed substance use intervention focusing on peer relations and activity spaces as mechanisms for behavioral change. PNC-txt has shown evidence of reducing tobacco and cannabis use with adolescents and young adults, but it has not been tested in the context of legal cannabis use. The current randomized controlled trial sought to expand the evidence regarding the context of PNC-txt effects, comparing one state in which cannabis is legal (Colorado) and one state in which it is not (Tennessee). We hypothesized that participants randomized to PNC-txt would show significant reductions in cannabis use compared to controls, with larger reductions for females and those in Colorado, and that peer relations and activity space would mediate effects.

METHODS

One thousand, seventy eight 18-25 year olds (CO: 551; TN: 527) who met screening criteria for CUD and biologically-verified cannabis use were randomly assigned to PNC-txt or waitlist control condition. Every other day for 4 weeks, participants assigned to PNC-txt received pre-programmed text conversations, tailored via data from the baseline assessment. Self-report and biological indicators of cannabis use were measured at 1-, 3-, and 6-months.

DISCUSSION

Data analysis is underway. Results will provide evidence regarding whether, and how, PNC-txt reduces cannabis use in young adults with CUD.

TRIAL REGISTRATION

This trial was prospectively registered on September 28, 2020 with ClinicalTrials.gov (NCT04567394).

Change in Adolescents' Perceived ADHD Symptoms Across 17 Days of Ecological Momentary Assessment

OBJECTIVE

To test whether adolescents' perceived ADHD symptoms may improve while monitoring them throughout the day.

METHOD

In a sample of 90 adolescents (Mage = 14.7; 66% boys, 34% girls; 76.7% White, 13.3% Black or African American, 8.9% more than one race, 1.1% "other") treated for ADHD by their pediatricians, this study examined: (1) whether self-rated ADHD symptoms decreased across 17 days of 4 times daily ecological momentary assessment (EMA) of symptoms and (2) whether completing versus missing an EMA survey was associated with lower self-rated ADHD symptoms in the subsequent hours.

RESULTS

Multilevel regression analyses showed that, on average, adolescents' perceived ADHD symptoms (inattention, hyperactivity, impulsivity, and total across domains) decreased across 17 days of EMA. Within person, symptoms were lower following completed versus missed EMA surveys. Significant moderating effects showed that the effect of completing the prior EMA survey weakened across the day and over the course of the 17 days.

CONCLUSIONS

This study is the first to document acute improvements in self-rated ADHD symptoms using EMA in adolescents' naturalistic environments. Symptom monitoring throughout the day may help adolescents improve their day-to-day ADHD, at least acutely, and holds promise as one component of mobile-health ADHD interventions.

The Experience Sampling Method: A New Way of Assessing Variability of the Emotional Dimensions of Religiosity and Spirituality in a Dutch Psychiatric Population

Religiosity and spirituality (R/S) are often regarded as being relatively stable over time. The present exploratory experience sampling method (ESM) study aims to assess the variability of three R/S parameters concerning affective representations of God and spiritual experiences in a psychiatric population. Depressed in- and outpatients self-identifying as being spiritual or religious participated, from two Dutch mental health care institutions. The twenty-eight participants rated momentary affective R/S-variables up to 10 times per day over a 6-day period when prompted by a mobile application. All three examined R/S parameters varied significantly within the day. ESM examination of R/S showed good compliance and little reactivity. This indicates that ESM offers a feasible, usable, and valid way to explore R/S in a psychiatric population.

Feasibility and Acceptability of Ecological Momentary Assessment With Young Adults Who Are Currently or Were Formerly Homeless: Mixed Methods Study

Background

Ecological momentary assessment (EMA) has been used with young people experiencing homelessness to gather information on contexts associated with homelessness and risk behavior in real time and has proven feasible in this population. However, the extent to which EMA may affect the attitudes or behaviors of young adults who are currently or were formerly homeless and are residing in supportive housing has not been well investigated.

Objective

This study aims to describe the feedback regarding EMA study participation from young adults who are currently or were formerly homeless and examine the reactivity to EMA participation and compliance.

Methods

This mixed methods study used cross-sectional data collected before and after EMA, intensive longitudinal data from a 7-day EMA prompting period, and focus groups of young adults who are currently or were formerly homeless in Los Angeles, California, between 2017 and 2019.

Results

Qualitative data confirmed the quantitative findings. Differences in the experience of EMA between young adults who are currently or were formerly homeless were found to be related to stress or anxiety, interference with daily life, difficulty charging, behavior change, and honesty in responses. Anxiety and depression symptomatology decreased from before to after EMA; however, compliance was not significantly associated with this decrease.

Conclusions

The results point to special considerations when administering EMA to young adults who are currently or were formerly homeless. EMA appears to be slightly more burdensome for young adults who are currently homeless than for those residing in supportive housing, which are nuances to consider in the study design. The lack of a relationship between study compliance and symptomatology suggests low levels of reactivity.

Crowdsourced Community Support Resources Among Patients Discharged From the Emergency Department During the COVID-19 Pandemic: Pilot Feasibility Study

BACKGROUND

The COVID-19 pandemic has placed strains on communities. During this public health crisis, health systems have created remote methods of monitoring symptom progression and delivering care virtually.

OBJECTIVE

Using an SMS text message-based system, we sought to build and test a remote model to explore community needs, connect individuals to curated resources, and facilitate community health worker intervention when needed during the pandemic. The primary aims of this pilot study were to establish the feasibility (ie, engagement with the text line) and acceptability (ie, participant ratings of resources and service) of delivering automated well-being resources via smartphone technology.

METHODS

Eligible patients (aged 18 years or older, having a cell phone with SMS text messaging capability, and recently visited the emergency department) were identified using the electronic health record. The patients were consented to enroll and begin receiving COVID-19-related information and links to community resources. We collected open-ended and close-ended resource and mood ratings. We calculated the frequencies and conducted a thematic review of the open-ended responses.

RESULTS

In 7 weeks, 356 participants were enrolled; 13,917 messages were exchanged including 333 resource ratings (mean 4) and 673 well-being scores (mean 6.8). We received and coded 386 open-ended responses, most of which elaborated upon their self-reported mood score (29%). Overall, 77% (n=274) of our participants rated the platform as a service they would highly recommend to a family member or friend.

CONCLUSIONS

This approach is designed to broaden the reach of health systems, tailor to community needs in real time, and connect at-risk individuals with robust community health support.

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Background

Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use.

Objective

The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both.

Methods

A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information.

Results

The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially.

Conclusions

A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BACKGROUND

Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare.

METHODS

Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period.

RESULTS

Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles.

CONCLUSIONS

The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.

Practices for monitoring and responding to incoming data on self-injurious thoughts and behaviors in intensive longitudinal studies: A systematic review

Advancements in the understanding and prevention of self-injurious thoughts and behaviors (SITBs) are urgently needed. Intensive longitudinal data collection methods-such as ecological momentary assessment-capture fine-grained, "real-world" information about SITBs as they occur and thus have the potential to narrow this gap. However, collecting real-time data on SITBs presents complex ethical and practical considerations, including about whether and how to monitor and respond to incoming information about SITBs from suicidal or self-injuring individuals during the study. We conducted a systematic review of protocols for monitoring and responding to incoming data in previous and ongoing intensive longitudinal studies of SITBs. Across the 61 included unique studies/samples, there was no clear most common approach to managing these ethical and safety considerations. For example, studies were fairly evenly split between either using automated notifications triggered by specific survey responses (e.g., indicating current suicide risk) or monitoring and intervening upon (generally with a phone-based risk assessment) incoming responses (36%), using both automated notifications and monitoring/intervening (35%), or neither using automated notifications nor monitoring/intervening (29%). Certain study characteristics appeared to influence the safety practices used. Future research that systematically evaluates optimal, feasible strategies for managing risk in real-time monitoring research on SITBs is needed.

Perceptions towards an interaction partner predict social anxiety: an ecological momentary assessment study

Social anxiety occurs in everyday social interactions, yet the real-world factors that shape the moment-to-moment experience of social anxiety have not been fully explored. Using ecological momentary assessments (smartphone-based, five signals a day for 21 days), the present study examined the associations between state social anxiety (SSA) and characteristics of interaction partners in varied contexts, and how these momentary associations differed with trait social anxiety (TSA). Ninety-two participants (54% female, age from 18 to 34) completed 4185 momentary reports. Results from multilevel models showed that perceived judgmentalness and unfamiliarity of interaction partners were positively associated with SSA, and the associations were stronger for the high TSA group (n = 30) compared to a control group (n = 62). Exploratory analyses with various types of interaction partners and social settings revealed noticeable group differences in how the types were associated with SSA (e.g. acquaintance, close friend/romantic partner) and how they influenced the effect of judgmentalness and unfamiliarity on SSA (e.g. authority, work/school). Overall, the findings highlight the role of contextual associations in social anxiety, and the benefits and the need for more comprehensive approaches with EMA in studying social anxiety, particularly its contextual aspects.

Discovering different profiles in the dynamics of depression based on real-time monitoring of mood: a first exploration

BACKGROUND

Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons.

METHODS

After completing baseline measures, mildly-to-moderately depressed persons (n = 37) were prompted to rate their current mood (1-10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants.

RESULTS

Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 (n = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 (n = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively).

CONCLUSIONS

The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons.

More Light? Opportunities and Pitfalls in Digitalized Psychotherapy Process Research

While the evidence on the effectiveness of different psychotherapies is often strong, it is not settled whereby and how these therapies work. Knowledge on the causal factors and change mechanisms is of high clinical and public relevance, as it contributes to the empirically informed advancement of psychotherapeutic interventions. Here, digitalized research approaches might possess the potential to generate new insights into human behavior change, contributing to augmented interventions and mental healthcare practices with better treatment outcomes. In this perspective article, we describe recent findings of research into change mechanisms that were only feasible with digital tools and outline important future directions for this rather novel branch of research. Furthermore, we indicate several challenges and pitfalls that are to be solved, in order to advance digitalized psychotherapy process research, both methodologically and technologically.

Improving Mood Through Community Connection and Resources Using an Interactive Digital Platform: Development and Usability Study

BACKGROUND

COVID-19 continues to disrupt global health and well-being. In April-May 2020, we generated a digital, remote interactive tool to provide health and well-being resources and foster connectivity among community members through a text messaging platform.

OBJECTIVE

This study aimed to prospectively investigate the ability of a health system-based digital, remote, interactive tool to provide health and well-being resources to local community participants and to foster connectivity among them during the early phases of COVID-19.

METHODS

We performed descriptive and nonparametric longitudinal statistical analyses to describe and compare the participants' mood ratings over time and thematic analysis of their responses to text messages to further assess mood.

RESULTS

From among 393 individuals seeking care in an urban emergency department in an academic setting, engaged in a two-way text messaging platform, we recorded 287 mood ratings and 368 qualitative responses. We observed no difference in the initial mood rating by week of enrollment [Kruskal-Wallis chi-square H(5)=1.34; P=.93], and the average mood rating did not change for participants taken together [Friedman chi-square Q(3)=0.32; P=.96]. However, of participants providing mood ratings at baseline, mood improved significantly among participants who reported a low mood rating at baseline [n=25, 14.97%; Q(3)=20.68; P<.001] but remained stable among those who reported a high mood rating at baseline [n=142, 85.03%; Q(3)=2.84; P=.42]. Positive mood elaborations most frequently included words related to sentiments of thankfulness and gratitude, mostly for a sense of connection and communication; in contrast, negative mood elaborations most frequently included words related to anxiety.

CONCLUSIONS

Our findings suggest the feasibility of engaging individuals in a digital community with an emergency department facilitation. Specifically, for those who opt to engage in a text messaging platform during COVID-19, it is feasible to assess and respond to mood-related queries with vetted health and well-being resources.

Context-Aware Delivery of Ecological Momentary Assessment

Ecological Momentary Assessment (EMA) is an in-the-moment data collection method which avoids retrospective biases and maximizes ecological validity. A challenge in designing EMA systems is finding a time to ask EMA questions that increases participant engagement and improves the quality of data collection. In this work, we introduce SEP-EMA, a machine learning-based method for providing transition-based context-aware EMA prompt timings. We compare our proposed technique with traditional time-based prompting for 19 individuals living in smart homes. Results reveal that SEP-EMA increased participant response rate by 7.19% compared to time-based prompting. Our findings suggest that prompting during activity transitions makes the EMA process more usable and effective by increasing EMA response rates and mitigating loss of data due to low response rates.

Effects of intense assessment on statistical power in randomized controlled trials: Simulation study on depression

Smartphone-based devices are increasingly recognized to assess disease symptoms in daily life (e.g. ecological momentary assessment, EMA). Despite this development in digital psychiatry, clinical trials are mainly based on point assessments of psychopathology. This study investigated expectable increases in statistical power by intense assessment in randomized controlled trials (RCTs). A simulation study, based on three scenarios and several empirical data sets, estimated power gains of two- or fivefold pre-post-assessment. For each condition, data sets of various effect sizes were generated, and AN(C)OVAs were applied to the sample of interest (N = 50-N = 200). Power increases ranged from 6% to 92%, with higher gains in more underpowered scenarios and with higher number of repeated assessments. ANCOVA profited from a more precise estimation of the baseline covariate, resulting in additional gains in statistical power. Fivefold pre-post EMA resulted in highest absolute statistical power and clearly outperformed traditional questionnaire assessments. For example, ANCOVA of automatized PHQ-9 questionnaire data resulted in absolute power of 55 (for N = 200 and d = 0.3). Fivefold EMA, however, resulted in power of 88.9. Non-parametric and multi-level analyses resulted in comparable outcomes. Besides providing psychological treatment, digital mental health can help optimizing sensitivity in RCT-based research. Intense assessment appears advisable whenever psychopathology needs to be assessed with high precision at pre- and post-assessment (e.g. small sample sizes, small treatment effects, or when applying optimization problems like machine learning). First empiric studies are promising, but more evidence is needed. Simulations for various effects and a short guide for popular power software are provided for study planning.

Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study

BACKGROUND

The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels.

OBJECTIVE

In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals' perceived stress reactivity moderates stress level changes while using TYS.

METHODS

Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity.

RESULTS

Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04).

CONCLUSIONS

TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.

Response Time as an Implicit Self-Schema Indicator for Depression Among Undergraduate Students: Preliminary Findings From a Mobile App-Based Depression Assessment

Background

Response times to depressive symptom items in a mobile-based depression screening instrument has potential as an implicit self-schema indicator for depression but has yet to be determined; the instrument was designed to readily record depressive symptoms experienced on a daily basis. In this study, the well-validated Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) was adopted.

Objective

The purpose of this study was to investigate the relationship between depression severity (ie, explicit measure: total K-CESD-R Mobile scores) and the latent trait of interest in schematic self-referent processing of depressive symptom items (ie, implicit measure: response times to items in the K-CESD-R Mobile scale). The purpose was to investigate this relationship among undergraduate students who had never been diagnosed with, but were at risk for, major depressive disorder (MDD) or comorbid MDD with other neurological or psychiatric disorders.

Methods

A total of 70 participants—36 males (51%) and 34 females (49%)—aged 19-29 years (mean 22.66, SD 2.11), were asked to complete both mobile and standard K-CESD-R assessments via their own mobile phones. The mobile K-CESD-R sessions (binary scale: yes or no) were administered on a daily basis for 2 weeks. The standard K-CESD-R assessment (5-point scale) was administered on the final day of the 2-week study period; the assessment was delivered via text message, including a link to the survey, directly to participants’ mobile phones.

Results

A total of 5 participants were excluded from data analysis. The result of polynomial regression analysis showed that the relationship between total K-CESD-R Mobile scores and the reaction times to the depressive symptom items was better explained by a quadratic trend—F (2, 62)=21.16, P<.001, R²=.41—than by a linear trend—F (1, 63)=25.43, P<.001, R²=.29. It was further revealed that the K-CESD-R Mobile app had excellent internal consistency (Cronbach alpha=.94); at least moderate concurrent validity with other depression scales, such as the Korean version of the Quick Inventory for Depressive Symptomatology-Self Report (ρ=.38, P=.002) and the Patient Health Questionnaire-9 (ρ=.48, P<.001); a high adherence rate for all participants (65/70, 93%); and a high follow-up rate for 10 participants whose mobile or standard K-CESD-R score was 13 or greater (8/10, 80%).

Conclusions

As hypothesized, based on a self-schema model for depression that represented both item and person characteristics, the inverted U-shaped relationship between the explicit and implicit self-schema measures for depression showed the potential of an organizational breakdown; this also showed the potential for a subsequent return to efficient processing of schema-consistent information along a continuum, ranging from nondepression through mild depression to severe depression. Further, it is expected that the updated K-CESD-R Mobile app can play an important role in encouraging people at risk for depression to seek professional follow-up for mental health care.

ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions

In this paper we introduce a new Android library, called ULTEMAT, for the delivery of ecological momentary assessments (EMAs) on mobile devices and we present its use in the MoodBuster app developed in the H2020 E-COMPARED project. We discuss context-aware, or event-based, triggers for the presentation of EMAs and discuss the potential they have to improve the effectiveness of mobile provision of mental health interventions as they allow for the delivery of assessments to the patients when and where these are most appropriate. Following this, we present the abilities of ULTEMAT to use such context-aware triggers to schedule EMAs and we discuss how a similar approach can be used for Ecological Momentary Interventions (EMIs).