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

Implementation of an Ecological Momentary Assessment (EMA) in Naturalistic Psychotherapy Settings: Qualitative Insights from Patients, Therapists, and Supervisors Perspectives

Ecological momentary assessment (EMA) allows measuring intra-individual processes moment by moment, identifying and modeling, in a naturalistic way, individual levels and changes in different psychological processes. However, active EMA requires a high degree of adherence, as it implies a significant burden for patients. Moreover, there is still no consensus on standardized procedures for implementation. There have been few results in detecting desirable characteristics for the design and implementation of an EMA device. Studies that address these issues from the perspectives of participants in psychotherapeutic processes are needed. To analyze the perspectives of patients, therapists and supervisors on the implementation of an EMA device in a psychotherapeutic treatment for depression. The sample will include eight patients, eleven therapists and five supervisors, taken from a research project that implemented an EMA system for monitoring the dynamics of affectivity at the beginning of psychotherapies for depression. Semi-structured interviews specific to each group are being conducted and analyzed from a qualitative approach based on consensual qualitative research (CQR). Participants reported having a positive evaluation of the study's informational resources and implementation. Difficulties were expressed in responding in the morning hours and the importance of having a customized EMA that is tailored to the needs of the patients was expressed. Furthermore, patients and therapists agreed that the impact of the use of the monitoring system on treatment was neutral or positive. In contrast, patients considered the EMA to be positive for their daily life.

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Background

Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.

Objective

This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.

Methods

We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.

Results

A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.

Conclusions

This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

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.

eSano - An eHealth Platform for Internet- and Mobile-based Interventions

The prevention and treatment of mental disorders and chronic somatic diseases is a core challenge for health care systems of the 21th century. Mental- and behavioral health interventions provide the means for lowering the public health burden. However, structural deficits, reluctance to use existing services, perceived stigma and further personal and environmental reasons restrict the uptake of these evidence-based approaches. Internet- and mobile-based interventions (IMIs) might overcome some of the limitations of on-site interventions by providing an anonymous, scalable, time- and location-independent, yet evidence-based approach. In order to implement digital mental and behavioral health concepts across the life-span into practice, a technical solution to support the design, creation, and execution of IMIs is needed. However, there are various conceptual, technical as well as legal challenges to implementing a corresponding software solution in the healthcare domain. Therefore, the work at hand (1) identifies these challenges and derives a number of respective requirements, (2) introduces the eHealth platform eSano, a software project developed by an interdisciplinary team of computer scientists, psychologists, therapists, and other domain experts, with the aim to serve as a flexible basis for mental and behavioral research and health care, and (3) provides technical insights into the developed platform and its approach to address the aforementioned requirements.

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review

BACKGROUND

Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal.

OBJECTIVE

This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues.

METHODS

The review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed.

RESULTS

A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests.

CONCLUSIONS

Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.

Immediate and long-term effectiveness of adding an Internet intervention for depression to routine outpatient psychotherapy: Subgroup analysis of the EVIDENT trial

OBJECTIVE

To examine immediate and long-term effectiveness of an adjunctive Internet intervention for depression in a large sample of patients undergoing routine psychotherapy.

METHOD

The current study evaluated a subgroup of patients from the Evident trial, a randomized investigation of a 12-week minimally guided Internet intervention (Deprexis) for the treatment of mild to moderate depression. 340 adults (mean age = 43.3 years; 71.7 % female) of the original sample received routine outpatient psychotherapy during the trial period, resulting in a standard psychotherapy group (n = 174) and an augmented therapy group (n = 166). Outcomes were assessed at baseline, post-treatment and 6-month follow-up.

RESULTS

Intention-to-treat analyses indicated that combined treatment led to a greater reduction in symptoms of depression (effect size d = 0.32; p = .002), improved therapeutic progress (d = 0.36; p = .003), and higher mental health-related quality of life (d = 0.34; p = .004). There was no intervention effect on physical health-related quality of life. The same pattern was found at 6-month follow-up, and adjunctive treatment also resulted in increased rates of clinical improvement. Treatment success was independent from therapeutic orientation of combined face-to-face therapy.

CONCLUSION

Results indicate that the adjunctive use of the investigated intervention can produce additional and lasting effects in routine outpatient psychotherapy for mild to moderate levels of depression. The study adds to the ongoing evidence on augmented effects of blended treatment. Future studies should investigate different types of blends in diverse populations by means of change-sensitive assessment strategies.

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.

Unraveling the Black Box: Exploring Usage Patterns of a Blended Treatment for Depression in a Multicenter Study

BACKGROUND

Blended treatments, combining digital components with face-to-face (FTF) therapy, are starting to find their way into mental health care. Knowledge on how blended treatments should be set up is, however, still limited. To further explore and optimize blended treatment protocols, it is important to obtain a full picture of what actually happens during treatments when applied in routine mental health care.

OBJECTIVE

The aims of this study were to gain insight into the usage of the different components of a blended cognitive behavioral therapy (bCBT) for depression and reflect on actual engagement as compared with intended application, compare bCBT usage between primary and specialized care, and explore different usage patterns.

METHODS

Data used were collected from participants of the European Comparative Effectiveness Research on Internet-Based Depression Treatment project, a European multisite randomized controlled trial comparing bCBT with regular care for depression. Patients were recruited in primary and specialized routine mental health care settings between February 2015 and December 2017. Analyses were performed on the group of participants allocated to the bCBT condition who made use of the Moodbuster platform and for whom data from all blended components were available (n=200). Included patients were from Germany, Poland, the Netherlands, and France; 64.5% (129/200) were female and the average age was 42 years (range 18-74 years).

RESULTS

Overall, there was a large variability in the usage of the blended treatment. A clear distinction between care settings was observed, with longer treatment duration and more FTF sessions in specialized care and a more active and intensive usage of the Web-based component by the patients in primary care. Of the patients who started the bCBT, 89.5% (179/200) also continued with this treatment format. Treatment preference, educational level, and the number of comorbid disorders were associated with bCBT engagement.

CONCLUSIONS

Blended treatments can be applied to a group of patients being treated for depression in routine mental health care. Rather than striving for an optimal blend, a more personalized blended care approach seems to be the most suitable. The next step is to gain more insight into the clinical and cost-effectiveness of blended treatments and to further facilitate uptake in routine mental health care.

Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review

Ecological momentary assessment (EMA) and ecological momentary intervention (EMI) are alternative approaches to retrospective self-reports and face-to-face treatments, and they make it possible to repeatedly assess patients in naturalistic settings and extend psychological support into real life. The increase in smartphone applications and the availability of low-cost wearable biosensors have further improved the potential of EMA and EMI, which, however, have not yet been applied in clinical practice. Here, we conducted a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to explore the state of the art of technology-based EMA and EMI for major depressive disorder (MDD). A total of 33 articles were included (EMA = 26; EMI = 7). First, we provide a detailed analysis of the included studies from technical (sampling methods, duration, prompts), clinical (fields of application, adherence rates, dropouts, intervention effectiveness), and technological (adopted devices) perspectives. Then, we identify the advantages of using information and communications technologies (ICTs) to extend the potential of these approaches to the understanding, assessment, and intervention in depression. Furthermore, we point out the relevant issues that still need to be addressed within this field, and we discuss how EMA and EMI could benefit from the use of sensors and biosensors, along with recent advances in machine learning for affective modelling.

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Objective

We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables.

Materials and Methods

We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.

Results

We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.

Conclusion

While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

Current state and future directions of technology-based ecological momentary assessments and interventions for major depressive disorder: protocol for a systematic review

BACKGROUND

Ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) represent a novel approach for the assessment and delivery of psychological support to depressed patients in daily life. Beyond the classical paper-and-pencil daily diaries, the more recent progresses in Information and Communication Technologies (ICT) enabled researchers to bring all the needed processes together in only one device, i.e., response signaling, repeated symptom collection, information storage, secure data transfer, and psychological support delivery. Despite evidence showing the feasibility and acceptability of these techniques, EMAs are only beginning to be applied in real clinical practice, whether the development of EMIs for clinically depressed patients is still very limited. The objective of this systematic review is to provide the state of the art of technology-based EMAs and EMIs for major depressive disorder (MDD), with the aim of leading the way to possible future directions for the clinical practice.

METHODS

We will conduct a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Data sources will include two bibliographic databases, PubMed and Web of Science (Web of Knowledge), supplemented by searches for unpublished or ongoing studies. Eligible studies will report data for adult (≥ 18 years old) with a primary (both current and past) diagnosis of MDD, defined by a valid criterion standard. We will consider studies adopting technology-based EMAs and EMIs for the investigation and/or assessment of depression and for the delivery of a psychological intervention. We will exclude studies adopting paper-and-pencil tools.

DISCUSSION

The proposed systematic review will provide new insights on the advantages and benefits of adopting technology-based EMAs and EMIs for MDD in the traditional clinical practice, taking into consideration both clinical and technological issues. The potential of using sensors and biosensors along with machine learning for affective modeling will also be discussed.

Using Mobile Phones to Examine and Enhance Perceptions of Control in Mildly Depressed and Nondepressed Volunteers: Intervention Study

BACKGROUND

Perceived control is strongly linked to healthy outcomes, mental healthiness, and psychological well-being. This is particularly important when people have little control over things that are happening to them. Perceived control studies have been performed extensively in laboratory settings and show that perceived control can be increased by experimental manipulations. Although these studies suggest that it may be possible to improve people's mental health by increasing their perceived control, there is very little evidence to date to suggest that perceived control can also be influenced in the real world.

OBJECTIVE

The first aim of this study was to test for evidence of a link between noncontrol situations and psychological well-being in the real world using a mobile phone app. The second and arguably more important aim of the study was to test whether a simple instructional intervention on the nature of alternative causes would enhance people's perceptions of their own control in these noncontrol situations.

METHODS

We implemented a behavioral action-outcome contingency judgment task using a mobile phone app. An opportunity sample of 106 healthy volunteers scoring low (n=56, no depression) or high (n=50, mild depression) on a depression scale participated. They were given no control over the occurrence of a low- or high-frequency stimulus that was embedded in everyday phone interactions during a typical day lasting 8 hours. The intervention involved instructions that either described a consistent alternative cause against which to assess their own control, or dynamic alternative causes of the outcome. Throughout the day, participants rated their own control over the stimulus using a quantitative judgment scale.

RESULTS

Participants with no evidence of depression overestimated their control, whereas those who were most depressed were more accurate in their control ratings. Instructions given to all participants about the nature of alternative causes significantly affected the pattern of perceived control ratings. Instructions describing discrete alternative causes enhanced perceived control for all participants, whereas dynamic alternative causes were linked to less perceived control.

CONCLUSIONS

Perceptions of external causes are important to perceived control and can be used to enhance people's perceptions. Theoretically motivated interventions can be used to enhance perceived control using mobile phone apps. This is the first study to do so in a real-world setting.