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

[Decentralized Health Promotion in Nuremberg according to the Prevention Bill: Assessment of results and experiences of the project "Health for Everyone in the District"]

BACKGROUND

The project "Health for Everyone in the District" was implemented in Nuremberg from May 2017 to October 2022 as part of the law passed to strengthen health promotion and disease prevention with funding from Public Health Insurance, Bavaria. The aim was to implement health promotion measures through a decentralized system in four deprived parts of the city and thus promote health equity on site. Among other aspects, program loyalty, project scope, and acceptance, as well as continuity and establishment of permanent structures underwent external assessment.

METHOD

As part of the evaluation, quantitative data from the paper-and-pencil feedback forms of the measures (n=580), four qualitative focus group interviews with participants of the project (n=20), and an in-depth partially standardized predominantly quantitative online survey of participants and course instructors from the districts (n=67) were conducted.

RESULTS

The programs were accepted by those most in need, namely women, elderly people and those with a migration background. Women, senior citizens and people with a migration background were well reached by the measures. The very high level of satisfaction with the measures showed that there were opportunities for implementation of health promotion measures into daily life taking into consideration the local environment and deprived target groups. The specifications of the guidelines for prevention, however, represented a hurdle for the long-term establishment of the measures in these districts.

CONCLUSION

The project "Health for Everyone in the District " represents a local low-threshold approach to social situation-related health promotion in the municipal setting and is suitable for reaching deprived target groups with health-promoting measures. Adjustments to the guidelines for prevention could help create permanent structures on a broader scale.

Measurement reactivity in ambulatory assessment: Increase in emotional clarity over time independent of sampling frequency

Ambulatory assessment (AA) studies are frequently used to study emotions, cognitions, and behavior in daily life. But does the measurement itself produce reactivity, that is, are the constructs that are measured influenced by participation? We investigated individual differences in intraindividual change in momentary emotional clarity and momentary pleasant-unpleasant mood over the course of an AA study. Specifically, we experimentally manipulated sampling frequency and hypothesized that the intraindividual change over time would be stronger when sampling frequency was high (vs. low). Moreover, we assumed that individual differences in dispositional mood regulation would moderate the direction of intraindividual change in momentary pleasant-unpleasant mood over time. Students (n = 313) were prompted either three or nine times a day for 1 week (data collection took place in 2019 and 2020). Multilevel growth curve models showed that momentary emotional clarity increased within participants over the course of the AA phase, but this increase did not differ between the two sampling frequency groups. Pleasant-unpleasant mood did not show a systematic trend over the course of the study, and mood regulation did not predict individual differences in mood change over time. Again, results were not moderated by the sampling frequency group. We discuss limitations of our study (e.g., WEIRD sample) and potential practical implications regarding sampling frequency in AA studies. Future studies should further systematically investigate the circumstances under which measurement reactivity is more likely to occur.

Enhancing mHealth data collection applications with sensing capabilities

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

The Impact of Coping Styles and Gender on Situational Coping: An Ecological Momentary Assessment Study With the mHealth Application TrackYourStress

The aim of this study was to investigate the impact of different coping styles on situational coping in everyday life situations and gender differences. An ecological momentary assessment study with the mobile health app TrackYourStress was conducted with 113 participants. The coping styles Positive Thinking, Active Stress Coping, Social Support, Support in Faith, and Alcohol and Cigarette Consumption of the Stress and Coping Inventory were measured at baseline. Situational coping was assessed by the question “How well can you cope with your momentary stress level” over 4 weeks. Multilevel models were conducted to test the effects of the coping styles on situational coping. Additionally, gender differences were evaluated. Positive Thinking (p = 0.03) and Active Stress Coping (p = 0.04) had significant positive impacts on situational coping in the total sample. For women, Social Support had a significant positive effect on situational coping (p = 0.046). For men, Active Stress Coping had a significant positive effect on situational coping (p = 0.001). Women had higher scores on the SCI scale Social Support than men (p = 0.007). These results suggest that different coping styles could be more effective in daily life for women than for men. Taking this into account, interventions tailored to users’ coping styles might lead to better coping outcomes than generalized interventions.

Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform

Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.

Comparing stress prediction models using smartwatch physiological signals and participant self-reports

Recent advances in wearable technology have facilitated the non-obtrusive monitoring of physiological signals, creating opportunities to monitor and predict stress. Researchers have utilized machine learning methods using these physiological signals to develop stress prediction models. Many of these prediction models have utilized objective stressor tasks (e.g., a public speaking task or solving math problems). Alternatively, the subjective user responses with self-reports have also been used for measuring stress. In this paper, we describe a methodological approach (a) to compare the prediction performance of models developed using objective markers of stress using participant-reported subjective markers of stress from self-reports; and (b) to develop personalized stress models by accounting for inter-individual differences. Towards this end, we conducted a laboratory-based study with 32 healthy volunteers. Participants completed a series of stressor tasks-social, cognitive and physical-wearing an instrumented commercial smartwatch that collected physiological signals and participant responses using timed self-reports. After extensive data preprocessing using a combination of signal processing techniques, we developed two types of models: objective stress models using the stressor tasks as labels; and subjective stress models using participant responses to each task as the label for that stress task. We trained and tested several machine learning algorithms-support vector machine (SVM), random forest (RF), gradient boosted trees (GBT), AdaBoost, and Logistic Regression (LR)-and evaluated their performance. SVM had the best performance for the models using the objective stressor (i.e., stressor tasks) with an AUROC of 0.790 and an F-1 score of 0.623. SVM also had the highest performance for the models using the subjective stress (i.e., participant self-reports) with an AUROC of 0.719 and an F-1 score of 0.520. Model performance improved with a personalized threshold model to an AUROC of 0.751 and an F-1 score of 0.599. The performance of the stress models using an instrumented commercial smartwatch was comparable to similar models from other state-of-the-art laboratory-based studies. However, the subjective stress models had a lower performance, indicating the need for further research on the use of self-reports for stress-related studies. The improvement in performance with the personalized threshold-based models provide new directions for building stress prediction models.

Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.

The Use of Digitally Assessed Stress Levels to Model Change Processes in CBT - A Feasibility Study on Seven Case Examples

In psychotherapy research, the measurement of treatment processes and outcome are predominantly based on self-reports. However, given new technological developments, other potential sources can be considered to improve measurements. In a feasibility study, we examined whether Ecological Momentary Assessments (EMA) using digital phenotyping (stress level) can be a valuable tool to investigate change processes during cognitive behavioral therapy (CBT). Seven outpatients undergoing psychological treatment were assessed using EMA. Continuous stress levels (heart rate variability) were assessed via fitness trackers (Garmin) every 3 min over a 2-week time period (6,720 measurements per patient). Time-varying change point autoregressive (TVCP-AR) models were employed to detect both gradual and abrupt changes in stress levels. Results for seven case examples indicate differential patterns of change processes in stress. More precisely, inertia of stress level changed gradually over time in one of the participants, whereas the other participants showed both gradual and abrupt changes. This feasibility study demonstrates that intensive longitudinal assessments enriched by digitally assessed stress levels have the potential to investigate intra- and interindividual differences in treatment change processes and their relations to treatment outcome. Further, implementation issues and implications for future research and developments using digital phenotyping are discussed.

Frequently repeated measurements -our experience of collecting data with SMS

Background

As technology is advancing, so are the possibilities for new data collection methods in research, potentially improving data quality and validity of the results. In Sweden, a system using frequent repeated data collection using text messages, SMS Track, has been used in clinical research for more than a decade. In this paper, compliance with repeated text message questions was examined across five different studies, i.e. if compliance was

1: associated with study-specific factors (age or gender of the subjects, the condition, its’ severity or course, i.e. improvement, relapse or steady state) and/or.

2: associated with the methodology itself (the question being asked, the frequency and number of questions, duration of data collection, initial compliance or the management of the system).

Methods

Descriptive comparisons were done across five studies. Three studies were collecting weekly responses over at least 52 weeks (“Weekly studies”) and were used to investigate the effect of age, sex and pain severity on compliance, the effect of early compliance for late compliance, and finally the early occurrence of two successive weeks with non-compliance.

Result

Compliance was excellent across all five studies, and only influenced somewhat by age, sex and pain-level. The factor “study” remained significant in the final model thus the observed differences may be a result of the conditions studied but does not seem to be attributable to severity or development of these conditions. Number and frequency of questions did not influence compliance, nor did study duration.

Conclusions

Compliance was excellent in the included studies and was not affected by population factors. However, differences in compliance were observed that cannot be easily explained and warrant further investigation. In particular, the nature of the variables or the management of the study are potential areas for further investigations.

Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain

The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.