Towards Incentive Management Mechanisms in the Context of Crowdsensing Technologies based on TrackYourTinnitus Insights

Acceptability and satisfaction with emma, a smartphone application dedicated to suicide ecological assessment and prevention

BACKGROUND

As mHealth may contribute to suicide prevention, we developed emma, an application using Ecological Momentary Assessment and Intervention (EMA/EMI).

OBJECTIVE

This study evaluated emma usage rate and acceptability during the first month and satisfaction after 1 and 6 months of use.

METHODS

Ninety-nine patients at high risk of suicide used emma for 6 months. The acceptability and usage rate of the EMA and EMI modules were monitored during the first month. Satisfaction was assessed by questions in the monthly EMA (Likert scale from 0 to 10) and the Mobile App Rating Scale (MARS; score: 0-5) completed at month 6. After inclusion, three follow-up visits (months 1, 3, and 6) took place.

RESULTS

Seventy-five patients completed at least one of the proposed EMAs. Completion rates were lower for the daily than weekly EMAs (60 and 82%, respectively). The daily completion rates varied according to the question position in the questionnaire (lower for the last questions, LRT = 604.26, df = 1, p-value < 0.0001). Completion rates for the daily EMA were higher in patients with suicidal ideation and/or depression than in those without. The most used EMI was the emergency call module (n = 12). Many users said that they would recommend this application (mean satisfaction score of 6.92 ± 2.78) and the MARS score at month 6 was relatively high (overall rating: 3.3 ± 0.87).

CONCLUSION

Emma can target and involve patients at high risk of suicide. Given the promising users' satisfaction level, emma could rapidly evolve into a complementary tool for suicide prevention.

UNITI Mobile-EMI-Apps for a Large-Scale European Study on Tinnitus

More and more observational studies exploit the achievements of mobile technology to ease the overall implementation procedure. Many strategies like digital phenotyping, ecological momentary assessments or mobile crowdsensing are used in this context. Recently, an increasing number of intervention studies makes use of mobile technology as well. For the chronic disorder tinnitus, only few long-running intervention studies exist, which use mobile technology in a larger setting. Tinnitus is characterized by its heterogeneous patient's symptom profiles, which complicates the development of general treatments. In the UNITI project, researchers from different European countries try to unify existing treatments and interventions to cope with this heterogeneity. One study arm (UNITI Mobile) exploits mobile technology to investigate newly implemented interventions types, especially within the pan-European setting. The goals are to learn more about the validity and usefulness of mobile technology in this context. Furthermore, differences among the countries shall be investigated. Practically, two native intervention apps have been developed for UNITI and the mobile study arm, which pose features not presented so far in other apps of the authors. Along the implementation procedure, it is discussed whether these features might leverage similar types of studies in future. Since instruments like the mHealth evidence reporting and assessment checklist (mERA), developed by the WHO mHealth technical evidence review group, indicate that aspects shown for UNITI Mobile are important in the context of health interventions using mobile phones, our findings may be of a more general interest and are therefore being discussed in the work at hand.

Using a visual analog scale (VAS) to measure tinnitus-related distress and loudness: Investigating correlations using the Mini-TQ results of participants from the TrackYourTinnitus platform

INTRODUCTION

Tinnitus, a perception of ringing and buzzing sound in the ear, has not been completely understood yet. It is well known that tinnitus-related distress and loudness can change over time. However, proper comparability for the data collection approaches requires further focused studies. In this context, technology such as the use of mobile devices may be a promising approach. Repeated assessments of tinnitus-related distress and loudness in Ecological Momentary Assessment (EMA) studies require a short assessment, and a Visual Analogic Scale (VAS) is often used in this context. Yet, their comparability with psychometric questionnaires remains unclear and thus was the focus of this study. Research goals: The evaluation of the appropriateness of VAS in measuring tinnitus-related distress and loudness is pursued in this paper.

METHODS

The Mini Tinnitus Questionnaire (Mini-TQ) measured tinnitus-related distress once. Tinnitus-related distress and tinnitus loudness were measured repeatedly using VAS on a daily basis during 7 days in the TrackYourTinnitus (TYT) smartphone app and were summarized per day using mean and median results. Then, correlations between summarized VAS tinnitus-related distress and summarized VAS tinnitus loudness, on the one side, and Mini-TQ, on the other side, were calculated.

RESULTS

Correlations between Mini-TQ and VAS tinnitus-related distress ranged between r = 0.36 and r = 0.52, while correlations between Mini-TQ and VAS tinnitus loudness ranged between r = 0.25 and r = 0.36. The more time difference between the Mini-TQ and the VAS assessments is, the lower the correlations between them. Mean and median VAS values per day resulted in similar correlations.

CONCLUSIONS

Mobile-based VAS seems to be an appropriate approach to utilize daily measurements of tinnitus-related distress.

Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring

The recording of Ecological Momentary Assessments (EMA) can assist people with chronic diseases in monitoring their health state. However, many users quickly lose interest in their respective EMA platforms. Therefore, we studied the adherence of users of the mHealth app TrackYourTinnitus (TYT). The app is used to record EMA in people with tinnitus. 1292 users, who interacted with the app between April 2014 and February 2017, were analyzed in this work. We defined “adherence” based on the dimensions of interaction duration and interaction continuity. We propose methods that are able to predict the (dis)continuation of interaction with the app and identify user segments that are characterized by similar patterns of adherence. For the prediction task we used the data of the questionnaires MiniTF and TSCHQ, which are filled in when the users enter TYT for the first time. Additionally, time series of the eight items of the daily EMA questionnaire were used. The distribution of user activity pertaining to the adherence dimension of interaction duration revealed a very skewed distribution, with most users giving up after only 1 day of interaction. However, many users returned after interrupting for some time. Some of the MiniTF items indicated that the worries of users might have lead to an increased likelihood of returning back to the app. The MiniTF score itself was not predictive, though. The answers to the TSCHQ items, in turn, pointed to user strata (more than 65 years of age at registration), which tended towards higher interaction continuity. As the registration questionnaires predicted adherence only to a limited extent, it is promising to study the activities of the users in the very first days of interaction more deeply. It turned out in this context that the effects of interaction stimulants like personalized and non-personalized tips, pointers to information sources, and mechanisms used in online treatments for tinnitus (e.g., in iCBT) should be further investigated.

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more in-depth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform.

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.