The statistical analysis plan for the unification of treatments and interventions for tinnitus patients randomized clinical trial (UNITI-RCT).

BACKGROUND: Tinnitus is a leading cause of disease burden globally. Several therapeutic strategies are recommended in guidelines for the reduction of tinnitus distress; however, little is known about the potentially increased effectiveness of a combination of treatments and personalized treatments for each tinnitus patient. METHODS: Within the Unification of Treatments and Interventions for Tinnitus Patients project, a multicenter, randomized clinical trial is conducted with the aim to compare the effectiveness of single treatments and combined treatments on tinnitus distress (UNITI-RCT). Five different tinnitus centers across Europe aim to treat chronic tinnitus patients with either cognitive behavioral therapy, sound therapy, structured counseling, or hearing aids alone, or with a combination of two of these treatments, resulting in four treatment arms with single treatment and six treatment arms with combinational treatment. This statistical analysis plan describes the statistical methods to be deployed in the UNITI-RCT. DISCUSSION: The UNITI-RCT trial will provide important evidence about whether a combination of treatments is superior to a single treatment alone in the management of chronic tinnitus patients. This pre-specified statistical analysis plan details the methodology for the analysis of the UNITI trial results. TRIAL REGISTRATION: ClinicalTrials.gov NCT04663828 . The trial is ongoing. Date of registration: December 11, 2020. All patients that finished their treatment before 19 December 2022 are included in the main RCT analysis.

Prediction meets time series with gaps: User clusters with specific usage behavior patterns.

With mHealth apps, data can be recorded in real life, which makes them useful, for example, as an accompanying tool in treatments. However, such datasets, especially those based on apps with usage on a voluntary basis, are often affected by fluctuating engagement and by high user dropout rates. This makes it difficult to exploit the data using machine learning techniques and raises the question of whether users have stopped using the app. In this extended paper, we present a method to identify phases with varying dropout rates in a dataset and predict for each. We also present an approach to predict what period of inactivity can be expected for a user in the current state. We use change point detection to identify the phases, show how to deal with uneven misaligned time series and predict the user's phase using time series classification. In addition, we examine how the evolution of adherence develops in individual clusters of individuals. We evaluated our method on the data of an mHealth app for tinnitus, and show that our approach is appropriate for the study of adherence in datasets with uneven, unaligned time series of different lengths and with missing values.

A baseline study of microplastic pollution in a Southern Indian Estuary.

Knowledge on the processes controlling the vertical distribution of microplastics (MPs) in estuaries is less. This research was carried out to determine the MP distribution in the surface, middle, and bottom layers of the Udyavara River Estuary in southwest India. The mean (± standard deviation) concentrations were 320.83 (± 98.30), 514.55 (± 352.16), and 755.03 (± 400.96) particles/m3, respectively. Fibres, films, and fragments dominated, and 57 % of the MPs had a size range of 0.3-1 mm, while 43 % had a size of 1-5 mm. The main polymers were high-density polyethylene and polyethylene terephthalate. A positive correlation (r = 0.421, p = 0.0205, n = 30) exists between the MPs and salinity, suggesting that the MPs are held by dense saline waters. The mean pollution load index value was 2.25 indicating severe pollution. Microplastic pollution is due to harbour, fishing, industrial activities, the effects of southwest monsoon rain, and tidal currents.

Radially aligned hierarchical N-doped porous carbon beads derived from oil-sand asphaltene for long-life water filtration and wastewater treatment.

The application of waste-derived highly efficient adsorbent for organic pollutants removal from water and wastewater is presented. Highly porous carbon beads with radially aligned macrochannels were prepared from asphaltene. Well-ordered inwardly aligned macrovoids favored solute diffusion and maximized the liquid accommodation capacity. A further N-doping could modulate the sorbent hydrophilicity leading to an outstanding absorption performance for a range of organic solvents and oily chemicals. N-doped carbon beads were effective sorbents of lopinavir (LNV) and ritonavir (RNV) from water and wastewater. The process of sorption was fast, and the highest removal was noted for RNV than LPV. N-doping favored LNV and RNV adsorption due to the increased porous structure of N-doped asphaltene beads. The chemisorption of both LPV and RTV was a rate-limiting step. The presence of co-pollutants in treated wastewater enhanced LPV and RNV removal and an up to 470 % increase was noted. The presence of LPV or RTV in distilled water was not toxic to Aliivibrio fischeri or even can stimulate their growth. However, after the adsorption process, the solution of RTV reduced its toxicity significantly and the final solution was not toxic. The opposite effect was noted for LPV. Given the repeatability, high removal performance, and cost-effectiveness of the asphaltene-based carbon microtubes when compared to other well-known sorbents such as carbon nanotubes, they demonstrated great potential as a low-cost and effective agent for long-life water filtration and wastewater treatment.

Assessment of pollution and risks associated with microplastics in the riverine sediments of the Western Ghats: a heritage site in southern India.

There is very little knowledge on microplastic pollution in the Western Ghats (WG), a heritage site in southwest India. To address this, we have studied the spatiotemporal variations of sedimentary microplastics (MPs) from the River Sharavathi, a pristine river in the Western Ghats (WG), southern India. The rich biodiversity in the region makes it relevant to analyse the distribution of this emerging pollutant that is causing harm to the biota and the ecosystem. We analysed the sedimentological and carbon content (organic and inorganic) of these sediments and explored their relationship with MPs. Finally, risk assessment indices such as the Pollution Load Index (PLI), the Polymer Hazard Index (PHI), and the Potential Ecological Risk Index (PERI) were calculated to detect the levels of plastic pollution. The concentration of MPs ranged from 2.5 to 57.5 pieces/kg and 0 to 15 pieces/kg during the pre-monsoon and post-monsoon seasons, respectively. The dip in the MPs' abundance during the post-monsoon season was due to the extremely high rainfall in the river basin during July-August 2019, which would have entrained the sedimentary MPs and transported them to the coast/Arabian Sea. Smaller MPs (0.3-1 mm) were more abundant than the larger MPs (1-5 mm), mainly due to the breakdown of sedimentary plastics by physical processes. Fragments, films, foams, and fibres were the main categories of MPs, and the main polymers were polyethylene, polyethylene terephthalate, and polypropylene. No significant relationship was observed between the sedimentological properties and microplastics, which may be due to the different physical properties of sediments and microplastics. The PLI, PHI, and PERI indices suggest different contamination levels in the river basin. Based on the PLI scores, all the samples belong to the hazardous level I suggesting minor risk category, and the risk of microplastic pollution falls under the high to hazardous risk category based on the PHI values. The PERI value ranged from 160 to 440 and 40 to 2240 during the pre-monsoon and post-monsoon seasons, respectively. The risk assessment in a region known for its rich biodiversity is crucial, as the data can be used by the district administration to mitigate plastic pollution.

Perception of Oral Health and Practices among Children Residing in Orphanages in Bengaluru.

Background: Children residing in orphanages often have accumulated oral health needs as they are unaware of the importance of maintaining good oral hygiene. Due to financial constraints, the provision of oral health care is often neglected. Aims and Objectives: This study attempts to assess the perception of oral health and practices among children residing in orphanages in Bengaluru. Materials and Methods: A cross-sectional survey was conducted among 269 inmates, which assessed their perception and practices concerning oral health. Results: The self-assessment and practices of the study participants varied significantly. The proportion of substance abuse was very low, and the oral hygiene practices of the majority of participants were good. Conclusion: Interventions need to be undertaken to improve and reinforce oral health awareness among the study participants.

Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit.

Ecological Momentary Assessments (EMA) deliver insights on how patients perceive tinnitus at different times and how they are affected by it. Moving to the next level, an mHealth app can support users more directly by predicting a user's next EMA and recommending personalized services based on these predictions. In this study, we analyzed the data of 21 users who were exposed to an mHealth app with non-personalized recommendations, and we investigate ways of predicting the next vector of EMA answers. We studied the potential of entity-centric predictors that learn for each user separately and neighborhood-based predictors that learn for each user separately but take also similar users into account, and we compared them to a predictor that learns from all past EMA indiscriminately, without considering which user delivered which data, i.e., to a "global model." Since users were exposed to two versions of the non-personalized recommendations app, we employed a Contextual Multi-Armed Bandit (CMAB), which chooses the best predictor for each user at each time point, taking each user's group into account. Our analysis showed that the combination of predictors into a CMAB achieves good performance throughout, since the global model was chosen at early time points and for users with few data, while the entity-centric, i.e., user-specific, predictors were used whenever the user had delivered enough data-the CMAB chose itself when the data were "enough." This flexible setting delivered insights on how user behavior can be predicted for personalization, as well as insights on the specific mHealth data. Our main findings are that for EMA prediction the entity-centric predictors should be preferred over a user-insensitive global model and that the choice of EMA items should be further investigated because some items are answered more rarely than others. Albeit our CMAB-based prediction workflow is robust to differences in exposition and interaction intensity, experimentators that design studies with mHealth apps should be prepared to quantify and closely monitor differences in the intensity of user-app interaction, since users with many interactions may have a disproportionate influence on global models.

Juxtaposing Medical Centers Using Different Questionnaires Through Score Predictors.

Background: Chronic tinnitus is a clinically multidimensional phenomenon that entails audiological, psychological and somatosensory components. Previous research has demonstrated age and female gender as potential risk factors, although studies to this regard are heterogeneous. Moreover, whilst recent research has begun to identify clinical "phenotypes," little is known about differences in patient population profiles at geographically separated and specialized treatment centers. Identifying such differences might prevent potential biases in joint randomized controlled trials (RCTs) and allow for population-specific treatment adaptations. Method: Two German tinnitus treatment centers were compared regarding pre-treatment data distributions of their patient population bases. To identify overlapping as well as center-specific factors, juxtaposition-, similarity-, and meta-data-based methods were applied. Results: Between centers, significant differences emerged. One center demonstrated some predictive power of the patients of the other center with regard to questionnaire score after treatment, indicating similarities in treatment response across center populations. Furthermore, adherence to the completion of the questionnaires was found to be an important factor in predicting post-treatment data. Discussion: Differential age and gender distributions per center should be considered as regards RCT design and individualized treatment planning.

Standardized Clinical Profiling in Spanish Patients with Chronic Tinnitus.

BACKGROUND: Tinnitus is a heterogeneous condition. The aim of this study as to compare the online and hospital responses to the Spanish version of European School for Interdisciplinary Tinnitus Research screening-questionnaire (ESIT-SQ) in tinnitus individuals by an unsupervised age clustering. METHODS: A cross-sectional study was performed including 434 white Spanish patients with chronic tinnitus to assess the demographic and clinical profile through the ESIT-SQ, with 204 outpatients and 230 individuals from an online survey; a K-means clustering algorithm was used to classify both responses according to age. RESULTS: Online survey showed a high proportion of Meniere's disease (MD) patients compared to both the general population and the outpatient cohort. The responses showed statistically significant differences between groups regarding education level, tinnitus-related hearing disorders (MD, hyperacusis), sleep difficulties, dyslipidemia, and other tinnitus characteristics, including duration, type of onset, the report of mitigating factors and the use of treatments. However, these differences were partially confirmed after adjusting for age. CONCLUSIONS: Self-reported tinnitus surveys are a low confidence source for tinnitus phenotyping. Additional clinical evaluation is needed for tinnitus research to reach the diagnosis. Age-based cluster analysis might help to better define clinical profiles and to compare responses in ESIT-SQ among subgroups of patients with tinnitus.

Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users.

Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.

Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment.

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients' characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient.The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature.The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level.In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection.

Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI.

Tinnitus is the perception of a phantom sound and the patient's reaction to it. Although much progress has been made, tinnitus remains a scientific and clinical enigma of high prevalence and high economic burden, with an estimated prevalence of 10%-20% among the adult population. The EU is funding a new collaborative project entitled "Unification of Treatments and Interventions for Tinnitus Patients" (UNITI, grant no. 848261) under its Horizon 2020 framework. The main goal of the UNITI project is to set the ground for a predictive computational model based on existing and longitudinal data attempting to address the question of which treatment or combination of treatments is optimal for a specific patient group based on certain parameters. Clinical, epidemiological, genetic and audiological data, including signals reflecting ear-brain communication, as well as patients' medical history, will be analyzed making use of existing databases. Predictive factors for different patient groups will be extracted and their prognostic relevance validated through a Randomized Clinical Trial (RCT) in which different patient groups will undergo a combination of tinnitus therapies targeting both auditory and central nervous systems. From a scientific point of view, the UNITI project can be summarized into the following research goals: (1) Analysis of existing data: Results of existing clinical studies will be analyzed to identify subgroups of patients with specific treatment responses and to identify systematic differences between the patient groups at the participating clinical centers. (2) Genetic and blood biomarker analysis: High throughput Whole Exome Sequencing (WES) will be performed in well-characterized chronic tinnitus cases, together with Proximity Extension Assays (PEA) for the identification of blood biomarkers for tinnitus. (3) RCT: A total of 500 patients will be recruited at five clinical centers across Europe comparing single treatments against combinational treatments. The four main treatments are Cognitive Behavioral Therapy (CBT), hearing aids, sound stimulation, and structured counseling. The consortium will also make use of e/m-health applications for the treatment and assessment of tinnitus. (4) Decision Support System: An innovative Decision Support System will be implemented, integrating all available parameters (epidemiological, clinical, audiometry, genetics, socioeconomic and medical history) to suggest specific examinations and the optimal intervention strategy based on the collected data. (5) Financial estimation analysis: A cost-effectiveness analysis for the respective interventions will be calculated to investigate the economic effects of the interventions based on quality-adjusted life years. In this paper, we will present the UNITI project, the scientific questions that it aims to address, the research consortium, and the organizational structure.

The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications.

Chronic tinnitus, the perception of a phantom sound in the absence of corresponding stimulus, is a condition known to affect patients' quality of life. Recent advances in mHealth have enabled patients to maintain a 'disease journal' of ecologically-valid momentary assessments, improving patients' own awareness of their disease while also providing clinicians valuable data for research. In this study, we investigate the effect of non-personalised tips on patients' perception of tinnitus, and on their continued use of the application. The data collected from the study involved three groups of patients that used the app for 16 weeks. Groups A & Y were exposed to feedback from the start of the study, while group B only received tips for the second half of the study. Groups A and Y were run by different supervisors and also differed in the number of hospital visits during the study. Users of Group A and B underwent assessment at baseline, mid-study, post-study and follow-up, while users of group Y were only assessed at baseline and post-study. It is seen that the users in group B use the app for longer, and also more often during the day. The answers of the users to the Ecological Momentary Assessments are seen to form clusters where the degree to which the tinnitus distress depends on tinnitus loudness varies. Additionally, cluster-level models were able to predict new unseen data with better accuracy than a single global model. This strengthens the argument that the discovered clusters really do reflect underlying patterns in disease expression.

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.