Gender-Specific Differences in Patients With Chronic Tinnitus - Baseline Characteristics and Treatment Effects

Study examining (1) how male and female tinnitus patients differ, (2) the extent to which gender is related to (tinnitus) questionnaire responses, and (3) which baseline questionnaire items are related to tinnitus distress, depression, and treatment response, only one gender.

Paper authors: Uli Niemann, Benjamin Boecking, Petra Brueggemann, Birgit Mazurek, and Myra Spiliopoulou

Frontiers in Neuroscience

By Uli Niemann in Research Tinnitus

May 25, 2020


Whilst some studies have identified gender-specific differences, there is no consensus about gender-specific determinants for prevalence rates or concomitant symptoms of chronic tinnitus such as depression or anxiety. However, gender-associated differences in psychological response profiles and coping strategies may differentially affect tinnitus chronification and treatment success rates. Thus, understanding gender-associated differences may facilitate a more detailed identification of symptom profiles, heighten treatment response rates, and help to create access for vulnerable populations that are potentially less visible in clinical settings. Our research questions are: RQ1: how do male and female tinnitus patients differ regarding tinnitus-related distress, depression severity, and treatment response, RQ2: to what extent are answers to questionnaires administered at baseline associated with gender, and RQ3: which baseline questionnaire items are associated with tinnitus distress, depression, and treatment response, while relating to one gender only? In this work, we present a data analysis workflow to investigate gender-specific differences in N = 1,628 patients with chronic tinnitus (828 female, 800 male) who completed a 7-day multimodal treatment encompassing cognitive behavioral therapy (CBT), physiotherapy, auditory attention training, and information counseling components. For this purpose, we extracted 181 variables from 7 self-report questionnaires on socio-demographics, tinnitus-related distress, tinnitus frequency, loudness, localization, and quality as well as physical and mental health status. Our workflow comprises (i) training machine learning models, (ii) a comprehensive evaluation including hyperparameter optimization, and (iii) post-learning steps to identify predictive variables. We found that female patients reported higher levels of tinnitus-related distress, depression and response to treatment (RQ1). Female patients indicated higher levels of tension, stress, and psychological coping strategies rates. By contrast, male patients reported higher levels of bodily pain associated with chronic tinnitus whilst judging their overall health as better (RQ2). Variables measuring depression, sleep problems, tinnitus frequency, and loudness were associated with tinnitus-related distress in both genders and indicators of mental health and subjective stress were found to be associated with depression in both genders (RQ3). Our results suggest that gender-associated differences in symptomatology and treatment response profiles suggest clinical and conceptual needs for differential diagnostics, case conceptualization and treatment pathways.

Important figure

Figure 3. Top-8 variables on gender (LT1). Gender-stratified item answer frequencies for the top-5% variables with the highest attribution toward model prediction quality according to model reliance (MR).

Predictive variables for gender.

BibTeX citation

  author    = {Niemann, Uli and Boecking, Benjamin and Brueggemann, Petra and Mazurek, Birgit and Spiliopoulou, Myra},
  journal   = {Frontiers in Neuroscience},
  title     = {{Gender-Specific Differences in Patients With Chronic Tinnitus \textemdash{} Baseline Characteristics and Treatment Effects}},
  year      = {2020},
  issn      = {1662-453X},
  number    = {487},
  pages     = {1--11},
  volume    = {14},
  doi       = {10.3389/fnins.2020.00487},
  url       = {},
Posted on:
May 25, 2020
3 minute read, 471 words
Research Tinnitus
Predictive Modeling Explainable AI
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