@article{PrakashUnnikrishnanPryssetal.2021,
  author    = {Prakash, Subash and Unnikrishnan, Vishnu and Pryss, R{\"u}diger and Kraft, Robin and Schobel, Johannes and Hannemann, Ronny and Langguth, Berthold and Schlee, Winfried and Spiliopoulou, Myra},
  title     = {Interactive system for similarity-based inspection and assessment of the well-being of mHealth users},
  series = {Entropy},
  volume    = {23},
  journal   = {Entropy},
  number    = {12},
  issn      = {1099-4300},
  doi       = {10.3390/e23121695},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252333},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{UnnikrishnanSchleicherShahetal.2020,
  author    = {Unnikrishnan, Vishnu and Schleicher, Miro and Shah, Yash and Jamaludeen, Noor and Pryss, Ruediger and Schobel, Johannes and Kraft, Robin and Schlee, Winfried and Spiliopoulou, Myra},
  title     = {The effect of non-personalised tips on the continued use of self-monitoring mHealth applications},
  series = {Brain Sciences},
  volume    = {10},
  journal   = {Brain Sciences},
  number    = {12},
  issn      = {2076-3425},
  doi       = {10.3390/brainsci10120924},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219435},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}