{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:41:23Z","timestamp":1769942483934,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users\u2019 individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.<\/jats:p>","DOI":"10.3390\/s22010170","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T01:20:43Z","timestamp":1640654443000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0657-3232","authenticated-orcid":false,"given":"Robin","family":"Kraft","sequence":"first","affiliation":[{"name":"Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany"},{"name":"Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2536-4153","authenticated-orcid":false,"given":"Manfred","family":"Reichert","sequence":"additional","affiliation":[{"name":"Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1522-785X","authenticated-orcid":false,"given":"R\u00fcdiger","family":"Pryss","sequence":"additional","affiliation":[{"name":"Institute of Clinical Epidemiology and Biometry, University of W\u00fcrzburg, 97078 W\u00fcrzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.3389\/fnins.2020.00164","article-title":"Combining mobile crowdsensing and ecological momentary assessments in the healthcare domain","volume":"14","author":"Kraft","year":"2020","journal-title":"Front. 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