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Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. However, knowledge about the effects of the long-term provision of health-aware recommendations in real-life situations is limited. This study investigates the impact of a mobile, personalized recommender system named Nutrilize. Our system offers automated personalized visual feedback and recommendations based on individual dietary behaviour, phenotype, and preferences. By using quantitative and qualitative measures of 34 participants during a study of 2\u20133\u00a0months, we provide a deeper understanding of how our nutrition application affects the users\u2019 physique, nutrition behaviour, system interactions and system perception. Our results show that Nutrilize positively affects nutritional behaviour (conditional <jats:inline-formula><jats:alternatives><jats:tex-math>$$R^2=.342$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mi>R<\/mml:mi>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mo>.<\/mml:mo>\n                    <mml:mn>342<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) measured by the optimal intake of each nutrient. The analysis of different application features shows that reflective visual feedback has a more substantial impact on healthy behaviour than the recommender (conditional <jats:inline-formula><jats:alternatives><jats:tex-math>$$R^2=.354$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mi>R<\/mml:mi>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mo>.<\/mml:mo>\n                    <mml:mn>354<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>). We further identify system limitations influencing this result, such as a lack of diversity, mistrust in healthiness and personalization, real-life contexts, and personal user characteristics with a qualitative analysis of semi-structured in-depth interviews. Finally, we discuss general knowledge acquired on the design of personalized mobile nutrition recommendations by identifying important factors, such as the users\u2019 acceptance of the recommender\u2019s taste, health, and personalization.\n<\/jats:p>","DOI":"10.1007\/s11257-021-09301-y","type":"journal-article","created":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T16:40:41Z","timestamp":1634316041000},"page":"923-975","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6840-5341","authenticated-orcid":false,"given":"Hanna","family":"Hauptmann","sequence":"first","affiliation":[]},{"given":"Nadja","family":"Leipold","sequence":"additional","affiliation":[]},{"given":"Mira","family":"Madenach","sequence":"additional","affiliation":[]},{"given":"Monika","family":"Wintergerst","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Lurz","sequence":"additional","affiliation":[]},{"given":"Georg","family":"Groh","sequence":"additional","affiliation":[]},{"given":"Markus","family":"B\u00f6hm","sequence":"additional","affiliation":[]},{"given":"Kurt","family":"Gedrich","sequence":"additional","affiliation":[]},{"given":"Helmut","family":"Krcmar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"9301_CR1","unstructured":"Achananuparp, P., Weber, I.: Extracting food substitutes from food diary via distributional similarity (2016). arXiv preprint arXiv:1607.08807"},{"key":"9301_CR2","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1007\/978-3-319-91800-6_9","volume-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"M Alrige","year":"2018","unstructured":"Alrige, M., Chatterjee, S.: Easy nutrition: a customized dietary app to highlight the food nutritional value. 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