{"id":950,"date":"2025-03-19T09:44:22","date_gmt":"2025-03-19T09:44:22","guid":{"rendered":"https:\/\/igormatias.com\/?p=950"},"modified":"2026-02-02T13:01:15","modified_gmt":"2026-02-02T13:01:15","slug":"use-of-personal-mobile-technologies-for-peer-based-assessment-of-stress-a-systematic-literature-review","status":"publish","type":"post","link":"https:\/\/igormatias.com\/pt\/2025\/03\/19\/use-of-personal-mobile-technologies-for-peer-based-assessment-of-stress-a-systematic-literature-review\/","title":{"rendered":"Use of personal mobile technologies for peer-based assessment of stress: a systematic literature review"},"content":{"rendered":"<p><span class=\"LabelSearchRepeater\">Abstract: <\/span>The use of personal mobile technologies has grown in recent years, providing a method for collecting high-frequency and high-quality data on human behaviors and states, amongst the others, on stress levels. Mobile technologies can play a significant role in peer-based stress assessment, particularly in e-mental health and well-being. It is accessible, convenient, and reliable compared to traditional self-report methods, making it a popular choice for collecting data. This systematic literature review aimed to explore the use of mobile technologies for peer-based assessment of stress. We analyzed existing literature to understand how mobile technologies have been used to assess stress levels through peer feedback\u2014from relatives, friends, or others with close and daily contact with the individual. The results of the review showed that mobile technologies have the potential to be a valuable tool for peer-based stress assessment, as they can provide real-time and convenient data collection. However, although its popularity has grown in recent years, it is worth noting that the use of paper and pen questionnaires has remained prevalent in peer-based stress assessment over the last decade. This indicates that there is still a need for further exploration and evaluation of the benefits and limitations of both methods.<\/p>\n<p>Antoine Bellanger<sup>1<\/sup>, Igor Matias<sup>1, 2<\/sup>, Katarzyna Wac<sup>1<\/sup><\/p>\n<p>[<sup>1<\/sup>Quality of Life Technologies Lab, <sup>2<\/sup>Cognitive Aging Lab], University of Geneva, Switzerland<\/p>\n<p><span id=\"ContentPlaceHolder1_LinkPaperPage_LinkPaperContent_AuxInProceeding\" class=\"PublicationsDetailNormal\">In EAI MobiCASE 2024.<\/span><\/p>\n<p>Article: <a href=\"https:\/\/doi.org\/10.4108\/eetpht.11.8941\">here<\/a><\/p>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Wearable devices are a useful and widely used source of continuous and temporal dependant data. In contrast to the traditional clinical environment, these devices allow time series data collection in an individual\u2019s daily living environ- ment. However, missing data can occur while using them. Many techniques have been applied to solve these data gaps; nonetheless, missing time series data poses extra challenges, such as maintaining the temporal dependency. In this article, we addressed the forecast of sleep trackers data (sleeping heart rate (HR) and time asleep) for 2 main reasons: (1) to design models capable of accurately forecasting missing data from those devices, and (2) to apply those models to empower sleep interventions that may increase its quality, by forecasting future sleep events. We collected wearables data over 290 days (per individual) from 12 participants using a smartwatch and made this dataset publicly available. We then explored several hyperparameters of 2 Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). We further elaborated and compared the performance of 3 approaches to training those RNNs. Although similar performance, slightly more accurate results were obtained after training a GRU network on an entire population\u2019s dataset, which was able to forecast the average, minimum, and maximum sleeping HR with a root-mean-squared error (RMSE) of 4.4 (\u00b1 1.4), 4.9 (\u00b1 2.6), and 12.1 (\u00b1 4.0) beats per minute, respectively. However, the total time asleep was impossible to forecast with low error.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11],"tags":[],"class_list":["post-950","post","type-post","status-publish","format-standard","hentry","category-conference"],"gutentor_comment":0,"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Use of personal mobile technologies for peer-based assessment of stress: a systematic literature review<\/title>\n<meta name=\"description\" content=\"Abstract: The use of personal mobile technologies has grown in recent years, providing a method for collecting high-frequency...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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