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It remains unknown whether de-identifying wearable biometric monitoring data is sufficient to protect the privacy of individuals in the dataset. This systematic review seeks to shed light on this. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on December 6, 2021 (PROSPERO CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Though our search strategy had no language restrictions, all retrieved studies were in English. We included studies demonstrating re-identification, identification, or authentication using data from wearables. Our search returned 17,625 studies, and 72 studies met our inclusion criteria. Our findings demonstrate that substantial re-identification risk exists in data from sensors generally not thought to generate identifiable information, such as the electrocardiogram and electromyogram. In many cases, only a small amount of data (1-300 seconds of recording) is sufficient for re-identification.

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Background Effective monitoring of dietary habits is critical for promoting healthy lifestyles and preventing or delaying the onset and progression of diet-related diseases, such as type 2 diabetes. Recent advances in speech recognition technologies and natural language processing present new possibilities for automated diet capture; however, further exploration is necessary to assess the usability and acceptability of such technologies for diet logging. Objective This study explores the usability and acceptability of speech recognition technologies and natural language processing for automated diet logging. Methods We designed and developed base2Diet—an iOS smartphone application that prompts users to log their food intake using voice or text. To compare the effectiveness of the 2 diet logging modes, we conducted a 28-day pilot study with 2 arms and 2 phases. A total of 18 participants were included in the study, with 9 participants in each arm (text: n=9, voice: n=9). During phase I of the study, all 18 participants received reminders for breakfast, lunch, and dinner at preselected times. At the beginning of phase II, all participants were given the option to choose 3 times during the day to receive 3 times daily reminders to log their food intake for the remainder of the phase, with the ability to modify the selected times at any point before the end of the study. Results The total number of distinct diet logging events per participant was 1.7 times higher in the voice arm than in the text arm (P=.03, unpaired t test). Similarly, the total number of active days per participant was 1.5 times higher in the voice arm than in the text arm (P=.04, unpaired t test). Furthermore, the text arm had a higher attrition rate than the voice arm, with only 1 participant dropping out of the study in the voice arm, while 5 participants dropped out in the text arm. Conclusions The results of this pilot study demonstrate the potential of voice technologies in automated diet capturing using smartphones. Our findings suggest that voice-based diet logging is more effective and better received by users compared to traditional text-based methods, underscoring the need for further research in this area. These insights carry significant implications for the development of more effective and accessible tools for monitoring dietary habits and promoting healthy lifestyle choices.

BACKGROUND Effective monitoring of dietary habits is critical for promoting healthy lifestyles and preventing or delaying the onset and progression of diet-related diseases, such as type 2 diabetes. Recent advances in speech recognition technologies and natural language processing present new possibilities for automated diet capture; however, further exploration is necessary to assess the usability and acceptability of such technologies for diet logging. OBJECTIVE This study explores the usability and acceptability of speech recognition technologies and natural language processing for automated diet logging. METHODS We designed and developed base2Diet—an iOS smartphone application that prompts users to log their food intake using voice or text. To compare the effectiveness of the 2 diet logging modes, we conducted a 28-day pilot study with 2 arms and 2 phases. A total of 18 participants were included in the study, with 9 participants in each arm (text: n=9, voice: n=9). During phase I of the study, all 18 participants received reminders for breakfast, lunch, and dinner at preselected times. At the beginning of phase II, all participants were given the option to choose 3 times during the day to receive 3 times daily reminders to log their food intake for the remainder of the phase, with the ability to modify the selected times at any point before the end of the study. RESULTS The total number of distinct diet logging events per participant was 1.7 times higher in the voice arm than in the text arm (<i>P</i>=.03, unpaired <i>t</i> test). Similarly, the total number of active days per participant was 1.5 times higher in the voice arm than in the text arm (<i>P</i>=.04, unpaired <i>t</i> test). Furthermore, the text arm had a higher attrition rate than the voice arm, with only 1 participant dropping out of the study in the voice arm, while 5 participants dropped out in the text arm. CONCLUSIONS The results of this pilot study demonstrate the potential of voice technologies in automated diet capturing using smartphones. Our findings suggest that voice-based diet logging is more effective and better received by users compared to traditional text-based methods, underscoring the need for further research in this area. These insights carry significant implications for the development of more effective and accessible tools for monitoring dietary habits and promoting healthy lifestyle choices.