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BACKGROUND: It is recommended that children should avoid eating dinner, being physically active, or using screens in the hour before bed to ensure good sleep health. However, the evidence base behind these guidelines is weak and limited to cross-sectional studies using questionnaires. OBJECTIVE: The aim of this randomized crossover trial was to use objective measures to experimentally determine whether recommendations to improve sleep by banning electronic media, physical activity, or food intake in the hour before bed, impact sleep quantity and quality in the youth. METHODS: After a baseline week to assess usual behavior, 72 children (10-14.9 years old) will be randomized to four conditions, which are (1) avoid all 3 behaviors, (2) use screens for at least 30 minutes, (3) be physically active for at least 30 minutes, and (4) eat a large meal, during the hour before bed on days 5 to 7 of weeks 2 to 5. Families can choose which days of the week they undertake the intervention, but they must be the same days for each intervention week. Guidance on how to undertake each intervention will be provided. Interventions will only be undertaken during the school term to avoid known changes in sleep during school holidays. Intervention adherence and shuteye latency (time from getting into bed until attempting sleep) will be measured by wearable and stationary PatrolEyes video cameras (StuntCams). Sleep (total sleep time, sleep onset, and wake after sleep onset) will be measured using actigraphy (baseline, days 5 to 7 of each intervention week). Mixed effects regression models with a random effect for participants will be used to estimate mean differences (95% CI) for conditions 2 to 4 compared with condition 1. RESULTS: Recruitment started in March 2024, and is anticipated to finish in April 2025. Following data analysis, we expect that results will be available later in 2026. CONCLUSIONS: Using objective measures, we will be able to establish if causal relationships exist between prebedtime behaviors and sleep in children. Such information is critical to ensure appropriate and achievable sleep guidelines. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12624000206527; https://tinyurl.com/3kcjmfnj. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63692.

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Unlabelled: To assess the utility of wearable cameras in medical examinations, we created a physician-view video-based examination question and explanation, and the survey results indicated that these cameras can enhance the evaluation and educational capabilities of medical examinations.

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A small wearable camera, SenseCam, passively captured pictures from everyday experience that were later used to evaluate the accuracy and completeness of autobiographical memory. Nine undergraduates wore SenseCams that took pictures every 10 s for two days. After one week and one month, participants first recalled their experiences from specific time periods (timeslices), then reviewed the corresponding pictures to make corrections and report information omitted from initial recall. Results demonstrated the utility of wearable cameras as research tools, and illustrated several characteristics of everyday memory. Recall contents reflected the structure of undergraduate lives. Three different types of omissions were reported: neglected, reminded, and forgotten. Pictures stimulated memory, even for non-visual information (e.g., feelings, thoughts), increasing recall by 23%. The mean completeness of initial recall was 79% (upper bound), with at least 21% forgetting. Accuracy was self-scored by participants (M = 89%), and the mean error rate (11%) provided evidence against strong reconstructive and copy theories of memory. The characteristics of errors shed light on the cognitive processes underlying them. Ratings of recall (confidence, reliving, knowledge, and frequency) supported the episodic/semantic distinction, the dual-process theory of repetition, and reconstructive imagery. Metamemory measures showed a positive correlation between confidence and accuracy.

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BACKGROUND: Globally, heart failure (HF) affects more than 64 million people, and attempts to reduce its social and economic burden are a public health priority. Interventions to support people with HF to self-manage have been shown to reduce hospitalizations, improve quality of life, and reduce mortality rates. Understanding how people self-manage is imperative to improve future interventions; however, most approaches to date, have used self-report methods to achieve this. Wearable cameras provide a unique tool to understand the lived experiences of people with HF and the daily activities they undertake, which could lead to more effective interventions. However, their potential for understanding chronic conditions such as HF is unclear. OBJECTIVE: This study aimed to determine the potential utility of wearable cameras to better understand the activities of daily living in people living with HF. METHODS: The "Seeing is Believing (SIB)" study involved 30 patients with HF who wore wearable cameras for a maximum of 30 days. We used the E-Myscéal web-based lifelog retrieval system to process and analyze the wearable camera image data set. Search terms for 7 daily activities (physical activity, gardening, shopping, screen time, drinking, eating, and medication intake) were developed and used for image retrieval. Sensitivity analysis was conducted to compare the number of images retrieved using different search terms. Temporal patterns in daily activities were examined, and differences before and after hospitalization were assessed. RESULTS: E-Myscéal exhibited sensitivity to specific search terms, leading to significant variations in the number of images retrieved for each activity. The highest number of images returned were related to eating and drinking, with fewer images for physical activity, screen time, and taking medication. The majority of captured activities occurred before midday. Notably, temporal differences in daily activity patterns were observed for participants hospitalized during this study. The number of medication images increased after hospital discharge, while screen time images decreased. CONCLUSIONS: Wearable cameras offer valuable insights into daily activities and self-management in people living with HF. E-Myscéal efficiently retrieves relevant images, but search term sensitivity underscores the need for careful selection.

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Background: Previous physical activity and sedentary behaviour studies during the pandemic have largely utilized online surveys, with known limitations including recall bias. Employing both device-based and self-reported measurements may provide a more comprehensive picture of both behaviours. Physical activity and sedentary behaviour research in adolescents is still limited in low- and middle-income countries (LMICs), including Indonesia. Male adolescents had been identified as more active than females but have had a greater decrease in physical activity during the pandemic. The present study aimed to investigate the quantity, temporal patterns, contexts, and biopsychosocial factors of physical activity and sedentary behaviour during the COVID-19 pandemic in a small group of male Indonesian adolescents. Methods: Male adolescents (n = 5; 14-15 years old) from Yogyakarta wore accelerometers and automated wearable cameras for four days, and completed diaries and interviews in November 2020. Results: Participants' activity was dominated by light intensity (67% of all physical activity). Sedentary behaviour was high; accelerometer, school days: 456 ± 145 min (78 ± 10% of wear time), non-school days: 344 ± 160 min (79 ± 17% of wear time); camera, school days: 176 ± 101 min (81 ± 46% of wear time), non-school days: 210 ± 165 min (86 ± 67% of wear time). Sedentary behaviour was mainly done during school hours on school days and from late afternoon to evening on non-school days. Screen time was largely for leisure purposes and action games were most favoured. Smartphones were the most used device, mainly used in a solitary context in the bedroom. Non-screen-based sedentary behaviour was consistently low. Interviews suggested that during the pandemic, supporting factors for physical activity are: self-determination, enjoyment, parental support, and physical education; meanwhile, factors influencing screen time are: educational demands, device and internet availability, screen time opportunities, parental control, social facilitators, phone notifications, and emotional state. Conclusions: Most participants were not able to stay active during the pandemic. Using digital platforms may be beneficial to shift some screen-based sedentary behaviour to 'screen-based' or 'screen-prompted' physical activity. Supplementary Information: The online version contains supplementary material available at 10.1186/s44167-022-00014-0.

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BACKGROUND: Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs. METHODS: Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records. RESULTS: The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. CONCLUSIONS: Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.

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Recording a video scene as seen by an observer, materializing where is focused his visual attention and allowing an external person to point at a given object in this scene, could be beneficial for various applications such as medical education or remote training. Such a versatile device, although tested at the experimental laboratory demonstrator stage, has never been integrated in a compact and portable way in a real environment. In this context, we built a low-cost, light-weight, head-mounted device integrating a miniature camera and a laser pointer that can be remotely controlled or servo-controlled by an eye tracker. Two motorizations were implemented and tested (pan/tilt and Rilsey-prisms-based). The video was both recorded locally and transmitted wirelessly. Risley prisms allowed finer remote control of camera or laser pointer orientation (0.1° vs. 0.35°), but data processing and Wi-Fi transmission incur significant latency (~0.5 s) limiting the servo-controlling by eye movements. The laser beam was spatially shaped by a Diffractive Optical Element to facilitate object illumination or recognition. With this first proof-of-concept prototype, the data stream needs to be optimized to make full use of the eye tracker, but this versatile device can find various applications in education, healthcare or research.

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Wearable Cameras (WCs) enable researchers to capture objective descriptions of what participants see and experience as they go about their normal lives. When studying interactions between individuals (e.g. between a parent and child), using multiple WCs can provide highly detailed descriptions of interactions with levels of ecological validity not possible with other methods. However, the use of WCs brings challenges too, and understanding these is key to developing and optimising these methods. We captured the challenges experienced by a variety of stakeholders, namely parents and a range of different researcher roles (academics, field-workers and data processors) involved in a large UK study exploring parent-child interactions using low-cost, off-the-shelf WCs. High among the challenges identified were difficulties caused when subjects are temporarily not in view in the video footage captured. This and other factors identified were used as criteria to select a new, improved WC. The new WCs reduced the time faces were not in view by 75%. We report this and the other challenges identified, and suggest how these can be used to guide and help optimise future studies of this kind.

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Autobiographical memory (AM) impairments influence both sense of identity and social functioning of patients with schizophrenia. However, cognitive remediation methods addressing these difficulties do not sufficiently consider the heterogeneity of this disorder and frequently face methodological limitations. The aim of the present study was to evaluate the efficacy of a method using a wearable camera (NarrativeClip®), through an alternating treatments design across two types of AM training. In parallel, repeated measures were used to appreciate the efficacy, specificity, and generalizability of the programme's benefits. Three patients were invited to wear the camera during 24 personal events. Ten of these events memories were trained by visual cueing (wearable camera condition), 10 others by verbal cueing (written diary condition) and 4 were not trained (control condition). Using pictures collected by the wearable camera seemed particularly relevant, since it promoted more detailed recalls than the diary method, from the first training session and until the end of a one-year follow-up. In addition, the repeated measures performed revealed (1) the efficacy (improvement in AM capacities after participating in the programme), (2) specificity (persistence of working memory deficits), and (3) generalizability (improvement in measures of episodic memory) of our cognitive remediation programme's effects.

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Automated detection and validation of fine-grained human activities from egocentric vision has gained increased attention in recent years due to the rich information afforded by RGB images. However, it is not easy to discern how much rich information is necessary to detect the activity of interest reliably. Localization of hands and objects in the image has proven helpful to distinguishing between hand-related fine-grained activities. This paper describes the design of a hand-object-based mask obfuscation method (HOBM) and assesses its effect on automated recognition of fine-grained human activities. HOBM masks all pixels other than the hand and object in-hand, improving the protection of personal user information (PUI). We test a deep learning model trained with and without obfuscation using a public egocentric activity dataset with 86 class labels and achieve almost similar classification accuracies (2% decrease with obfuscation). Our findings show that it is possible to protect PUI at smaller image utility costs (loss of accuracy).

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Screen time is associated with several health risk behaviors including mindless eating, sedentary behavior, and decreased academic performance. Screen time behavior is traditionally assessed with self-report measures, which are known to be burdensome, inaccurate, and imprecise. Recent methods to automatically detect screen time are geared more towards detecting television screens from wearable cameras that record high-resolution video. Activity-oriented wearable cameras (i.e., cameras oriented towards the wearer with a fisheye lens) have recently been designed and shown to reduce privacy concerns, yet pose a greater challenge in capturing screens due to their orientation and fewer pixels on target. Methods that detect screens from low-power, low-resolution wearable camera video are needed given the increased adoption of such devices in longitudinal studies. We propose a method that leverages deep learning algorithms and lower-resolution images from an activity-oriented camera to detect screen presence from multiple types of screens with high variability of pixel on target (e.g., near and far TV, smartphones, laptops, and tablets). We test our system in a real-world study comprising 10 individuals, 80 hours of data, and 1.2 million low-resolution RGB frames. Our results outperform existing state-of-the-art video screen detection methods yielding an F1-score of 81%. This paper demonstrates the potential for detecting screen-watching behavior in longitudinal studies using activity-oriented cameras, paving the way for a nuanced understanding of screen time's relationship with health risk behaviors.

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Studies have shown that young adults follow less structured eating patterns compared with older cohorts. This may have implications for dietary assessment methods which rely on memory and structured meal patterns. Our aim was to describe the intra-individual variation of eating times in young adults aged 18−30 years. Participants (n = 41) wore an Autographer camera that captured first-person perspective images every 30 s for three consecutive days. All images were timestamped and those showing food consumption were used to extract data such as the timing of the first and last eating occasions (EOs), number of EOs per day, and length of eating window. Intra-individual variability was calculated from these data using composite phase deviation (CPD) and coefficient of variation (CV). The number of individuals with high or very high variability was 28 and 18 for timing of first and last EOs, respectively (CPD > 1.70), and 27 and 17 for number of EOs and eating window, respectively (CV > 20%). In this sample of young adults, the lack of regularity in eating patterns should be considered when selecting a dietary assessment method.

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PURPOSE: Most stroke survivors have remaining upper limb impairment six months after stroke and require additional rehabilitation and help from family members to enhance their performance of daily activities. First-person (egocentric) video has been proposed to capture the activities of daily living (ADLs) of stroke survivors in order to assess their hand function at home. This study explored the experiences and expectations of stroke survivors regarding the use of egocentric cameras in daily life for rehabilitation applications. METHODS: Twenty-one chronic stroke survivors recruited for the study were asked to record three sessions of 1.5 h of video of their ADLs at home over two weeks. Their experiences and expectations after completing the recordings were discussed using a structured questionnaire and a semi-structured interview. The questionnaire and interview data were analysed using descriptive statistics and content analysis, respectively. The results were further integrated using a mixed methods analysis for mutual explanation and elaboration. RESULTS: The themes generated were Camera Usability, Privacy Concerns Related to Home Recordings, Future Use of the Camera in Public, and Information Usefulness. The participants perceived that the camera was easy to use, the information obtained from the recordings was beneficial, and no major concerns about recording at home. A discreet camera and a solution to privacy issues were prerequisites to recording tasks in public. CONCLUSIONS: There was high acceptance among stroke survivors regarding the use of wearable cameras for rehabilitation purposes in the future. Concerns to be managed include discomfort, self-consciousness, and the privacy of others.Implications for rehabilitationThe egocentric camera was easy for the stroke survivors to use at home. However, they expressed a preference for cameras to be less noticeable and lighter in the future to minimize self-consciousness and discomfort.Expectations for future use of an egocentric camera for upper limb rehabilitation at home from the perspectives of stroke survivors included receiving feedback on their hand function in daily life and guidance on how to improve function.Privacy concerns of stroke survivors regarding recording activities of daily living were mostly avoidable by planning in advance. However, some personal hygiene tasks and virtual meetings were recorded by accident. A checklist of common activities that may raise privacy issues can be provided along with the camera to serve as a reminder to avoid these issues.

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Traditional methods of dietary assessment are prone to measurement error, with energy intake often under-reported. The 24-h recall is widely used in dietary assessment, however, its reliance on self-report without verification of consumption can result in inaccuracies in true nutrient intake. Wearable cameras may provide a complementary approach to improve self-report accuracy by providing an objective and passive measure of food consumption. The purpose of the present study was to determine whether a wearable camera improves the accuracy of a 24-h recall compared with a 24-h recall alone in twenty adults aged 18-65 years. The study also explored limitations associated with wearable cameras. Participants wore the camera for 1 d and a 24-h recall was then conducted the following day, before and after viewing the camera images. Dietary data were analysed using Nutritics dietary analysis software, while eating habits were assessed by a self-report questionnaire. Energy and nutrient intakes were compared between the recall alone and the camera-assisted recall. Results showed a significant increase in mean energy intake with the camera-assisted recall compared with the recall alone (9677⋅8 ± 2708⋅0 kJ/d v. 9304⋅6 ± 2588⋅5 kJ/d, respectively, P = 0⋅003). Intakes of carbohydrates, total sugars and saturated fats were also significantly higher with the camera-assisted recall. In terms of challenges, there were occasionally technological issues such as proper positioning of the camera by the participants. In conclusion, reporting of energy and nutrient intake may be enhanced when a traditional method of dietary assessment, the 24-h recall, is assisted by a wearable camera.

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Automated, wearable cameras can benefit health-related research by capturing accurate and objective information about individuals' daily experiences. However, wearable cameras present unique privacy- and confidentiality-related risks due to the possibility of the images capturing identifying or sensitive information from participants and third parties. Although best practice guidelines for ethical research with wearable cameras have been published, limited information exists on the risks of studies using wearable cameras. The aim of this literature review was to survey risks related to using wearable cameras, and precautions taken to reduce those risks, as reported in empirical research. Forty-five publications, comprising 36 independent studies, were reviewed, and findings revealed that participants' primary concerns with using wearable cameras included physical inconvenience and discomfort in certain situations (e.g., public settings). None of the studies reviewed reported any serious adverse events. Although it is possible that reported findings do not include all risks experienced by participants in research with wearable cameras, our findings suggest a low level of risk to participants. However, it is important that investigators adopt recommended precautions, which can promote autonomy and reduce risks, including participant discomfort.

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Wearable cameras offer an innovative way to discover new insights into the lived experience of people with cardiovascular conditions. Wearable cameras can be used alone or supplement more traditional research methods, such as interviews and participant observations. This paper provides an overview of the benefits of using wearable cameras for data collection and outlines some key considerations for researchers and clinicians interested in this method. We provide a case study describing a study design using wearable cameras and how the data were used.

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OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically. MATERIALS AND METHODS: We propose a deep learning approach to classify activities of patient work collected from wearable cameras, thereby studying self-management routines more effectively. Twenty-six people with type 2 diabetes and comorbidities wore a wearable camera for a day, generating more than 400 h of video across 12 daily activities. To classify these video images, a weighted ensemble network that combines Linear Discriminant Analysis, Deep Convolutional Neural Networks, and Object Detection algorithms is developed. Performance of our model is assessed using Top-1 and Top-5 metrics, compared against manual classification conducted by 2 independent researchers. RESULTS: Across 12 daily activities, our model achieved on average the best Top-1 and Top-5 scores of 81.9 and 86.8, respectively. Our model also outperformed other non-ensemble techniques in terms of Top-1 and Top-5 scores for most activity classes, demonstrating the superiority of leveraging weighted ensemble techniques. CONCLUSIONS: Deep learning can be used to automatically classify daily activities of patient work collected from wearable cameras with high levels of accuracy. Using wearable cameras and a deep learning approach can offer an alternative approach to investigate patient work, one not subjected to biases commonly associated with self-report methods.

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Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women's time use in rural Eastern Ugandan. Women's (n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland-Altman limits of agreement (LOA) method of analysis and Cronbach's coefficient alpha (time allocation) or Cohen's κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (-1.1 to 3.7) and AWC-IAR (-3.2 to 3.2). Cronbach's alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women's time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women's time allocations and nutrition outcomes.

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Background/Objective: Exploring comprehensive information on the duration, pattern and context of physical activity and sedentary behaviour is important to develop effective policies and interventions. Especially in lower- and middle-income countries, our understanding of these health-behaviours is limited. Our study aimed to investigate physical activity and sedentary behaviour of female Indonesian adolescents by using a multi-method approach. Methods: Female adolescents (n = 5; 13-15 years old) from Yogyakarta, Indonesia wore accelerometers and automated wearable cameras for four days, and completed diaries, and interviews between February and March 2020. Results: Participants' activity, especially on non-school days, was dominated by light-intensity physical activity. Four of the 5 participants did not meet the physical activity guidelines. Participants spent a great proportion of time on screen-based sedentary behaviour (school days: 83.2% of wear time; non-school days: 75.7% of wear time). During school days, most physical activity and sedentary behaviour was done at school. Screen time was mainly done on the school day evenings and weekend mornings. Participants mostly used smartphones in the bedroom and living room in a solitary environment. Interviews suggest that the high amount of screen time seemed to be influenced by a lack of awareness of current guidelines, the feeling of urgency to check information, and the lack of parental supervision. Non-screen-based sedentary behaviour comprised just over 10% of total camera images. Conclusion: The use of a multi-method approach facilitated a rich understanding of the duration, patterns, and contexts of physical activity and sedentary behaviour in participants. Future studies might consider using similar methods in a larger sample.

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Understanding cattle behavior is important for discerning their health and management status. However, manual observations of cattle are time-consuming and labor-intensive. Moreover, during manual observations, the presence or position of a human observer may alter the normal behavior of the cattle. Wearable cameras are small and lightweight; therefore, they do not disturb cattle behavior when attached to their bodies. Thus, this study aimed to evaluate the suitability of wearable cameras for monitoring and analyzing cattle behavior. From December 18 to 27, 2017, this study used four 2-month-old, group-housed Holstein calves at the Field Science Center of the Obihiro University of Agriculture and Veterinary Medicine, Japan. Calf behavior was recorded every 30 s using a wearable camera (HX-A1H, Panasonic, Japan) from 10:00 to 15:30 and observed directly from 11:00 to 12:00 and 14:00 to 15:00. In addition, the same observer viewed the camera recordings corresponding to the direct observation periods, and the results were compared. The correlation coefficients of all behavioral data from direct and wearable camera video observations were significant (p < 0.01). We conclude that wearable cameras are suitable for observing calf behavior, particularly their posture (standing or lying), as well as their ruminating and feeding behaviors.