Background Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. Objective The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. Methods This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. Results A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. Conclusions Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

Background Greater adoption of wearable devices with multiple sensors may enhance personalized health monitoring, facilitate early detection of some diseases, and further scale up population health screening. However, few studies have explored the utility of data from wearable fitness trackers in cardiovascular and metabolic disease risk prediction. Objective This study aimed to investigate the associations between a range of activity metrics derived from a wearable consumer-grade fitness tracker and major modifiable biomarkers of cardiometabolic disease in a working-age population. Methods This was a cross-sectional study of 83 working adults. Participants wore Fitbit Charge 2 for 21 consecutive days and went through a health assessment, including fasting blood tests. The following clinical biomarkers were collected: BMI, waist circumference, waist-to-hip ratio, blood pressure, triglycerides (TGs), high-density lipoprotein (HDL) and low-density lipoprotein cholesterol, and blood glucose. We used a range of wearable-derived metrics based on steps, heart rate (HR), and energy expenditure, including measures of stability of circadian activity rhythms, sedentary time, and time spent at various intensities of physical activity. Spearman rank correlation was used for preliminary analysis. Multiple linear regression adjusted for potential confounders was used to determine the extent to which each metric of activity was associated with continuous clinical biomarkers. In addition, pairwise multiple regression was used to investigate the significance and mutual dependence of activity metrics when two or more of them had significant association with the same outcome from the previous step of the analysis. Results The participants were predominantly middle aged (mean age 44.3 years, SD 12), Chinese (62/83, 75%), and male (64/83, 77%). Blood biomarkers of cardiometabolic disease (HDL cholesterol and TGs) were significantly associated with steps-based activity metrics independently of age, gender, ethnicity, education, and shift work, whereas body composition biomarkers (BMI, waist circumference, and waist-to-hip ratio) were significantly associated with energy expenditure–based and HR-based metrics when adjusted for the same confounders. Steps-based interdaily stability of circadian activity rhythm was strongly associated with HDL (beta=5.4 per 10% change; 95% CI 1.8 to 9.0; P=.005) and TG (beta=−27.7 per 10% change; 95% CI −48.4 to −7.0; P=.01). Average daily steps were negatively associated with TG (beta=−6.8 per 1000 steps; 95% CI −13.0 to −0.6; P=.04). The difference between average HR and resting HR was significantly associated with BMI (beta=−.5; 95% CI −1.0 to −0.1; P=.01) and waist circumference (beta=−1.3; 95% CI −2.4 to −0.2; P=.03). Conclusions Wearable consumer-grade fitness trackers can provide acceptably accurate and meaningful information, which might be used in the risk prediction of cardiometabolic disease. Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health. Study findings should be further replicated with larger population studies.

It is estimated that in urbanized and industrialized countries people spend on average between 80% to 90% of their lives indoors. 1,2

IntroductionRapid advancements in technology and the ubiquity of personal mobile digital devices have brought forth innovative methods of acquiring healthcare data. Smartphones can capture vast amounts of data both passively through inbuilt sensors or connected devices and actively via user engagement. This scoping review aims to evaluate evidence to date on the use of passive digital sensing/phenotyping in assessment and prediction of mental health.Methods and analysisThe methodological framework proposed by Arksey and O’Malley will be used to conduct the review following the five-step process. A three-step search strategy will be used: (1) Initial limited search of online databases namely, MEDLINE for literature on digital phenotyping or sensing for key terms; (2) Comprehensive literature search using all identified keywords, across all relevant electronic databases: IEEE Xplore, MEDLINE, the Cochrane Database of Systematic Reviews, PubMed, the ACM Digital Library and Web of Science Core Collection (Science Citation Index Expanded and Social Sciences Citation Index), Scopus and (3) Snowballing approach using the reference and citing lists of all identified key conceptual papers and primary studies. Data will be charted and sorted using a thematic analysis approach.Ethics and disseminationThe findings from this systematic scoping review will be reported at scientific meetings and published in a peer-reviewed journal.

BackgroundThe rise of social media proved to be a fertile ground for the expansion of the acquired immune deficiency syndrome (AIDS)-denialist movement (in the form of online communities). While there is substantial literature devoted to disproving AIDS-denialist views, there is a lack of studies exploring AIDS-denialists online communities that interact with an external environment.ObjectiveWe explored three research areas: (1) reasons for newcomers to come to an AIDS-denialist community, (2) the patterns of interactions of the community with the newcomers, and (3) rhetorical strategies that denialists use for persuasion in the veracity of their views.MethodsWe studied the largest AIDS-denialist community on one of the most popular social networking services in Russia. We used netnography as a method for collecting data for qualitative analysis and observed the community for 9 months (at least 2-3 times a week). While doing netnography, we periodically downloaded community discussions. In total, we downloaded 4821 posts and comments for analysis. Grounded theory approach was used for data analysis.ResultsMost users came to the community for the following reasons: their stories did not fit the unitary picture of AIDS disease progression translated by popular medical discourse, health problems, concern about HIV-positive tests, and desire to dissuade community members from false AIDS beliefs. On the basis of strength in AIDS-denialist beliefs, we constructed a typology of the newcomers consisting of three ideal-typical groups: (1) convinced: those who already had become denialists before coming to the group, (2) doubters: those who were undecided about the truth of either human immunodeficiency virus (HIV) science theory or AIDS-denialist theory, and (3) orthodox: those who openly held HIV science views. Reception of a newcomer mainly depended on the newcomer’s belief status. Reception was very warm for the convinced, cold or slightly hostile for the doubters, and extremely hostile or derisive for the orthodox. We identified seven main rhetorical strategies of persuasion used by the denialists on the “undecided”.ConclusionsContrary to the widespread public health depiction of AIDS denialists as totally irrational, our study suggests that some of those who become AIDS denialists have sufficiently reasonable grounds to suspect that “something is wrong” with scientific theory, because their personal experience contradicts the unitary picture of AIDS disease progression. Odd and inexplicable practices of some AIDS centers only fuel these people’s suspicions. We can conclude that public health practitioners’ practices may play a role in generating AIDS-denialist sentiments. In interactions with the newcomers, the experienced community members highlighted the importance of personal autonomy and freedom of choice in decision making consistent with the consumerist ideology of health care. The study findings suggest that health care workers should change a one-size-fits-all mode of counseling for a more complex and patient...

Developing underground spaces serves a range of common urban functions, including workspaces. However, underground workplaces, work-related factors and the indoor environmental quality (IEQ) parameters within them may negatively affect worker's mental health. This study assessed the prevalence of psychological distress with repeated measures over time in aboveground and underground workspaces, and assessed the association between perceived IEQ parameters and work-related factors with psychological distress. A total of 329 workers in similar aboveground and underground workspaces were followed-up in three assessments over 12 months in Singapore. Psychological distress was assessed using the 12-item General Health Questionnaire (GHQ-12) and defined as a GHQ-12 score ≥2. Perceived IEQ (air quality, temperature, noise, light) in the workplace were collected using the OFFICAIR questionnaire. We used generalised estimating equation models to assess the association between working underground, perceived IEQ, and work-related factors with psychological distress. The overall prevalence of psychological distress was 21.9%, 26.1% and 21.9%, at baseline, 3-and 12-months follow-up, respectively. The fully-adjusted multivariable analysis did not show any association between working underground and psychological distress however, perceived IEQ parameters and longer working hours were significantly associated with psychological distress. Regardless of working in under or aboveground workplaces, perceived IEQ was associated with psychological distress. Future studies are needed in order to examine the relationship between objective measures of IEQ and psychological distress and the impact of healthy building policies and improved IEQ on psychological distress.

With rapid growth of online social network sites, the issue of health-related online communities and its social and behavioral implications has become increasingly important for public health. Unfortunately, online communities often become vehicles for promotion of pernicious misinformation, in particular, that HIV virus is a myth (AIDS denialism). This study seeks to explore online users’ behavior and interactions within AIDS-denialist community to identify and estimate the number of those, who potentially are most susceptible to AIDS-denialist arguments—“the risk group” in terms of becoming AIDS denialists. Social network analysis was used for examining the most numerous AIDS-denialist community (over 15,000 members) in the most popular Russian SNS “ VK.com .” In addition, content analysis was used for collecting data on attitudes toward AIDS-denialist arguments and participants’ self-disclosed HIV status. Two data sets were collected to analyze friendship ties and communication interactions among community members. We have identified the core of online community—cohesive and dedicated AIDS denialists, and the risk group: users who communicate with core members, and, thus, can be more susceptible to the AIDS-denialist propaganda and their health behaviors (e.g., refusing treatment). Analysis allowed to significantly reduce the target audience for possible intervention campaigns and simultaneously increase the accuracy of determining the risk group composition.

BackgroundWe aimed to examine the association between shift work and sleep quality in a diverse occupational type. MethodsThis was a cross-sectional study of self-reported sleep quality in 424 workers aged �21 using the Pittsburgh Sleep Quality Index (PSQI). We divided workers into two categories based on their PSQI score: (a) �5 (good sleep quality) and (b) >5 (poor sleep quality). We used multiple logistic regressions to assess the association between shift work and sleep quality adjusted for potential confounders. ResultsThe mean age was 39.2 (SD = 11.3) years, with shift workers being older than their counterparts. Most workers were of Chinese ethnicity (63.9%). Males were significantly more likely to undertake shift work than females (89% v 11%, p-value<0.001), but it should be noted that the majority of workers was male (78.8%) in this sample of workers. Shift workers had a 198% increased odds of poor sleep quality compared to non-shift workers (OR = 2.98; 95% CI:1.53-5.81). ConclusionShift work was significantly and independently associated with increased odds of poor sleep quality in this sample of workers. The present findings may inform employment guidelines and help develop workplace health promotion interventions aimed at improving sleep quality among workers and ultimately lead to a healthier workforce.