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This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children. 59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis. Fitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs. Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

Social isolation and loneliness are increasing public health concerns and have been associated with increased risk of cardiovascular disease (CVD) among older adults. To examine the associations of social isolation and loneliness with incident CVD in a large cohort of postmenopausal women and whether social support moderated these associations. This prospective cohort study, conducted from March 2011 through March 2019, included community-living US women aged 65 to 99 years from the Women's Health Initiative Extension Study II who had no history of myocardial infarction, stroke, or coronary heart disease. Social isolation and loneliness were ascertained using validated questionnaires. The main outcome was major CVD, which was physician adjudicated using medical records and included coronary heart disease, stroke, and death from CVD. Continuous scores of social isolation and loneliness were analyzed. Hazard ratios (HRs) and 95% CIs for CVD were calculated for women with high social isolation and loneliness scores (midpoint of the upper half of the distribution) vs those with low scores (midpoint of the lower half of the distribution) using multivariable Cox proportional hazards regression models adjusting for age, race and ethnicity, educational level, and depression and then adding relevant health behavior and health status variables. Questionnaire-assessed social support was tested as a potential effect modifier. Among 57 825 women (mean [SD] age, 79.0 [6.1] years; 89.1% White), 1599 major CVD events occurred over 186 762 person-years. The HR for the association of high vs low social isolation scores with CVD was 1.18 (95% CI, 1.13-1.23), and the HR for the association of high vs low loneliness scores with CVD was 1.14 (95% CI, 1.10-1.18). The HRs after additional adjustment for health behaviors and health status were 1.08 (95% CI, 1.03-1.12; 8.0% higher risk) for social isolation and 1.05 (95% CI, 1.01-1.09; 5.0% higher risk) for loneliness. Women with both high social isolation and high loneliness scores had a 13.0% to 27.0% higher risk of incident CVD than did women with low social isolation and low loneliness scores. Social support was not a significant effect modifier of the associations (social isolation × social support: r, -0.18; P = .86; loneliness × social support: r, 0.78; P = .48). In this cohort study, social isolation and loneliness were independently associated with modestly higher risk of CVD among postmenopausal women in the US, and women with both social isolation and loneliness had greater CVD risk than did those with either exposure alone. The findings suggest that these prevalent psychosocial processes merit increased attention for prevention of CVD in older women, particularly in the era of COVID-19.

Weight loss interventions based solely on text messaging (short message service [SMS]) have been shown to be modestly effective for short periods of time and in some populations, but limited evidence is available for positive longer-term outcomes and for efficacy in Hispanic populations. Also, little is known about the comparative efficacy of weight loss interventions that use SMS coupled with brief, technology-mediated contact with health coaches, an important issue when considering the scalability and cost of interventions. We examined the efficacy of a 1-year intervention designed to reduce weight among overweight and obese English- and Spanish-speaking adults via SMS alone (ConTxt) or in combination with brief, monthly health-coaching calls. ConTxt offered 2-4 SMS/day that were personalized, tailored, and interactive. Content was theory- and evidence-based and focused on reducing energy intake and increasing energy expenditure. Monthly health-coaching calls (5-10 minutes' duration) focused on goal-setting, identifying barriers to achieving goals, and self-monitoring. English- and Spanish-speaking adults were recruited from October 2011 to March 2013. A total of 298 overweight (body mass index [BMI] 27.0 to 39.9 kg/m2) adults (aged 21-60 years; 77% female; 41% Hispanic; 21% primarily Spanish speaking; 44% college graduates or higher; 22% unemployed) were randomly assigned (1:1) to receive either ConTxt only (n = 101), ConTxt plus health-coaching calls (n = 96), or standard print materials on weight reduction (control group, n = 101). We used computer-based permuted-block randomization with block sizes of three or six, stratified by sex and Spanish-speaking status. Participants, study staff, and investigators were masked until the intervention was assigned. The primary outcome was objectively measured percent of weight loss from baseline at 12 months. Differences between groups were evaluated using linear mixed-effects regression within an intention-to-treat framework. A total of 261 (87.2%) and 253 (84.9%) participants completed 6- and 12-month visits, respectively. Loss to follow-up did not differ by study group. Mean (95% confidence intervals [CIs]) percent weight loss at 12 months was -0.61 (-1.99 to 0.77) in the control group, -1.68 (-3.08 to -0.27) in ConTxt only, and -3.63 (-5.05 to -2.81) in ConTxt plus health-coaching calls. At 12 months, mean (95% CI) percent weight loss, adjusted for baseline BMI, was significantly different between ConTxt plus health-coaching calls and the control group (-3.0 [-4.99 to -1.04], p = 0.003) but not between the ConTxt-only and the control group (-1.07 [-3.05 to 0.92], p = 0.291). Differences between ConTxt plus health-coaching calls and ConTxt only were not significant (-1.95 [-3.96 to 0.06], p = 0.057). These findings were consistent across other weight-related secondary outcomes, including changes in absolute weight, BMI, and percent body fat at 12 months. Exploratory subgroup analyses suggested that Spanish speakers responded more favorably to ConTxt plus health-coaching calls than English speakers (Spanish contrast: -7.90 [-11.94 to -3.86], p < 0.001; English contrast: -1.82 [-4.03 to 0.39], p = 0.107). Limitations include the unblinded delivery of the intervention and recruitment of a predominantly female sample from a single site. A 1-year intervention that delivered theory- and evidence-based weight loss content via daily personalized, tailored, and interactive SMS was most effective when combined with brief, monthly phone calls. ClinicalTrials.gov NCT01171586.

Information about genetic and phenotypic risk of type 2 diabetes is now widely available and is being incorporated into disease prevention programs. Whether such information motivates behavior change or has adverse effects is uncertain. We examined the effect of communicating an estimate of genetic or phenotypic risk of type 2 diabetes in a parallel group, open, randomized controlled trial. We recruited 569 healthy middle-aged adults from the Fenland Study, an ongoing population-based, observational study in the east of England (Cambridgeshire, UK). We used a computer-generated random list to assign participants in blocks of six to receive either standard lifestyle advice alone (control group, n = 190) or in combination with a genetic (n = 189) or a phenotypic (n = 190) risk estimate for type 2 diabetes (intervention groups). After 8 wk, we measured the primary outcome, objectively measured physical activity (kJ/kg/day), and also measured several secondary outcomes (including self-reported diet, self-reported weight, worry, anxiety, and perceived risk). The study was powered to detect a between-group difference of 4.1 kJ/kg/d at follow-up. 557 (98%) participants completed the trial. There were no significant intervention effects on physical activity (difference in adjusted mean change from baseline: genetic risk group versus control group 0.85 kJ/kg/d (95% CI -2.07 to 3.77, p = 0.57); phenotypic risk group versus control group 1.32 (95% CI -1.61 to 4.25, p = 0.38); and genetic risk group versus phenotypic risk group -0.47 (95% CI -3.40 to 2.46, p = 0.75). No significant differences in self-reported diet, self-reported weight, worry, and anxiety were observed between trial groups. Estimates of perceived risk were significantly more accurate among those who received risk information than among those who did not. Key limitations include the recruitment of a sample that may not be representative of the UK population, use of self-reported secondary outcome measures, and a short follow-up period. In this study, we did not observe short-term changes in behavior associated with the communication of an estimate of genetic or phenotypic risk of type 2 diabetes. We also did not observe changes in worry or anxiety in the study population. Additional research is needed to investigate the conditions under which risk information might enhance preventive strategies. (Current Controlled Trials ISRCTN09650496; Date applied: April 4, 2011; Date assigned: June 10, 2011). The trial is registered with Current Controlled Trials, ISRCTN09650496.

Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively). Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

Background The present study examined the prevalence of symptoms of depression throughout pregnancy and the early postpartum period (first trimester through six weeks postpartum) in low-income Latina women. Further, this study examined whether established risk factors in non-Latina women also predict perinatal depressive symptom trajectories in low-income Latinas. Methods The sample included 240 Latina women from a federally qualified health center (FQHC). Results Latent growth curve modeling found that, on average, symptoms of depression decreased from the first trimester of pregnancy through six weeks postpartum. Change in symptoms of depression from the first trimester through six weeks postpartum were predicted by limited English proficiency and age. Conclusions Findings of the current study add to existing evidence that can guide clinical care for Latina maternal mental health throughout the perinatal period.

Background: Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data. Methods: Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended. Results: Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample. Conclusions: Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

Background As many as 2.4 million Americans are affected by chronic Hepatitis C Virus (HCV) in the United States.In 2018, the estimated number of adults with a history of HCV infection in San Diego County was 55,354 (95% CI: 25,411–93,329). This corresponded to a seroprevalence of 2.1% (95% CI: 2.1–3.4%). One-third of infections were among PWID. Published research has demonstrated that direct-acting antivirals (DAAs) have high efficacy and can now be used by primary care providers to treat HCV. In addition, limited evidence exists to support the effectiveness of simplified algorithms in clinical trial and real-world settings. Even with expanded access to HCV treatment in primary care settings, there are still groups, especially people who inject drugs (PWID) and people experiencing homelessness, who experience treatment disparities due to access and treatment barriers. The current study extends the simplified algorithm with a streetside ‘one-stop-shop’ approach with integrated care (including the offer of buprenorphine prescriptions and abscess care) using a mobile clinic situated adjacent to a syringe service program serving many homeless populations. Rates of HCV treatment initiation and retention will be compared between patients offered HCV care in a mobile clinic adjacent to a syringe services program (SSP) and homeless encampment versus those who are linked to a community clinic’s current practice of usual care, which includes comprehensive patient navigation. Methods A quasi-experimental, prospective, interventional, comparative effectiveness trial with allocation of approximately 200 patients who inject drugs and have chronic HCV to the \"simplified care\" pathway (intervention group) or the \"usual care\" pathway (control group). Block randomization will be performed with a 1:1 randomization. Discussion Previous research has demonstrated acceptable outcomes for patients treated using simplified algorithms for DAAs and point-of-care testing in mobile medical clinics; however, there are opportunities to explore how these new, innovative systems of care impact treatment initiation rates or other HCV care cascade outcomes among PWID. Trial registration We have registered our study with ClinicalTrials.gov, a resource of the United States National Library of Medicine. This database contains research studies from United States and other countries around the world. Our study has not been previously published. The ClinicalTrials.gov registration identifier is NCT04741750.

Background Monoclonal antibody (mAb) treatment for COVID-19 is associated with improved clinical outcomes. However, there is limited information regarding the impact of treatment on symptoms and the prevalence of post-COVID Conditions (PCC). Understanding of the association between time to mAb infusion and the development of PCC is also limited. Methods This longitudinal study was conducted among patients with COVID-19 who received mAb infusions at a Federally Qualified Health Center in San Diego, CA. A series of telephone interviews were conducted at baseline and follow-up (14 days and 28+ days). A comprehensive symptom inventory was completed and physical and mental health status were measured using PROMIS-29 and PHQ-2. Pearson’s Chi-squared tests and independent two-sample t-tests were performed to test for association between time to mAb infusion and outcomes at follow-up. A Poisson regression model was used to analyze whether time to mAb infusion predicts risk of developing PCC. Results Participants (N = 411) were 53% female, ranged in age from 16 to 92 years (mean 50), and a majority (56%) were Latino/Hispanic. Cross-sectional findings revealed a high symptom burden at baseline (70% of patients had cough, 50% had fever, and 44% had headache). The prevalence of many symptoms decreased substantially by the final follow-up survey (29% of patients had cough, 3% had fever, and 28% had headache). Longitudinal findings indicated that 10 symptoms decreased in prevalence from baseline to final follow-up, 2 remained the same, and 14 increased. The severity of symptoms and most patient-reported physical and mental health measure scores decreased over time. The prevalence of PCC was 69% when PCC was defined as ≥ 1 symptom at final follow-up. Time to mAb infusion was not significantly associated with any outcome at follow-up. Time to infusion was not associated with PCC status at final follow-up in the crude or adjusted Poisson regression models. Conclusions The prevalence of PCC was high among this patient population following COVID-19 mAb treatment. Time to mAb infusion did not predict the development of PCC. Further research in these areas is essential to answer urgent clinical questions about effective treatments of COVID-19.