The majority of Black women with hypertension in the United States have smartphones or tablets and use social media, and many use wearable activity trackers and health or wellness apps, digital tools that can be used to support lifestyle changes and medication adherence.

BACKGROUND: The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people\'s sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep.
OBJECTIVE: This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period.
METHODS: We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables.
RESULTS: The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) (β=.003, 95% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) (β=.02, 95% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST (β=.15, 95% CI 0.05-0.27; P=.006) and MS (β=.17, 95% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST (β=.37, 95% CI 0.14-0.61; P=.004) and lower variability in TST (β=-.15, 95% CI -0.27 to -0.05; P<.001). Engaging in PA later in the day was associated with longer TST (β=.03, 95% CI 0.02-0.04; P<.001) and less variability in TST (β=-.01, 95% CI -0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST (β=-.26, 95% CI -0.29 to -0.23; P<.001), earlier MS (β=-.29, 95% CI -0.33 to -0.26; P<.001), and reduced variability in TST (β=-.16, 95% CI -0.23 to -0.09; P<.001).
CONCLUSIONS: Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce.

UNASSIGNED: Sedentary time (ST) is associated with mortality independent of moderate-to-vigorous physical activity in patients with COPD. The proper processing methods for the measurement data and factors related to ST are still unknown. We investigated several conditions for determining the proper processing of ST accelerometric data and created a reference equation for ST using ST-related factors.
UNASSIGNED: In Study 1, we evaluated the minimum required number of days to obtain repeatability at different measurement times and assessed the effects of rainy days or weekend days on ST in patients with COPD. In Study 2, we detected the ST-related factors among 28 parameters and created a reference equation for ST using the detected factors.
UNASSIGNED: In Study 1, 38 patients with stable COPD were analyzed. The minimum number of days required for repeatability was 3 for 8-h wearing and 2 for 10-h wearing. The ST was significantly prolonged on rainy days, but not on weekends. In Study 2, 216 patients with stable COPD were analyzed. BMI, FEV1%pred, 6MWD, and mMRC were detected as ST-related factors, and a reference equation could be created using these four factors. The equation was validated for patients whose ST was ≥ 6 h.
UNASSIGNED: By using properly processed measurement data of ST, we created a reference equation for assessing ST that is expected to be useful for providing individual guidance on the shortening of ST to patients with COPD.
When measuring sedentary time (ST) objectively, we should recognize that the minimum required number of days is three and that ST is prolonged on rainy days. The reference equation for ST could be created using four ST-related factors. It might serve as a guide for shortening ST in COPD.

UNASSIGNED: Lung function constraints and comorbidities such as coronary heart disease, sarcopenia, and mood disorders make chronic obstructive pulmonary disease (COPD) patients avoid physical activity (PA). However, PA represents an important pillar of COPD management and is explicitly recommended by professional associations to enhance physical functioning and positively modulate disease progression.
UNASSIGNED: In this monocentric, prospective, observational feasibility study, it was our primary objective to investigate the association between PA and the evolution of the COPD assessment test (CAT) and the occurrence of acute exacerbations of COPD (AECOPD), respectively. To this end, we equipped 42 COPD patients with an activity tracking wearable and telemonitored their daily PA levels over one year using a dedicated web-based interface. Patients additionally provided weekly CAT scores using the same telehealth platform and came in for 3 study visits to assess functional parameters and biochemical markers related to nutrition and inflammation.
UNASSIGNED: A principal study finding was that PA was inversely associated with CAT score (drop of 0.21 points associated with an increase of 1000 daily steps, p = 0.004), and that the 50% of patients with higher PA levels showed less CAT score progression over time (0.42 points per year) than the 50% of patients with lower PA levels (3.26 points per year) (p < 0.001). In addition, higher PA levels were significantly associated with a lower likelihood of experiencing a moderate-to-severe AECOPD (31% risk reduction associated with an increase of 1000 daily steps, p = 0.0097).
UNASSIGNED: Our study demonstrates the relevance of PA for key COPD outcome metrics in a real-world setting and underpins the importance of PA for COPD self-management in everyday life. Our study paves the way for future intervention trials to prospectively identify medically relevant PA thresholds and establish training recommendations for different patient subgroups.

BACKGROUND: Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain.
OBJECTIVE: This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events.
METHODS: Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies.
RESULTS: A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%).
CONCLUSIONS: Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability.
BACKGROUND: Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.

UNASSIGNED: Arrhythmia recurrence after pulmonary vein isolation (PVI) is common. We conducted a multicenter, randomized trial to determine the impact of increased physical activity on atrial fibrillation recurrence after PVI.
UNASSIGNED: From 2018 to 2020, we randomly assigned 200 patients with atrial fibrillation to the ACTION or NO-ACTION group in 4 different centers in the local country of Brandenburg, Germany. Patients were eligible if they were scheduled to undergo PVI, aged ≥50 to ≤77 years, body mass index ≥23 to ≤35 kg/m2, and accepted wearing an activity tracker allowing 24-hour activity monitoring via mobile app. Patients in the ACTION group were actively remote-controlled via transmitted activity data by a physiotherapist, and individual motivational interviewing call sessions were scheduled with each ACTION patient every 2 weeks. The primary end point was the composite of recurrence of any atrial arrhythmia >30 seconds, additional ablation procedure, cardioversion, and new onset of antiarrhythmic drugs earliest after 90 days after index PVI over 12 months.
UNASSIGNED: Overall, the median age of patients was 66 (interquartile range, 61-71) years, 33.5% were women, and 52% had persistent atrial fibrillation. The number of steps per day increased in both groups of patients from baseline to 12 months (P<0.001). The absolute increase in steps per day did not differ between patients in the ACTION group with +3205 steps (597-4944) compared with those in the NO-ACTION group +2423 steps (17-4284), P=0.325. Unadjusted intention-to-treat analysis showed no difference in the primary composite end point in the ACTION group (27.3%) versus the NO-ACTION group (32.7%), P=0.405.
UNASSIGNED: Physical activity improved in patients after PVI. The present randomized controlled trial shows that activity tracker and motivational calls to increase physical activity versus activity tracker alone did not reduce the occurrence of the primary composite end point of atrial fibrillation recurrence or the absolute increase in steps per day.
UNASSIGNED: URL: https://www.cochranelibrary.com; Unique identifier: DRKS00012914.

UNASSIGNED: To elucidate physical activity in the first year after myocardial infarction (MI), and to explore differences in various subgroups, delineated by age, participation in exercise-based cardiac rehabilitation (exCR), or restrictions due to the covid-19 pandemic. Secondly, to explore associations between changes in physical activity variables with blood pressure and lipid levels.
UNASSIGNED: A longitudinal study in 2017-2023. Physical activity variables were assessed via accelerometers at two- and twelve months post-MI. The intensity was divided into, sedentary, light, moderate, and vigorous-intensity physical activity, according to established cut-offs. Blood pressure and lipids were measured by standardized procedures at the same time points.
UNASSIGNED: There were 178 patients included at baseline, 81% male, mean age of 64 (9 SD) years. Patients spent 72% of their time sedentary, followed by light (19%), moderate (8%), and vigorous physical activity (1%). Patients included during covid-19 restrictions and younger patients had a higher level of moderate-intensity physical activity compared to patients included during non-pandemic restrictions and older patients. At 12-month follow-up, patients overall increased time (1%) in sedentary behavior (p = 0.03) and decreased time (0.6%) in moderate-intensity physical activity (p = 0.04), regardless of participation in exCR or age. There was a positive association between the change in mean physical activity intensity and HDL-cholesterol (p = 0.047).
UNASSIGNED: Participants had a low fraction of time in moderate-to-vigorous-intensity physical activity two months post-MI, which deteriorated during the first year. This emphasizes the need for improved implementation of evidence-based interventions to support and motivate patients to perform regular physical activity.

OBJECTIVE: To compare different types of activity trackers recording physical activity energy expenditure (PAEE) and examine their criterion validity against indirect calorimetry (IC) as the gold standard in adults over 60 years of age with a special focus on women with cardiovascular risk.
METHODS: Synchronous registrations of PAEE were performed with up to four different devices to determine criterion validity against IC while participants performed a protocol of simulated activities in a laboratory setting.
METHODS: Thirty-four participants (25 women, 9 men) with at least a light cardiac risk performed a protocol of simulated activities in a laboratory setting (daily living activities, cycle ergometer test). PAEE was simultaneously assessed by IC, two research-grade activity trackers (ActiGraph-wGT3X-BT and Actiheart-4) and two consumer-level activity trackers (OMRON pedometer and Fitbit Charge-3). Tracker-derived PAEE was compared with PAEE calculated from IC descriptively and by Bland-Altman plots.
RESULTS: The ActiGraph (0.7 ± 0.4 kcal/min), the Actiheart (1.1 ± 0.6 kcal/min) and the OMRON (0.8 ± 0.6 kcal/min) underestimated, while the Fitbit (3.4 ± 1.2 kcal/min) overestimated PAEE compared to IC-PAEE (2.0 ± 0.5 kcal/min). The Bland-Altman limits of agreement (LoA) against IC were +0.5/+2.2 kcal/min for the ActiGraph, -0.3/+2.1 kcal/min for the Actiheart, -3.7/+1.0 kcal/min for the Fitbit, and -0.5/+2.9 kcal/min for the OMRON. The magnitude of the deviation varied considerably depending on the activity (e.g. walking, cleaning, cycle ergometer test).
CONCLUSIONS: The research-grade activity trackers estimated PAEE with higher validity than the commercially available activity trackers. The partly very wide LoA have to be critically considered when assessing PAEE in the context of health service research, as individual Physical Activity behaviour may be under- or overestimated.

BACKGROUND: Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings.
OBJECTIVE: This study aimed to develop a consensus-based framework for the optimal use of WATs in health care.
METHODS: A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants\' opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist\'s clarity, utility, and appropriateness.
RESULTS: Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients\' data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4.
CONCLUSIONS: This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector.

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities.