Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6-minute bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.

To validate measures of sleep and heart rate (HR) during sleep generated by a commercially-available activity tracker against those derived from polysomnography (PSG) in healthy adolescents. Sleep data were concurrently recorded using FitbitChargeHR™ and PSG, including electrocardiography (ECG), during an overnight laboratory sleep recording in 32 healthy adolescents (15 females; Age, mean±SD: 17.3±2.5 years). Sleep and HR measures were compared between FitbitChargeHR™ and PSG using paired t-tests and Bland-Altman plots. Epoch-by-epoch analysis showed that FitbitChargeHR™ had high overall accuracy (91%), high sensitivity (97%) in detecting sleep, and poor specificity (42%) in detecting wake on a min-to-min basis. On average, FitbitChargeHR™ significantly but negligibly overestimated total sleep time by 8min and sleep efficiency by 1.8%, and underestimated wake after sleep onset by 5.6min (p<0.05). Within FitbitChargeHR™ epochs of sleep, the average HR was 59.3±7.5 bpm, which was significantly but negligibly lower than that calculated from ECG (60.2±7.6 bpm, p<0.001), with no change in mean discrepancies throughout the night. FitbitChargeHR™ showed good agreement with PSG and ECG in measuring sleep and HR during sleep, supporting its use in assessing sleep and cardiac function in healthy adolescents. Further validation is needed to assess its reliability over prolonged periods of time in ecological settings and in clinical populations.

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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. Methods 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. Results 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.

Background: Few studies characterize older adult physical activity and sitting patterns using accurate accelerometer and concurrent posture measures. In this descriptive paper, we report accelerometer data collection protocols, consent rates, and physical behavior measures from a population-based cohort study (Adult Changes in Thought, ACT). Methods: The ACT study holds enrollment steady at approximately 2000 members of Kaiser Permanente Washington aged 65+ without dementia undergoing detailed biennial assessments. In 2016 the ACT-Activity Monitor (ACT-AM) sub-study was initiated to obtain data from wearing activPAL and ActiGraph devices for 7 days following regular biennial visits. We describe the methods protocol of ACT-AM and present characteristics of people who did and did not consent to wear devices. We compute inverse probability of response weights and incorporate these weights in linear regression models to estimate means and 95% confidence intervals (CI) of device-based pattern metrics, adjusted for wear time and demographic factors, and weighted to account for potential selection bias due to device-wear consent. Results: Among 1885 eligible ACT participants, 56% agreed to wear both devices (mean age 77 years, 56% female, 89% non-Hispanic white, 91% with post-secondary education). On average, those who agreed to wear devices were younger and healthier. Estimated mean (95% CI) activPAL-derived sitting, standing, and stepping times were 10.2 h/ day (603-618 min/day), 3.9 h/day (226-239 min/day), and 1.4 h/day (79-84 min/day), respectively. Estimated mean ActiGraph derived sedentary (Vector Magnitude [VM] < =18 counts/15 s), light intensity (VM 19-518 counts/15 s), and moderate-to-vigorous intensity (VM > 518 counts/15 s) physical activity durations were 9.5 h/day (565-577 min/ day), 4.5 h/day (267-276 min/day), and 1.0 h/day (59-64 min/day). Participants who were older, had chronic conditions, and were unable to walk a half-mile had higher sedentary time and less physical activity. Conclusions: Our recruitment rate demonstrates the feasibility of cohort participants to wear two devices that measure sedentary time and physical activity. Data indicate high levels of sitting time in older adults but also high levels of physical activity using cut-points developed for older adults. These data will help researchers test hypotheses related to physical behavior and health in older adults in the future.

ImportanceEpisodic memory and executive function are essential aspects of cognitive functioning that decline with aging. This decline may be ameliorable with lifestyle interventions.ObjectiveTo determine whether mindfulness-based stress reduction (MBSR), exercise, or a combination of both improve cognitive function in older adults.Design, Setting, and ParticipantsThis 2 × 2 factorial randomized clinical trial was conducted at 2 US sites (Washington University in St Louis and University of California, San Diego). A total of 585 older adults (aged 65-84 y) with subjective cognitive concerns, but not dementia, were randomized (enrollment from November 19, 2015, to January 23, 2019; final follow-up on March 16, 2020).InterventionsParticipants were randomized to undergo the following interventions: MBSR with a target of 60 minutes daily of meditation (n = 150); exercise with aerobic, strength, and functional components with a target of at least 300 minutes weekly (n = 138); combined MBSR and exercise (n = 144); or a health education control group (n = 153). Interventions lasted 18 months and consisted of group-based classes and home practice.Main Outcomes and MeasuresThe 2 primary outcomes were composites of episodic memory and executive function (standardized to a mean [SD] of 0 [1]; higher composite scores indicate better cognitive performance) from neuropsychological testing; the primary end point was 6 months and the secondary end point was 18 months. There were 5 reported secondary outcomes: hippocampal volume and dorsolateral prefrontal cortex thickness and surface area from structural magnetic resonance imaging and functional cognitive capacity and self-reported cognitive concerns.ResultsAmong 585 randomized participants (mean age, 71.5 years; 424 [72.5%] women), 568 (97.1%) completed 6 months in the trial and 475 (81.2%) completed 18 months. At 6 months, there was no significant effect of mindfulness training or exercise on episodic memory (MBSR vs no MBSR: 0.44 vs 0.48; mean difference, –0.04 points [95% CI, –0.15 to 0.07]; P = .50; exercise vs no exercise: 0.49 vs 0.42; difference, 0.07 [95% CI, –0.04 to 0.17]; P = .23) or executive function (MBSR vs no MBSR: 0.39 vs 0.31; mean difference, 0.08 points [95% CI, –0.02 to 0.19]; P = .12; exercise vs no exercise: 0.39 vs 0.32; difference, 0.07 [95% CI, –0.03 to 0.18]; P = .17) and there were no intervention effects at the secondary end point of 18 months. There was no significant interaction between mindfulness training and exercise (P = .93 for memory and P = .29 for executive function) at 6 months. Of the 5 prespecified secondary outcomes, none showed a significant improvement with either intervention compared with those not receiving the intervention.Conclusions and RelevanceAmong older adults with subjective cognitive concerns, mindfulness training, exercise, or both did not result in significant differences in improvement in episodic memory or executive function at 6 months. The findings do not support the use of these interventions for improving cognition in older adults with subjective cognitive concerns.Trial RegistrationClinicalTrials.gov Identifier: NCT02665481

Summary Hyperkyphosis commonly affects older persons and is associated with morbidity and mortality. Many have hypothesized that hyperkyphosis increases fall risk. Within this prospective study of older adults, kyphosis was significantly associated with incident falls over 1 year. Measures of hyperkyphosis could enhance falls risk assessments during primary care office visits. Introduction To determine the association between four measures of kyphosis and incident and injurious falls in older persons. Methods Community-dwelling adults aged 65 and older (n = 72) residing in southern California were invited to participate in a prospective cohort study. Participants had kyphosis assessed four ways. Two standing measures included a flexicurve ruler placed against the back to derive a kyphotic index and the Debrunner kyphometer, a protractor used to measure the kyphotic angle in degrees. Two lying measures included the blocks method (number of 1.7 cm blocks needed to achieve a neutral head position while lying supine) and traditional Cobb angle calculation derived from DXA based lateral vertebral assessment. Baseline demographic, clinical, and other health information (including a timed up and go (TUG) test) were assessed at a clinic visit. Participants were followed monthly through email or postcard for 1 year, with falls outcomes confirmed through telephone interview. Results Mean age was 77.8 (± 7.1) among the 52 women and 20 men. Over 12 months, 64% of participants experienced at least one incident fall and 35% experienced an injurious fall. Each standard deviation increase in kyphosis resulted in more than doubling the adjusted odds of an incident fall, even after adjusting for TUG. Odds of injurious falls were less consistent across measures; after adjusting for TUG, only the blocks method was associated with injurious falls. Conclusions Each kyphosis measure was independently associated with incident falls. Findings were inconsistent for injurious falls; the blocks measure suggested the strongest association. If these findings are replicated, the blocks measure could be incorporated into office visits as a quick and efficient tool to identify patients at increased fall risk.

A randomized controlled trial is being conducted in the United States to test the efficacy of a personalized interactive mobile health intervention (iSTEP) designed to increase physical activity (PA) and improve neurocognitive functioning among HIV-positive persons. This article describes an initial qualitative study performed to develop iSTEP for the HIV-positive population, including assessment of PA barriers and facilitators. Two focus groups, with 9 and 12 unique HIV-positive individuals, respectively, were administered to evaluate barriers limiting PA and potential iSTEP content created to encourage greater PA. Group discussions revealed prominent PA barriers, including HIV symptoms (neuropathy, lipoatrophy), antiretroviral medication effects, and fatigue; significant PA facilitators included self-monitoring and family support. Participants provided feedback on strategies to increase PA and expressed positive support for a mobile intervention adapted to personal priorities. These findings will assist the development of novel PA interventions focused on treating the epidemic of HIV-associated neurocognitive disorders.