The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

BACKGROUND: Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings.
OBJECTIVE: The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting.
METHODS: Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices.
RESULTS: We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity.
CONCLUSIONS: Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death.
OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death.
METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data.
RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively.
CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes.
BACKGROUND: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768.
UNASSIGNED: RR2-10.1136/bmjopen-2021-054675.

BACKGROUND: The purpose of this study was to evaluate the impact of direct anterior approach (DAA) or posterior approach (PA) on step and stair counts after total hip arthroplasty using a remotely monitored mobile application with a smartwatch while controlling for baseline characteristics.
METHODS: This is a secondary data analysis from a prospective cohort study of patients utilizing a smartphone-based care management platform. The primary outcomes were step and stair counts and changes from baseline through one year. Step and stair counts were available for 1,501 and 847 patients, respectively. Longitudinal regression models were created to control for baseline characteristics.
RESULTS: Patients in the DAA group had significantly lower body mass index (P = .049) and comorbidities (P = .028), but there were no significant differences in age (P = .225) or sex (P = .315). The DAA patients had a higher average and improvement from baseline in step count at 2 and 3 weeks postoperatively after controlling for patient characteristics (P = .028 and P = .044, respectively). The average stair counts were higher for DAA patients at one month postoperatively (P = .035), but this difference was not significant after controlling for patient demographics. Average stair ascending speeds and changes from baseline were not different between DAA and PA patients. Descending stair speed was higher at 2 weeks postoperatively for DAA patients, but was no longer higher after controlling for baseline demographics.
CONCLUSIONS: After controlling for baseline characteristics, DAA patients demonstrate earlier improvement in step count than PA patients after total hip arthroplasty. However, patient selection and surgeon training may continue to influence outcomes through a surgical approach.

The purposes were to examine the criterion-related validity of the steps estimated by consumer-wearable activity trackers (wrist-worn activity trackers: Fitbit Ace 2, Garmin Vivofit Jr, and Xiomi Mi Band 5; smartphone applications: Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health) and their comparability in primary schoolchildren under controlled conditions. An initial sample of 66 primary schoolchildren (final sample = 56; 46.4% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), wore three wrist-worn activity trackers (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on their non-dominant wrist and had three applications in two smartphones (Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health for Android/iOS installed in Samsung Galaxy S20+/iPhone 11 Pro Max) in simulated front trouser pockets. Primary schoolchildren\'s steps estimated by the consumer-wearable activity trackers and the video-based counting independently by two researchers (gold standard) were recorded while they performed a 200-meter course in slow, normal and brisk pace walking, and running conditions. Results showed that the criterion-related validity of the step scores estimated by the three Samsung applications and the Garmin Vivofit Jr 2 were good-excellent in the four walking/running conditions (e.g., MAPE = 0.6-2.3%; lower 95% CI of the ICC = 0.81-0.99), as well as being comparable. However, the Apple applications, Fitbit Ace 2, and Xiaomi Mi Band 5 showed poor criterion-related validity and comparability on some walking/running conditions (e.g., lower 95% CI of the ICC < 0.70). Although, as in real life primary schoolchildren also place their smartphones in other parts (e.g., schoolbags, hands or even somewhere away from the body), the criterion-related validity of the Garmin Vivofit Jr 2 potentially would be considerably higher than that of the Samsung applications. The findings of the present study highlight the potential of the Garmin Vivofit Jr 2 for monitoring primary schoolchildren\'s steps under controlled conditions.

UNASSIGNED: Increasing physical activity may prevent cognitive decline. Previous studies primarily focused on older adults and used self-reported questionnaires to assess physical activity. We examined the relationship between step count, an objective measure of physical activity, and cognitive function in community-based middle-aged and older Japanese men.
UNASSIGNED: The Shiga Epidemiological Study of Subclinical Atherosclerosis randomly recruited community-dwelling healthy men aged 40-79 years from Shiga, Japan, and measured their step counts over 7 consecutive days using a pedometer at baseline (2006-2008). Among men who returned for follow-up (2009-2014), we assessed their cognitive function using the Cognitive Abilities Screening Instrument (CASI) score. We restricted our analyses to those with valid 7-day average step counts at baseline and those who remained free of stroke at follow-up (n = 676). Using analysis of covariance, we calculated the adjusted means of the CASI score according to the quartiles of the average step counts.
UNASSIGNED: The mean (standard deviation) of age and unadjusted CASI score were 63.8 (9.1) years and 90.8 (5.8), respectively. The CASI score was elevated in higher quartiles of step counts (90.2, 90.4, 90.6, and 91.8 from the lowest to the highest quartile, respectively, [p for trend = 0.004]) in a model adjusted for age and education. Further adjustment for smoking, drinking, and other cardiovascular risk factors resulted in a similar pattern of association (p for trend = 0.005).
UNASSIGNED: In apparently healthy middle-aged and older Japanese men, a greater 7-day average step count at baseline was associated with significantly higher cognitive function score.

BACKGROUND: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in-depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time-consuming and costly but also require systematic and cost-effective approaches.
OBJECTIVE: The main objective of this study was to investigate the feasibility of training an end-to-end deep learning (DL) model by directly inputting time-series activity tracking and sleep data obtained from consumer-grade wrist-worn activity trackers to identify comorbid depression and anxiety.
METHODS: To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non-time-series data. The basic structure of the DL model was designed to process mixed-input data and perform multi-label-based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short-term memory (LSTM), were applied to process the time-series data, and model selection was conducted by comparing the performances of the hyperparameters.
RESULTS: This study achieved significant results in the multi-label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed-input DL model based on activity tracking data. The comparison of hyper-parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results.
CONCLUSIONS: This study can be considered as the first to develop a mixed-input DL model based on activity tracking data for the multi-label identification of late-life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer-grade wrist-worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi-label classification framework for identifying the complex symptoms of depression and anxiety.

BACKGROUND: There are disparities in the prevalence of physical activity (PA) with women engaging in less PA than men, a gap which widens during midlife. Walking is a generally accepted form of PA among women and should be encouraged. Motivations, barriers, and attitudes to engaging in walking change with age, but the influencing factors are not well understood nor are the features of mobile apps that facilitate daily walking.
OBJECTIVE: This study explores the relationship between age and women\'s self-reported motivations, barriers, attitudes, and beliefs toward daily walking. It further assesses attitudes toward features of a mobile app designed to sync with a wearable step tracker to increase and maintain levels of daily walking among women.
METHODS: A web-based anonymous survey was completed by 400 women, aged 21-75 years. The 31-item survey captured women\'s perceived barriers and motivators toward daily walking and attitudes toward mobile apps to support and maintain daily walking. For analysis, responses to the survey were grouped into 2 categories of women: ages 21-49 years and ages 50-75 years. Bivariate analyses were conducted through SPSS (IBM Corp) for each of the survey questions using chi-square for dichotomous variables and 1-tailed t tests for scales and continuous variables to identify significant differences between the groups. One-tailed t tests were run for scaled variables to identify significant differences between the 10-year age increments.
RESULTS: Significant barriers to daily walking were observed in the 21-49-year group for personal and work responsibilities, motivational and psychosocial factors, and physical and environmental factors. Motivators to walk daily in the 21- 49-year group were significantly higher to reduce stress and anxiety, and motivators to walk daily in the 50-75-year group were significantly higher to help manage or lose weight and to reduce the risk of chronic illness. Women\'s walking preferences, beliefs around their walking behaviors, and their perceived importance of the features of a future mobile app for walking designed specifically for women showed significant variation according to age. When asked about the importance of features for a mobile app, women aged 21-49 years indicated a significantly higher number of positive responses for the following features: digital community support, rewards or point system, and seeing a daily or weekly or monthly progress chart.
CONCLUSIONS: Our findings indicate that barriers, motivators, and beliefs around daily walking and the importance of preferred features of a mobile app vary according to women\'s ages. Messaging and app features should be tailored to different age groups of women. These study results can be viewed as a foundation for future research and development of mobile health interventions to effectively increase daily walking among women of all ages.

BACKGROUND: Physical activity (PA) is an important contributor to one\'s physical and mental health both acutely and across the lifespan. Much research has done on the ambient environment\'s impact on PA; however, these studies have used absolute values of atmospheric measures such as temperature and humidity, which vary spatiotemporally and make comparisons between studies which differ in location or time of year difficult to square with one another.
METHODS: Here, we employ the Global Weather Type Classification, Version 2, to determine the combined impact of temperature and humidity on PA in a sample of insufficiently active young adults. We conducted secondary analyses of data from a single-group behavioral intervention trial that varied the number of digital messages sent daily. Young adults (n = 81) wore Fitbit Versa smartwatches for a 6-month period sometime between April 2019 and July 2020, and location was tracked using a custom smartphone application.
RESULTS: Mixed linear models indicated that, across 8179 person-days, PA was significantly lower on days with humid conditions and significantly higher on warm dry days, though the latter relationship was no longer significant when controlling for timing in relation to the COVID-19 pandemic declaration. Demographic factors did not affect the relationship between weather and PA.
CONCLUSIONS: Results are a first step in providing additional guidance for encouraging PA in insufficiently active individuals given forecasted daily weather conditions. Future work should examine seasonal variability in the weather type-PA relationship without the influence of a world-altering event influencing results.

OBJECTIVE: Coronary heart disease has several risk factors that require a multifactorial community intervention approach in prevention efforts. Prevalence of coronary heart disease and its risk factors have been disproportionately high among American Indians. The objective of this study is to evaluate the impact of ambulatory activity levels on the development of coronary heart disease in this population.
METHODS: Using pedometer data and other lifestyle and clinical factors from 2492 participants in the Strong Heart Family Study, we examined the associations of average daily step counts with incident coronary heart disease during an 18 to 20 year follow-up.
RESULTS: After adjusting for potential confounders, participants with daily step counts in the 4th quartile (>7282 steps per day) had significantly lower odds of developing coronary heart disease compared to those in the 1st quartile (<3010 steps per day) (p = 0.035).
CONCLUSIONS: Higher daily step count (over 7282 steps per day) is significantly associated with lower incidence of coronary heart disease among American Indian participants of the Strong Heart Family Study in a 20-year follow-up period.