Physical Activity Tracking Research Articles

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data.

Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant's needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

A study on the benefits of participation in an electronic tracking physical activity program and motivational interviewing during a three-month period

Background: The purpose was to investigate if participation in a three-month electronic tracking outdoor physical activity and a motivational interviewing (MI) intervention led to positive behavioural, psychological, and physiological outcomes. Methods: Based on a two-group pre-post design, 12 middle-aged women and 6 men were randomly assign to an experimental and a control group. Physical activity data were collected by wrist-worn activity sensors, and pre-post data were collected on the GHQ-12, the BREQ-2, body mass, body fat mass and total body muscle. Measures of cardiovascular fitness were taken pre to post. The experimental group was supported through individual MI coaching sessions and resistance-training for use in an outdoor gym. Magnitude based inferences (MBI) were calculated based on the disposition of the confidence limits for the mean differences to the smallest worthwhile changes. Results: The experimental group had a beneficial increase in its physical activity behaviour (steps). The control group had a medium decrease in identified regulation, the experimental group maintained the same level at the post-measure. Conclusion: Few studies have investigated how the combination of MI and the use of activity-tracking devices effect physical and mental health. This study investigates the use of both MI and activity-tracking devices on psychological well-being, motivation, and physical health in an outdoor context. Future research recommendations are given.

Exploring Saudi Individuals' Perspectives and Needs to Design a Hypertension Management Mobile Technology Solution: Qualitative Study.

Hypertension is a chronic condition caused by a poor lifestyle that affects patients’ lives. Adherence to self-management programs increases hypertension self-monitoring, and allows greater prevention and disease management. Patient compliance with hypertension self-management is low in general; therefore, mobile health applications (mHealth-Apps) are becoming a daily necessity and provide opportunities to improve the prevention and treatment of chronic diseases, including hypertension. This research aims to explore Saudi individuals’ perspectives and needs regarding designing a hypertension management mobile app to be used by hypertension patients to better manage their illnesses. Semi-structured interviews were conducted with 21 Saudi participants to explore their perspectives and views about the needs and requirements in designing a hypertension mobile technology solution, as well as usability and culture in the Saudi context. The study used NVivo to analyze data and divided the themes into four main themes: the app’s perceived health benefits, features and usability, suggestions for the app’s content, and security and privacy. The results showed that there are many suggestions for improvements in mobile health apps that developers should take into consideration when designing apps. The mobile health apps should include physical activity tracking, related diet information, and reminders, which are promising, and could increase adherence to healthy lifestyles and consequently improve the self-management of hypertension patients. Mobile health apps provide opportunities to improve hypertension patients’ self-management and self-monitoring. However, this study asserts that mobile health apps should not share users’ data, and that adequate privacy disclosures should be implemented.

The Accuracy of Commercially Available Fitness Trackers in Patients after Stroke.

Background: Fitness trackers could represent an easy-to-use and cheap tool for continuous tracking of physical activity of stroke survivors during the period of their recovery at home. The aim of the study was to examine the accuracy of the Fitbit activity tracker in locomotor activity monitoring of stroke survivors with respect to gait disorders, walking speed, walking aid, and placement of the tracker on body. Methods: Twenty-four ambulatory stroke survivors (15 men and 9 women) with locomotion/gait disorder were involved in the study. Patients underwent two walking tests with the Fitbit Alta HR trackers attached on 5 different places on body. The accuracy of the trackers has been analyzed on 3 groups of patients—those walking without any walking aid, those using a single-point stick and those using a rolling walker. Results: For no-aid patients, the most accurate place was the waist. Patients with a single-point stick revealed the smallest deviations for a tracker attached to a healthy lower limb, and patients with a rolling walker revealed the smallest deviations for a tracker attached on the paretic lower limb. Conclusions: An accuracy comparable with the healthy population can be reached for all of the three groups of patients, while fulfilling the conditions for minimum speed of 2 km/h and optimal placement of the trackers with respect to a walking aid and aspect to impairment.

P10-04 Effects of a web-based versus a print-based physical activity intervention for community-dwelling older adults: Results of a randomized trial with a cross-over design

BackgroundDespite the crucial role of regular physical activity (PA) for preventing chronic non-communicable diseases, fewer than half of older adults in Germany engage in the recommended levels.Objective: The aim of this study was to compare acceptance and effectiveness of two interventions for PA promotion among initially inactive community-dwelling adults aged 60 years and above in a nine-month randomized trial with a cross-over design.MethodsParticipants were recruited offline and randomized to (a) a print-based intervention (PRINT n = 113) and (b) a web-based intervention (WEB, n = 129). Thirty percent (n = 38) of those in group (b) received a PA tracker in addition to WEB (WEB+, (c)). All intervention groups were offered ten weekly face-to-face group sessions led by trained student assistants. Afterwards, participants could choose to stay in their group or cross over to one of the other groups. Group sessions were continued monthly for another six months. Three-dimensional accelerometers to assess PA at baseline (T0), three-months (T1) and nine-months (T2) were employed. Intervention acceptance was assessed via self-administered paper-based questionnaires. Linear mixed models were used to calculate differences in moderate-to-vigorous PA (MVPA) between time points and intervention groups.ResultsOf the initially recruited n = 242 participants, n = 91 (37.6%) were randomized to the WEB group, n = 38 (15.7%) to WEB+ and n = 113 (46.7%) to PRINT and n = 195 participants completed T1. Only n = 1 moved from WEB to PRINT and n = 15 from PRINT to WEB (WEB-WEB: n = 103, PRINT-PRINT: n = 76), when offered to cross over at T1. One-hundred and sixty participants completed T2. MVPA in min per day increased between baseline and T1, but these within-group changes in time disappeared after adjusting for covariates. MVPA decreased by 9 min/day between baseline and T2 (βtime = -9.37, 95% CI: [-18.58; -0.16]), regardless of intervention group (WEB vs. PRINT: βgroup*time = -3.76, 95% CI: [-13.33; 5.82], WEB+ vs. PRINT: βgroup*time = 1.40, 95% CI: [-11.04; 13.83]). Intervention acceptance was generally high.ConclusionsDespite high levels of acceptance of web- and print-based interventions for PA promotion and little movement between groups at T1, when given the choice, participation was not associated with increases in PA over time.

Stable physical activity tracking during children's guided active play.

ObjectivePhysical activity (PA) decreases during childhood with PA tracking statistics showing moderate coefficients in early childhood moving to poor coefficients in late childhood. The age-related instability of PA tracking is attributed to variations in age-related PA behaviors when quantifying PA in different settings (in/out of school, sports camps, and habitual PA). This study has examined the stability of age-related PA for children (from 7 to 11 years) during self-paced guided active play (GAP) sessions separated by 12 months.MethodsChildren (n = 65) recruited from community camps in two consecutive years were assessed for growth and PA participation during GAP sessions (1 h.d−1 on 2 d.wk−1) using cooperative games. Accelerometer outputs were used to quantify PA and estimate energy expenditure (EE) and moderate-to-vigorous PA (MVPA). Tracking statistics were assessed by Spearman coefficients (r) and agreement scores for percentile rankings (Kappa (k)) after a 1-year interval.ResultsGrouped data for PA tracking (EE) over the 1-year interval showed strong coefficients (r = 0.88, p = 0.001) and moderate agreement scores (k = 0.54). Boys and girls showed similar results. During the 1-year interval, the MVPA tracking coefficient was r = 0.54 (p = 0.01) with a moderate k score of 0.47. Age-related PA (EE) tracking coefficients at 7 (r = 0.90), 8 (r = 0.88), 10 (r = 0.82), and 11 (r = 0.83) years showed strong coefficients except at 9 years (r = 0.51).ConclusionChildren's age-related PA tracking (EE and MVPA) is stable during self-paced GAP assessed over a 1-year interval. Since PA tracking is stable in self-paced GAP, GAP should be included in children's PA intervention strategies to improve health and fitness.

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment.

Wearable electronic devices are becoming key players in monitoring the body signals predominantly altered during physical activity tracking. Considering the growing interest in telemedicine and personalized care driven by the rise of the Internet of Things era, wearable sensors have expanded their field of application into healthcare. To ensure the collection of clinically relevant data, these devices need to establish conformable interfaces with the human body to provide high-signal-quality recordings and long-term operation. To this end, this paper presents a method to easily fabricate conformable thin tattoo- and soft textile-based sensors for their application as wearable organic electronic devices in a broad spectrum of surface electrophysiological recordings. The sensors are developed through a cost-effective and scalable process of cutaneous electrode patterning using poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS), the most popular conductive polymer in bioelectronics, on off-the-shelf, wearable substrates. This paper presents key steps in electrode characterization through impedance spectroscopy to investigate their performance in signal transduction when coupled with the skin. Comparative studies are required to position the performance of novel sensors with respect to the clinical gold standard. To validate the fabricated sensors' performance, this protocol shows how to perform various biosignal recordings from different configurations through a user-friendly and portable electronic setup in a laboratory environment. This methods paper will allow multiple experimental initiatives to advance the current state of the art in wearable sensors for human body health monitoring.

Influence of upper limb activity on the step count and accuracy of sleep time of a wristband-type physical activity tracker.

BackgroundA wristband-type consumer physical activity tracker (PAT) is commonly used in rehabilitation to assess an individual’s physical activity. However, under the free-living setting, the wristband-type PAT tends to overestimate step counts when compared with the research-standard criterion. Also, daily rhythm characteristics, such as sleep time, are difficult to monitor accurately based solely on self-reporting.PurposeTo identify the conditions measured as step counts by a wristband-type consumer PAT when using the upper limbs in daily living, and the measurement accuracy of the sleeping time estimated from the wristband-type PAT.MethodsForty participants (20 females, mean age 32.65 ± 9.52 years) were enrolled in two experiments in this study. In Experiment 1, we measured the influence of upper limbs activity (movement speed and distance) on step counts of wristband-type and waist holder-type PAT in two upper limb tasks. In Experiment 2, we verified the measurement accuracy of two sleep times by wristband-type PAT using a self-reported survey for 3 days.ResultsThe results of Experiment 1 revealed that the step counts using wristband-type PAT were influenced by upper limbs activity depending on movement distance (F (1, 19) = 31.705, p < 0.001) but not speed (F (1, 19) = 2.669, p < 0.117). Whereas, there was no relationship between step counts and upper limb activity in waist holder-type PAT. The results of Experiment 2 showed that the sleep times of wristband-type and self-report had a strong correlation (coefficient value = 0.93, p < 0.001).ConclusionsThis PAT is useful for capturing changes in the amount of physical activity and the daily rhythm within the individual. It can be expected to be used for rehabilitation support centered on upper limb activity and daily rhythm.

The Effect of a Future-Self Avatar Mobile Health Intervention (FutureMe) on Physical Activity and Food Purchases: Randomized Controlled Trial.

BackgroundInsufficient physical activity and unhealthy diets are contributing to the rise in noncommunicable diseases. Preventative mobile health (mHealth) interventions may help reverse this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence of whether such interventions can change health behavior is lacking.ObjectiveWe aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs.MethodsWe conducted a 12-week parallel randomized controlled trial (RCT), followed by semistructured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention, a physical activity and food purchase tracking mobile phone app that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition by leveraging digital receipts from loyalty card data and analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals, and nonparametric tests were conducted to test for within- and between-group differences.ResultsWe recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44 years (IQR 19), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power. The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median −1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system.ConclusionsPreventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change.Trial RegistrationClinicalTrials.gov NCT04505124; https://clinicaltrials.gov/ct2/show/NCT04505124

The Effect of Tailored, Daily, Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: the SMARTER Randomized Clinical Trial.

BackgroundSelf-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials.ObjectiveThe aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m2.MethodsAll participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants’ dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal).ResultsParticipants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m2. The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: –2.12%, 95% CI –3.04% to –1.21%, P<.001; SM: –2.39%, 95% CI –3.32% to –1.47%; P<.001), but no difference between the groups (–0.27%; 95% CI –1.57% to 1.03%; t =–0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=–0.10; 95% CI –0.13 to –0.07; t =–5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001).ConclusionsThere were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.Trial RegistrationClinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936

Adherence to a caloric budget and body weight change vary by season, gender, and BMI: An observational study of daily users of a mobile health app.

Self-monitoring, one of the most important behaviors for successful weight loss, can be facilitated through mobile health applications (mHealth apps). Therefore, it is of interest to determine whether consistent users of these apps succeed in achieving their weight goals. This study used data from an mHealth app that enabled tracking of caloric intake, body weight, and physical activity and provided a caloric budget depending on weight goal. The primary objective was to evaluate adherence to caloric budget and body weight change among the most consistent (i.e., daily) trackers of caloric intake over a calendar year (n=9372, 50% male). Gender-stratified linear mixed models were conducted to examine the effects of quarter of year (Q1-Q4 as season proxies) and body mass index (BMI) group (normal weight, overweight, obesity) on adherence to a caloric budget (kcal/day). Change in body weight was analyzed using a subset of users (n=5808) who entered their weight in the app at least once per week, once per month, or once in Q1 and Q4. Physical activity entries were evaluated in exploratory analyses. Only users with obesity met their caloric budget in Q1. Deviation from budget increased for all groups from Q1 to Q2 (mean change[±standard error of the mean]: +23.7[±1.8] and +39.7[±2.2]kcal/day for female and male users, p<0.001), was stable between Q2 and Q3, and fluctuated thereafter depending on gender and BMI, with greater deviation among males with overweight. Users with obesity with weight entries at least once per month lost the most weight (-6.1[±0.3] and -4.5[±0.3]kg for females and males, p<0.001). Physical activity was highest in the summer months. Among consistent calorie trackers, adherence to a caloric budget and body weight vary by season, gender, and BMI. Self-monitoring of body weight in addition to calorie tracking may lead to improved weight loss outcomes.

38-LB: Physical Activity and Glycemia among Young Adults with Type 1 Diabetes and Overweight or Obesity—Results from Advancing Care for Type 1 Diabetes and Obesity Network (ACT1ON)

The ACT1ON pilot study evaluated the feasibility of three dietary strategies to optimize weight and glycemic management among young adults with T1D and overweight or obesity. As a secondary measure, self-reported physical activity (PA) was collected at baseline, 3-, 6-, and 9-months from 68 young adults with T1D (age 25.5 ± 3.1 years, 72.1% female, HbA1c 7.9 ± 1.8%, BMI 30.4 (27.9 - 33.9)) . Using the Global Physical Activity Questionnaire (GPAQ, n=195) and Previous Day Physical Activity Recalls (PDPAR, n=123) , we estimated weekly minutes of moderate-to-vigorous physical activity (MVPA) . Following the COVID-19 outbreak, a subset of participants wore Garmin Vivosmart4® PA trackers for two weeks at each visit (44 measurements from 27 participants) . Mixed effects regression models assessed the relationship between weekly minutes of MVPA and HbA1c using each PA measure. Median weekly minutes of MVPA were 33% lower following the COVID-19 outbreak compared to pre-pandemic PA levels (p=0.02) per the GPAQ, but not PDPAR (-7.7%, p=0.34) . After adjusting for design, demographic, clinical, and dietary variables, a 1 standard deviation increase in weekly minutes of MVPA (GPAQ) was associated with an absolute increase of 0.27% HbA1c (p&amp;gt;0.001) . A small, statistically non-significant association was observed for PDPAR (β=0.13, p=0.19) ; however, we observed a borderline statistically significant association using the PA tracker data (β=0.231, p=0.08) , despite a smaller sample size (n=44) . These results suggest that among young adults with T1D and overweight and obesity, higher levels of PA may lead to challenges in achieving optimal glycemia. Future work is needed to determine how to best support young adults with T1D and overweight and obesity in attaining both their PA and glycemic management goals. Disclosure F. R. Muntis: None. R. E. Pratley: Other Relationship; Bayer AG, Corcept Therapeutics, Dexcom, Inc., Hanmi Pharm. Co., Ltd., Merck &amp; Co., Inc., Metavention, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. K. Corbin: None. E. J. Mayer-davis: None. D. P. Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D. Igudesman: None. A. Cristello: None. J. Thomas: None. N. Arrizon- ruiz: None. J. Hooper: None. A. Addala: None. J. Crandell: n/a. D. M. Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. Funding National Institutes of Health (1DP3DK113358-01)

EFFECT OF MOBILE APPLICATIONS FOR PHYSICAL ACTIVITY TRACKING ON EXERCISE TOLERANCE IN PATIENTS WITH HYPERTENSION AND CORONARY ARTERY DISEASE

Objective: To assess the effect of smartphone applications designed for physical activity tracking on exercise tolerance of patients with arterial hypertension (HTN) and coronary artery disease (CAD) during rehabilitation program. Design and method: We examined 464 patients with HTN (men 176, 37.9%) aged 61.7 ± 11.0 who attended rehabilitation programs in 5 days to 6 months after myocardial infarction (median 24 [6; 92] days) in health campus. Most of the patients undergone coronary revascularization: 371 (80.0%) - percutaneous coronary intervention and 75 (16.1%) coronary artery bypass grafting; 18 patients (3.9%) had non-invasive management. Patients were randomized in two groups: I (n = 238, 51.3%) were using mobile apps with activity trackers and II (control, n = 226, 48.7%) without intervention. The six-minute walk test (6MWT) was performed at the time of admission, in the middle of the rehabilitation course (the 6th day) and before discharge (the 12th day) (6MWT1, 6MWT2 and 6MWT3, respectively). We compared groups using Mann-Whitney U test and assessed changes in 6MWT distance as the difference between measurements (6MWT2-1 = 6MWT2 – 6MWT1 and 6MWT3-1 = 6MWT3 – 6MWT1). Results: The 6MWT1 distance on admission did not differ between the experimental and control groups (440.1 ± 82.3 m vs 421.3 ± 78.1 m, p = 0.81). The 6MWT distance increased over time in both groups. In the middle and at the end of the course difference between the groups became statistically significant: 6MWT2 in I group 499.3 ± 88.5 m vs 446.0 ± 82.7 in group II, p &lt; 0.001; 6MWT3 was 570.6 ± 89.5 m vs 473.6 ± 88.3 m, respectively, p &lt; 0.001. Experimental group had a more pronounced increase in 6MWT after the 2nd measurement (6MWT2-1 59.1 ± 26.3 m vs 25.6 ± 17.7m, p &lt; 0.001) and the 3rd measurement (6MWT3-1 was 130.5 ± 45.2 m vs 52.5 ± 30.6 m, p &lt; 0.001). Conclusions: The use of mobile apps for physical activity tracking in patients with HTN and CAD after MI was associated with an increase in the 6MWT distance on the 6th and 12th day of cardiac rehabilitation program compared with the control group.

Objectively Measured Physical Activity and Sedentary Time of Suburban Toddlers Aged 12-36 Months.

Low levels of physical activity may predispose children to the development of obesity and related chronic diseases in later life. The aims of this study were as follows: (1) quantitatively describe the levels of habitual physical activity in a contemporary sample of suburban children aged 12 to 36 months; (2) assess for gender differences in physical activity and sedentary behavior; (3) examine the specific effects of ethnicity, gender and overweight status on the objectively measured physical activity; and (4) quantify the tracking of physical activity in a subset of children over 1 year. During year one, 142 participants wore the GT3X Actigraph for 3 days. At a 1-year follow-up, a subset of 25 participants wore the Actigraph for 7 consecutive days. GLM and t-tests as appropriate were carried out to assess the influence of gender on the physical activity level. Spearman rank correlations, percentage agreement and kappa statistics assessed the tracking of physical activity. The results showed no significant gender differences in any anthropometric measurements, sedentary behavior or MVPA (p > 0.05). There were also no significant gender, ethnicity or overweight interaction for sedentary behavior, time spent in light PA and time spent in MVPA (p > 0.05). For tracking, there was a moderate strength of agreement for MVPA. Considering the disproportionate effects of obesity in minority groups, culturally appropriate interventions targeting the reduction in sedentary behavior are urgently required.

Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study.

BackgroundConsumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed.ObjectiveThe objective of this study was to compare the Polar Vantage watch (Polar Electro Oy) and Oura ring (generation 2; Ōura Health Oy) activity trackers to research-based instruments for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration in free-living adults.MethodsA total of 21 participants wore 2 consumer-based activity trackers (Polar watch and Oura ring), an ActiGraph accelerometer (ActiGraph LLC), and an Actiheart accelerometer and heart rate monitor (CamNtech Ltd) and completed a sleep diary for up to 7 days. We assessed Polar watch and Oura ring validity and comparability for measuring physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analyzed repeated measures correlations, Bland-Altman plots, and mean absolute percentage errors.ResultsThe Polar watch and Oura ring values strongly correlated (P<.001) with the ActiGraph values for steps (Polar: r=0.75, 95% CI 0.54-0.92; Oura: r=0.77, 95% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar: r=0.76, 95% CI 0.62-0.88; Oura: r=0.70, 95% CI 0.49-0.82), and total energy expenditure (Polar: r=0.69, 95% CI 0.48-0.88; Oura: r=0.70, 95% CI 0.51-0.83) and strongly or very strongly correlated (P<.001) with the sleep diary–derived sleep durations (Polar: r=0.74, 95% CI 0.56-0.88; Oura: r=0.82, 95% CI 0.68-0.91). Oura ring–derived resting heart rates had a very strong correlation (P<.001) with the Actiheart-derived resting heart rates (r=0.9, 95% CI 0.85-0.96). However, the mean absolute percentage error was high for all variables except Oura ring–derived sleep duration (10%) and resting heart rate (3%), which the Oura ring underreported on average by 1 beat per minute.ConclusionsThe Oura ring can potentially be used as an alternative to the Actiheart to measure resting heart rate. As for sleep duration, the Polar watch and Oura ring can potentially be used as replacements for a manual sleep diary, depending on the acceptable error. Neither the Polar watch nor the Oura ring can replace the ActiGraph when it comes to measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but they may be used as additional sources of physical activity measures in some settings. On average, the Polar Vantage watch reported higher outputs compared to those reported by the Oura ring for steps, moderate-to-vigorous physical activity, and total energy expenditure.

Spatio-temporal and contextual cues to support reflection in physical activity tracking

Physical activity reconstruction is a process whereby self-trackers reflect on their physical activities and goals in an episodic fashion by recalling a series of past experiences and events. Tracking tools often include spatio-temporal cues (i.e. maps and timelines) to provide further context to these physical activity patterns, yet little is known about whether these features actually aid reflection. Here, we report a four-week user study (N=26) run with FitReconstruct, an app that fosters reconstruction of physical activity data using spatio-temporal and contextual cues to support recall of episodic information. We found that compared to a baseline version, presenting spatio-temporal and contextual cues increased the quality of reflection and user satisfaction. These findings inform design recommendations to support explicit reflective prompts in physical activity tracking apps.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

BackgroundOn-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation.ObjectiveThe aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients’ changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility.MethodsFrom June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission.ResultsOf the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (β=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (β=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (β=–.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (β=–.13; SE 0.06; P=.04) was associated with toileting activities.ConclusionsThis study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients’ progress or decline.

Asthma and Technology in Emerging African American Adults (The ATHENA Project): Protocol for a Trial Using the Multiphase Optimization Strategy Framework.

BackgroundAsthma causes substantial morbidity and mortality in the United States, particularly among African American emerging adults (AAEAs; aged 18-30 years), but very few asthma programs have targeted this population. Interventions that provide education and address underlying motivation for managing asthma may be the most effective. However, intensive face-to-face interventions are often difficult to implement in this population.ObjectiveThe purpose of this study is to develop an effective mobile asthma management intervention to improve control among AAEAs.MethodsWe will assess the ability of multiple technologic components to assist and improve traditional asthma education. The first component is the Motivational Enhancement System for asthma management. It is a mobile 4-session intervention using supported self-regulation and motivational interviewing. Personalized content is based on each participant’s activity level, daily experiences, and goals. The second component is supportive accountability. It is administered by asthma nurses using targeted mobile support (Skype/voice calls) to provide education, promote self-efficacy, and overcome barriers through a motivational interviewing–based framework. The third component is SMS text messaging. It provides reminders for asthma education, medication adherence, and physical activity. The fourth component is physical activity tracking. It uses wearable technology to help meet user-defined physical activity goals. Using a multiphase optimization strategy (MOST) framework, we will test intervention components and combinations of components to identify the most effective mobile intervention. The MOST framework is an innovative, and cost- and time-effective framework that uses engineering principles to produce effective behavioral interventions. We will conduct a component selection experiment using a factorial research design to build an intervention that has been optimized for maximum efficacy, using a clinically significant improvement in asthma. Participants (N=180) will be randomized to 1 of 6 intervention arms. Participants will be recruited from multiple sites of the American Lung Association-Airway Clinical Research Centers network and ambulatory care clinics at the Detroit Medical Center. Data collections will occur at baseline, and 3, 6, and 12 months.ResultsAt study completion, we will have an empirically supported optimized mobile asthma management intervention to improve asthma control for AAEAs. We hypothesize that postintervention (3, 6, and 12 months), participants with uncontrolled asthma will show a clinically significant improvement in asthma control. We also hypothesize that improvements in asthma management behaviors (including physical activity), quality of life, symptoms, adherence, and exacerbation (secondary outcomes) will be observed.ConclusionsAAEAs are disproportionately impacted by asthma, but have been underrepresented in research. Mobile asthma management interventions may help improve asthma control and allow people to live healthier lives. During this project, we will use an innovative strategy to develop an optimized mobile asthma management intervention using the most effective combination of nurse-delivered asthma education, a smartphone app, and text messaging.International Registered Report Identifier (IRRID)PRR1-10.2196/37946

P132 Does weight-bearing physical activity track through the lifecourse? Findings from the Hertfordshire Cohort Study

Abstract Background/Aims Physical activity is known to be beneficial for musculoskeletal health across the lifecourse. We have previously demonstrated that regular participation in weight-bearing physical activity (WBPA) at the time of peak bone mass acquisition is associated with higher hip bone mineral density in late adulthood in women. Understanding how WBPA tracks across the lifecourse may help us develop interventions to promote WBPA for musculoskeletal benefit; we consider these relationships here. Methods The study population consisted of 128 men and 130 women from the Hertfordshire Cohort Study. Questionnaire data on participation in sports and leisure time exercise involving WBPA e.g. running, racquet sports, football, rugby, hockey and dancing, but not walking, cycling or swimming were collected for the following age bands: &amp;lt;18 years; ages 18-29 years; ages 30-49 years; and ≥50 years. Responses were coded as none/ once a month/ once a week/ ≥once a week. Data on current lifestyle and physical activity levels were also collected. Results The mean age of participants was 75.4 (SD 2.5) years in men and 75.7 (SD 2.6) years in women. Men tended to report higher levels of past WBPA throughout the lifecourse, with significant differences in WBPA up to the age of 18 years (p = 0.006) and 18-29 years (p &amp;lt; 0.001). Table 1 shows kappa statistics of relationships between reported WBPA levels at different ages. These decreased over time, and while broadly similar, the tracking was generally stronger in women than men. For example, we observed that participants who were active at 30-49 years were more likely to remain active in late adulthood with kappa in men 0.393 (95%CI: 0.295-0.442) and kappa in women 0.492 (95% CI: 0.400-0.555). Conclusion WBPA decreased with advancing age in both sexes, though was generally higher in men than women in this retrospective cohort study. The correlation between WBPA at different stages of the lifecourse weakened as time between the measurements increased in both sexes. Our results highlight the need for further work to understand the drivers to encourage WBPA at different stages in the lifecourse and to facilitate development of appropriate interventions to promote WBPA for musculoskeletal benefit. Disclosure J. Zhang: None. K. Jameson: None. L. Westbury: None. N. Fuggle: None. K. Ward: None. C. Cooper: None. E. Dennison: None.

Evaluation of the Immediate Effects of Web-Based Intervention Modules for Goals, Planning, and Coping Planning on Physical Activity: Secondary Analysis of a Randomized Controlled Trial on Weight Loss Maintenance.

BackgroundThe use of digital interventions can be accurately monitored via log files. However, monitoring engagement with intervention goals or enactment of the actual behaviors targeted by the intervention is more difficult and is usually evaluated based on pre-post measurements in a controlled trial.ObjectiveThe objective of this paper is to evaluate if engaging with 2 digital intervention modules focusing on (1) physical activity goals and action plans and (2) coping with barriers has immediate effects on the actual physical activity behavior.MethodsThe NoHoW Toolkit (TK), a digital intervention developed to support long-term weight loss maintenance, was evaluated in a 2 x 2 factorial randomized controlled trial. The TK contained various modules based on behavioral self-regulation and motivation theories, as well as contextual emotion regulation approaches, and involved continuous tracking of weight and physical activity through connected commercial devices (Fitbit Aria and Charge 2). Of the 4 trial arms, 2 had access to 2 modules directly targeting physical activity: a module for goal setting and action planning (Goal) and a module for identifying barriers and coping planning (Barriers). Module visits and completion were determined based on TK log files and time spent in the module web page. Seven physical activity metrics (steps; activity; energy expenditure; fairly active, very active and total active minutes; and distance) were compared before and after visiting and completing the modules to examine whether the modules had immediate or sustained effects on physical activity. Immediate effect was determined based on 7-day windows before and after the visit, and sustained effects were evaluated for 1 to 8 weeks after module completion.ResultsOut of the 811 participants, 498 (61.4%) visited the Goal module and 406 (50.1%) visited the Barriers module. The Barriers module had an immediate effect on very active and total active minutes (very active minutes: before median 24.2, IQR 10.4-43.0 vs after median 24.9, IQR 10.0-46.3; P=.047; total active minutes: before median 45.1, IQR 22.9-74.9 vs after median 46.9, IQR 22.4-78.4; P=.03). The differences were larger when only completed Barriers modules were considered. The Barriers module completion was also associated with sustained effects in fairly active and total active minutes for most of the 8 weeks following module completion and for 3 weeks in very active minutes.ConclusionsThe Barriers module had small, significant, immediate, and sustained effects on active minutes measured by a wrist-worn activity tracker. Future interventions should pay attention to assessing barriers and planning coping mechanisms to overcome them.Trial RegistrationISRCTN Registry ISRCTN88405328; https://www.isrctn.com/ISRCTN88405328