Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors.

BACKGROUND: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. METHODS: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis. RESULTS: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00-01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. CONCLUSIONS: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.

Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study.

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.

Engagement, acceptability, usability and satisfaction with Active for Life, a computer-tailored web-based physical activity intervention using Fitbits in older adults.

BACKGROUND: Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with 'Active for Life,' a web-based physical activity intervention providing computer-tailored physical activity advice to older adults. METHODS: Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12 weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction. RESULTS: At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p = .14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37 min in total, p = .56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p = .41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p = .003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options. CONCLUSIONS: This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901.

Effects of Wearable Fitness Trackers and Activity Adequacy Mindsets on Affect, Behavior, and Health: Longitudinal Randomized Controlled Trial.

BACKGROUND: There is some initial evidence suggesting that mindsets about the adequacy and health consequences of one's physical activity (activity adequacy mindsets [AAMs]) can shape physical activity behavior, health, and well-being. However, it is unknown how to leverage these mindsets using wearable technology and other interventions. OBJECTIVE: This research examined how wearable fitness trackers and meta-mindset interventions influence AAMs, affect, behavior, and health. METHODS: A total of 162 community-dwelling adults were recruited via flyers and web-based platforms (ie, Craigslist and Nextdoor; final sample size after attrition or exclusion of 45 participants). Participants received an Apple Watch (Apple Inc) to wear for 5 weeks, which was equipped with an app that recorded step count and could display a (potentially manipulated) step count on the watch face. After a baseline week of receiving no feedback about step count, participants were randomly assigned to 1 of 4 experimental groups: they received either accurate step count (reference group; 41/162, 25.3%), 40% deflated step count (40/162, 24.7%), 40% inflated step count (40/162, 24.7%), or accurate step count+a web-based meta-mindset intervention teaching participants the value of adopting more positive AAMs (41/162, 25.3%). Participants were blinded to the condition. Outcome measures were taken in the laboratory by an experimenter at the beginning and end of participation and via web-based surveys in between. Longitudinal analysis examined changes within the accurate step count condition from baseline to treatment and compared them with changes in the deflated step count, inflated step count, and meta-mindset conditions. RESULTS: Participants receiving accurate step counts perceived their activity as more adequate and healthier, adopted a healthier diet, and experienced improved mental health (Patient-Reported Outcomes Measurement Information System [PROMIS]-29) and aerobic capacity but also reduced functional health (PROMIS-29; compared with their no-step-count baseline). Participants exposed to deflated step counts perceived their activity as more inadequate; ate more unhealthily; and experienced more negative affect, reduced self-esteem and mental health, and increased blood pressure and heart rate (compared with participants receiving accurate step counts). Inflated step counts did not change AAM or most other outcomes (compared with accurate step counts). Participants receiving the meta-mindset intervention experienced improved AAM, affect, functional health, and self-reported physical activity (compared with participants receiving accurate step counts only). Actual step count did not change in either condition. CONCLUSIONS: AAMs--induced by trackers or adopted deliberately--can influence affect, behavior, and health independently of actual physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT03939572; https://www.clinicaltrials.gov/ct2/show/NCT03939572.

RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) Evaluation of the Use of Activity Trackers in the Clinical Care of Adults Diagnosed With a Chronic Disease: Integrative Systematic Review.

BACKGROUND: Chronic diseases are a leading cause of adult mortality, accounting for 41 million deaths globally each year. Low levels of physical activity and sedentary behavior are major risk factors for adults to develop a chronic disease. Physical activity interventions can help support patients in clinical care to be more active. Commercial activity trackers that can measure daily steps, physical activity intensity, sedentary behavior, and distance moved are being more frequently used within health-related interventions. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is a planning and evaluation approach to explore the reach, effectiveness, adoption, implementation, and maintenance of interventions. OBJECTIVE: The objective of this study is to conduct an integrative systematic review and report the 5 main RE-AIM dimensions in interventions that used activity trackers in clinical care to improve physical activity or reduce sedentary behavior in adults diagnosed with chronic diseases. METHODS: A search strategy and study protocol were developed and registered on the PROSPERO platform. Inclusion criteria included adults (18 years and older) diagnosed with a chronic disease and have used an activity tracker within their clinical care. Searches of 10 databases and gray literature were conducted, and qualitative, quantitative, and mixed methods studies were included. Screening was undertaken by more than 1 researcher to reduce the risk of bias. After screening, the final studies were analyzed using a RE-AIM framework data extraction evaluation tool. This tool assisted in identifying the 28 RE-AIM indicators within the studies and linked them to the 5 main RE-AIM dimensions. RESULTS: The initial search identified 4585 potential studies. After a title and abstract review followed by full-text screening, 15 studies were identified for data extraction. The analysis of the extracted data found that the RE-AIM dimensions of adoption (n=1, 7% of studies) and maintenance (n=2, 13% of studies) were underreported. The use of qualitative thematic analysis to understand the individual RE-AIM dimensions was also underreported and only used in 3 of the studies. Two studies used qualitative analysis to explore the effectiveness of the project, while 1 study used thematic analysis to understand the implementation of an intervention. CONCLUSIONS: Further research is required in the use of activity trackers to support patients to lead a more active lifestyle. Such studies should consider using the RE-AIM framework at the planning stage with a greater focus on the dimensions of adoption and maintenance and using qualitative methods to understand the main RE-AIM dimensions within their design. These results should form the basis for establishing long-term interventions in clinical care. TRIAL REGISTRATION: PROSPERO CRD42022319635; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=319635.

Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy (the NeoFit Trial): protocol for a national, multicenter single-arm trial.

BACKGROUND: Breast cancer (BC) has particular characteristics in young women, with diagnosis at more advanced stages, a poorer prognosis and highly aggressive tumors. In NeoFit, we will use an activity tracker to identify and describe various digital profiles (heart rate, physical activity, and sleep patterns) in women below the age of 45 years on neoadjuvant chemotherapy for BC. METHODS: NeoFit is a prospective, national, multicenter, single-arm open-label study. It will include 300 women below the age of 45 years treated with neoadjuvant chemotherapy for BC. Participants will be asked to wear a Withing Steel HR activity tracker round the clock for 12 months. The principal assessments will be performed at baseline, at the end of neoadjuvant chemotherapy and at 12 months. We will evaluate clinical parameters, such as toxicity and the efficacy of chemotherapy, together with quality of life, fatigue, and parameters relating to lifestyle and physical activity. The women will complete REDCap form questionnaires via a secure internet link. DISCUSSION: In this study, the use of an activity tracker will enable us to visualize changes in the lifestyle of young women on neoadjuvant chemotherapy for BC, over the course of a one-year period. This exploratory study will provide crucial insight into the digital phenotypes of young BC patients on neoadjuvant chemotherapy and the relationship between these phenotypes and the toxicity and efficacy of treatment. This trial will pave the way for interventional studies involving sleep and physical activity interventions. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT05011721 . Registration date: 18/08/2021.

The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial.

BACKGROUND: Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults. OBJECTIVE: The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians. METHODS: Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants' Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)-measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms. RESULTS: A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI -24% to 40%) or the tailoring-only group (-4%, 95% CI -24% to 30%); however, a significant decline was seen in the control group (-35%, 95% CI -52% to -11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%). CONCLUSIONS: A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000646246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618000646246.

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

BACKGROUND: Self-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. OBJECTIVE: The 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. METHODS: All 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). RESULTS: Participants (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). CONCLUSIONS: There 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 REGISTRATION: Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936.

Effects of a Long-Term Wearable Activity Tracker-Based Exercise Intervention on Cardiac Morphology and Function of Patients with Cystic Fibrosis.

Several studies have shown that patients with cystic fibrosis (CF), even at a young age, have pulmonary and cardiac abnormalities. The main complications are cardiac right ventricular (RV) systolic and/or diastolic dysfunction and pulmonary hypertension, which affects their prognosis. Exercise training (ET) is recommended in patients with CF as a therapeutic modality to improve physical fitness and health-related quality of life. However, questions remain regarding its optimal effective and safe dose and its effects on the patients' cardiac function. The study aimed to provide a wearable activity tracker (WAT)-based ET to promote physical activity in CF patients and assess its effects on cardiac morphology and function. Forty-two stable CF individuals (aged 16.8 ± 3.6 years) were randomly assigned to either the intervention (Group A) or the control group (Group B). Group A participated in a 1-year WAT-based ET program three times per week. All patients underwent a 6-min walking test (6-MWT) and an echocardiographic assessment focused mainly on RV anatomy and function at the baseline and the end of the study. RV systolic function was evaluated by measuring the tricuspid annular plane systolic excursion (TAPSE), the systolic tricuspid annular velocity (TVS'), the RV free-wall longitudinal strain (RVFWSL), and the right ventricular four-chamber longitudinal strain (RV4CSL). RV diastolic function was assessed using early (TVE) and late (TVA) diastolic transtricuspid flow velocity and their ratio TVE/A. Pulmonary artery systolic pressure (PASP) was also estimated. In Group A after ET, the 6MWT distance improved by 20.6% (p < 0.05), TVA decreased by 17% (p < 0.05), and TVE/A increased by 13.2% (p < 0.05). Moreover, TAPSE, TVS', RVFWSL, and RV4CSL increased by 8.3% (p < 0.05), 9.0% (p < 0.05), 13.7% (p < 0.05), and 26.7% (p < 0.05), respectively, while PASP decreased by 7.6% (p < 0.05). At the end of the study, there was a significant linear correlation between the number of steps and the PASP (r = −0.727, p < 0.01) as well as the indices of RV systolic function in Group A. In conclusion, WAT is a valuable tool for implementing an effective ET program in CF. Furthermore, ET has a positive effect on RV systolic and diastolic function.

Monitoring the recovery time of children after tonsillectomy using commercial activity trackers.

An observational prospective feasibility study in which children received a tracker 2 weeks before a tonsillectomy and were required to wear it until four weeks postoperatively. The parents used a diary to log the estimated steps of their child. As primary endpoint, the compliance of complete datasets was compared between the tracker and the diary. As secondary endpoints, the agreement of steps between tracker and diary, and the recovery time after tonsillectomy were analyzed.Twenty-four patients (50% male) with a median age of 6 years were recruited. The tracker had a complete dataset compliance of 91.7% in the pre-operative and 58.3% in postoperative period, whereas the diary's compliance was 62.5% in the pre-operative and 12.5% in the postoperative period. The difference of 29.2% and 45.8% in the pre-operative and postoperative periods between the tracker and the diary was significant (p < 0.005). The tracker and diary had a mean agreement difference of 1063 steps per day. Mean recovery time was 21 days after tonsillectomy.Conclusion: The results of this pilot study support the use of a tracker in terms of compliance and practicability. Consumer-level activity trackers are a viable alternative to conventional manual logging for clinical use in pediatric research.Trial registration: ClinicalTrials.gov Identifier: NCT03174496 What is known: • Consumer-level activity trackers are already used in clinical research to monitor steps and physical activity. • The use of consumer-level activity trackers in clinical studies has mostly been validated in the adult population. What is new: • This study proves the feasibility of using physical activity trackers in a pediatric population before and after a surgical intervention. • Recovery of a patient could be assessed with an activity tracker.

Feasibility of promoting physical activity using mHEALTH technology in rural women: the step-2-it study.

BACKGROUND: Rural women are more likely to be obese and have a higher risk for chronic disease than their non-rural counterparts. Inadequate physical activity (PA) at least in part contributes to this increased risk. Rural women face personal, social and environmental barriers to PA engagement. Interventions promoting walking among rural women have demonstrated success; however, few of these studies use text messaging to promote PA. METHODS: Step-2-It was a pilot study to assess the feasibility, acceptability, and effectiveness of text-messaging combined with a pedometer to promote PA, specifically walking among English-speaking women, aged 40 and older, living in a rural, northwest Illinois county. Enrolled participants completed baseline assessments, received pedometers and two types of automated text messages: motivational messages to encourage walking, and accountability messages to report pedometer steps. Participants engaged in 3, 6, 9, and 12-week follow-ups to download pedometer data, and completed post-intervention assessments at 12 weeks. RESULTS: Of the 44 enrolled participants, 35 participants (79.5%) completed the intervention. Among completers, the proportion meeting PA guidelines increased from 31.4% (11/35) at baseline to 48.6% (17/35) at post-intervention, those with no PA decreased from 20% (7/35) to 17.1% (6/35). During weeks 1-12, when participants received motivational text messages, average participant daily step count was 5926 ± 3590, and remained stable during the intervention. Pedometer readings were highly correlated with self-reported steps (r = 0.9703; p < 0.001). CONCLUSION: Step-2-It was a feasible and acceptable walking intervention for older rural women. Technology, including text messaging, should be investigated further as an enhancement to interventions for rural women. Trial Registration on Clinicaltrials.gov: NCT04812756, registered on March 22, 2021.

The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study.

BACKGROUND: Engagement is positively associated with the effectiveness of digital health interventions. It is unclear whether tracking devices that automatically synchronize data (eg, Fitbit) produce different engagement levels compared with manually entering data. OBJECTIVE: This study examines how different step logging methods in the freely available 10,000 Steps physical activity program differ according to age and gender and are associated with program engagement. METHODS: A subsample of users (n=22,142) of the free 10,000 Steps physical activity program were classified into one of the following user groups based on the step-logging method: Website Only (14,617/22,142, 66.01%), App Only (2100/22,142, 9.48%), Fitbit Only (1705/22,142, 7.7%), Web and App (2057/22,142, 9.29%), and Fitbit Combination (combination of web, app, and Fitbit; 1663/22,142, 7.51%). Generalized linear regression and binary logistic regression were used to examine differences between user groups' engagement and participation parameters. The time to nonusage attrition was assessed using Cox proportional hazards regression. RESULTS: App Only users were significantly younger and Fitbit user groups had higher proportions of women compared with other groups. The following outcomes were significant and relative to the Website Only group. The App Only group had fewer website sessions (odds ratio [OR] -6.9, 95% CI -7.6 to -6.2), whereas the Fitbit Only (OR 10.6, 95% CI 8.8-12.3), Web and App (OR 1.5, 95% CI 0.4-2.6), and Fitbit Combination (OR 8.0; 95% CI 6.2-9.7) groups had more sessions. The App Only (OR -0.7, 95% CI -0.9 to -0.4) and Fitbit Only (OR -0.5, 95% CI -0.7 to -0.2) groups spent fewer minutes on the website per session, whereas the Fitbit Combination group (OR 0.2, 95% CI 0.0-0.5) spent more minutes. All groups, except the Fitbit Combination group, viewed fewer website pages per session. The mean daily step count was lower for the App Only (OR -201.9, 95% CI -387.7 to -116.0) and Fitbit Only (OR -492.9, 95% CI -679.9 to -305.8) groups but higher for the Web and App group (OR 258.0, 95% CI 76.9-439.2). The Fitbit Only (OR 5.0, 95% CI 3.4-6.6), Web and App (OR 7.2, 95% CI 5.9-8.6), and Fitbit Combination (OR 15.6, 95% CI 13.7-17.5) groups logged a greater number of step entries. The App Only group was less likely (OR 0.65, 95% CI 0.46-0.94) and other groups were more likely to participate in Challenges. The mean time to nonusage attrition was 35 (SD 26) days and was lower than average in the Website Only and App Only groups and higher than average in the Web and App and Fitbit Combination groups. CONCLUSIONS: Using a Fitbit in combination with the 10,000 Steps app or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that synchronize data automatically into real-world physical activity interventions is one strategy for improving engagement.

A comparison of the utility of different step-indices to translate the physical activity recommendation in adolescents.

Previous studies translating the daily moderate-to-vigorous physical activity (MVPA) recommendation of total steps/day among adolescents are inconsistent, and those with cadence-based steps are scarce. The main purpose was to compare the accuracy of different daily steps index-based cut-points related to the daily 60 minutes of MVPA recommendation measured by a waist-worn accelerometer for adolescents. Following a cross-sectional design, 428 Spanish adolescents (final sample 351, 50.4% males), aged 13-16 years old, wore an ActiGraph GT3X/+ accelerometer (reference standard = MVPA; index tests = total steps/day, average steps/min and peak 1-min cadence) on the right hip for eight consecutive days. 32.5% of the adolescents met the daily MVPA recommendation. The multiple ROC curve comparisons showed that the accuracy of the daily total step-based recommendation (AUC = 0.97) was statistically higher than for those with the steps/min (AUC = 0.90) and peak 1-min cadence (AUC = 0.58) (p < 0.001). The 10,000-step-per-day cut-point (k= 0.59-0.83) showed highest accuracy values than the 12,000 steps/day (k= 0.20-0.32). Daily total step-based recommendations are more accurate than those with steps/min and peak 1-min cadence for classifying adolescents as being physically active or inactive. A 10,000-step-per-day target is simple and accurate for both male and female adolescents.

Monitoring behavioral symptoms of dementia using activity trackers.

Tertiary disease prevention for dementia focuses on improving the quality of life of the patient. The quality of life of people with dementia (PwD) and their caregivers is hampered by the presence of behavioral and psychological symptoms of dementia (BPSD), such as anxiety and depression. Non-pharmacological interventions have proved useful in dealing with these symptoms. However, while most PwD exhibit BPSD, their manifestation (in frequency, intensity and type) varies widely among patients, thus the need to personalize the intervention and its assessment. Traditionally, instruments to measure behavioral symptoms of dementia, such as NPI-NH and CMAI, are used to evaluate these interventions. We propose the use of activity trackers as a complement to monitor behavioral symptoms in dementia research. To illustrate this approach we describe a nine week Cognitive Stimulation Therapy conducted with the assistance of a social robot, in which the ten participants wore an activity tracker. We describe how data gathered from these wearables complements the assessment of traditional behavior assessment instruments with the advantage that this assessment can be conducted continuously and thus be used to tailor the intervention to each PwD.

Measurement of sedentary time and physical activity in rheumatoid arthritis: an ActiGraph and activPAL™ validation study.

Accurate measurement of sedentary time and physical activity (PA) is essential to establish their relationships with rheumatoid arthritis (RA) outcomes. Study objectives were to: (1) validate the GT3X+ and activPAL3µ™, and develop RA-specific accelerometer (count-based) cut-points for measuring sedentary time, light-intensity PA and moderate-intensity PA (laboratory-validation); (2) determine the accuracy of the RA-specific (vs. non-RA) cut-points, for estimating free-living sedentary time in RA (field-validation). Laboratory-validation: RA patients (n = 22) were fitted with a GT3X+, activPAL3µ™ and indirect calorimeter. Whilst being video-recorded, participants undertook 11 activities, comprising sedentary, light-intensity and moderate-intensity behaviours. Criterion standards for devices were indirect calorimetry (GT3X+) and direct observation (activPAL3µ™). Field-validation: RA patients (n = 100) wore a GT3X+ and activPAL3µ™ for 7 days. The criterion standard for sedentary time cut-points (RA-specific vs. non-RA) was the activPAL3µ™. Results of the laboratory-validation: GT3X-receiver operating characteristic curves generated RA-specific cut-points (counts/min) for: sedentary time = ≤ 244; light-intensity PA = 245-2501; moderate-intensity PA ≥ 2502 (all sensitivity ≥ 0.87 and 1-specificity ≤ 0.11). ActivPAL3µ™-Bland-Altman 95% limits of agreement (lower-upper [min]) were: sedentary = (- 0.1 to 0.2); standing = (- 0.7 to 1.1); stepping = (- 1.2 to 0.6). Results of the field-validation: compared to the activPAL3µ™, Bland-Altman 95% limits of agreement (lower-upper) for sedentary time (min/day) estimated by the RA-specific cut-point = (- 42.6 to 318.0) vs. the non-RA cut-point = (- 19.6 to 432.0). In conclusion, the activPAL3µ™ accurately quantifies sedentary, standing and stepping time in RA. The RA-specific cut-points offer a validated measure of sedentary time, light-intensity PA and moderate-intensity PA in these patients, and demonstrated superior accuracy for estimating free-living sedentary time, compared to non-RA cut-points.

Physical activity preferences before and after participation in a 6-month physical activity intervention among women with metastatic breast cancer.

OBJECTIVE: This secondary analysis of the ABLE Trial (ClinicalTrials.gov NCT03148886) aimed to assess physical activity preferences before and after a 6-month physical activity intervention for women recently diagnosed with metastatic breast cancer and to investigate demographic and clinical correlates of these preferences. METHODS: Forty-nine patients participated in the ABLE Trial, a single-arm, unsupervised 6-month physical activity intervention with activity trackers. At baseline and 6 months, physical activity preferences, physical activity level, clinical variables, demographics and social vulnerability were assessed. RESULTS: At baseline, 49 participants were included, among whom 85% were interested in receiving physical activity counselling and 89% were interested in following a physical activity programme designed for metastatic breast cancer. At the end of the study, more participants preferred practising in a community fitness centre (66%) rather than at home (19% vs. 44% at baseline, p = .03). A higher social vulnerability score and not being treated by chemotherapy at baseline were significantly associated with lower desire to receive physical activity counselling (p = .01 and p = .04 respectively). CONCLUSIONS: This study will help design future studies within patients with metastatic breast cancer in accordance with their preferences. Designing tailored physical activity interventions according to the participant's preferences may be one key to success for adherence.

Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: a mixed methods approach.

BACKGROUND: Lack of physical activity (PA) is a risk factor for death and non-communicable disease. Despite this, more than one fourth of adults worldwide do not follow PA guidelines. As part of a feasibility study to test a complex intervention for increasing PA, we included a consumer-based activity tracker (AT) as a tool to measure PA outcomes and to track heart rate during exercise sessions. The aim of the present study was to identify factors that increase wear time when using a consumer-based AT for monitoring of participants in clinical research. METHODS: Sixteen participants aged 55-74 years, with obesity, sedentary lifestyle, and elevated cardiovascular risk were recruited to a 12-month feasibility study. Participants wore a Polar M430 AT to collect continuous PA data during a six-month intervention followed by 6 months of follow-up. We performed quantitative wear time analysis, tested the validity of the AT, and completed two rounds of qualitative interviews to investigate how individual wear-time was linked to participant responses. RESULTS: From 1 year of tracking, mean number of valid wear days were 292 (SD = 86), i.e. 80%. The Polar M430 provides acceptable measurements for total energy expenditure. Motivations for increased wear time were that participants were asked to wear it and the ability to track PA progress. Perceived usefulness included time keeping, heart rate- and sleep tracking, becoming more conscious about day-to-day activity, and improved understanding of which activity types were more effective for energy expenditure. Sources of AT annoyance were measurement inaccuracies and limited instruction for use. Suggestions for improvement were that the AT was big, unattractive, and complicated to use. CONCLUSIONS: Adherence to wearing a consumer-based AT was high. Results indicate that it is feasible to use a consumer-based AT to measure PA over a longer period. Potential success factors for increased wear time includes adequate instruction for AT use, allowing participants to choose different AT designs, and using trackers with accurate measurements. To identify accurate trackers, AT validation studies in the target cohort may be needed. TRIAL REGISTRATION: U.S. National Library of Medicine, Clinical Trial registry: NCT03807323 ; Registered 16 September 2019 - Retrospectively registered.

Patterns in Weight and Physical Activity Tracking Data Preceding a Stop in Weight Monitoring: Observational Analysis.

BACKGROUND: Self-regulation for weight loss requires regular self-monitoring of weight, but the frequency of weight tracking commonly declines over time. OBJECTIVE: This study aimed to investigate whether it is a decline in weight loss or a drop in motivation to lose weight (using physical activity tracking as a proxy) that may be prompting a stop in weight monitoring. METHODS: We analyzed weight and physical activity data from 1605 Withings Health Mate app users, who had set a weight loss goal and stopped tracking their weight for at least six weeks after a minimum of 16 weeks of continuous tracking. Mixed effects models compared weight change, average daily steps, and physical activity tracking frequency between a 4-week period of continuous tracking and a 4-week period preceding the stop in weight tracking. Additional mixed effects models investigated subsequent changes in physical activity data during 4 weeks of the 6-week long stop in weight tracking. RESULTS: People lost weight during continuous tracking (mean -0.47 kg, SD 1.73) but gained weight preceding the stop in weight tracking (mean 0.25 kg, SD 1.62; difference 0.71 kg; 95% CI 0.60 to 0.81). Average daily steps (beta=-220 daily steps per time period; 95% CI -320 to -120) and physical activity tracking frequency (beta=-3.4 days per time period; 95% CI -3.8 to -3.1) significantly declined from the continuous tracking to the pre-stop period. From pre-stop to post-stop, physical activity tracking frequency further decreased (beta=-6.6 days per time period; 95% CI -7.12 to -6.16), whereas daily step count on the day's activity was measured increased (beta=110 daily steps per time period; 95% CI 50 to 170). CONCLUSIONS: In the weeks before people stop tracking their weight, their physical activity and physical activity monitoring frequency decline. At the same time, weight increases, suggesting that declining motivation for weight control and difficulties with making use of negative weight feedback might explain why people stop tracking their weight. The increase in daily steps but decrease in physical activity tracking frequency post-stop might result from selective measurement of more active days.

Wearable Activity Trackers for Monitoring Adherence to Home Confinement During the COVID-19 Pandemic Worldwide: Data Aggregation and Analysis.

BACKGROUND: In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations' adherence to home confinement to adapt policies and control measures accordingly. OBJECTIVE: The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals' natural activity patterns regardless of their location. METHODS: We analyzed big data from individuals using activity trackers (Withings) that count the wearer's average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens' activities. RESULTS: Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people's activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. CONCLUSIONS: Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.

Clusters of Adolescent Physical Activity Tracker Patterns and Their Associations With Physical Activity Behaviors in Finland and Ireland: Cross-Sectional Study.

BACKGROUND: Physical activity trackers (PATs) such as apps and wearable devices (eg, sports watches, heart rate monitors) are increasingly being used by young adolescents. Despite the potential of PATs to help monitor and improve moderate-to-vigorous physical activity (MVPA) behaviors, there is a lack of research that confirms an association between PAT ownership or use and physical activity behaviors at the population level. OBJECTIVE: The purpose of this study was to examine the ownership and use of PATs in youth and their associations with physical activity behaviors, including daily MVPA, sports club membership, and active travel, in 2 nationally representative samples of young adolescent males and females in Finland and Ireland. METHODS: Comparable data were gathered in the 2018 Finnish School-aged Physical Activity (F-SPA 2018, n=3311) and the 2018 Irish Children's Sport Participation and Physical Activity (CSPPA 2018, n=4797) studies. A cluster analysis was performed to obtain the patterns of PAT ownership and usage by adolescents (age, 11-15 years). Four similar clusters were identified across Finnish and Irish adolescents: (1) no PATs, (2) PAT owners, (3) app users, and (4) wearable device users. Adjusted binary logistic regression analyses were used to evaluate how PAT clusters were associated with physical activity behaviors, including daily MVPA, membership of sports clubs, and active travel, after stratification by gender. RESULTS: The proportion of app ownership among Finnish adolescents (2038/3311, 61.6%) was almost double that of their Irish counterparts (1738/4797, 36.2%). Despite these differences, the clustering patterns of PATs were similar between the 2 countries. App users were more likely to take part in daily MVPA (males, odds ratio [OR] 1.27, 95% CI 1.04-1.55; females, OR 1.49, 95% CI 1.20-1.85) and be members of sports clubs (males, OR 1.37, 95% CI 1.15-1.62; females, OR 1.25, 95% CI 1.07-1.50) compared to the no PATs cluster, after adjusting for country, age, family affluence, and disabilities. These associations, after the same adjustments, were even stronger for wearable device users to participate in daily MVPA (males, OR 1.83, 95% CI 1.49-2.23; females, OR 2.25, 95% CI 1.80-2.82) and be members of sports clubs (males, OR 1.88, 95% CI 1.55-2.88; females, OR 2.07, 95% CI 1.71-2.52). Significant associations were observed between male users of wearable devices and taking part in active travel behavior (OR 1.39, 95% CI 1.04-1.86). CONCLUSIONS: Although Finnish adolescents report more ownership of PATs than Irish adolescents, the patterns of use and ownership remain similar among the cohorts. The findings of our study show that physical activity behaviors were positively associated with wearable device users and app users. These findings were similar between males and females. Given the cross-sectional nature of this data, the relationship between using apps or wearable devices and enhancing physical activity behaviors requires further investigation.