Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial.

BACKGROUND: Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA. OBJECTIVE: We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA. METHODS: This study was a single-blind, pilot randomized controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 BCT conditions: self-monitoring (feedback on participants' own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed. RESULTS: Participants showed excellent adherence (mean valid-wear time of Fitbit=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). Fitbit-assessed step count during the intervention period was slightly higher in the goal setting and social comparison groups than in the self-monitoring group, although the effects did not reach statistical significance (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal setting group (P=.04). CONCLUSIONS: Fitbit-implemented BCTs promote readiness and motivation for PA, although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA. TRIAL REGISTRATION: Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b.

The efficacy of online physical activity interventions with added mobile elements within adults aged 50 years and over: Randomized controlled trial.

Electronic health (eHealth) and mobile health (mHealth) could stimulate physical activity (PA) in a time-efficient and cost-effective way. This randomized controlled trial aims to investigate effects on moderate-to-vigorous PA (MVPA) of different combined computer- and mobile-based PA interventions targeted at adults aged 50 years and over. Participants (N = 954) were randomly allocated to a basic existing computer-based intervention (Active Plus [AP] or I Move [IM]) supplemented with one of three mobile elements being (1) activity tracker (AT), (2) ecological momentary intervention (EMI), or (3) chatbot (CB) or a control group (CG). MVPA was assessed via the SQUASH at baseline (T0), 3 months (T1), and 6 months (T2) and via accelerometers at T0 and T2. No intervention effects were found on objective (p = .502) and subjective (p = .368) MVPA for main research groups (AP/IM + AT, AP/IM + EMI, AP/IM + CB). Preliminary MVPA findings for subgroups (AP + AT, AP + EMI, AP + CB, IM + AT, IM + EMI, IM + CB) combined with drop-out data showed potential for the computer-based intervention AP with an integrated AT. Based on these preliminary findings, eHealth developers can be recommended to integrate ATs with existing computer-based PA interventions. However, further research is recommended to confirm the findings as a result of the exploratory nature of the subgroup analyses.

An App-Based Physical Activity Intervention in Community-Dwelling Chinese-, Tagalog-, and Vietnamese-Speaking Americans: Single-Arm Intervention Study.

BACKGROUND: Physical inactivity is associated with adverse health outcomes among Asian Americans, who exhibit the least adherence to physical activity guidelines compared with other racial and ethnic groups. Mobile app-based interventions are a promising approach to promote healthy behaviors. However, there is a lack of app-based interventions focused on improving physical activity among Asian Americans whose primary language is not English. OBJECTIVE: This pilot study aimed to assess the feasibility and acceptability of a 5-week intervention using a culturally and linguistically adapted, evidence-based mobile phone app with an accelerometer program, to promote physical activity among Chinese-, Tagalog-, or Vietnamese-speaking Americans. METHODS: Participants were recruited through collaborations with community-based organizations. The intervention was adapted from a 12-month physical activity randomized controlled trial involving the app and accelerometer for English-speaking adults. Sociodemographic characteristics, lifestyle factors, and physical measurements were collected at the baseline visit. A 7-day run-in period was conducted to screen for the participants who could wear a Fitbit One (Fitbit LLC) accelerometer and complete the app's daily step diary. During the 4-week intervention period, participants wore the accelerometer and reported their daily steps in the app. Participants also received daily messages to reinforce key contents taught during an in-person educational session, remind them to input steps, and provide tailored feedback. Feasibility measures were the percentage of eligible participants completing the run-in period and the percentage of participants who used the app diary for at least 5 out of 7 days during the intervention period. We conducted poststudy participant interviews to explore overall intervention acceptability. RESULTS: A total of 19 participants were enrolled at the beginning of the study with a mean age of 47 (SD 13.3; range 29-70) years, and 58% (n=11) of them were female. Of the participants, 26% (n=5) were Chinese, 32% (n=6) were Vietnamese, and 42% (n=8) were Filipino. All participants met the run-in criteria to proceed with the intervention. Adherence to the app diary ranged from 74% (n=14) in week 2 to 95% (n=18) in week 4. The daily average steps per week from accelerometers increased each week from 8451 (SD 3378) steps during the run-in period to 10,930 (SD 4213) steps in week 4. Participants reported positive experiences including an increased motivation to walk and the enjoyment of being able to monitor their physical activity. CONCLUSIONS: This is the first pilot study of a multicomponent intervention and evidence-based mobile phone app to promote physical activity among Asian Americans who use apps in traditional Chinese, Tagalog, or Vietnamese, which demonstrated high feasibility and acceptability. Future work focused on multilingual mobile apps to address disparities in physical inactivity among Asian Americans should be considered.

Cardiopulmonary Exercise Test and Daily Physical Activity in Pediatric Congenital Heart Disease: an Exploratory Analysis.

Sedentary lifestyle is reported to be associated with diminished exercise capacity, resulting in increased cardiovascular risk in adults with congenital heart disease (CHD). This cross-sectional study examined the association between objectively measured physical activity (PA) and exercise capacity in children and adolescents with CHD. Therefore, 107 patients (aged 13.0 ± 2.7 years, 41 girls) with various CHD performed a cardiopulmonary exercise test to quantify their peak oxygen uptake (peakV'O2). Moderate to vigorous PA (MVPA) and daily step count were assessed using Garmin vivofit jr. (Garmin, Germany) for 7 consecutive days. For association between PA and submaximal exercise capacity, Spearman correlation was performed. Patients with CHD showed almost normal values compared with the reference (79.5 ± 17.2% [31.6 to 138.1] %peakV'O2 predicted), with roughly normal ventilatory anerobic thresholds (50.6 ± 14.0% [20.3 to 97.9] % oxygen uptake at ventilatory anaerobic threshold [VATV'O2]). Step counts are below the recommendations (9,304 ± 3,792 steps/day [1,701 to 20,976]), whereas MVPA data are above the recommendations for children with ≥60 min/day (83.6 ± 34.6 min/day [10.1 to 190.9]). The Spearman rho showed significant positive correlations to VATV'O2 (r = 0.353, p <0.001) and %VATV'O2 (r = 0.307, p = 0.001), with similar results regarding MVPA (VATV'O2: r = 0.300, p = 0.002 and %VATV'O2: r = 0.270, p = 0.005). In conclusion, submaximal exercise capacity and PA correlate positively, making both assessments relevant in a clinical setting: PA in the context of cardiovascular prevention and peakV'O2 as the strongest predictor for morbidity and mortality.

Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.

BACKGROUND: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. OBJECTIVE: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. METHODS: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. RESULTS: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. CONCLUSIONS: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements.

BACKGROUND: The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity. OBJECTIVE: This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X). METHODS: The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities. RESULTS: Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration-extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor. CONCLUSIONS: Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day.

The Wearable Activity Tracker Checklist for Healthcare (WATCH): a 12-point guide for the implementation of wearable activity trackers in healthcare.

Increasing physical activity in patients offers dual benefits, fostering improved patient health and recovery, while also bolstering healthcare system efficiency by minimizing costs related to extended hospital stays, complications, and readmissions. Wearable activity trackers offer valuable opportunities to enhance physical activity across various healthcare settings and among different patient groups. However, their integration into healthcare faces multiple implementation challenges related to the devices themselves, patients, clinicians, and systemic factors. This article presents the Wearable Activity Tracker Checklist for Healthcare (WATCH), which was recently developed through an international Delphi study. The WATCH provides a comprehensive framework for implementation and evaluation of wearable activity trackers in healthcare. It covers the purpose and setting for usage; patient, provider, and support personnel roles; selection of relevant metrics; device specifications; procedural steps for issuance and maintenance; data management; timelines; necessary adaptations for specific scenarios; and essential resources (such as education and training) for effective implementation. The WATCH is designed to support the implementation of wearable activity trackers across a wide range of healthcare populations and settings, and in those with varied levels of experience. The overarching goal is to support broader, sustained, and systematic use of wearable activity trackers in healthcare, therefore fostering enhanced physical activity promotion and improved patient outcomes.

Attitudes, Barriers, and Motivators Toward Daily Walking and a Mobile App to Increase Walking Among Women: Web-Based Anonymous Survey.

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

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study.

BACKGROUND: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. OBJECTIVE: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. METHODS: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. RESULTS: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%). CONCLUSIONS: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

Lower cardiac rehabilitation enrolment occurs in acute coronary syndrome patients who report low levels of physical activity at four weeks post-event: A prospective observational study using physical activity tracker data.

BACKGROUND: Physical activity (PA) and cardiac rehabilitation (CR) attendance are important for recovery and prognosis following acute coronary syndrome (ACS). However, PA patterns early post-ACS are not well known. OBJECTIVES: Investigate the level of PA at 4-weeks post-ACS and any potential associations with CR enrolment. METHODS: We recruited patients admitted for ACS from cardiac wards and clinics at two hospital sites in Sydney, Australia. PA data were collected using wearable activity trackers worn at 4-weeks post-ACS, and CR enrolment was self-reported. RESULTS: Participants (n = 61) were aged 66.7 ± 10.3 years, 74 % male, 61 % were married or partnered, and 33 % were diagnosed with ST-elevation myocardial infarction. Patients engaged in 7514±3355 steps per day and 44.6 ± 37.5 min of moderate-to-vigorous physical activity (MVPA). Patients who enrolled in CR exhibited higher daily step counts (p = 0.044), MVPA minutes (p = 0.001), and were more likely to meet PA guidelines. ACS patients who engaged in higher levels of MVPA were more likely to enrol in CR (odds ratio [OR] 1.46; 95 % confidence interval [CI] 1.08, 1.98). CR enrolment was also positively associated with being married or in an intimate partnership (OR 9.93; 95 % CI 1.83, 53.85) and absence of depressive symptoms (OR 11.86; 95 % CI 1.91, 73.74). CONCLUSION: Lower CR enrolment rates were observed among less physically active patients at 4-weeks post-ACS. However, each 10 min increment in MVPA increased the odds of CR enrolment by 46 %. Future research should explore strategies to target this inactive and high-risk group, given the potential for a large prognostic gain with CR participation.

The experiences of people with chronic obstructive pulmonary disease (COPD) using activity monitors in everyday life: an interpretative phenomenological study.

PURPOSE: Understanding the experiences of people with Chronic Obstructive Pulmonary Disease (COPD) using activity monitors in daily life could support the utilisation of technology within healthcare to increase physical activity and support self-management. This qualitative study aimed to explore the experiences of people with COPD using activity monitors at home in everyday life. METHODS: Semi-structured face-to-face or telephone interviews were conducted with seven people with COPD between August 2018 and June 2020. Participants had all used an activity monitor within the last year (Fitbit, Garmin, or Apple Watch). Interviews were analysed in-depth using Interpretative Phenomenological Analysis (IPA). RESULTS: Four themes, developed using IPA, highlight participants' engagement with activity monitors and integrating them into their lives: (1) Motivational features to monitor activity, (2) Importance of setting achievable goals, (3) Developing knowledge and awareness, and (4) Integration into everyday life for self-management. CONCLUSION: Activity monitors were perceived to be beneficial and useful to people with COPD, not just for monitoring their activity, but also helping to self-manage their condition. Activity monitors may be a useful tool within rehabilitation and healthcare services for COPD.

Activity monitors were beneficial for people with Chronic Obstructive Pulmonary Disease (COPD) to monitor their physical activity and support self-management of their COPD.People with COPD could see and make sense of their activity levels, set activity goals and increase their motivation from the objectively monitored activity.Activity monitors can help to support individual goal setting and facilitate ownership, but support is needed to set achievable and realistic goals.Healthcare practitioners need to be aware of the potential negative effects of using activity monitors on mental or physical health and wellbeing and support people with COPD to manage pressure and expectations of meeting their goals.

Feasibility and Preliminary Effectiveness of the INSPIRE Pilot Study for Adolescent Girls.

INTRODUCTION: This pilot study evaluated the feasibility and preliminary effectiveness of the Intervention to Support Physical Activity Independence and Routine Everyday program. METHOD: This quasi-experimental single-group pretest/posttest study included adolescent girls (n = 15) who received a Fitbit Inspire 2 wearable activity tracker and participated in the seven-week Intervention to Support Physical Activity Independence and Routine Everyday program on a college campus in the Midwest. Feasibility outcomes included intervention participation, fidelity, and satisfaction. Preliminary effectiveness outcomes included moderate-to-vigorous physical activity, cardiorespiratory fitness, and resting heart rate. RESULTS: For intervention sessions, the mean attendance rate was 83.42%, the mean minutes of moderate-to-vigorous physical activity was 62.03, and the mean satisfaction score was 28.90. Moderate-to-vigorous physical activity and resting heart rate improved significantly from baseline to postintervention, but cardiorespiratory fitness did not. DISCUSSION: Results suggest the need for a pilot randomized controlled trial to improve cardiorespiratory fitness and incorporating an objective measure of moderate-to-vigorous physical activity.

Biofeedback and Digitalized Motivational Interviewing to Increase Daily Physical Activity: Series of Factorial N-of-1 Randomized Controlled Trials Piloting the Precious App.

BACKGROUND: Insufficient physical activity is a public health concern. New technologies may improve physical activity levels and enable the identification of its predictors with high accuracy. The Precious smartphone app was developed to investigate the effect of specific modular intervention elements on physical activity and examine theory-based predictors within individuals. OBJECTIVE: This study pilot-tested a fully automated factorial N-of-1 randomized controlled trial (RCT) with the Precious app and examined whether digitalized motivational interviewing (dMI) and heart rate variability-based biofeedback features increased objectively recorded steps. The secondary aim was to assess whether daily self-efficacy and motivation predicted within-person variability in daily steps. METHODS: In total, 15 adults recruited from newspaper advertisements participated in a 40-day factorial N-of-1 RCT. They installed 2 study apps on their phones: one to receive intervention elements and one to collect ecological momentary assessment (EMA) data on self-efficacy, motivation, perceived barriers, pain, and illness. Steps were tracked using Xiaomi Mi Band activity bracelets. The factorial design included seven 2-day biofeedback interventions with a Firstbeat Bodyguard 2 (Firstbeat Technologies Ltd) heart rate variability sensor, seven 2-day dMI interventions, a wash-out day after each intervention, and 11 control days. EMA questions were sent twice per day. The effects of self-efficacy, motivation, and the interventions on subsequent steps were analyzed using within-person dynamic regression models and aggregated data using longitudinal multilevel modeling (level 1: daily observations; level 2: participants). The analyses were adjusted for covariates (ie, within- and between-person perceived barriers, pain or illness, time trends, and recurring events). RESULTS: All participants completed the study, and adherence to activity bracelets and EMA measurements was high. The implementation of the factorial design was successful, with the dMI features used, on average, 5.1 (SD 1.0) times of the 7 available interventions. Biofeedback interventions were used, on average, 5.7 (SD 1.4) times out of 7, although 3 participants used this feature a day later than suggested and 1 did not use it at all. Neither within- nor between-person analyses revealed significant intervention effects on step counts. Self-efficacy predicted steps in 27% (4/15) of the participants. Motivation predicted steps in 20% (3/15) of the participants. Aggregated data showed significant group-level effects of day-level self-efficacy (B=0.462; P<.001), motivation (B=0.390; P<.001), and pain or illness (B=-1524; P<.001) on daily steps. CONCLUSIONS: The automated factorial N-of-1 trial with the Precious app was mostly feasible and acceptable, especially the automated delivery of the dMI components, whereas self-conducted biofeedback measurements were more difficult to time correctly. The findings suggest that changes in self-efficacy and motivation may have same-day effects on physical activity, but the effects vary across individuals. This study provides recommendations based on the lessons learned on the implementation of factorial N-of-1 RCTs.

Social Participation and Physical Activity Incentive at Home in Stroke Survivor: Contribution of Technologies.

Increased physical activity has been demonstrated as a relevant treatment after a stroke, with positive effects on impairment recovery, activity limitation, social participation and quality of life. Furthermore, PA is now recommended as part of the stroke recovery pathway, starting during inpatient care and extending through rehabilitation and community integration. The purpose of this presentation is to describe how current technologies may facilitate a continuity of care for stroke survivors. We present a synthesis of 8 studies that we have conducted to date to assess and monitor the activity level of post-stroke patients at home. The results of these studies show that home rehabilitation of post-stroke patients requires the use of individualized monitoring criteria to optimize patient care. To encourage the patient to increase his level of moderate physical activity and reduce his sedentary time, it would be recommended to propose a regularly monitored and structured program.

Views and experiences of healthcare practitioners supporting people with COPD who have used activity monitors: "More than just steps".

INTRODUCTION: Activity monitors (apps and wearables) are increasingly used by the general population, including people with Chronic Obstructive Pulmonary Disease (COPD). There is potential for activity monitors to support increases in physical activity for people with COPD and healthcare practitioners (HCPs) are likely to be key in supporting their use, but little is currently known about HCPs' views or experiences. This qualitative research aimed to explore HCPs' views and experiences of supporting people with COPD who have used activity monitors. METHODS: Seventeen semi-structured telephone or online interviews were conducted with HCPs between September 2020 and May 2021. HCPs included two nurses, an occupational therapist, a physician, and 13 physiotherapists. Participants were recruited via social media advertisements. They all had experience of supporting people with COPD who had used activity monitors. Interviews were analysed using reflexive thematic analysis. FINDINGS: Four themes were developed highlighting the challenges and benefits of HCPs supporting patients with using activity monitors and utilising patient-collected activity data; 1) Skills and experience are needed to increase accessibility and engagement, 2) Objectively monitored physical activity can support exercise prescription, 3) Applications of activity monitors vary across different settings, and 4) Support is needed for future use of activity monitors. DISCUSSION: HCPs recognised the potential for activity monitors to impact patients' ability to self-manage their COPD. However, there is a lack of guidance and information to support integration within practice. Future research is needed to co-develop information and guidelines for people with COPD and HCPs.

Effect of a Wearable Device-Based Physical Activity Intervention in North Korean Refugees: Pilot Randomized Controlled Trial.

BACKGROUND: Effective health interventions for North Korean refugees vulnerable to metabolic disorders are currently unelucidated. OBJECTIVE: This study aimed to evaluate the effects of digital health interventions in North Korean refugees using a wearable activity tracker (Fitbit device). METHODS: We conducted a prospective, randomized, open-label study on North Korean refugees aged 19-59 years between June 2020 and October 2021 with a 12-week follow-up period. The participants were randomly assigned to either an intervention group or a control group in a 1:1 ratio. The intervention group received individualized health counseling based on Fitbit data every 4 weeks, whereas the control group wore the Fitbit device but did not receive individualized counseling. The primary and secondary outcomes were the change in the mean daily step count and changes in the metabolic parameters, respectively. RESULTS: The trial was completed by 52 North Korean refugees, of whom 27 and 25 were in the intervention and control groups, respectively. The mean age was 43 (SD 10) years, and 41 (78.8%) participants were women. Most participants (44/52, 95.7%) had a low socioeconomic status. After the intervention, the daily step count in the intervention group increased, whereas that in the control group decreased. However, there were no significant differences between the 2 groups (+83 and -521 steps in the intervention and control groups, respectively; P=.500). The effects of the intervention were more prominent in the participants with a lower-than-average daily step count at baseline (<11,667 steps/day). After the 12-week study period, 85.7% (12/14) and 46.7% (7/15) of the participants in the intervention and control groups, respectively, had an increased daily step count (P=.05). The intervention prevented the worsening of the metabolic parameters, including BMI, waist circumference, fasting blood glucose level, and glycated hemoglobin level, during the study period. CONCLUSIONS: The wearable device-based physical activity intervention did not significantly increase the average daily step count in the North Korean refugees in this study. However, the intervention was effective among the North Korean refugees with a lower-than-average daily step count; therefore, a large-scale, long-term study of this intervention type in an underserved population is warranted. TRIAL REGISTRATION: Clinical Research Information Service KCT0007999; https://cris.nih.go.kr/cris/search/detailSearch.do/23622.

A Longitudinal Pilot Study of Stress and Sleep in First-Year Osteopathic Medical Students.

OBJECTIVE: Poor sleep quality is thought to be a contributor to medical student stress. The authors evaluated the effect of high and low periods of academic stress on sleep quality and quantity in first-year medical students. METHODS: A group of 25 students in their first year of medical school were provided Fitbit Charge 3 activity trackers for continual use and were surveyed at 4 intervals to assess stress level, sleep quantity, and sleep quality. Fitbit data were collected through the Fitbit mobile app and uploaded to the Fitabase (Small Steps Labs, LLC) server. Data collection times were scheduled around the academic exam schedule. Weeks in which testing occurred were identified as high-stress periods. Results from assessments were compared to nontesting periods of low stress. RESULTS: During stressful periods, students slept an average of one hour less per 24-h period, took more naps, and reported poorer sleep quality than during the low-stress periods. No significant change was seen in the 4 surveyed intervals in sleep efficiency or sleep stages. CONCLUSION: Students slept less and had poorer quality sleep in their main sleep event during stressful periods but attempted to compensate with increased napping and weekend catchup sleep. The objective Fitbit activity tracker data were consistent with and validated the self-reported survey data. Activity trackers could potentially be used to optimize the efficiency and quality of both student napping and main sleep events as one component of a stress reduction program for medical students.

Validity of the Xiaomi Mi Band 2, 3, 4 and 5 Wristbands for Assessing Physical Activity in 12-to-18-Year-Old Adolescents under Unstructured Free-Living Conditions. Fit-Person Study.

The purpose was to assess the validity of four generations of Xiaomi Mi Band wristbands for the assessment of step count and physical activity (PA) levels among adolescents aged 12-18 years under free-living conditions. One hundred adolescents were invited to participate in the present study. The final sample consisted of 62 high-school students (34 females), aged 12-18 years old (Mage = 14.1 ± 1.6 years), who wore an ActiGraph accelerometer on their hip (PA and step count reference measures) and four activity wristbands (Xiaomi Mi Band 2, 3, 4, and 5) on their non-dominant wrist during the waking time of one day. Results showed that the agreement between daily PA levels (i.e., slow, brisk, and slow-brisk pace walking, total PA and moderate-to-vigorous PA) measured by Xiaomi Mi Band wristbands and the accelerometer were poor (ICC, 95% CI = 0.06-0.78, 0.00-0.92; MAPE = 50.1-150.6%). However, agreement between daily step count measured by the accelerometer and the Xiaomi Mi Band wristbands were between acceptable (MAPE = 12.2-13.6%) to excellent (ICC, 95% CI = 0.94-0.95, 0.90-0.97). Furthermore, the Xiaomi Mi Band wristbands have a good to excellent validity for correctly classifying adolescents as meeting or not meeting the recommended 10,000 steps per day (P = 0.89-0.95, k = 0.71-0.87) and the recommended 60 minutes of moderate-to-vigorous PA per day (P = 0.89-0.94, k = 0.69-0.83). Furthermore, comparability between the four Xiaomi Mi Band generations were poor to excellent (ICC, 95% CI = 0.22-0.99, 0.00-1.00) for the daily PA levels outputs, although it was excellent (ICC, 95% CI = 0.99-1.00, 0.96-1.00; MAPE = 0.0-0.1%) for daily step count. Different models of Xiaomi Mi Band wristbands were comparable and presented good validity for measuring adolescents' step count, and they accurately classified adolescents as meeting or not meeting the PA recommendations under free-living conditions.

Physical activity maintenance among young adult cancer survivors in an mHealth intervention: Twelve-month outcomes from the IMPACT randomized controlled trial.

BACKGROUND: Most physical activity (PA) interventions in young adult cancer survivors (YACS) have focused on short-term outcomes without evaluating longer-term outcomes and PA maintenance. This study examined the effects of an mHealth PA intervention at 12 months, after 6 months of tapered contacts, relative to a self-help group among 280 YACS. METHODS: YACS participated in a 12-month randomized trial that compared self-help and intervention groups. All participants received an activity tracker, smart scale, individual videochat session, and access to a condition-specific Facebook group. Intervention participants also received lessons, tailored feedback, adaptive goal setting, text messages, and Facebook prompts for 6 months, followed by tapered contacts. Accelerometer-measured and self-reported PA (total [primary outcome], moderate-to-vigorous [MVPA], light, steps, sedentary behaviors) were collected at baseline, 6, and 12 months. Generalized estimating equation analyses evaluated group effects on outcomes from baseline to 12 months. RESULTS: From baseline to 12 months, there were no between- or within-group differences in accelerometer-measured total PA min/week, while increases in self-reported total PA were greater in the intervention versus self-help group (mean difference = +55.8 min/week [95% CI, 6.0-105.6], p = 0.028). Over 12 months, both groups increased accelerometer-measured MVPA (intervention: +22.5 min/week [95% CI, 8.8-36.2] vs. self-help: +13.9 min/week [95% CI, 3.0-24.9]; p = 0.34), with no between-group differences. Both groups maintained accelerometer-measured and self-reported PA (total, MVPA) from 6 to 12 months. At 12 months, more intervention participants reported meeting national PA guidelines than self-help participants (47.9% vs. 33.1%, RR = 1.45, p = 0.02). CONCLUSION: The intervention was not more effective than the self-help group at increasing accelerometer-measured total PA over 12 months. Both groups maintained PA from 6 to 12 months. Digital approaches have potential for promoting sustained PA participation in YACS, but additional research is needed to identify what strategies work for whom, and under what conditions.