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.

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.

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.

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.

Efficacy of an mHealth Behavior Change Intervention for Promoting Physical Activity in the Workplace: Randomized Controlled Trial

BACKGROUND

Insufficient physical activity (PA) is a well-established risk factor for several noncommunicable diseases such as cardiovascular diseases, cancer, diabetes, depression, and dementia. The World Health Organization (WHO) advises that individuals engage in 150 minutes of moderate PA per week or 75 minutes of intense PA per week. According to the WHO's latest report, 23% of adults fail to meet the minimum recommended level of PA. The percentage was even higher in a recent global study that showed 27% of adults were insufficiently active and reported a 5% increase in the prevalence trend of insufficient PA between 2001 and 2016. The study also showed the rate of insufficient PA among countries varied significantly. For instance, it was estimated that 40% were insufficiently active in the United States, and the percentage was even higher in Saudi Arabia (more than 50%). Governments are actively developing policies and methods to successfully establish a PA-inducing environment that encourages a healthy lifestyle in order to address the global steady decline in PA.

OBJECTIVE

The purpose of this study was to determine the effectiveness of mobile health (mHealth) interventions, particularly SMS text messaging interventions, to improve PA and decrease BMI in healthy adults in the workplace.

METHODS

In this parallel, 2-arm randomized controlled trial, healthy adults (N=327) were randomized to receive an mHealth intervention (tailored text messages combined with self-monitoring (intervention; n=166) or no intervention (control; n=161). Adults who were fully employed in an academic institution and had limited PA during working hours were recruited for the study. Outcomes, such as PA and BMI, were assessed at baseline and 3 months later.

RESULTS

Results showed significant improvement in PA levels (weekly step counts) in the intervention group (β=1097, 95% CI 922-1272, P<.001). There was also a significant reduction in BMI (β=0.60, 95% CI 0.50-0.69, P<.001).

CONCLUSIONS

Combining tailored text messages and self-monitoring interventions to improve PA and lower BMI was significantly effective and has the potential to leverage current methods to improve wellness among the public.

A Qualitative Exploration of Exercise During Pregnancy

OBJECTIVE

To explore the experience of women engaging in physical activity during pregnancy.

DESIGN

This was the qualitative arm of the Starting Pregnancy With Robustness for Optimal Upward Trajectories (SPROUT) pilot project. Thematic analysis was used to realize patterns of meaning and significance from data on participants' experiences in engaging in physical activity during pregnancy.

SETTING

One-on-one structured interviews via video conferencing.

PARTICIPANTS

Eighteen women in the first trimester of their pregnancy were recruited from local obstetric practices and randomized into one of three exercise groups. All three groups of women were followed for their entire pregnancy and for 6 months postpartum.

METHODS

Interviews were recorded and analyzed using thematic analysis.

RESULTS

Six major themes important for clinical practice resulted from the analysis: Physical Activity During Pregnancy Is Desirous for Health Benefits, Activity Monitors Provide Motivation, Human Connection HelpsSupportPhysical Activity, More Guidance Is Needed on How to Be Physically Active During Pregnancy, A Supervised Physical Activity Program Is Preferred if Available and Flexible, and Participants Would Choose to Be Physically Active in Subsequent Pregnancies.

CONCLUSION

Human interaction, education on physical activity guidelines, and exercise advice increased motivation, accountability, and confidence in the women. Using a tracking device such as an activity watch provided real-world feedback in addition to fostering motivation.

Wearable Activity Trackers Objectively Measure Incidental Physical Activity in Older Adults Undergoing Aortic Valve Replacement

BACKGROUND

For older adults with severe aortic stenosis (AS) undergoing aortic valve replacement (AVR), recovery of physical function is important, yet few studies objectively measure it in real-world environments. This exploratory study explored the acceptability and feasibility of using wearable trackers to measure incidental physical activity (PA) in AS patients before and after AVR.

METHODS

Fifteen adults with severe AS wore an activity tracker at baseline, and ten at one month follow-up. Functional capacity (six-minute walk test, 6MWT) and HRQoL (SF 12) were also assessed.

RESULTS

At baseline, AS participants (n = 15, 53.3% female, mean age 82.3 ± 7.0 years) wore the tracker for four consecutive days more than 85% of the total prescribed time, this improved at follow-up. Before AVR, participants demonstrated a wide range of incidental PA (step count median 3437 per day), and functional capacity (6MWT median 272 m). Post-AVR, participants with the lowest incidental PA, functional capacity, and HRQoL at baseline had the greatest improvements within each measure; however, improvements in one measure did not translate to improvements in another.

CONCLUSION

The majority of older AS participants wore the activity trackers for the required time period before and after AVR, and the data attained were useful for understanding AS patients' physical function.

Implementing an Activity Tracker to Increase Motivation for Physical Activity in Patients With Diabetes in Primary Care: Strengths, Weaknesses, Opportunities and Threats (SWOT) Analysis

BACKGROUND

Many projects related to technology implementation in the context of chronic diseases have been developed over the years to better manage lifestyle medicine interventions and improve patient care. However, technology implementation in primary care settings remains challenging.

OBJECTIVE

The aim is to carry out a strengths, weaknesses, opportunities, and threats (SWOT) analysis (1) to assess satisfaction among patients with type 2 diabetes using an activity tracker to increase motivation for physical activity (PA) and (2) to explore the research and health care team's perceptions of this technology's implementation in a primary care setting.

METHODS

A 3-month hybrid type 1 study, which included 2 stages, was conducted in an academic primary health center in Quebec City, Quebec, Canada. In stage 1, a total of 30 patients with type 2 diabetes were randomized to the intervention (activity tracker) group or the control group. In stage 2, a SWOT analysis was performed on both patients and health care professionals to determine the components of successful technology implementation. Two questionnaires were used to gather feedback: a satisfaction and acceptability questionnaire concerning an activity tracker (15 patients in the intervention group) and a questionnaire based on the SWOT elements (15 patients in the intervention group and 7 health care professionals). Both questionnaires contained quantitative and qualitative questions. Qualitative variables from open questions were synthesized in a matrix and ranked according to apparition frequency and global importance. A thematic analysis was performed by the first author and validated by 2 coauthors separately. The information gathered was triangulated to propose recommendations that were then approved by the team. Both quantitative (randomized controlled trial participants) and qualitative (randomized controlled trial participants and team) results were combined for recommendations.

RESULTS

In total, 86% (12/14) of the participants were satisfied with their activity tracker use and 75% (9/12) felt that it incited them to stick to their PA program. The main strengths of the team members' perspectives were the project initiation and involvement of a patient partner, the study design, the team, and the device. The weaknesses were the budgetary constraints, the turnover, and the technical issues. The opportunities were the primary care setting, the loan of equipment, and common technology. The threats were recruitment issues, administrative challenges, technological difficulties, and a single research site.

CONCLUSIONS

Patients with type 2 diabetes were satisfied with their activity tracker used to improve motivation for PA. Health care team members agreed that implementation can be done in primary care, but some challenges remain in using this technological tool in clinical practice regularly.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03709966; https://clinicaltrials.gov/ct2/show/NCT03709966.

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

BACKGROUND

Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants' physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants' physical activity evolves.

OBJECTIVE

The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes?

METHODS

The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis.

RESULTS

All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%).

CONCLUSIONS

Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression

Purpose

To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial.

Methods

This post hoc analysis used data from the ENGAGE-2 trial where adults (n=106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M).

Results

Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, β=-0.78, p<.05; 6M, β=-0.80, p<.05) and low depressive symptoms (6M, β=-0.15, p<.05). Continuous decline in step count was associated with high weight (2M, β=0.58, p<.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M.

Conclusions

Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

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.

Assessing the Acceptability and Effectiveness of Mobile-Based Physical Activity Interventions for Midlife Women During Menopause: Systematic Review of the Literature

BACKGROUND

Midlife women with menopausal symptoms are less likely to meet the recommended level of physical activity (PA). Promoting PA among women in midlife could reduce their risk of cardiovascular diseases and perhaps improve menopausal symptoms. Mobile PA interventions in the form of smartphone apps and wearable activity trackers can potentially encourage users to increase PA levels and address time and resource barriers to PA. However, evidence on the acceptability and effectiveness of these interventions among midlife women is unclear.

OBJECTIVE

This systematic review evaluated the effectiveness, acceptability, and active behavior change techniques (BCTs) of mobile PA technologies among midlife menopausal women.

METHODS

A mixed methods systematic review of qualitative and quantitative studies was conducted. MEDLINE (Ovid), Embase, Scopus, CINAHL, Web of Science, SPORTDiscus, CENTRAL, PsycINFO, and the ProQuest Sports Medicine and Education Index were systematically searched. Studies were selected and screened according to predetermined eligibility criteria. In total, 2 reviewers independently assessed the risk of bias using the Mixed Methods Appraisal Tool and completed BCT mapping of the included interventions using the BCT Taxonomy v1.

RESULTS

A total of 12 studies were included in this review. Overall risk of bias was "Moderate to high" in 58% (7/12) of the included studies and "low" in 42% (5/12) of the studies. Of the 12 studies, 7 (58%) assessed changes in PA levels. The pooled effect size of 2 randomized controlled trials resulted in a small to moderate increase in moderate to vigorous PA of approximately 61.36 weekly minutes among midlife women, at least in the short term (95% CI 17.70-105.01; P=.006). Although a meta-analysis was not feasible because of heterogeneity, positive improvements were also found in a range of menopause-related outcomes such as weight reduction, anxiety management, sleep quality, and menopause-related quality of life. Midlife women perceived mobile PA interventions to be acceptable and potentially helpful in increasing PA and daily steps. The average number of BCTs per mobile PA intervention was 8.8 (range 4-13) according to the BCT Taxonomy v1. "Self-monitoring of behaviour," "Biofeedback," and "Goal setting (behaviour)" were the most frequently described BCTs across the included interventions.

CONCLUSIONS

This review demonstrated that mobile PA interventions in the form of smartphone apps and wearable trackers are potentially effective for small to moderate increases in moderate to vigorous PA among midlife women with menopausal symptoms. Although menopause is a natural condition affecting half the population worldwide, there is a substantial lack of evidence to support the acceptability and effectiveness of mobile PA interventions on menopause-related outcomes, which needs further investigation.

TRIAL REGISTRATION

PROSPERO CRD42021273062; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=273062.

Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

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.

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.

A Digital Educational Intervention With Wearable Activity Trackers to Support Health Behaviors Among Childhood Cancer Survivors: Pilot Feasibility and Acceptability Study

Background

Childhood cancer survivors are at increased risk of cardiometabolic complications that are exacerbated by poor health behaviors. Critically, many survivors do not meet physical activity guidelines.

Objective

The primary aim was to evaluate the feasibility and acceptability of iBounce, a digital health intervention for educating and engaging survivors in physical activity. Our secondary aims were to assess the change in survivors’ physical activity levels and behaviors, aerobic fitness, and health-related quality of life (HRQoL) after participating in the iBounce program.

Methods

We recruited survivors aged 8 to 13 years who were ≥12 months post cancer treatment completion. The app-based program involved 10 educational modules, goal setting, and home-based physical activities monitored using an activity tracker. We assessed objective physical activity levels and behaviors using cluster analysis, aerobic fitness, and HRQoL at baseline and after the intervention (week 12). Parents were trained to reassess aerobic fitness at home at follow-up (week 24).

Results

In total, 30 participants opted in, of whom 27 (90%) completed baseline assessments, and 23 (77%) commenced iBounce. Our opt-in rate was 59% (30/51), and most (19/23, 83%) of the survivors completed the intervention. More than half (13/23, 57%) of the survivors completed all 10 modules (median 10, IQR 4-10). We achieved a high retention rate (19/27, 70%) and activity tracker compliance (15/19, 79%), and there were no intervention-related adverse events. Survivors reported high satisfaction with iBounce (median enjoyment score 75%; ease-of-use score 86%), but lower satisfaction with the activity tracker (median enjoyment score 60%). Parents reported the program activities to be acceptable (median score 70%), and their overall satisfaction was 60%, potentially because of technological difficulties that resulted in the program becoming disjointed. We did not observe any significant changes in physical activity levels or HRQoL at week 12. Our subgroup analysis for changes in physical activity behaviors in participants (n=11) revealed five cluster groups: most active, active, moderately active, occasionally active, and least active. Of these 11 survivors, 3 (27%) moved to a more active cluster group, highlighting their engagement in more frequent and sustained bouts of moderate-to-vigorous physical activity; 6 (56%) stayed in the same cluster; and 2 (18%) moved to a less active cluster. The survivors’ mean aerobic fitness percentiles increased after completing iBounce (change +17, 95% CI 1.7-32.1; P=.03) but not at follow-up (P=.39).

Conclusions

We demonstrated iBounce to be feasible for delivery and acceptable among survivors, despite some technical difficulties. The distance-delivered format provides an opportunity to engage survivors in physical activity at home and may address barriers to care, particularly for regional or remote families. We will use these pilot findings to evaluate an updated version of iBounce.

Trial Registration

Australian New Zealand Clinical Trials Registry ACTRN12621000259842; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=ACTRN12621000259842

Technologies measuring manual wheelchair propulsion metrics: a scoping review

The aim of this review is to investigate existing and developing technologies assessing metrics of manual wheelchair propulsion. A scoping review of scientific and gray literature was performed. Five databases were searched - Medline, Scopus, CINAHL, Institute of Electrical and Electronics Engineers (IEEE), and Embase. The 38 retained articles identified 27 devices categorized into accelerometers, wheelchair-mounted devices, instrumented wheels, and wearables. The devices included in this review can be used by manual wheelchair users to monitor propulsion effort and activity goals, by clinicians to assess rehabilitation programs, and to inform and guide future research. The findings support a need for further research into the development of custom algorithms for manual wheelchair user populations as well as further validation in broader free-living environments with equitable participant populations.

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

BACKGROUND

Consumer-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.

OBJECTIVE

The 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.

METHODS

A 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.

RESULTS

The 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.

CONCLUSIONS

The 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.

Examining the Dose-Response Relationship between Outdoor Jogging and Physical Health of Youths: A Long-Term Experimental Study in Campus Green Space

Many studies have demonstrated that outdoor physical activity positively affects the physical health of young people. Here, we aimed to examine the extent to which outdoor jogging was associated with the physical health of youths, and then to decipher whether a dose–response relationship exists between them. A total of 2852 youths from a Chinese university were enrolled in a long-term experimental study between September 2018 and September 2019. We conducted two waves of physical health tests for 2852 youths (before and after the jogging interventions in 2018 and 2019, respectively) using China’s National Student Physical Health Standard (NSPHS). Paired t-tests were used to examine statistical differences. A multiple regression model was used to evaluate the associations between jogging and physical health. The results showed that: statistically significant changes in the two waves of physical health outcomes were suggested after jogging interventions; outdoor jogging in campus green space was associated with participants’ physical health after controlling for covariates; and a dose–response relationship between jogging and physical health outcomes was revealed, with 120–140 km/year (approximately 3.43–4 km/week) being the most effective intervention dose. Our findings have implications for promoting physical health in youth groups by encouraging outdoor physical activity.

Effect of an ongoing pharmacist service to reduce medicine-induced deterioration and adverse reactions in aged-care facilities (nursing homes): a multicentre, randomised controlled trial (the ReMInDAR trial)

Objective

To assess the effectiveness of a pharmacist-led intervention using validated tools to reduce medicine-induced deterioration and adverse reactions.

Design and setting

Multicenter, open-label parallel randomised controlled trial involving 39 Australian aged-care facilities.

Participants

Residents on ≥4 medicines or ≥1 anticholinergic or sedative medicine.

Intervention

Pharmacist-led intervention using validated tools to detect signs and symptoms of medicine-induced deterioration which occurred every 8 weeks over 12 months.

Comparator

Usual care (Residential Medication Management Review) provided by accredited pharmacists.

Outcomes

Primary outcome was change in Frailty Index at 12 months. Secondary outcomes included changes in cognition, 24-hour movement behaviour by accelerometry, grip strength, weight, adverse events and quality of life.

Results

248 persons (median age 87 years) completed the study; 120 in the interventionand, 128 in control arms. In total 575 pharmacist, sessions were undertaken in the intervention arm. There was no statistically significant difference for change in frailty between groups (mean difference: 0.009, 95% CI: −0.028, 0.009, P = 0.320). A significant difference for cognition was observed, with a mean difference of 1.36 point change at 12 months (95% CI: 0.01, 2.72, P = 0.048). Changes in 24-hour movement behaviour, grip strength, adverse events and quality of life were not significantly different between groups. Point estimates favoured the intervention arm at 12 months for frailty, 24-hour movement behaviour and grip strength.

Conclusions

The use of validated tools by pharmacists to detect signs of medicine-induced deterioration is a model of practice that requires further research, with promising results from this trial, particularly with regards to improved cognition.

"I Do What I Like": 8- to 10-Year-Old Children's Physical Activity Behavior Is Already Interrelated With Their Automatic Affective Processes

The majority of middle-age children do not meet current physical activity guidelines. There is growing evidence that adults' physical activity is partially influenced by automatic affective processes, which are derived from affective experiences with physical activity. However, little is known about whether these processes are interrelated with children's physical activity level. A prospective design was used to examine whether automatic affective processes assessed by an evaluative priming procedure predict physical activity of children. Physical activity of 48 children (8.71 ± 0.71 years; 65% girls) was measured for 1 week with activity trackers. In a linear regression model, automatic affective processes (β = 0.36) significantly predicted physical activity, accounting for 11.02% of variance. These results indicate that physical-activity-related automatic affective processes are associated with children's physical activity, as has previously been found in adults. This study emphasizes the importance of fostering positive affective experiences associated with physical activity during childhood.

Impact of Activity Tracker Usage in Combination with a Physical Activity Intervention on Physical and Cognitive Parameters in Healthy Adults Aged 60+: A Randomized Controlled Trial

Regular physical activity (PA) is of central importance for healthy aging and has a well-known impact on helping older adults maintain their cognitive and physical health. Thus, we aimed to compare the effectiveness of two physical activity interventions primarily conducted at home (print-based or web-based vs. web-based plus the use of an activity tracker) on cognitive and physical health parameters in older adults. Data of participants (n = 551, 60–80 years) were analyzed after being randomly allocated to a waitlist control group (CG), a web-based or print-based intervention group (IG) or a web-based intervention group that also included the use of an activity tracker (AG). Measured parameters were grip strength, endurance (two-minute step test), gait speed (four-meter walk test), cognition (Simon task; balanced integration score (BIS), reaction time and accuracy) and physical self-concept (Physical Self-Description Questionnaire (PSDQ)). We found the highest effect sizes in all measured dimensions for AG (grip strength, endurance, gait speed, reaction time, physical self-concept), followed by IG (endurance, gait speed, reaction time, physical self-concept) and CG (endurance, gait speed, BIS). Findings suggest that a combined web-based and activity tracker intervention may improve physical functions, physical self-concept, and cognition in community-dwelling older adults.

The Interplay Between Affect, Dog's Physical Activity and Dog-Owner Relationship

Leaving a dog home alone is part of everyday life for most dog owners. Previous research shows that dog–owner relationship has multifarious effects on dog behavior. However, little is known about the interplay between dog–owner relationship, physical activity of the dog, and affective experiences at the time of the owner leaving home and reunion when the owner comes home. In this paper, we explored how the general (daily, home alone, and over the 2-week study period) physical activity of the dog, and owner's perceptions of the dog's affective state were correlated at those particular moments. Nineteen volunteer dog owners had their dogs (N = 19) wear two activity trackers (ActiGraph wGT2X-GT and FitBark2) for 2 weeks 24 h/day. Prior to the 2-week continuous physical activity measurement period, the owners filled in questionnaires about the dog–owner relationship and the dog behavior. In daily questionnaires, owners described and assessed their own and their perception of the emotion-related experiences of their dog and behavior of the dog at the moment of separation and reunion. The results indicated that the dog–owner relationship has an interplay with the mean daily and weekly physical activity levels of the dog. An indication of strong emotional dog–owner relationship (especially related to the attentiveness of the dog, continuous companionship, and time spent together when relaxing) correlated positively with the mean daily activity levels of the dog during the first measurement week of the study. Results also suggest that the mean daily and over the 2-week measurement period physical activity of the dog correlated the affective experiences of the dog and owner as reported by the owner when the dog was left home alone. More research is needed to understand the interplay between affect, physical activity of the dog, dog–owner relationship, and the effects of these factors on, and their interplay with, the welfare of dogs.

Remote Monitoring of the Performance Status and Burden of Symptoms of Patients With Gastrointestinal Cancer Via a Consumer-Based Activity Tracker: Quantitative Cohort Study

BACKGROUND

The number of older patients with gastrointestinal cancer is increasing due to an aging global population. Minimizing reliance on an in-clinic patient performance status test to determine a patient's prognosis and course of treatment can improve resource utilization. Further, current performance status measurements cannot capture patients' constant changes. These measurements also rely on self-reports, which are subjective and subject to bias. Real-time monitoring of patients' activities may allow for a more accurate assessment of patients' performance status while minimizing resource utilization.

OBJECTIVE

This study investigates the validity of consumer-based activity trackers for monitoring the performance status of patients with gastrointestinal cancer.

METHODS

A total of 27 consenting patients (63% male, median age 58 years) wore a consumer-based activity tracker 7 days before chemotherapy and 14 days after receiving their first treatment. The provider assessed patients using the Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scale and Memorial Symptom Assessment Scale-Short Form (MSAS-SF) before and after chemotherapy visits. The statistical correlations between ECOG-PS and MSAS-SF scores and patients' daily step counts were assessed.

RESULTS

The daily step counts yielded the highest correlation with the patients' ECOG-PS scores after chemotherapy (P<.001). The patients with higher ECOG-PS scores experienced a higher fluctuation in their step counts. The patients who walked more prechemotherapy (mean 6071 steps per day) and postchemotherapy (mean 5930 steps per day) had a lower MSAS-SF score (lower burden of symptoms) compared to patients who walked less prechemotherapy (mean 5205 steps per day) and postchemotherapy (mean 4437 steps per day).

CONCLUSIONS

This study demonstrates the feasibility of using inexpensive, consumer-based activity trackers for the remote monitoring of performance status in the gastrointestinal cancer population. The findings need to be validated in a larger population for generalizability.

Exploring the Use of Fitbit Consumer Activity Trackers to Support Active Lifestyles in Adults with Type 2 Diabetes: A Mixed-Methods Study

Background: People with type 2 diabetes are less active than those without the condition. Physical activity promotion within diabetes health care is limited. This project explored the use of Fitbit activity trackers (Fitbit, San Francisco, CA, USA) to support active lifestyles in adults with type 2 diabetes through a mixed-methods study. Methods: Two stages were conducted. In stage 1, adults with type 2 diabetes used a Fitbit Charge 4 (Fitbit, San Francisco, CA, USA) for 4 weeks. Fitbit and self-reported physical activity data was examined through quantitative analysis. Qualitative analysis was conducted to explore the experiences of participants. In stage 2, health professionals were interviewed to examine their views on using Fitbit activity trackers within type 2 diabetes care. Results: Adults with type 2 diabetes were recruited for stage 1 and adult health care and fitness professionals were recruited for stage 2. Stage 1 participants’ self-reported increases in physical activity (mean weekly minutes of walking increased from 358.75 to 507.50 min, p = 0.046) and a decrease in sedentary behaviour (mean daily hours of sedentary behaviour decreased from 10.65 to 10.05 h, p = 0.575). Fitbit activity data ranges identified individuals who led inactive and sedentary lifestyles below levels recommended and in need of physical activity support to reduce the risk to their health. During interviews, participants stated that the Fitbit activity tracker motivated them to be more active. Stage 2 participants intimated that Fitbit activity trackers could improve the promotion of physical activity within type 2 diabetes care. Interventions involving the Fitbit premium service, community prescription and combined use of Fitbits with physical activity behaviour change models were recommended by stage 2 participants. Conclusions: This study found that there is future scope for using Fitbit activity trackers to support active lifestyles in adults diagnosed with type 2 diabetes.

Asthma control is not associated with physical activity level in children with asthma during regular follow-up

OBJECTIVE

Engagement in physical activity (PA) is one of the important aims of long-term asthma treatment. The objective of this study is to evaluate whether improvement of asthma control is associated with enhanced PA during regular follow-up in children with asthma.

METHODS

Children, 6-18 years, with asthma were eligible for inclusion when their asthma was uncontrolled at a regular follow-up visit. Participants completed a seven-day recall questionnaire to assess engagement in different physical activities (Physical Activity Questionnaires for Children) at baseline and at the time asthma control was achieved according to predefined criteria. They were also instructed to wear an accelerometer (Actigraph^TM GT1M) for seven consecutive days at these timepoints.

RESULTS

Thirty children (21 boys), aged 10.5 (2.9) years, with uncontrolled asthma were included. After a median (IQR) follow up time of 163 (94-253) days PA was assessed again. Accelerometer obtained moderate vigorous PA (median (IQR) 56 (43-66) versus 53 (35-63) minutes) as well as self-reported PA (median (IQR) PAQ score 7.4 (5.9-10.1) versus 7.2 (6.5-11.0)) were not significantly different at the time of uncontrolled and controlled asthma. Moderate vigorous PA increased in 46.2%, was comparable in 23.1%, and decreased in 30.7% of patients, respectively. Self-reported PA increased in 19.0%, was comparable in 52.4%, and decreased in 28.6% of patients, respectively.

CONCLUSIONS

Based on the results of this study we conclude that asthma control is not associated with self-reported and accelerometer obtained level of physical activity during regular follow-up in children with asthma.

An Activity Tracker-Guided Physical Activity Program for Patients Undergoing Radiotherapy: Protocol for a Prospective Phase III Trial (OnkoFit I and II Trials)

Background

The positive impact that physical activity has on patients with cancer has been shown in several studies over recent years. However, supervised physical activity programs have several limitations, including costs and availability. Therefore, our study proposes a novel approach for the implementation of a patient-executed, activity tracker–guided exercise program to bridge this gap.

Objective

Our trial aims to investigate the impact that an activity tracker–guided, patient-executed exercise program for patients undergoing radiotherapy has on cancer-related fatigue, health-related quality of life, and preoperative health status.

Methods

Patients receiving postoperative radiotherapy for breast cancer (OnkoFit I trial) or neoadjuvant, definitive, or postoperative treatment for other types of solid tumors (OnkoFit II trial) will be randomized (1:1:1) into 3-arm studies. Target accrual is 201 patients in each trial (50 patients per year). After providing informed consent, patients will be randomized into a standard care arm (arm A) or 1 of 2 interventional arms (arms B and C). Patients in arms B and C will wear an activity tracker and record their daily step count in a diary. Patients in arm C will receive personalized weekly targets for their physical activity. No further instructions will be given to patients in arm B. The target daily step goals for patients in arm C will be adjusted weekly and will be increased by 10% of the average daily step count of the past week until they reach a maximum of 6000 steps per day. Patients in arm A will not be provided with an activity tracker. The primary end point of the OnkoFit I trial is cancer-related fatigue at 3 months after the completion of radiotherapy. This will be measured by the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire. For the OnkoFit II trial, the primary end point is the overall quality of life, which will be assessed with the Functional Assessment of Cancer Therapy-General sum score at 6 months after treatment to allow for recovery after possible surgery. In parallel, blood samples from before, during, and after treatment will be collected in order to assess inflammatory markers.

Results

Recruitment for both trials started on August 1, 2020, and to date, 49 and 12 patients have been included in the OnkoFit I and OnkoFit II trials, respectively. Both trials were approved by the institutional review board prior to their initiation.

Conclusions

The OnkoFit trials test an innovative, personalized approach for the implementation of an activity tracker–guided training program for patients with cancer during radiotherapy. The program requires only a limited amount of resources.

Trial Registration

ClinicalTrials.gov NCT04506476; https://clinicaltrials.gov/ct2/show/NCT04506476. ClinicalTrials.gov NCT04517019; https://clinicaltrials.gov/ct2/show/NCT04517019.

International Registered Report Identifier (IRRID)

DERR1-10.2196/28524

Individualised, targeted step count intervention following gastrointestinal cancer surgery: The Fit-4-Home randomised clinical trial

BACKGROUND

To determine the effectiveness of an individualised, daily targeted step count intervention and usual care compared with usual care alone on improving surgical and patient reported outcomes.

METHODS

The Fit-4-Home trial was a pragmatic, randomised controlled trial conducted from April 2019 to February 2021. Patients undergoing elective surgery for liver, stomach or pancreatic cancer in two Australian hospitals were recruited. Participants were randomly allocated to receive an individualised, targeted step count intervention and usual care (intervention) or usual care alone (control). A wearable activity tracker was provided to the intervention group to monitor their daily step count target. Primary outcome was the length of stay in the gastrointestinal ward. Secondary outcomes included postoperative complication rates, discharge destination, quality of life, physical activity, pain, fatigue, distress and hospital re-admission within 30 days. Outcome measures were compared between groups using non-parametric statistics.

RESULTS

Of the 96 patients recruited, 47 were randomised to the intervention group and 49 were randomised to the control group. The median (interquartile) length of stay in the ward was 7 days (5.0-13.0) in the intervention group and 7 days (5.0- 12.0) in the control group (p = 0.330). Fatigue scores were worse in the intervention group when compared to control (p = 0.018). No other differences between groups were observed.

CONCLUSIONS

An individualised, daily targeted step count intervention and usual care did not confer additional benefits in reducing the length of stay in the ward compared to usual care alone for patients undergoing gastrointestinal cancer surgery.

TRIAL REGISTRATION

Registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12619000194167).

Comparison of Energy Expenditure Assessed Using Wrist- and Hip-Worn ActiGraph GT3X in Free-Living Conditions in Young and Older Adults

The World Health Organization has presented their recommendations for energy expenditure to improve public health. Activity trackers do represent a modern solution for measuring physical activity, particularly in terms of steps/day and energy expended in physical activity (active energy expenditure). According to the manufacturer's instructions, these activity trackers can be placed on different body locations, mostly at the wrist and the hip, in an undifferentiated manner. The objective of this study was to compare the absolute error rate of active energy expenditure measured by a wrist-worn and hip-worn ActiGraph GT3X+ over a 24-h period in free-living conditions in young and older adults. Over the period of a 24-h period, 22 young adults and 22 older adults were asked to wear two ActiGraph GT3X+ at two different body locations recommended by the manufacturer, namely one around the wrist and one above the hip. Freedson algorithm was applied for data analysis. For both groups, the absolute error rate tended to decrease from 1,252 to 43% for older adults and from 408 to 46% for young participants with higher energy expenditure. Interestingly, for both young and older adults, the wrist-worn ActiGraph provided a significantly higher values of active energy expenditure (943 ± 264 cal/min) than the hip-worn (288 ± 181 cal/min). Taken together, these results suggest that caution is needed when using active energy expenditure as an activity tracker-based metric to quantify physical activity.

Exergaming Using Postural Feedback From Wearable Sensors and Exercise Therapy to Improve Postural Balance in People With Nonspecific Low Back Pain: Protocol for a Factorial Pilot Randomized Controlled Trial

Background

Physical exercise is a common treatment for people with low back pain (LBP). Wearable sensors that provide feedback on body movements and posture during exercise may enhance postural balance and motor control in people with LBP.

Objective

This study aims to investigate whether physical exercising with postural feedback (EPF) improves postural balance, motor control, and patient-reported outcomes in people with LBP.

Methods

The study was an assessor-blinded 2×2 factorial trial. We planned to recruit 80 participants with nonspecific LBP who did not receive treatment for LBP. In addition, we aimed to recruit 40 patients with chronic, nonspecific LBP who were receiving exercise therapy (ET) at the University Hospital Zurich. Both ET patients and participants without treatment were randomized to receive either an additional EPF intervention or no additional intervention. This resulted in four different combinations of interventions: ET+EPF, ET, EPF, and no intervention. The participants underwent outcome assessments at inclusion (T1); 3 weeks later, at randomization (T2); after an intervention period of 3 weeks with a predefined exercise schedule for participants receiving EPF (T3); and after an additional 6 weeks, during which participants assigned to the EPF groups could exercise as much as they wished (T4). Patients receiving ET completed their regularly prescribed therapies during the study period. Balance was assessed during quiet standing on a force platform, and motor control was assessed during a lifting task and a waiter’s bow task. Physical activity was recorded using an activity tracker and the participants’ mobile phones during the study. The predefined EPF schedule consisted of nine sessions of 20 minutes of exercise with a tablet and inertial measurement unit sensors at home. Participants performed a series of trunk and hip movements and received feedback on their movements in a gamified environment displayed on the tablet.

Results

The first participant was recruited in May 2019. Data collection was completed in October 2020, with 3 patients and 32 eligible people without therapy who passed the eligibility check.

Conclusions

Although it will not be possible to investigate differences in patients and people without other therapies, we expect this pilot study to provide insights into the potential of EPF to improve balance in people with LBP and adherence to such interventions.

International Registered Report Identifier (IRRID)

DERR1-10.2196/26982

Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.

Is the number of daily walking steps in sedentary workers affected by age, gender, body mass index, education, and overall energy expenditure?

BACKGROUND

Healthy adults should take 10,000 steps per day to gain the resulting health benefits. Knowledge regarding the individual characteristics associated with daily walking steps would enhance resource allocation to those most likely to benefit from the 10,000-steps-per-day campaign.

OBJECTIVE

To determine the extent to which age, gender, body mass index (BMI), education, and energy expenditure influence daily walking steps in white-collar workers and to assess the correlation of daily walking steps among pedometer, wristband activity tracker, and smartphone application.

METHODS

A cross-sectional study was conducted on 49 sedentary workers. Daily walking steps were simultaneously assessed by three activity trackers in free-living conditions for 7 consecutive days. Associations between daily walking steps and individual factors were examined using linear regression. Correlation tests were conducted to assess the association among the three devices.

RESULTS

Multiple regression analyses showed that BMI was associated with daily walking steps. A moderate to good correlation in daily walking steps was found between the wristband activity tracker and pedometer, as well as between the smartphone application and pedometer.

CONCLUSIONS

BMI influenced daily walking steps in white-collar workers. Daily walking steps assessed by the wristband activity tracker and smartphone application differed from those assessed by the pedometer.

Reducing occupational sitting time in adults with type 2 diabetes: Qualitative experiences of an office-adapted mHealth intervention

AIM

Understanding barriers and facilitators for limiting occupational sitting and what impact it has on health on those with type 2 diabetes is essential for future trials and intervention development in primary healthcare settings. This study aimed to explore the feasibility and acceptability of an intervention using mobile health (mHealth) technology, together with counselling by a diabetes specialist nurse, to reduce occupational sitting in adults with type 2 diabetes.

METHODS

Individual semi-structured interviews were conducted in 15 participants with type 2 diabetes who completed a 3-month intervention including mHealth; activity tracker (Garmin Vivofit3) and SMS reminders, one initial face-to-face patient-centred counselling session and three telephone follow-up calls by a diabetes specialist nurse within the primary healthcare system in Sweden. The interviews were recorded, transcribed verbatim and analysed using qualitative content analysis.

RESULTS

Two themes were identified: (1) 'From baby steps to milestones' reflecting three categories; 'Small changes make it easier to reduce sitting', 'Encouraged by trustworthy coaching', 'Physical and mental rewards matter' and (2) 'Tailoring strategies that fit me and my workplace' reflecting four categories; 'It's up to me', 'Taking advantage of the support', 'Using creativity to find practical solutions for interrupting sitting' and 'Living up to expectations'.

CONCLUSION

The intervention was perceived as feasible and acceptable in different office workplaces, and led to increased awareness of sedentary behaviour in adults with type 2 diabetes. Stepwise goal setting together with personalization of the mHealth intervention should be emphasized in individual type 2 diabetes programmes aiming to reduce workplace sitting.

Effect of Wearable Technology-Based Physical Activity Interventions on Breast Cancer Survivors' Physiological, Cognitive, and Emotional Outcomes: A Systematic Review

This systematic review synthesized all randomized controlled trials (RCTs) and controlled trials examining the effects of wearable health technology-based physical activity interventions on physiological, cognitive, and emotional outcomes in breast cancer survivors (BCS). We searched NCBI, Academic Search Premier, EMBASE, Web of Science, PubMed, and Medline from inception to March 2021. We included studies which: (1) were RCTs or controlled trials ≥8 weeks in duration; (2) were peer-reviewed and published in English; (3) sampled BCS in full remission and had not received treatment for at least six months; (4) utilized wearable health technology (e.g., Fitbit, Garmin xGC30); and (5) examined physiological, emotional, and/or cognitive outcomes. Sixty-six studies were identified and 14 were included in the review. Most of the observed effects were statistically significant and those which employed multi-component interventions generally yielded greater effects. Overall, the use of wearable health technology reduced sedentary behavior and increased moderate-to-vigorous intensity physical activity. Further, increased moderate-to-vigorous intensity physical activity was observed to be associated with increased perceived cognition and higher cognitive performance. Multiple studies also observed significant improvements in attitude, worry, and anxiety. Overall, findings suggested wearable health technology-based physical activity interventions to be effective for improving physical activity, attitude, and cognitive functions and for reducing sedentary behavior, anxiety, and worry in BCS.

Effect of Wearable Technology-Based Physical Activity Interventions on Breast Cancer Survivors' Physiological, Cognitive, and Emotional Outcomes: A Systematic Review

This systematic review synthesized all randomized controlled trials (RCTs) and controlled trials examining the effects of wearable health technology-based physical activity interventions on physiological, cognitive, and emotional outcomes in breast cancer survivors (BCS). We searched NCBI, Academic Search Premier, EMBASE, Web of Science, PubMed, and Medline from inception to March 2021. We included studies which: (1) were RCTs or controlled trials ≥8 weeks in duration; (2) were peer-reviewed and published in English; (3) sampled BCS in full remission and had not received treatment for at least six months; (4) utilized wearable health technology (e.g., Fitbit, Garmin xGC30); and (5) examined physiological, emotional, and/or cognitive outcomes. Sixty-six studies were identified and 14 were included in the review. Most of the observed effects were statistically significant and those which employed multi-component interventions generally yielded greater effects. Overall, the use of wearable health technology reduced sedentary behavior and increased moderate-to-vigorous intensity physical activity. Further, increased moderate-to-vigorous intensity physical activity was observed to be associated with increased perceived cognition and higher cognitive performance. Multiple studies also observed significant improvements in attitude, worry, and anxiety. Overall, findings suggested wearable health technology-based physical activity interventions to be effective for improving physical activity, attitude, and cognitive functions and for reducing sedentary behavior, anxiety, and worry in BCS.

Comparison of free-living physical activity data obtained from a Fitbit Zip, the Apple iPhone Health app and a modified Bouchard Activity Record

ISSUE ADDRESSED

Physical activity tracking devices have potential to improve public health, but their data needs to be reliable. No study has compared movement data between the Fitbit Zip, Apple iPhone Health app and physical activity records in a community setting over 10 days.

METHODS

University students aged 18+ years wore both a Fitbit Zip and an iPhone at/near their right waist and completed a modified Bouchard Activity Record (BAR) for 10 days in a free-living setting. Comparisons were made between the Fitbit Zip and iPhone for the number of steps and the distance travelled and between the Fitbit Zip and BAR for the minutes of activity in three different intensities.

RESULTS

Eighteen students provided sufficient data for inclusion. There were strong correlations between steps per day (r = .87) and distance travelled (r = .88) between the Fitbit Zips and iPhones. However, the Fitbit Zip measured significantly more steps per day (mean 8437 vs 7303; P ≤ .001) and greater distances (mean 5.9 vs 4.9; P ≤ .001) than the iPhone. Correlations between the Fitbit Zips and the BARs were moderate for minutes of total (r = .51) and light (r = .40) activity and weak for moderate/fairly active (r = .20) and vigorous/very active (r = .25).

CONCLUSIONS

There were strong correlations between the physical activity data measured by Fitbit Zips and iPhones, but the iPhone Health app significantly underestimated the number of steps per day taken and the distance travelled when compared to the Fitbit Zip. SO WHAT?: Understanding the comparability of accelerometer devices provides useful information for future pragmatic physical activity measurement.

Feasibility and Convergent Validity of an Activity Tracker for Low Back Pain Within a Clinical Study: Cross-sectional Study

BACKGROUND

Low back pain (LBP) is a highly prevalent condition affecting individuals of all ages. To manage the symptoms and prevent recurrences and flare-ups, physical activity in conjunction with self-management education is recommended. Tools such as diaries and questionnaires have been the gold standard for tracking physical activity in clinical studies. However, there are issues with consistency, accuracy, and recall with the use of these outcome measures. Given the growth of technology in today's society, consumer-grade activity monitors have become a common and convenient method of recording physical activity data.

OBJECTIVE

The aim of this study is to test the feasibility and convergent validity of a Garmin Vivofit 3 activity tracker in evaluating physical activity levels in a clinical trial of patients with LBP.

METHODS

We recruited 17 individuals with nonspecific LBP referred from health care professionals or self-referred through advertisements in the community. The participants entered into a 12-week physical activity and self-management program. Physical activity was assessed using a self-reported questionnaire and the Garmin activity tracker. Activity tracker data (eg, steps taken, distance walked, and intensity minutes) were extracted weekly from the Garmin Connect online platform. Outcomes of pain and activity limitation were assessed weekly using a mobile app. A linear regression was conducted to evaluate if demographic factors (ie, age, gender, pain level) affected the adherence rates to the activity monitor. We also used Pearson correlations to evaluate the convergent validity of the Garmin activity tracker with the physical activity questionnaire.

RESULTS

The mean daily adherence rate for activity monitors was 70% (SD 31%) over the 26 weeks of study. The mean response rate for the weekly physical activity measures using REDCap for the first 12 weeks of the study was 91% (SD 17%). None of the hypothesized variables or questionnaires were predictors of response rate.

CONCLUSIONS

The majority of participants were compliant with wearing the tracker, and demographic factors were not found to be predictors of adherence to wearing the device. However, there were poor correlations between the modified International Physical Activity Questionnaire Short Form (IPAQ-SF) and the activity monitor, demonstrating problems with convergent validity.

Acceptability of a Mobile Health Behavior Change Intervention for Cancer Survivors With Obesity or Overweight: Nested Mixed Methods Study Within a Randomized Controlled Trial

Background

A significant proportion of cancer survivors have overweight or obesity. Although this has negative implications for health, weight management is not a standard component of oncology aftercare. Mobile health (mHealth) technology, in combination with behavior change techniques (BCTs), has the potential to support positive lifestyle changes. Few studies have been carried out with cancer survivors; therefore, the acceptability of these tools and techniques requires further investigation.

Objective

The aim of this study is to examine the acceptability of a behavior change intervention using mHealth for cancer survivors with a BMI of 25 or more and to gather constructive feedback from participants.

Methods

The intervention consisted of educational sessions and an 8-week physical activity goal setting intervention delivered using mobile technology (ie, Fitbit activity monitor plus SMS contact). In the context of a two-arm randomized controlled trial, semistructured interviews were conducted to assess the retrospective acceptability of the intervention from the perspective of the recipients. The theoretical framework for the acceptability of health care interventions was used to inform a topic guide. The interviews were transcribed and analyzed using thematic analysis. A quantitative survey was also conducted to determine the acceptability of the intervention. A total of 13 participants were interviewed, and 36 participants completed the quantitative survey.

Results

The results strongly support the acceptability of the intervention. The majority of the survey respondents held a positive attitude toward the intervention (35/36, 97%). In qualitative reports, many of the intervention components were enjoyed and the mHealth components (ie, Fitbit and goal setting through text message contact) were rated especially positively. Responses were mixed as to whether the burden of participating in the intervention was high (6/36, 17%) or low (5/36, 14%). Participants perceived the intervention as having high efficacy in improving health and well-being (34/36, 94%). Most respondents said that they understood how the intervention works (35/36, 97%), and qualitative data show that participants’ understanding of the aim of the intervention was broader than weight management and focused more on moving on psychologically from cancer.

Conclusions

On the basis of the coherence of responses with theorized aspects of intervention acceptability, we are confident that this intervention using mHealth and BCTs is acceptable to cancer survivors with obesity or overweight. Participants made several recommendations concerning the additional provision of social support. Future studies are needed to assess the feasibility of delivery in clinical practice and the acceptability of the intervention to those delivering the intervention.

International Registered Report Identifier (IRRID)

RR2-10.2196/13214

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data

The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19.

The EnergyKids Pilot Study: Comparing Energy Balance of Primary School Children during School and Summer Camp

Children's energy requirements may vary during school and summer camp days. To evaluate energy balance during these two periods, seventy-eight children (45% females, 8-10 years) living in Parma, Italy, were enrolled in this observational study. Participants completed a 3-day food diary and wore an activity tracker for three consecutive days during a school- and a summer camp-week to estimate energy intake (EI) and energy expenditure (TEE). Height and body weight were measured at the beginning of each period to define children's weight status. BMI and EI (school: 1692 ± 265 kcal/day; summer camp: 1738 ± 262 kcal/day) were similar during both periods. Both physical activity and TEE (summer camp: 1948 ± 312; school: 1704 ± 263 kcal/day) were higher during summer camp compared to school time. Therefore, energy balance was more negative during summer camp (-209 ± 366 kcal/day) compared to school time (-12 ± 331 kcal/day). Similar results were observed when males and females were analyzed separately but, comparing the sexes, males had a higher TEE and a more negative energy balance than females, during both periods. The results strongly suggest that an accurate evaluation of children's energy balance, that considers both diet and physical activity, is needed when planning adequate diets for different situations.

Without Alarms to Increase Postoperative Ambulation: A Randomized Control Trial

Early ambulation is a key component to postoperative recovery; however, measuring steps taken is often inconsistent and nonstandardized. This study aimed to determine whether an activity tracker with alarms would increase postoperative ambulation in patients after elective colorectal procedures. Forty-eight patients were randomly assigned to either trackers with 5 daily alarms or activity trackers alone. Over 223 total patient days, the trackers recorded a complete data set for 216 patient days (96.9%). Increasing the postoperative day significantly affected the number of steps taken, while age, sex, Risk Analysis Index score, and approach (laparoscopic versus open) did not show a significant effect. The mean steps per day in the intervention group were 1468 (median 495; interquartile range (IQR) 1345) and in the control group was 1645 (median 1014; IQR 2498). The use of trackers with alarms did not significantly affect the number of daily steps compared to trackers alone (ANOVA, P = .93). Although activity trackers with alarms did not increase postoperative ambulation compared with trackers with no alarms, we demonstrated a strategy to operationalize the use of trackers into postoperative care to provide a quantitative value for ambulation. This enables quantification of a key component in the Enhanced Recovery After Surgery protocol.

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Background

In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process.

Objective

This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined.

Methods

We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]).

Results

In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892).

Conclusions

ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted.

Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers

Background

The health internet-of-things (IoT) can potentially provide insights into the present health condition, potential pitfalls, and support of a healthier lifestyle. However, to enjoy these benefits, people need skills to use the IoT. These IoT skills are expected to differ across the general population, thereby causing a new digital divide.

Objective

This study aims to assess whether a sample of the general Dutch population can use health IoT by focusing on data and strategic IoT skills. Furthermore, we determine the role of gender, age, and education, and traditional internet skills.

Methods

From April 1, 2019, to December 12, 2019, 100 individuals participated in this study. Participants were recruited via digital flyers and door-to-door canvassing. A selective quota sample was divided into equal subsamples of gender, age, and education. Additional inclusion criteria were smartphone possession and no previous experience of using activity trackers. This study was conducted in 3 waves over a period of 2 weeks. In wave 1, a questionnaire was administered to measure the operational, mobile, and information internet skills of the participants, and the participants were introduced to the activity tracker. After 1 week of getting acquainted with the activity tracker, a task-based performance test was conducted in wave 2 to measure the levels of data IoT skills and the strategic IoT skill component—action plan construction. A week after the participants were asked to use the activity tracker more deliberately, a performance test was then conducted in wave 3 to measure the level of the strategic IoT skill component—action plan execution.

Results

The participants successfully completed 54% (13.5/25) of the data IoT skill tasks. Regarding strategic IoT tasks, the completion rates were 56% (10.1/18) for action plan construction and 43% (3.9/9) for action plan execution. None of the participants were able to complete all the data IoT skill tasks, and none of the participants were able to complete all the strategic IoT skill tasks regarding action plan construction or its execution. Age and education were important determinants of the IoT skill levels of the participants, except for the ability to execute an action plan strategically. Furthermore, the level of information internet skills of the participants contributed to their level of data IoT skills.

Conclusions

This study found that data and strategic IoT skills of Dutch citizens are underdeveloped with regard to health purposes. In particular, those who could benefit the most from health IoT were those who had the most trouble using it, that is, the older and lower-educated individuals.

Impact of a Pharmacist-Led Intensive Care Unit Sleep Improvement Protocol on Sleep Duration and Quality

BACKGROUND

Sleep improvement protocols are recommended for use in the intensive care unit (ICU) despite questions regarding which interventions to include, whether sleep quality or duration will improve, and the role of pharmacists in their development and implementation.

OBJECTIVE

To characterize the impact of a pharmacist-led, ICU sleep improvement protocol on sleep duration and quality as evaluated by a commercially available activity tracker and patient perception.

METHODS

Critical care pharmacists from a 40-bed, mixed ICU at a large community hospital led the development and implementation of an interprofessional sleep improvement protocol. It included daily pharmacist medication review to reduce use of medications known to disrupt sleep or increase delirium and guideline-based recommendations on both environmental and nonpharmacological sleep-focused interventions. Sleep duration and quality were compared before (December 2018 to December 2019) and after (January to June 2019) protocol implementation in non-mechanically ventilated adults using both objective (total nocturnal sleep time [TST] measured by an activity tracker (Fitbit Charge 2) and subjective (patient-perceived sleep quality using the Richards-Campbell Sleep Questionnaire [RCSQ]) measures.

RESULTS

Groups before (n = 48) and after (n = 29) sleep protocol implementation were well matched. After protocol implementation, patients had a longer TST (389 ± 123 vs 310 ± 147 minutes; P = 0.02) and better RCSQ-perceived sleep quality (63 ± 18 vs 42 ± 24 mm; P = 0.0003) compared with before implementation.

CONCLUSION AND RELEVANCE

A sleep protocol that incorporated novel elements led to objective and subjective improvements in ICU sleep duration and quality. Application of this study may result in increased utilization of sleep protocols and pharmacist involvement.

Asynchronous mHealth Interventions in Rheumatoid Arthritis: Systematic Scoping Review

BACKGROUND

Mobile devices such as smartphones and tablets have surged in popularity in recent years, generating numerous possibilities for their use in health care as mobile health (mHealth) tools. One advantage of mHealth is that it can be provided asynchronously, signifying that health care providers and patients are not communicating in real time. The integration of asynchronous mHealth into daily clinical practice might therefore help to make health care more efficient for patients with rheumatoid arthritis (RA). The benefits have been reviewed in various medical conditions, such as diabetes and asthma, with promising results. However, to date, it is unclear what evidence exists for the use of asynchronous mHealth in the field of RA.

OBJECTIVE

The objective of this study was to map the different asynchronous mHealth interventions tested in clinical trials in patients with RA and to summarize the effects of the interventions.

METHODS

A systematic search of Pubmed, Scopus, Cochrane, and PsycINFO was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Studies were initially screened and later assessed by two independent researchers. Disagreements on inclusion or exclusion of studies were resolved by discussion.

RESULTS

The literature search yielded 1752 abstracts. After deduplication and screening, 10 controlled intervention studies were included. All studies were assessed to be at risk for bias in at least one domain of the Cochrane risk-of-bias tool. In the 10 selected studies, 4 different types of mHealth interventions were used: SMS reminders (to increase medication adherence or physical activity; n=3), web apps (for disease monitoring and/or to provide medical information; n=5), smartphone apps (for disease monitoring; n=1), and pedometers (to increase and track steps; n=1). Measured outcomes varied widely between studies; improvements were seen in terms of medication compliance (SMS reminders), reaching rapid remission (web app), various domains of physical activity (pedometer, SMS reminders, and web apps), patient-physician interaction (web apps), and self-efficacy (smartphone app).

CONCLUSIONS

SMS reminders, web apps, smartphone apps, and pedometers have been evaluated in intervention studies in patients with RA. These interventions have been used to monitor patients or to support them in their health behavior. The use of asynchronous mHealth led to desirable outcomes in nearly all studies. However, since all studies were at risk of bias and methods used were very heterogeneous, high-quality research is warranted to corroborate these promising results.

Comparison of Older and Younger Adults' Attitudes Toward the Adoption and Use of Activity Trackers

Background

Activity tracking devices have significant potential in assisting older adults’ health care and quality of life, but this population lags behind in the adoption of these devices. While theoretical frameworks have been introduced to explain and increase the adoption of this technology by older adults, little effort has been made to validate the frameworks with people in other age groups.

Objective

The goal of this study was to validate the theoretical framework of technology acceptance by older adults that we previously proposed through a direct comparison of the attitudes to and experiences of activity trackers in older and younger users.

Methods

Semistructured interviews were conducted with 2 groups of 15 participants to investigate their experiences of using activity trackers. The recruitment criteria included age (between 18 years and 24 years for the younger participant group or 65 years and older for the older participant group) and prior experiences of using mobile devices or apps for activity tracking for 2 months and longer.

Results

Our findings showed that the phase of perceived ease of learning as a significant influencer of the acceptance of activity trackers existed only in the older participant group, but this phase never emerged in the younger participant group. In addition, this study confirmed that other phases exist in both age groups, but 2 distinct patterns emerged according to age groups: (1) the social influence construct influenced the older participants positively but the younger participants negatively and (2) older participants’ exploration in the system experiment phase was purpose-driven by particular needs or benefits but for younger participants, it was a phase to explore a new technology.

Conclusions

This study confirms the validity of the proposed theoretical framework to account for the unique aspect of older adults’ technology adoption. This framework can provide theoretical guidelines when designing technology for older adults as well as when generating new investigations and experiments for older adults and technology use.

Mental Condition Monitoring Based on Multimodality Biometry

We have developed a system with multimodality that monitors objective biomarkers for screening the mental distress in the office. A field study using a prototype of the system was performed over four months with 39 volunteers. We obtained PC operation patterns using a PC logger, sleeping time and activity levels using a wrist-band-type activity tracker, and brain activity and behavior data during a working memory task using optical topography. We also administered two standard questionnaires: the Brief Job Stress Questionnaire (BJS) and the Kessler 6 scale (K6). Supervised machine learning and cross validation were performed. The objective variables were mental scores obtained from the questionnaires and the explanatory variables were the biomarkers obtained from the modalities. Multiple linear regression models for mental scores were comprehensively searched and the optimum models were selected from 2,619,785 candidates. Each mental score estimated with each optimum model was well correlated with each mental score obtained with the questionnaire (correlation coefficient = 0.6-0.8) within a 24% of estimation error. Mental scores obtained by means of questionnaires have been in general use in mental health care for a while, so our multimodality system is potentially useful for mental healthcare due to the quantitative agreement on the mental scores estimated with biomarkers and the mental scores obtained with questionnaires.

One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial

BACKGROUND

In 2017, mobile app support for managing diabetes was available to 64% of the global population of adults with diabetes. One Drop's digital therapeutics solution includes an evidence-based mobile app with global reach, a Bluetooth-connected glucometer, and in-app coaching from Certified Diabetes Educators. Among people with type 1 diabetes and an estimated hemoglobin A_1c level≥7.5%, using One Drop for 3 months has been associated with an improved estimated hemoglobin A_1c level of 22.2 mg/dL (-0.80%). However, the added value of integrated activity trackers is unknown.

OBJECTIVE

We conducted a pragmatic, remotely administered randomized controlled trial to evaluate One Drop with a new-to-market activity tracker against One Drop only on the 3-month hemoglobin A_1c level of adults with type 1 diabetes.

METHODS

Social media advertisements and online newsletters were used to recruit adults (≥18 years old) diagnosed (≥1 year) with T1D, naïve to One Drop's full solution and the activity tracker, with a laboratory hemoglobin A_1c level≥7%. Participants (N=99) were randomized to receive One Drop and the activity tracker or One Drop only at the start of the study. The One Drop only group received the activity tracker at the end of the study. Multiple imputation, performed separately by group, was used to correct for missing data. Analysis of covariance models, controlling for baseline hemoglobin A_1c, were used to evaluate 3-month hemoglobin A_1c differences in intent-to-treat (ITT) and per protocol (PP) analyses.

RESULTS

The enrolled sample (N=95) had a mean age of 41 (SD 11) years, was 73% female, 88% White, diagnosed for a mean of 20 (SD 11) years, and had a mean hemoglobin A_1c level of 8.4% (SD 1.2%); 11% of the participants did not complete follow up. Analysis of covariance assumptions were met for the ITT and PP models. In ITT analysis, participants in the One Drop and activity tracker condition had a significantly lower 3-month hemoglobin A_1c level (mean 7.9%, SD 0.60%, 95% CI 7.8-8.2) than that of the participants in the One Drop only condition (mean 8.4%, SD 0.62%, 95% CI 8.2-8.5). In PP analysis, participants in the One Drop and activity tracker condition also had a significantly lower 3-month hemoglobin A_1c level (mean 7.9%, SD 0.59%, 95% CI 7.7-8.1) than that of participants in the One Drop only condition (mean 8.2%, SD 0.58%, 95% CI 8.0-8.4).

CONCLUSIONS

Participants exposed to One Drop and the activity tracker for the 3-month study period had a significantly lower 3-month hemoglobin A_1c level compared to that of participants exposed to One Drop only during the same timeframe. One Drop and a tracker may work better together than alone in helping people with type 1 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03459573; https://clinicaltrials.gov/ct2/show/NCT03459573.