Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort

Pilot evaluation of a consumer wearable device to assess sleep in a clinical polysomnography trial of suvorexant for treating insomnia in patients with Alzheimer's disease

The orexin receptor antagonist suvorexant was previously reported to significantly improve total sleep time (TST), by 28 min per night versus placebo after 4 weeks, in a sleep laboratory polysomnography (PSG) study of patients with Alzheimer's disease and insomnia. The study included an exploratory evaluation of a consumer-grade wearable "watch" device for assessing sleep that we report on here. Participants who met diagnostic criteria for both probable Alzheimer's disease dementia and insomnia were randomized to suvorexant 10-20 mg (N = 142) or placebo (N = 143) in a double-blind, 4-week trial. Patients were provided with a consumer-grade wearable watch device (Garmin vívosmart^® HR) to be worn continuously. Overnight sleep laboratory PSG was performed on three nights: screening, baseline and Night 29 (last dose). Watch treatment effects were assessed by change-from-baseline in watch TST at Week 4 (average TST per night). We also analysed Night 29 data only, with watch data restricted to the PSG recording time. In the 193 participants included in the Week 4 watch analysis (suvorexant = 97, placebo = 96), the suvorexant-placebo difference in watch TST was 4 min (p = .622). In patients with usable data for both assessments at the baseline and Night 29 PSG (suvorexant = 57, placebo = 50), the watch overestimated TST compared to PSG (e.g., placebo baseline = 412 min for watch and 265 min for PSG) and underestimated change-from-baseline treatment effects: the suvorexant-placebo difference was 20 min for watch TST (p = .405) and 35 min for PSG TST (p = .057). These findings show that the watch was less sensitive than PSG for evaluating treatment effects on TST.

Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured with a Wearable and Smartphone App: a Within-Subject Design Using Continuous Monitoring

Background

The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed.

Objective

This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association.

Methods

In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks.

Results

Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4).

Conclusions

To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.

Monitoring Patients Reported Outcomes after Valve Replacement Using Wearable Devices: Insights on Feasibility and Capability Study: Feasibility Results

Wearable devices (WDs) can objectively assess patient-reported outcomes (PROMs) in clinical trials. In this study, the feasibility and acceptability of using commercial WDs in elderly patients undergoing transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR) will be explored. This is a prospective observational study. Participants were trained to use a WD and a smartphone to collect data on their physical activity, rest heart rate and number of hours of sleep. Validated questionnaires were also used to evaluate these outcomes. A technology acceptance questionnaire was used at the end of the follow up. In our participants an overall good compliance in wearing the device (75.1% vs. 79.8%, SAVR vs. TAVR) was assessed. Half of the patients were willing to continue using the device. Perceived ease of use is one of the domains that scored higher in the technology acceptance questionnaire. In this study we observed that the use of a WD is accepted in our frail population for an extended period. Even though commercial WDs are not tailored for clinical research, they can produce useful information on patient behavior, especially when coordinated with intervention tailored to the single patient.

A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks

Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community’s interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system’s high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice.

Changes in Metabolic Activity and Gait Function by Dual-Task Cognitive Game-Based Treadmill System in Parkinson's Disease: Protocol of a Randomized Controlled Trial

Balance and gait impairments, and consequently, mobility restrictions and falls are common in Parkinson’s disease (PD). Various cognitive deficits are also common in PD and are associated with increased fall risk. These mobility and cognitive deficits are limiting factors in a person’s health, ability to perform activities of daily living, and overall quality of life. Community ambulation involves many dual-task (DT) conditions that require processing of several cognitive tasks while managing or reacting to sudden or unexpected balance challenges. DT training programs that can simultaneously target balance, gait, visuomotor, and cognitive functions are important to consider in rehabilitation and promotion of healthy active lives. In the proposed multi-center, randomized controlled trial (RCT), novel behavioral positron emission tomography (PET) brain imaging methods are used to evaluate the molecular basis and neural underpinnings of: (a) the decline of mobility function in PD, specifically, balance, gait, visuomotor, and cognitive function, and (b) the effects of an engaging, game-based DT treadmill walking program on mobility and cognitive functions. Both the interactive cognitive game tasks and treadmill walking require continuous visual attention, and share spatial processing functions, notably to minimize any balance disturbance or gait deviation/stumble. The ability to “walk and talk” normally includes activation of specific regions of the prefrontal cortex (PFC) and the basal ganglia (site of degeneration in PD). The PET imaging analysis and comparison with healthy age-matched controls will allow us to identify areas of abnormal, reduced activity levels, as well as areas of excessive activity (increased attentional resources) during DT-walking. We will then be able to identify areas of brain plasticity associated with improvements in mobility functions (balance, gait, and cognition) after intervention. We expect the gait-cognitive training effect to involve re-organization of PFC activity among other, yet to be identified brain regions. The DT mobility-training platform and behavioral PET brain imaging methods are directly applicable to other diseases that affect gait and cognition, e.g., cognitive vascular impairment, Alzheimer’s disease, as well as in aging.

Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting

Objectives

The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting.

Methods

Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement.

Results

The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%.

Conclusion

All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

Examining Response to Negative Life Events Through Fitness Tracker Data

Negative life events, such as the death of a loved one, are an unavoidable part of life. These events can be overwhelmingly stressful and may lead to the development of mental health disorders. To mitigate these adverse developments, prior literature has utilized measures of psychological responses to negative life events to better understand their effects on mental health. However, psychological changes represent only one aspect of an individual's potential response. We posit measuring additional dimensions of health, such as physical health, may also be beneficial, as physical health itself may be affected by negative life events and measuring its response could provide context to changes in mental health. Therefore, the primary aim of this work was to quantify how an individual's physical health changes in response to negative life events by testing for deviations in their physiological and behavioral state (PB-state). After capturing post-event, PB-state responses, our second aim sought to contextualize changes within known factors of psychological response to negative life events, namely coping strategies. To do so, we utilized a cohort of professionals across the United States monitored for 1 year and who experienced a negative life event while under observation. Garmin Vivosmart-3 devices provided a multidimensional representation of one's PB-state by collecting measures of resting heart rate, physical activity, and sleep. To test for deviations in PB-state following negative life events, One-Class Support Vector Machines were trained on a window of time prior to the event, which established a PB-state baseline. The model then evaluated participant's PB-state on the day of the life event and each day that followed, assigning each day a level of deviance relative to the participant's baseline. Resulting response curves were then examined in association with the use of various coping strategies using Bayesian gamma-hurdle regression models. The results from our objectives suggest that physical determinants of health also deviate in response to negative life events and that these deviations can be mitigated through different coping strategies. Taken together, these observations stress the need to examine physical determinants of health alongside psychological determinants when investigating the effects of negative life events.

Preoperative REM sleep is associated with complication development after colorectal surgery

BACKGROUND

While total sleep duration and rapid eye movement (REM) sleep duration have been associated with long-term mortality in non-surgical cohorts, the impact of preoperative sleep on postoperative outcomes has not been well studied.

METHODS

In this secondary analysis of a prospective observational cohort study, patients who recorded at least 1 sleep episode using a consumer wearable device in the 7 days before elective colorectal surgery were included. 30-day postoperative outcomes among those who did and did not receive at least 6 h of total sleep, as well as those who did and did not receive at least 1 h of rapid eye movement (REM) sleep, were compared.

RESULTS

34 out of 95 (35.8%) patients averaged at least 6 h of sleep per night, while 44 out of 82 (53.7%) averaged 1 h or more of REM sleep. Patients who slept less than 6 h had similar postoperative outcomes compared to those who slept 6 h or more. Patients who averaged less than 1 h of REM sleep, compared to those who achieved 1 h or more of REM sleep, had significantly higher rates of complication development (29.0% vs. 9.1%, P = 0.02), and return to the OR (10.5% vs. 0%, P = 0.04). After adjustment for confounding factors, increased REM sleep duration remained significantly associated with decreased complication development (increase in REM sleep from 50 to 60 min: OR 0.72, P = 0.009; REM sleep ≥ 1 h: OR 0.22, P = 0.03).

CONCLUSION

In this cohort of patients undergoing elective colorectal surgery, those who developed a complication within 30 days were less likely to average at least 1 h of REM sleep in the week before surgery than those who did not develop a complication. Preoperative REM sleep duration may represent a risk factor for surgical complications; however additional research is necessary to confirm this relationship.

Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study

BACKGROUND

Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms.

OBJECTIVE

The aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery.

METHODS

A total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual's typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically.

RESULTS

Models using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly.

CONCLUSIONS

Results suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions.

A Mobile Health Intervention to Increase Physical Activity in Pulmonary Arterial Hypertension

BACKGROUND

Supervised exercise training improves outcomes in patients with pulmonary arterial hypertension (PAH). The effect of an unsupervised activity intervention has not been tested.

RESEARCH QUESTION

Can a text-based mobile health intervention increase step counts in patients with PAH?

STUDY DESIGN AND METHODS

We performed a randomized, parallel arm, single-blind clinical trial. We randomized patients to usual care or a text message-based intervention for 12 weeks. The intervention arm received three automated text messages per day with real-time step count updates and encouraging messages rooted in behavioral change theory. Individual step targets increased by 20% every 4 weeks. The primary end point was mean week 12 step counts. Secondary end points included the 6-min walk test, quality of life, right ventricular function, and body composition.

RESULTS

Among 42 randomized participants, the change in raw steps between baseline and week 12 was higher in the intervention group (1,409 steps [interquartile range, -32 to 2,220] vs -149 steps [interquartile range, -1,010 to 735]; P = .02), which persisted after adjustment for age, sex, baseline step counts, and functional class (model estimated difference, 1,250 steps; P = .03). The intervention arm took a higher average number of steps on all days between days 9 and 84 (P < .05, all days). There was no difference in week 12 six-minute walk distance. Analysis of secondary end points suggested improvements in the emPHasis-10 score (adjusted change, -4.2; P = .046), a reduction in visceral fat volume (adjusted change, -170 mL; P = .023), and nearly significant improvement in tricuspid annular plane systolic excursion (model estimated difference, 1.2 mm; P = .051).

INTERPRETATION

This study demonstrated the feasibility of an automated text message-based intervention to increase physical activity in patients with PAH. Additional studies are warranted to examine the effect of the intervention on clinical outcomes.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov; No. NCT03069716; URL: www.clinicaltrials.gov.

Mobile Health Intervention Promoting Physical Activity in Adults Post Cardiac Rehabilitation: Pilot Randomized Controlled Trial

Background

Cardiac rehabilitation (CR) is an exercise-based program prescribed after cardiac events associated with improved physical, mental, and social functioning; however, many patients return to a sedentary lifestyle leading to deteriorating functional capacity after discharge from CR. Physical activity (PA) is critical to avoid recurrence of cardiac events and mortality and maintain functional capacity. Leveraging mobile health (mHealth) strategies to increase adherence to PA is a promising approach. Based on the social cognitive theory, we sought to determine whether mHealth strategies (Movn mobile app for self-monitoring, supportive push-through messages, and wearable activity tracker) would improve PA and functional capacity over 2 months.

Objective

The objectives of this pilot randomized controlled trial were to examine preliminary effects of an mHealth intervention on group differences in PA and functional capacity and group differences in depression and self-efficacy to maintain exercise after CR.

Methods

During the final week of outpatient CR, patients were randomized 1:1 to the intervention group or usual care. The intervention group downloaded the Movn mobile app, received supportive push-through messages on motivation and educational messages related to cardiovascular disease (CVD) management 3 times per week, and wore a Charge 2 (Fitbit Inc) activity tracker to track step counts. Participants in the usual care group wore a pedometer and recorded their daily steps in a diary. Data from the 6-minute walk test (6MWT) and self-reported questionnaires were collected at baseline and 2 months.

Results

We recruited 60 patients from 2 CR sites at a community hospital in Northern California. The mean age was 68.0 (SD 9.3) years, and 23% (14/60) were female; retention rate was 85% (51/60). Our results from 51 patients who completed follow-up showed the intervention group had a statistically significant higher mean daily step count compared with the control (8860 vs 6633; P=.02). There was no difference between groups for the 6MWT, depression, or self-efficacy to maintain exercise.

Conclusions

This intervention addresses a major public health initiative to examine the potential for mobile health strategies to promote PA in patients with CVD. Our technology-based pilot mHealth intervention provides promising results on a pragmatic and contemporary approach to promote PA by increasing daily step counts after completing CR.

Trial Registration

ClinicalTrials.gov NCT03446313; https://clinicaltrials.gov/ct2/show/NCT03446313

Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review

The availability of wearable devices (WDs) to collect biometric information and their use during activities of daily living is significantly increasing in the general population. These small electronic devices, which record fitness and health-related outcomes, have been broadly utilized in industries such as medicine, healthcare, and fitness. Since they are simple to use and progressively cheaper, they have also been used for numerous research purposes. However, despite their increasing popularity, most of these WDs do not accurately measure the proclaimed outcomes. In fact, research is equivocal about whether they are valid and reliable methods to specifically evaluate physical activity and health-related outcomes in older adults, since they are mostly designed and produced considering younger subjects' physical and mental characteristics. Additionally, their constant evolution through continuous upgrades and redesigned versions, suggests the need for constant up-to-date reviews and research. Accordingly, this article aims to scrutinize the state-of-the-art scientific evidence about the usefulness of WDs, specifically on older adults, to monitor physical activity and health-related outcomes. This critical review not only aims to inform older consumers but also aid researchers in study design when selecting physical activity and healthcare monitoring devices for elderly people.

Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review

Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.

A study protocol for MindMoves: A lifestyle physical activity and cognitive training intervention to prevent cognitive impairment in older women with cardiovascular disease

INTRODUCTION

Cognitive impairment (CI) and cardiovascular disease (CVD) disproportionately affect women compared to men, and CVD increases risk of CI. Physical activity and cognitive training can improve cognition in older adults and may have additive or synergistic effects. However, no combined intervention has targeted women with CVD or utilized a sustainable lifestyle approach. The purpose of the trial is to evaluate efficacy of MindMoves, a 24-week multimodal physical activity and cognitive training intervention, on cognition and serum biomarkers in older women with CVD. Three serum biomarkers (brain-derived neurotrophic factor [BDNF], vascular endothelial growth factor [VEGF], and insulin-like growth factor 1 [IGF-1]) were selected as a priori hypothesized indicators of the effects of physical activity and/or cognitive training on cognition.

METHODS

The study design is a randomized controlled trial with a 2 × 2 factorial design, to determine independent and combined efficacies of Mind (tablet-based cognitive training) and Move (lifestyle physical activity with goal-setting and group meetings) on change in cognition (primary outcome) and serum biomarkers (secondary outcomes). We will recruit 254 women aged ≥65 years with CVD and without CI from cardiology clinics. Women will be randomized to one of four conditions: (1) Mind, (2) Move, (3) MindMoves, or (4) usual care. Data will be obtained from participants at baseline, 24, 48, and 72 weeks.

DISCUSSION

This study will test efficacy of a lifestyle-focused intervention to prevent or delay cognitive impairment in older women with CVD and may identify relevant serum biomarkers that could be used as early indicators of intervention response.

A Co-Designed Active Video Game for Physical Activity Promotion in People With Chronic Obstructive Pulmonary Disease: Pilot Trial

BACKGROUND

People with chronic obstructive pulmonary disease (COPD) who are less active have lower quality of life, greater risk of exacerbations, and greater mortality than those who are more active. The effectiveness of physical activity interventions may facilitate the addition of game elements to improve engagement. The use of a co-design approach with people with COPD and clinicians as co-designers may also improve the effectiveness of the intervention.

OBJECTIVE

The primary aim of this study is to evaluate the feasibility of a co-designed mobile game by examining the usage of the game, subjective measures of game engagement, and adherence to wearing activity trackers. The secondary aim of this study is to estimate the effect of the game on daily steps and daily moderate-to-vigorous physical activity (MVPA).

METHODS

Participants with COPD who were taking part in the co-design of the active video game (n=9) acted as the experiment group, spending 3 weeks testing the game they helped to develop. Daily steps and MVPA were compared with a control group (n=9) of participants who did not co-design or test the game.

RESULTS

Most participants (8/9, 89%) engaged with the game after downloading it. Participants used the game to record physical activity on 58.6% (82/141) of the days the game was available. The highest scores on the Intrinsic Motivation Inventory were seen for the value and usefulness subscale, with a mean of 6.38 (SD 0.6). Adherence to wearing Fitbit was high, with participants in both groups recording steps on >80% of days. Usage of the game was positively correlated with changes in daily steps but not with MVPA.

CONCLUSIONS

The co-designed mobile app shows promise as an intervention and should be evaluated in a larger-scale trial in this population.

Multilevel mobile health approach to improve cardiovascular health in resource-limited communities with Step It Up: a randomised controlled trial protocol targeting physical activity

INTRODUCTION

Although physical activity (PA) reduces cardiovascular disease (CVD) risk, physical inactivity remains a pressing public health concern, especially among African American (AA) women in the USA. PA interventions focused on AA women living in resource-limited communities with scarce PA infrastructure are needed. Mobile health (mHealth) technology can increase access to PA interventions. We describe the development of a clinical protocol for a multilevel, community-based, mHealth PA intervention for AA women.

METHODS AND ANALYSIS

An mHealth intervention targeting AA women living in resource-limited Washington, DC communities was developed based on the socioecological framework for PA. Over 6 months, we will use a Sequential Multi-Assignment, Randomized Trial approach to compare the effects on PA of location-based remote messaging (named 'tailored-to-place') to standard remote messaging in an mHealth intervention. Participants will be randomised to a remote messaging intervention for 3 months, at which point the intervention strategy will adapt based on individuals' PA levels. Those who do not meet the PA goal will be rerandomised to more intensive treatment. Participants will be followed for another 3 months to determine the contribution of each mHealth intervention to PA level. This protocol will use novel statistical approaches to account for the adaptive strategy. Finally, effects of PA changes on CVD risk biomarkers will be characterised.

ETHICS AND DISSEMINATION

This protocol has been developed in partnership with a Washington, DC-area community advisory board to ensure feasibility and acceptability to community members. The National Institutes of Health Intramural IRB approved this research and the National Heart, Lung, and Blood Institute provided funding. Once published, results of this work will be disseminated to community members through presentations at community advisory board meetings and our quarterly newsletter.

TRIAL REGISTRATION NUMBER

NCT03288207.

Community-based intervention effects on older adults' physical activity and falls: Protocol and rationale for a randomized optimization trial (Ready Steady3.0)

The Ready Steady 3.0 trial is designed to test the main and interactive effects of two behavior change intervention components, within an 8-week physical activity intervention, on older adults' physical activity (PA). Each component is comprised of behavior change strategies that emphasize two different evidence-based ways to motivate older adults to be active: interpersonal and intrapersonal. 308 adults ≥70 years old will be randomized to 1 of 4 conditions in a 2 × 2 full factorial trial in which the two factors represent the receipt (No, Yes) of interpersonal or intrapersonal behavior change strategies. Participants will also receive two core intervention components: the Otago Exercise Program adapted for small groups and a PA monitor. Interventions across conditions will be delivered during 8 weekly, small group, meetings in community settings. The primary outcome of PA, measured objectively, and secondary outcomes of falls and the quality of life will be assessed at baseline and post-intervention: 1 week, 6 months, and 12 months. Findings will enable the identification of behavior change content that contributes to physical activity outcomes within a physical activity intervention for older adults. This study is one of the first to use the MOST framework to guide the development of a community-based physical activity intervention for older adults to reduce the public health problems of low PA and falls. The results will enable the optimization of behavior change content within a PA intervention for older adults and, in turn, other PA interventions for older adults.

The Physical Activity Wearables in the Police Force (PAW-Force) study: acceptability and impact

BACKGROUND

Policing is a highly stressful and increasingly sedentary occupation. The study aim was to assess the acceptability and impact of a mobile health (mHealth) technology intervention (Fitbit® activity monitor and 'Bupa Boost' smartphone app) to promote physical activity (PA) and reduce sedentary time in the police force.

METHODS

Single-group, pre-post, mixed methods pilot study. Police officers and staff (n = 180) were recruited from two police forces in South West England. Participants used the technology for 12 weeks (an 'individual' then 'social' phase) followed by 5 months of optional use. Data sources included Fitbit®-recorded objective step count, questionnaire surveys and semi-structured interviews (n = 32). Outcome assessment points were baseline (week 0), mid-intervention (week 6), post-intervention (week 12) and follow-up (month 8). Paired t-tests were used to investigate changes in quantitative outcomes. Qualitative analysis involved framework and thematic analysis.

RESULTS

Changes in mean daily step count were non-significant (p > 0.05), but self-reported PA increased in the short term (e.g. + 465.4 MET-minutes/week total PA baseline to week 12, p = 0.011) and longer term (e.g. + 420.5 MET-minutes/week moderate-to-vigorous PA baseline to month 8, p = 0.024). The greatest impact on behaviour was perceived by less active officers and staff. There were no significant changes in sedentary time; the qualitative findings highlighted the importance of context and external influences on behaviour. There were no statistically significant changes (all p-values > 0.05) in any secondary outcomes (physical and mental health-related quality of life, perceived stress and perceived productivity), with the exception of an improvement in mental health-related quality of life (SF-12 mental component score + 1.75 points, p = 0.020) from baseline to month 8. Engagement with and perceived acceptability of the intervention was high overall, but a small number of participants reported negative physical (skin irritation) and psychological (feelings of guilt and anxiety) consequences of technology use. Individual app features (such as goal-setting and self-monitoring) were generally preferred to social components (social comparison, competitions and support).

CONCLUSIONS

mHealth technology is an acceptable and potentially impactful intervention for increasing PA in the police force. The intervention was less useful for reducing sedentary time and the impact on secondary outcomes is unclear.

TRIAL REGISTRATION

NCT03169179 (registered 30th May 2017).

Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method

Inactivity, lack of sleep, and poor nutrition predispose individuals to health risks. Patient-Reported Outcomes (PROs) assess physical behaviours and psychological states but are subject of self-reporting biases. Conversely, wearables are an increasingly accurate source of behavioural Technology-Reported Outcomes (TechROs). However, the extent to which PROs and TechROs provide convergent information is unknown. We propose the coQoL PRO-TechRO co-calibration method and report its feasibility, reliability, and human factors influencing data quality. Thirty-nine seniors provided 7.4 ± 4.4 PROs for physical activity (IPAQ), social support (MSPSS), anxiety/depression (GADS), nutrition (PREDIMED, SelfMNA), memory (MFE), sleep (PSQI), Quality of Life (EQ-5D-3L), and 295 ± 238 days of TechROs (Fitbit Charge 2) along two years. We co-calibrated PROs and TechROs by Spearman rank and reported human factors guiding coQoL use. We report high PRO-TechRO correlations (rS≥ 0.8) for physical activity (moderate domestic activity-light+fair active duration), social support (family help-fair activity), anxiety/depression (numeric score-sleep duration), or sleep (duration to sleep-sleep duration) at various durations (7-120 days). coQoL feasibly co-calibrates constructs within physical behaviours and psychological states in seniors. Our results can inform designs of longitudinal observations and, whenever appropriate, personalized behavioural interventions.

Evaluations of Commercial Sleep Technologies for Objective Monitoring During Routine Sleeping Conditions

PURPOSE

The commercial market is saturated with technologies that claim to collect proficient, free-living sleep measurements despite a severe lack of independent third-party evaluations. Therefore, the present study evaluated the accuracy of various commercial sleep technologies during in-home sleeping conditions.

MATERIALS AND METHODS

Data collection spanned 98 separate nights of ad libitum sleep from five healthy adults. Prior to bedtime, participants utilized nine popular sleep devices while concurrently wearing a previously validated electroencephalography (EEG)-based device. Data collected from the commercial devices were extracted for later comparison against EEG to determine degrees of accuracy. Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, for each device.

RESULTS

Total sleep time (TST), total wake time (TWT), and sleep efficiency (SE) were measured with greater accuracy (lower percent errors) and limited bias by Fitbit Ionic [mean absolute percent error, bias (95% confidence interval); TST: 9.90%, 0.25 (-0.11, 0.61); TWT: 25.64%, -0.17 (-0.28, -0.06); SE: 3.49%, 0.65 (-0.82, 2.12)] and Oura smart ring [TST: 7.39%, 0.19 (0.04, 0.35); TWT: 36.29%, -0.18 (-0.31, -0.04); SE: 5.42%, 1.66 (0.17, 3.15)], whereas all other devices demonstrated a propensity to over or underestimate at least one if not all of the aforementioned sleep metrics. No commercial sleep technology appeared to accurately quantify sleep stages.

CONCLUSION

Generally speaking, commercial sleep technologies displayed lower error and bias values when quantifying sleep/wake states as compared to sleep staging durations. Still, these findings revealed that there is a remarkably high degree of variability in the accuracy of commercial sleep technologies, which further emphasizes that continuous evaluations of newly developed sleep technologies are vital. End-users may then be able to determine more accurately which sleep device is most suited for their desired application(s).

The accuracy of fitness watches for the measurement of heart rate and energy expenditure during moderate intensity exercise

BACKGROUND

As new technology emerges and updated fitness watches are released to the market, it is important to examine their accuracy. The aim of the current study was to examine the accuracy of three commercially available activity trackers in assessing heart rate (HR) and energy expenditure (EE) during moderate intensity exercise.

METHODS

Twenty healthy participants (Age: 20.5±0.7 yrs., Ht: 173.4±10.8 cm, BM: 72.8±13.9 kg, BMI: 24.0±2.5 kg/m²) wore two fitness watches (FB: Fitbit Versa^TM, San Francisco, CA, USA; and PI: Polar Ignite^TM, Polar Electro Oy, Kempele, Finland) and a chest-worn HR monitor (PTP: Polar TeamPro Sensor^TM, Polar Electro) during a 12-minute exercise protocol at incremental speeds. An electrocardiogram (ECG) and indirect calorimetry were used as criterion measures for HR and EE. Mean absolute percentage error (MAPE) was calculated to determine measurement error.

RESULTS

The MAPE values for HR were 11.6±8.7% for the FB, 11.0±10.0% for the PI, and 6.3±5.2% for the PTP. For EE, MAPE values were 9.6±7.2% for the FB, 16.7±19.6% for the PI and 13.8±13.0% for the PTP.

CONCLUSIONS

Fitness watches relying on optical measures of HR underestimate HR compared to criterion measures during moderate intensity exercise. Despite providing a more accurate measure of HR, a chest-worn monitor does not provide a more accurate estimate of EE compared to fitness watches. The Fitbit provided the most accurate measure of EE when compared to the Polar Ignite watch and chest-worn device.

Robust Stride Detector from Ankle-Mounted Inertial Sensors for Pedestrian Navigation and Activity Recognition with Machine Learning Approaches

In this paper, a stride detector algorithm combined with a technique inspired by zero velocity update (ZUPT) is proposed to reconstruct the trajectory of a pedestrian from an ankle-mounted inertial device. This innovative approach is based on sensor alignment and machine learning. It is able to detect 100% of both normal walking strides and more than 97% of atypical strides such as small steps, side steps, and backward walking that existing methods can hardly detect. This approach is also more robust in critical situations, when for example the wearer is sitting and moving the ankle or when the wearer is bicycling (less than two false detected strides per hour on average). As a consequence, the algorithm proposed for trajectory reconstruction achieves much better performances than existing methods for daily life contexts, in particular in narrow areas such as in a house. The computed stride trajectory contains essential information for recognizing the activity (atypical stride, walking, running, and stairs). For this task, we adopt a machine learning approach based on descriptors of these trajectories, which is shown to be robust to a large of variety of gaits. We tested our algorithm on recordings of healthy adults and children, achieving more than 99% success. The algorithm also achieved more than 97% success in challenging situations recorded by children suffering from movement disorders. Compared to most algorithms in the literature, this original method does not use a fixed-size sliding window but infers this last in an adaptive way.