Reporting adherence, validity and physical activity measures of wearable activity trackers in medical research: A systematic review

Risk of stroke with different levels of leisure-time physical activity: a systematic review and meta-analysis of prospective cohort studies

BACKGROUND

Leisure-time physical activity (LTPA) protects against vascular diseases. Whether and to what extent different levels of LTPA, including lower ones, benefit stroke prevention is still unclear.

METHODS

We searched prospective cohort studies, indexed on PubMed and Scopus, published in English up to 22 April 2023, that investigated, in a general healthy population, the relationship between different predefined LTPA levels, compared with inactivity, and the risk of any type of stroke. We applied random effect modelling for meta-analyses and meta-regression to control for the impact of age and sex.

RESULTS

Out of 3064 screened articles, 15 articles on 16 cohorts of subjects were included in meta-analyses, with a total of 752 050 followed-up subjects. Mean follow-up was 125.7±77.5 months. Included studies identified three (none, below target and ideal) to five (none, insufficient, low, moderate and intense) levels of LTPA. In the five studies identifying three levels of LTPA, compared with no LTPA, below target (risk ratio (RR)=0.82, 95% CI=0.75 to 0.88) and ideal LTPA significantly reduced stroke risk (RR=0.71, 95% CI=0.58 to 0.86).Lower levels of LTPA also mitigated stroke risk in studies reporting on four (n=6; RR=0.73, 95% CI=0.62 to 0.87 favouring moderate LTPA over no LTPA) and five levels (n=2; RR=0.71, 95% CI=0.58 to 0.88 favouring moderate LTPA over no LTPA). The benefits of LTPA were independent of age and sex.

CONCLUSIONS

According to our results, all levels of LTPA can be beneficial for stroke prevention, including levels currently regarded as low or insufficient. People should be encouraged to be physically active even at the lowest levels.

PROSPERO REGISTRATION NUMBER

CRD42023425302.

Increased Duration and Intensity of Physical Activity Are Associated With Increased Pain in Individuals With Femoroacetabular Impingement Syndrome: An Ecological Momentary Assessment Study

OBJECTIVE

To investigate the relation between accelerometer-measured physical activity and real-time pain in individuals with femoroacetabular impingement syndrome (FAIS). We tested the hypothesis that increased duration of high intensity activity would contribute to momentary increases in pain.

DESIGN

Observational study.

SETTING

Participants' natural, day-to-day environment.

PARTICIPANTS

Population-based sample of 33 individuals with unilateral FAIS. Important eligibility criteria included no concomitant hip disorders or previous hip surgery. Key sociodemographic features include that all participants were required to have a smartphone.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Duration and intensity of physical activity as measured by a waist-worn accelerometer, and instantaneous pain reported in real-time smartphone-based ecological momentary assessment surveys. Physical activity variables included each person's average sedentary time, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) in the 90 minutes proceeding all pain surveys as well as fluctuation in sedentary, LPA, and MVPA above or below average prior to each individual survey.

RESULTS

Linear mixed models revealed that the significant predictors of pain included fluctuation in sedentary time (B=-0.031, P<.001), average LPA (B=0.26, P=.035), and the interaction between fluctuation in LPA and fluctuation in MVPA (B=0.001, P<.001). Fluctuation in sedentary time above a person's average was associated with lower pain, while average LPA and fluctuations above average in both LPA and MVPA were associated with higher pain.

CONCLUSIONS

These results suggest that individuals with FAIS can engage in health-enhancing MVPA but should focus on avoiding concurrent increase above average in both high intensity and LPA in the same 90-minute period. Future work is warranted testing the efficacy of such an approach on pain. Additionally, given that high levels of LPA may arise from a host of socioeconomic factors, additional research is needed to disentangle the effect of LPA on pain in FAIS.

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

BACKGROUND

Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders.

METHODS

Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20-65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003-2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data.

RESULTS

Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as "Early-morning movers" and the profile characterized as "Highest activity" both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the "Lowest activity" profile. No significant differences were observed for the "Least sedentary movers" profile compared to the "Lowest activity" profile.

CONCLUSIONS

Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses

BACKGROUND

When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data.

METHODS

A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly.

DISCUSSION

The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices.

TRIAL REGISTRATION

NCT05481138.

Physical activity and sedentary behaviour of Bahraini people with type 2 diabetes: A cross-sectional study

Objective

Study patterns of physical activity and sedentary behaviour and the influence of demographics and body mass index (BMI) on these behaviours amongst Bahraini adults with type 2 diabetes over 10 weeks using an activity tracker.

Method

This cross-sectional observational study was conducted at a Bahrain government health centre. Thirty-three Bahraini Arab adults, 30-60 years old, with controlled type 2 diabetes, wore a Fitbit Flex 2TM activity tracker for 10 weeks. Data on age, sex, marital and employment status, education and BMI were collected at the start of the study.

Results

A total of N = 32 participants completed the study. The average steps per day were 7859 ± 4131, and there were no differences between baseline, week 5 and 10. A third of participants were sedentary, based on a threshold of 5000 steps/day. Females accumulated fewer average daily steps than males (6728 ± 2936 vs. 10,281 ± 4623, p = 0.018). Daily averages for physical activity intensity were as follows: sedentary (786 ± 109 min), light (250 ± 76 min), moderate (9 ± 10 min) and vigorous (12 ± 18 min). Males had higher daily averages versus females for moderate (13 ± 9 vs. 5 ± 9 min, p = 0.018) and vigorous physical activity (21 ± 23 vs. 5 ± 7 min, p = 0.034). 91% of participants wore the device ≥10 h/day. The adherence rate was 79% based on percentage of days the device was worn continuously over 10 weeks.

Conclusion

Future physical activity interventions should target sedentary and female participants with type 2 diabetes. In addition, we need to understand the facilitators and barriers to physical activity and the physical activity preferences of these two subgroups.

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

PURPOSE OF REVIEW

The aim of this review is to provide an overview of the use of commercial wrist-worn mobile health devices to track and monitor physiological outcomes in behavioral interventions as well as discuss considerations for selecting the optimal device.

RECENT FINDINGS

Wearable technology can enhance intervention design and implementation. The use of wrist-worn wearables provides the opportunity for tracking physiological outcomes, thus providing a unique approach for assessment and delivery of remote interventions. Recent findings support the utility, acceptability, and benefits of commercial wrist-worn wearables in interventions, and they can be used to continuously monitor outcomes, remotely administer assessments, track adherence, and personalize interventions. Wrist-worn devices show acceptable accuracy when measuring heart rate, blood pressure, step counts, and physical activity; however, accuracy is dependent on activity type, intensity, and device brand. These factors should be considered when designing behavioral interventions that utilize wearable technology.

SUMMARY

With the continuous advancement in technology and frequent product upgrades, the capabilities of commercial wrist-worn devices will continue to expand, thus increasing their potential use in intervention research. Continued research is needed to examine and validate the most recent devices on the market to better inform intervention design and implementation.

Wearable device use in older adults associated with physical activity guideline recommendations: Empirical research quantitative

AIMS AND OBJECTIVES

To (1) describe the socio-demographic and behavioural characteristics of older adults who use wearable devices for physical activity monitoring and (2) explore whether wearable device use increases the possibilities of meeting physical activity guideline recommendations among older adults and older adults with known cardiovascular disease or risk.

BACKGROUND

Finding ways to increase physical activity and reduce cardiovascular disease risk among older adults is a public health priority. Wearable technology has great potential for promoting physical activity among older adults.

DESIGN

A secondary analysis of the national data.

METHODS

A nationally representative sample of older adults aged 65 years and older (mean age = 73.79 years, N = 1484) and a subsample of older adults with known cardiovascular disease or cardiovascular disease risk (mean = 74.32 years, N = 1098) was used in the analysis. All analyses were weighted to account for the complex survey design. This study was reported according to the STROBE checklist.

RESULTS

The overall prevalence of wearable device use among older adults and older adults with cardiovascular disease risk was 16% and 14%, respectively. Older adults with higher household incomes, better self-rated health, and greater exercise enjoyment were more likely to use wearable devices. Compared with non-users, older adult users were more likely to meet the recommended levels of moderate (55% vs. 31%) and strengthening activity guidelines (46% vs. 25%), but not of the sedentary behaviour guideline (69% vs. 62%). Similar findings were also seen in older adults with known cardiovascular disease or risk.

CONCLUSION

The uptake of wearable devices in older adults, particularly those with known cardiovascular disease or risk is still low. The use of wearable devices is an important facilitator of physical activity. It is critical to provide individualised support for their engagement.

RELEVANCE TO CLINICAL PRACTICE

Age-friendly design and individualised support are recommended to increase older adults' adoption of wearable devices to improve their physical health.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution was involved in this study since we used publicly available data.

Wearable activity trackers for nurses' health: A qualitative acceptability study

AIM

To assess the practical, social and ethical acceptability of the use of a POLAR® H7 chest-strap wearable device to influence health behaviours among pre-registered nurses.

DESIGN

Qualitative acceptability study including a simulated test of use reported using COREQ guidelines.

METHODS

Pre-registered nurses simulated nine nursing tasks while wearing the chest strap in a clinical simulation facility in a Scottish university in 2016. Focus groups and semi-structured interviews were conducted to assess technology acceptance with participants who did and did not participate in the simulated nursing tasks. Focus groups and interviews were transcribed and analysed thematically guided by a theoretical model of technology acceptance.

RESULTS

Pre-registered nurses thought the use of chest-strap devices to monitor their own health in real-time was acceptable. However, participants shared that it was important that the use of technology was inclusive and supportive of nurses' health and cautioned against misuse of data from wearable devices for individual performance management or stigmatisation.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

Effect of Pre-Hospitalization Fall History on Physical Activity and Sedentary Behavior After the Implementation of a Behavioral Change Approach in Patients with Minor Ischemic Stroke: A Secondary Analysis of a Randomized Controlled Trial

BACKGROUND

We aimed to determine whether a history of falls before admission affected physical activity levels and sedentary behavior negatively after implementing a behavior modification approach in patients with minor ischemic stroke.

METHODS

This study constituted a secondary analysis of an intervention trial. In the intervention study, patients with minor ischemic stroke were randomly assigned to two groups: intervention and control groups. The intervention group was encouraged to reduce sedentary behavior during hospitalization and after discharge, while the control group was encouraged to increase physical activity levels solely during hospitalization. The study included 52 patients who completed the intervention trial. The exposure factor examined was a history of falls. Upon admission, patients were queried about any falls experienced in the year preceding admission and subsequently classified into fall and non-fall groups based on their responses. The primary outcome of interest focused on changes in physical activity levels (step count, light-intensity physical activity, and moderate-to-vigorous-intensity physical activity) and sedentary behavior. Measurements were obtained at two time points: before the intervention, during hospitalization (baseline), and 3 months after discharge (post-intervention).

RESULTS

Only a significantly lower change in the number of steps taken in the fall group than in the non-fall group was found.

CONCLUSION

Those with a history of falls showed a lesser change in the number of steps taken before and after implementing a behavior change approach compared with those without a history of falls. Those with a history of falls may have engaged in activities other than walking.

The efficacy of physical exercise interventions on mental health, cognitive function, and ADHD symptoms in children and adolescents with ADHD: an umbrella review

Background

A considerable number of published reviews have addressed the effects of physical exercise on mental health, cognitive function, or attention-deficit hyperactivity (ADHD) symptoms as outcomes in children and adolescents with ADHD. Their findings have often conflicted, therefore, there is an urgent need to synthesise a hierarchy of the evidence and examine the credibility of previous meta-analyses. To establish the robustness of these findings, we conducted an additional meta-analysis on a number of individual studies that were not covered in previous reviews but were suitable for inclusion in our own study.

Methods

Three reviewers independently searched Web of Science, Psych INFO, Embase, Cochrane Library, PubMed, SPORTDiscus, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for meta-analyses published between database inceptions to December 1, 2022. The individual studies were also screened from 1 January 2015 to 1 December 2022. We included meta-analyses and eligible individual studies that addressed the effects of exercise on at least one outcome of mental health, cognitive function, or ADHD symptoms in children and adolescents with ADHD. We excluded systematic reviews and articles that lacked sufficient data for a meaningful second analysis. The effect estimates (Hedges' g), 95% confidence interval (95% CI), 95% prediction interval (95% PI), small study effects, and excess significance bias were calculated. Finally, we categorised the meta-analyses based on the credibility of the evidence criteria and their quality using a Measurement Tool to Assess Systematic Reviews 2 checklist. This umbrella review was registered with PROSPERO, CRD42022361331.

Findings

Of 181 listed review articles and 60 individual papers, 10 reviews and 12 individual articles were included in the meta-analyses. This yielded 37 meta-analyses based on 106 study estimates. Evidence was highly suggestive for the effectiveness of exercise (class II) for improving inattention (G = 0.92, 95% CI: 0.44-1.39, 95%), inhibitory control (G = 0.82, 95% CI: 0.52-1.13), and cognitive flexibility (G = 0.52, 95% CI: 0.32-0.72). However, evidence for the effectiveness of exercise on emotional, social, and working memory outcomes was weak, and these results were not significant for hyperactivity and behavioural functioning.

Interpretation

Improvement of cognitive flexibility, inhibitory control, and inattention in children and adolescents with ADHD was highly suggested by exercise interventions. However, results were weak for other outcomes (emotional functioning, social functioning, and working memory). Further high-quality randomised controlled trials are, therefore, warranted to determine the effectiveness of exercise on weak outcomes.

Funding

None.

Objectively-measured movement behaviors, systemic low-grade inflammation, and plasma neurofilament light chain in older adults: a population-based study

BACKGROUND

Evidence has linked self-reported sedentary behavior (SB) and physical activity (PA) with cognitive impairment; however, the underlying mechanisms are poorly understood. We examined the associations of the accelerometer-measured movement behaviors with plasma neurofilament light chain (NfL) among older adults and the role of systemic low-grade inflammation in the associations.

RESULTS

This population-based study included 1,029 dementia-free older adults (age ≥ 60 years, range 60-88 years; 59.48% women) who undertook the ActiGraph substudy (March 2018-December 2020) in MIND-China. There were nonlinear relationships of daily SB and PA time with plasma NfL concentration, such that more daily SB time or less time spent in daily light-intensity physical activity (LPA) and moderate-to-vigorous-intensity physical activity (MVPA) was significantly associated with increased plasma NfL only when SB time ≥ 8.00 h/day or LPA time < 5.00 h/day or MVPA time < 2.00 h/day. Furthermore, more daily SB time or less daily LPA and MVPA time was significantly associated with higher serum low-grade inflammation score, a composite measure generated from serum IL-6, IL-8, TNF-α, and ICAM-1 (P < 0.05). Finally, low-grade inflammation score accounted for 14.5% to 17.8% of the associations between movement behaviors and plasma NfL.

CONCLUSIONS

More daily SB and less PA time are associated with neurodegeneration and systemic low-grade inflammation in older adults. The association of movement behaviors with neurodegeneration is partially mediated by low-grade inflammation.

The Impact of a Wearable Activity Tracker and Structured Feedback Program on Physical Activity in Hemodialysis Patients: The Step4Life Pilot Randomized Controlled Trial

RATIONALE & OBJECTIVE

People with end-stage kidney disease (ESKD) have very low physical activity, and the degree of inactivity is strongly associated with morbidity and mortality. We assessed the feasibility and effectiveness of a 12-week intervention coupling a wearable activity tracker (FitBit) and structured feedback coaching versus wearable activity tracker alone on changes in physical activity in hemodialysis patients.

STUDY DESIGN

Randomized controlled trial.

SETTING & PARTICIPANTS

55 participants with ESKD receiving hemodialysis who were able to walk with or without assistive devices recruited from a single academic hemodialysis unit between January 2019 and April 2020.

INTERVENTIONS

All participants wore a Fitbit Charge 2 tracker for a minimum of 12 weeks. Participants were randomly assigned 1:1 to a wearable activity tracker plus a structured feedback intervention versus the wearable activity tracker alone. The structured feedback group was counseled weekly on steps achieved after randomization.

OUTCOME

The outcome was step count, and the main parameter of interest was the absolute change in daily step count, averaged per week, from baseline to completion of 12 weeks intervention. In the intention-to-treat analysis, mixed-effect linear regression analysis was used to evaluate change in daily step count from baseline to 12-weeks in both arms.

RESULTS

Out of 55 participants, 46 participants completed the 12-week intervention (23 per arm). The mean age was 62 (± 14 SD) years; 44% were Black, and 36% were Hispanic. At baseline, step count (structured feedback intervention: 3,704 [1,594] vs wearable activity tracker alone: 3,808 [1,890]) and other participant characteristics were balanced between the arms. We observed a larger change in daily step count in the structured feedback arm at 12 weeks relative to use of the wearable activity tracker alone arm (Δ 920 [±580 SD] versus Δ 281 [±186 SD] steps; between-group difference Δ 639 [±538 SD] steps; P<0.05).

LIMITATIONS

Single-center study and small sample size.

CONCLUSION

This pilot randomized controlled trial demonstrated that structured feedback coupled with a wearable activity tracker led to a greater daily step count that was sustained over 12 weeks relative to a wearable activity tracker alone. Future studies are required to determine longer-term sustainability of the intervention and potential health benefits in hemodialysis patients.

FUNDING

Grants from industry (Satellite Healthcare) and government (National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK).

TRIAL REGISTRATION

Registered at ClinicalTrials.gov with study number NCT05241171.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Challenges of using a Fitbit smart wearable among people with dementia

OBJECTIVES

Limited research on using smart wearables such as Fitbit devices among people with dementia has shown favourable outcomes. The aim of this study was to explore the acceptability and feasibility of using a Fitbit Charge 3 among people with dementia, living in the community, who took part in the physical exercise component of the Comprehensive REsilience-building psychoSocial intervenTion pilot study.

METHODS

A mixed methods study was conducted; Quantitative data relating to wear rates for the Fitbit were recorded and qualitative data were collected by group and individual interviews with the people with dementia and their caregiver about their experience of wearing/using the Fitbit in the study.

RESULTS

Nine people with dementia and their caregiver completed the intervention. Only one participant wore the Fitbit consistently. Supporting set-up and use of the devices was time consuming and caregiver involvement was essential for day-to-day support: none of the people with dementia owned a smartphone. Few of them engaged with the Fitbit features, primarily only using it to check the time and only a minority wanted to keep the device beyond the intervention.

DISCUSSION

When designing a study using smart wearables such as a Fitbit among people with dementia, consideration should be given to the following: the possible burden on caregivers supporting the use of the device; a lack of familiarity with this technology in the target population; dealing with missing data, and the involvement of the researcher in setting up and supporting use of the device.

The association between wearable device metrics and clinical outcomes in oncology: a systematic review with evidence synthesis and meta-analysis

BACKGROUND

The emerging study of wearable devices (WDs) in patients with cancer provides opportunities to harness real-time patient data for predicting clinical outcomes. We conducted a systematic review with best evidence synthesis to examine the association between WD metrics and clinical outcomes in patients with cancer.

METHODS

MEDLINE and Embase were searched from inception until June 2022. Risk of bias assessment and best evidence synthesis were performed and, If possible, meta-analysis was conducted.

RESULTS

A total of 34 studies was included. We found moderate-to-strong evidence for associations between circadian rest-activity metrics and OS. Disrupted I<O was associated with increased hazard for death (HR 2.08; 95 %CI: 1.50-2.88). For most associations there was insufficient evidence due to lack of studies (n = 32) or inconsistent results (n = 14).

CONCLUSION

Meta-analysis was greatly hampered due to heterogeneity and different methodology used between studies. Studies primarily designed to investigate the association between WD metrics and clinical outcomes are warranted.

The Effectiveness of Wearable Devices in Non-Communicable Diseases to Manage Physical Activity and Nutrition: Where We Are?

Wearable devices are increasingly popular in clinical and non-clinical populations as a tool for exercise prescription, monitoring of daily physical activity and nutrition, and health-related parameters management. In this regard, smart devices not only assist people in pursuing a healthier lifestyle, but also provide a constant stream of physiological and metabolic data for management of non-communicable diseases (NCDs). Although the benefits of lifestyle-based interventions (exercise and nutrition) for NCDs are well known, the potential of wearable devices to promote healthy behaviors in clinical populations is still controversial. In this narrative review, we aimed to discuss the current application of wearable devices in NCDs, highlighting their role in prescribing and monitoring daily physical activity and dietary habits in the population living with chronic diseases. None of the studies considered specifically addressed the efficacy of the use of wearable devices, and limited are those that incorporate monitoring of both physical activity and nutrition for NCDs. However, there is evidence that such devices have helped improve physical activity levels, physical fitness, body composition, and metabolic and psychological parameters. Therefore, the authors believe that the benefits obtained from the use of wearable devices are likely to translate to public health and represent one of the important tools for the development of prevention plans in everyday life and clinical practice for optimal patient management.

Beyond colonoscopy: Physical activity as a viable adjunct to prevent colorectal cancer

Colorectal cancer (CRC) is a common cancer with an increasing incidence worldwide. The implementation of a mass screening program has been proven effective in reducing the global burden of CRC, but its effectiveness is not ideal and some metabolic derangements and lifestyle factors were reported to be attributable for such a deficit. Implementing positive lifestyle intervention as primary prevention therefore becomes critical because colorectal carcinogenesis can be promoted by several lifestyle factors, such as a lack of physical activity. Herein, we review the current evidence on the association and possible mechanisms between physical activity and CRC carcinogenesis. In addition, since CRC prevention heavily relies on resection of precancerous polyps and subsequent surveillance by colonoscopy, this review will also explore the impact of physical activity on populations with different colorectal polyp risks and its potential adjunct role in altering surveillance outcomes.

Accuracy of the Apple Watch Series 6 and the Whoop Band 3.0 for assessing heart rate during resistance exercises

This study aimed to determine heart rate accuracy measured by wearable devices during resistance exercises at various intensities. Twenty-nine participants (16 female) aged 19-37 years participated in this cross-sectional study. Participants completed five resistance exercises; Barbell Back Squat, Barbell Deadlift, Dumbbell Curl to Overhead Press, Seated Cable Row, and Burpees. During the exercises, heart rate was concurrently measured using the Polar H10, the Apple Watch Series 6 and the Whoop 3.0. The Apple Watch had high agreement with the Polar H10 during Barbell Back Squats, Barbell Deadlift, and Seated Cable Rows (rho > 0.832) and moderate to low agreement during Dumbbell Curl to Overhead Press and Burpees (rho > 0.364). The Whoop Band 3.0 had high agreement with the Polar H10 during Barbell Back Squats (r > 0.697), moderate agreement during Barbell Deadlift and Dumbbell Curl to Overhead Press (rho > 0.564) and low agreement during Seated Cable Rows and Burpees (rho > 0.383). The results varied across exercises and intensities and indicated the most favourable outcomes for the Apple Watch. In conclusion, our data suggest that the Apple Watch Series 6 is suitable for measuring heart rate during exercise prescription or monitoring resistance exercise performance.

Systemic neuroimmune responses in people with non-specific neck pain and cervical radiculopathy, and associations with clinical, psychological, and lifestyle factors

Neuroimmune responses remain understudied in people with neck pain. This study aimed to (1) compare a broad range of systemic neuroimmune responses in people with non-specific neck pain (N = 112), cervical radiculopathy (N = 25), and healthy participants (N = 23); and (2) explore their associations with clinical, psychological and lifestyle factors. Quantification of systemic neuroimmune responses involved ex vivo serum and in vitro evoked-release levels of inflammatory markers, and characterization of white blood cell phenotypes. Inflammatory indices were calculated to obtain a measure of total immune status and were considered the main outcomes. Differences between groups were tested using analyses of covariance (ANCOVA) and multivariable regression models. Compared to healthy participants, the ex vivo pro-inflammatory index was increased in people with non-specific neck pain (β = 0.70, p = 0.004) and people with cervical radiculopathy (β = 0.64, p = 0.04). There was no difference between non-specific neck pain and cervical radiculopathy (β = 0.23, p = 0.36). Compared to non-specific neck pain, people with cervical radiculopathy showed lower numbers of monocytes (β = -59, p = 0.01). There were no differences between groups following in vitro whole blood stimulation (p ≥ 0.23) or other differences in the number and phenotype of white blood cells (p ≥ 0.07). The elevated ex vivo neuroimmune responses in people with non-specific neck pain and radiculopathy support the contention that these conditions encompass inflammatory components that can be measured systemically. There were multiple significant associations with clinical, psychological and lifestyle factors, such as pain intensity (β = 0.25) and anxiety (β = 0.23) in non-specific neck pain, visceral adipose tissue (β = 0.43) and magnification (β = 0.59) in cervical radiculopathy, and smoking (β = 0.59) and visceral adipose tissue (β = 0.52) in healthy participants. These associations were modified by sex, indicating different neuroimmune associations for females and males.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

CLINICALTRIAL

Collapse

Adherence to eHealth-Delivered Exercise in Adults with no Specific Health Conditions: A Scoping Review on a Conceptual Challenge

Adherence has emerged as a focal point and critical determinant of success for physical activity interventions. The term is used for both traditional and digital interventions, and for prescribed and nonprescribed activities. Many other terms for adherence are being used interchangeably, as there is no consensus on its precise conceptualization. This scoping review aimed to advance the definition of adherence to eHealth programs, specifically for the adult population with no specific health conditions. A total of 2983 papers, published between 1 January 2016 and 13 March 2022, were retrieved from different databases (including grey literature). Of those, 13 studies met the eligibility criteria and were included for review. The selected studies used a wide array of technologies and consisted mainly of exercise interventions. Most of the reviewed publications contemplated exercise adherence as a percentage of expected dose. Most (8 out of 13) studies neither assessed nor specified an expected use of the involved technology. Results suggest a need for homogeneity in the conceptualization of adherence to physical activity and exercise, including those interventions delivered digitally.

mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials

Simple Summary

The number of cancer survivors has increased dramatically in the past several decades. Research indicates that health behaviors, including having a healthy diet and engaging in regular exercise, may improve the treatment outcomes and quality of life of cancer survivors. Behavioral interventions using web and mobile technology may be feasible and acceptable approaches to modify physical activity and dietary behaviors. This review summarizes the feasibility, acceptability and estimated effects of physical activity and dietary interventions using web and mobile technology from the published studies.

Abstract

Background: Technology-based interventions are increasingly used to improve physical activity (PA) and diet. Methods: We conducted a systematic review of randomized controlled trials (RCTs) published up to August 2021 that tested mobile health (mHealth) PA and/or dietary interventions among cancer survivors and reported on the feasibility, satisfaction, behavioral change, and/or quality of life (QOL) outcomes. Results: In total, 61 articles were identified on PubMed, and 23 of those met the inclusion criteria. The most common cancers were breast (n = 1000), prostate (n = 713), and colorectal (n = 650). Participants were predominantly White (median: 84%, interquartile range (IQR): 20%) and college-educated (58%). The interventions varied, but the most common combination of components (six studies) was a website/mobile app with an activity tracker and coaching. In terms of duration, 70% (n = 16) of the interventions lasted 12 weeks. The median total tracker wear was 87% of the study days (IQR: 6%) and the median text-message reply rate was 73% (IQR 4%). Most participants (median: 87%; IQR: 16%) were satisfied with at least one intervention component. Eleven out of 18 studies examining behavioral change reported significant between-group differences and six out of 11 studies examining QoL reported significant improvements. Conclusions: mHealth interventions are a promising approach to improving the PA and diets of cancer survivors. Research in racially/ethnically and socioeconomically diverse populations is needed.

Physical Activity Evaluation Using Activity Trackers for Type 2 Diabetes Prevention in Patients with Prediabetes

BACKGROUND

Prediabetes is a reversible condition, but lifestyle-changing measures, such as increasing physical activity, should be taken. This article explores the use of Fitbit activity trackers to assess physical activity and its impact on prediabetic patient health.

METHODS

Intervention study. In total, 30 volunteers (9 males and 21 females), aged 32-65 years, with impaired glucose levels and without diabetes or moving disorders, received Fitbit Inspire activity trackers and physical activity recommendations. A routine blood check was taken during the first and second visits, and body composition was analyzed. Physical activity variability in time was assessed using a Poincare plot.

RESULTS

The count of steps per day and variability differed between patients and during the research period, but the change in total physical activity was not statistically significant. Significant positive correlations between changes in lipid values, body mass composition, and variability of steps count, distance, and minutes of very active physical activity were observed.

CONCLUSIONS

When assessing physical activity, data doctors should evaluate not just the totals or the medians of the steps count, but also physical activity variability in time. The study shows that most changes were better linked to the physical activity variability than the total count of physical activity.

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment

Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.