Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X

Effects of a sedentary behaviour intervention in emergency dispatch centre phone operators: a study protocol for the SECODIS randomised controlled cross-over trial

INTRODUCTION

Sedentary behaviour is a public health problem. We mainly have sedentary behaviour at work, transforming them into occupational risk. To our knowledge, there is no intervention study on the reduction of occupational sedentary behaviour in a real work situation and its impact on health and biomarkers of stress. The main objective is to study changes in sedentary behaviour following a behavioural intervention (sit-and-stand desk and cycle ergometer).

METHODS AND ANALYSIS

This is a randomised controlled trial in cross-over design conducted in a single centre. The study will be proposed to emergency medical dispatchers of Clermont-Ferrand. Each volunteer will be followed during three cycles of 1 week (3 weeks in total). Each 1-week cycle is made up of 12 hours of work (three conditions: a control and two interventions), 12 hours of successive rest and 6 days of follow-up. For each condition, the measurements will be identical: questionnaire, measure of heart rate variability, electrodermal activity and level of physical activity, saliva and blood sampling. The primary outcome is sedentary behaviour at work (ie, number of minutes per day standing/active). Data will be analysed with both intention-to-treat and per protocol analysis. A p<0.05 will be considered as indicating statistical significance.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Ethics Committee Ouest IV, FRANCE. The study is registered in ClinicalTrials.gov. All patients will be informed about the details of the study and sign written informed consent before enrolment in the study. Results from this study will be published in a peer-reviewed journal. This study involves human participants and was approved by Comité de protection des personnes Ouest IVCPP reference: 23/132-2National number: 2022-A02730-43.

TRIAL REGISTRATION NUMBER

NCT05931406.

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BACKGROUND

Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.

METHODS

We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.

RESULTS

In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).

CONCLUSIONS

STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.

From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal

Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.

Stay on Track: A Pilot Randomized Control Trial on the Feasibility of a Diet and Exercise Intervention in Patients with Breast Cancer Receiving Radiotherapy

PURPOSE

Among patients with breast cancer undergoing radiotherapy, posttreatment cardiovascular disease and worsened quality of life (QoL) are leading causes of morbidity and mortality. To overcome these negative radiotherapy effects, this prospective, randomized clinical trial pilots a 12-week Stay on Track exercise and diet intervention for overweight patients with nonmetastatic breast cancer undergoing whole-breast radiotherapy.

EXPERIMENTAL DESIGN

The intervention group (n = 22) participated in three personal exercise and dietary counseling sessions, and received three text reminders/week to adhere to recommendations. The control group (n = 22) was administered a diet/exercise information binder. All patients received a Fitbit, and at baseline, 3 months, and 6 months, measurements of biomarkers, dual-energy X-ray absorptiometry scans, QoL and physical activity surveys, and food frequency questionnaires were obtained. A satisfaction survey was administered at 3 months.

RESULTS

Stay on Track was well received, with high rates of adherence and satisfaction. The intervention group showed an increase in self-reported physical activity and preserved QoL, a decrease in body mass index and visceral fat, and higher American Cancer Society/American Institute of Cancer Research dietary adherence. The control participants had reduced QoL, anti-inflammatory markers, and increased metabolic syndrome markers. Both groups had decreased overall body mass. These changes were within group effects. When comparing the intervention and control groups over time, there were notable improvements in dietary adherence in the intervention group.

CONCLUSIONS

Targeted lifestyle interventions during radiotherapy are feasible and could decrease cardiovascular comorbidities in patients with breast cancer. Larger-scale implementation with longer follow-up can better determine interventions that influence cardiometabolic health and QoL.

SIGNIFICANCE

This pilot study examines cardiometabolic benefits of a combined diet and exercise intervention for patients with breast cancer undergoing radiotherapy. The intervention included an activity tracker (FitBit) and text message reminders to promote adherence to lifestyle interventions. Large-scale implementation of such programs may improve cardiometabolic outcomes and overall QoL among patients with breast cancer.

Body composition, physical activity, and menopause symptoms: how do they relate?

OBJECTIVE

This study characterized the impact of physical activity (light, moderate, and vigorous [VIG] active minutes per day) and body composition (percent body fat [%BF] and fat-free mass index) on total menopausal symptoms (TMSs) in 72 premenopausal, perimenopausal (PERI), or postmenopausal women.

METHODS

Activity minutes were collected from wearable fitness trackers. Body composition was evaluated using a whole-body dual-energy x-ray absorptiometry scan. TMSs were quantified using The North American Menopause Society Questionnaire.

RESULTS

Significant associations were observed between TMSs and %BF ( r = 0.464, P < 0.001) and VIG ( r = -0.245, P = 0.038). %BF and VIG were significant predictors for TMSs across groups ( R2 = 0.146 and R2 = 0.092, respectively), but only %BF maintained for PERI ( R2 = 0.421, P < 0.001).

CONCLUSIONS

%BF predicted nearly half of the variance in PERI TMSs, whereas VIG predicted 9% of the sample variance, demonstrating an important influence of body fat accumulation and intense physical activity in the menopause transition. High-intensity exercise interventions to alleviate body composition changes may also reduce menopausal-related symptoms for PERI women.

Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker

OBJECTIVE

The aim of this study was to compare self-reported total energy intake (TEI) collected using an online multiple-pass 24-h dietary recall tool (Intake24) with total energy expenditure (TEE) estimated from Fitbit Charge 2-improved algorithms in adults from the NoHoW trial (12-mo weight maintenance after free-living weight loss).

METHODS

Bland-Altman plots were used to assess the level of agreement between TEI and TEE at baseline and after 12 mo. The ratio of TEI to TEE was also calculated.

RESULTS

Data from 1323 participants (71% female) was included in the analysis (mean ± SD: age 45 ± 12 y, body mass index 29.7 ± 5.4 kg/m2, initial weight loss 11.5 ± 6.5 kg). The TEI was lower than TEE on average by 33%, with limits of agreement ranging from -91% to +25%. Men, younger individuals, those with higher body mass index, those with the greater weight loss before enrollment, and those who gained weight during the study underestimated to a greater extent.

CONCLUSIONS

These findings contribute to the ongoing research examining the validity of technology-based dietary assessment tools.

Integrating 4 methods to evaluate physical function in patients with cancer (In4M): protocol for a prospective cohort study

INTRODUCTION

Accurate, patient-centred evaluation of physical function in patients with cancer can provide important information on the functional impacts experienced by patients both from the disease and its treatment. Increasingly, digital health technology is facilitating and providing new ways to measure symptoms and function. There is a need to characterise the longitudinal measurement characteristics of physical function assessments, including clinician-reported outcome, patient-reported ported outcome (PRO), performance outcome tests and wearable data, to inform regulatory and clinical decision-making in cancer clinical trials and oncology practice.

METHODS AND ANALYSIS

In this prospective study, we are enrolling 200 English-speaking and/or Spanish-speaking patients with breast cancer or lymphoma seen at Mayo Clinic or Yale University who will receive intravenous cytotoxic chemotherapy. Physical function assessments will be obtained longitudinally using multiple assessment modalities. Participants will be followed for 9 months using a patient-centred health data aggregating platform that consolidates study questionnaires, electronic health record data, and activity and sleep data from a wearable sensor. Data analysis will focus on understanding variability, sensitivity and meaningful changes across the included physical function assessments and evaluating their relationship to key clinical outcomes. Additionally, the feasibility of multimodal physical function data collection in real-world patients with breast cancer or lymphoma will be assessed, as will patient impressions of the usability and acceptability of the wearable sensor, data aggregation platform and PROs.

ETHICS AND DISSEMINATION

This study has received approval from IRBs at Mayo Clinic, Yale University and the US Food and Drug Administration. Results will be made available to participants, funders, the research community and the public.

TRIAL REGISTRATION NUMBER

NCT05214144; Pre-results.

The effectiveness of digital health interventions for the maintenance of physical activity following cardiac rehabilitation: A systematic review and meta-analysis

Objective

To summarise the effectiveness of digital health maintenance interventions for subjectively and objectively measured physical activity level (PA) and physical function, fitness and health-related quality of life (HRQoL) after completion of phase II cardiac rehabilitation (CR).

Methods

We conducted a search for studies in MEDLINE, Embase, CENTRAL, and CINAHL (inception to May 2024). Independent reviewers selected and included randomised controlled trials (RCTs) using digital health interventions to maintain PA in patients with cardiovascular disease after phase II CR. Independent reviewers conducted data extraction, assessed the risk of bias using the Cochrane Risk of Bias 2 tool and rated the certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation - registered at PROSPERO: CRD42023396629.

Results

From 17,455 hits, 20 RCTs with 1801 participants were included. Risk of bias for most studies reporting on PA outcomes was rated as 'some concerns'. Comparing digital health intervention with minimal intervention/usual care showed no effect of objective PA (standardised mean difference (SMD) 0.85, 95% CI: -0.07 to 1.77) and a small effect of subjective PA (SMD 0.37, 95% CI: 0.05 to 0.69) at the end of intervention both graded as very low certainty of evidence. We found very low certainty of evidence for moderate effects on physical function (SMD 0.63, 95% CI: 0.03 to 1.24), and low certainty of evidence for no effect on physical fitness (SMD 0.19, 95% CI: 0.05 to 0.34) and HRQoL (SMD 0.13, 95% CI: -0.02 to 0.28), I 2= 0.00%) at the end of intervention.

Conclusion

Digital health interventions showed a small effect on subjectively measured PA, and no effect on objective PA, physical fitness and HRQoL, but may increase physical function after CR completion. Yet, the certainty of evidence is low and higher quality studies with longer follow-up duration are needed to guide this area further.

Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes

BACKGROUND

Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH.

METHODS

Adults with T1D for ≥ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for ≥15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect.

RESULTS

Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl.

CONCLUSION

Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.

Convergent Validity of the Patient Reported Outcome Measurement Information System-Pediatric Physical Activity Instrument (PROMIS®-PA) with Wearable Devices in Adolescents

The study was conducted mainly to examine the convergent validity of the Patient Reported Outcome Measurement Information System-Pediatric Physical Activity instrument (PROMIS^®®®-PA) with step counts from wearable devices and another validated self-reported outcome measure. As a secondary aim, we explored the effect of different recall time frames (7-day, end-of-day [EoD], and ecological momentary assessment [EMA] time frames during the day) in terms of their feasibility and associations with each other and with step counts. This was a prospective cohort study that examined the associations between measures of PA in school-age children and adolescents (n = 84, aged 10-20). The participants wore Fitbit devices for 7 consecutive days, and then completed the 7-day-recall PROMIS-PA short form and Youth Activity Profile (YAP). Additional analyses were completed in a sub-sample (n = 25, aged 11-18 years) using the PROMIS-PA for the EMA at five intervals during the day (shorter form) and at the EoD. In the total sample, the PROMIS-PA results showed positive moderate correlations with the YAP and average daily steps (r = 0.533, p < 0.001 and r = 0.346, p = 0.002, respectively). In the sub-sample, the 7-day PROMIS-PA was highly correlated with the averaged EMA or EoD ratings for the week, and moderately correlated with the daily step counts. These findings support the validity of the PROMIS-PA as a measure of self-reported physical activity. Adolescents demonstrated higher compliance rates and preference for the 7-day recall and EoD assessments compared to more frequent EMA reporting.

Integrating 4 Measures to Evaluate Physical Function in Patients with Cancer (In4M): Protocol for a prospective study

Introduction

Accurate, patient-centered evaluation of physical function in patients with cancer can provide important information on the functional impacts experienced by patients both from the disease and its treatment. Increasingly, digital health technology is facilitating and providing new ways to measure symptoms and function. There is a need to characterize the longitudinal measurement characteristics of physical function assessments, including clinician-reported physical function (ClinRo), patient-reported physical function (PRO), performance outcome tests (PerfO) and wearable data, to inform regulatory and clinical decision-making in cancer clinical trials and oncology practice.

Methods and analysis

In this prospective study, we are enrolling 200 English- and/or Spanish-speaking patients with breast cancer or lymphoma seen at Mayo Clinic or Yale University who will receive standard of care intravenous cytotoxic chemotherapy. Physical function assessments will be obtained longitudinally using multiple assessment modalities. Participants will be followed for 9 months using a patient-centered health data aggregating platform that consolidates study questionnaires, electronic health record data, and activity and sleep data from a wearable sensor. Data analysis will focus on understanding variability, sensitivity, and meaningful changes across the included physical function assessments and evaluating their relationship to key clinical outcomes. Additionally, the feasibility of multi-modal physical function data collection in real-world patients with cancer will be assessed, as will patient impressions of the usability and acceptability of the wearable sensor, data aggregation platform, and PROs.

Ethics and dissemination

This study has received approval from IRBs at Mayo Clinic, Yale University, and the U.S. Food & Drug Administration. Results will be made available to participants, funders, the research community, and the public.

Registration Details

The trial registration number for this study is NCT05214144.

Strengths & Limitations

This study addresses an important unmet need by characterizing the performance characteristics of multiple patient-centered physical function measures in patients with cancerPhysical function is an important and undermeasured clinical outcome. Scientifically rigorous capture and measurement of physical function constitutes a key component of cancer treatment tolerability assessment both from a regulatory and clinical perspective.This study will include patients with lymphoma or breast cancer receiving a broad range of cytotoxic chemotherapy regimens. While recruitment will occur at two academic sites, patients who ultimately receive treatment at local community sites will be included.A patient-centered health data aggregating platform facilitates the delivery of patient-reported outcome measures and collection of wearable data to researchers, while reducing patient burden compared to traditional patient-generated data collection and aggregation methodsHeterogeneity in patient willingness or comfort engaging with mobile products including smartphones and wearables, enrollment primarily at large academic centers, and the modest sample size are potential limitations to the external validity of the study.

Accelerometer-measured Inpatient Physical Activity and Associated Outcomes after Major Abdominal Surgery: A Systematic Review (Preprint)

BACKGROUND

It remains unclear how inpatient physical activity after major abdominal surgery affects outcomes. Accelerometer research may provide further evidence for postoperative mobilization.

OBJECTIVE

We aimed to summarize the current literature evaluating the impact of accelerometer-measured postoperative physical activity on outcomes after major abdominal surgery.

METHODS

We searched PubMed and Google Scholar in October 2021 to conduct a systematic review. Studies were included if they used accelerometers to measure inpatient physical behaviors immediately after major abdominal surgery, defined as any nonobstetric procedures performed under general anesthesia requiring hospital admission. Studies were eligible only if they evaluated the effects of physical activity on postoperative outcomes such as postoperative complications, return of gastrointestinal function, hospital length of stay, discharge destination, and readmissions. We excluded studies involving participants aged <18 years. Risk of bias was assessed using the risk-of-bias assessment tool for nonrandomized studies (RoBANS) for observational studies and the revised Cochrane risk-of-bias tool for randomized trials (RoB 2) for randomized controlled trials (RCTs). Findings were summarized by qualitative synthesis.

RESULTS

We identified 15 studies. Risk of bias was high in 14 (93%) of the 15 studies. Most of the studies (11/15, 73%) had sample sizes of <100. Of the 15 studies, 13 (87%) included the general surgery population, 1 (7%) was a study of patients who had undergone gynecologic surgery, and 1 (7%) included a mixed (abdominal, thoracic, gynecologic, and orthopedic) surgical population. Of the 15 studies, 12 (80%) used consumer-grade accelerometers to measure physical behaviors. Step count was the most commonly reported physical activity outcome (12/15, 80%). In the observational studies (9/15, 60%), increased physical activity during the immediate postoperative period was associated with earlier return of gastrointestinal function, fewer surgical and pulmonary complications, shorter hospital length of stay, and fewer readmissions. In the RCTs (6/15, 40%), only 1 (17%) of the 6 studies demonstrated improved outcomes (shorter time to flatus and hospital length of stay) when a mobility-enhancing intervention was compared with usual care. Notably, mobility-enhancing interventions used in 4 (67%) of the 6 RCTs did not result in increased postoperative physical activity.

CONCLUSIONS

Although observational studies show strong associations between postoperative physical activity and outcomes after major abdominal surgery, RCTs have not proved the benefit of mobility-enhancing interventions compared with usual care. The overall risk of bias was high, and we could not synthesize specific recommendations for postoperative mobilization. Future research would benefit from improving study design, increasing methodologic rigor, and measuring physical behaviors beyond step counts to understand the impact of postoperative mobilization on outcomes after major abdominal surgery.

Substituting sedentary time with sleep or physical activity and subsequent weight-loss maintenance

OBJECTIVE

In this study, the associations between the substitution of sedentary time with sleep or physical activity at different intensities and subsequent weight-loss maintenance were examined.

METHODS

This prospective study included 1152 adults from the NoHoW trial who had achieved a successful weight loss of ≥5% during the 12 months prior to baseline and had BMI ≥25 kg/m² before losing weight. Physical activity and sleep were objectively measured during a 14-day period at baseline. Change in body weight was included as the primary outcome. Secondary outcomes were changes in body fat percentage and waist circumference. Cardiometabolic variables were included as exploratory outcomes.

RESULTS

Using isotemporal substitution models, no associations were found between activity substitutions and changes in body weight or waist circumference. However, the substitution of sedentary behavior with moderate-to-vigorous physical activity was associated with a decrease in body fat percentage during the first 6 months of the trial (-0.33% per 30 minutes higher moderate-to-vigorous physical activity [95% CI: -0.60% to -0.07%], p = 0.013).

CONCLUSIONS

Sedentary behavior had little or no influence on subsequent weight-loss maintenance, but during the early stages of a weight-loss maintenance program, substituting sedentary behavior with moderate-to-vigorous physical activity may prevent a gain in body fat percentage.

Sedentary behaviour, but not moderate-to-vigorous physical activity, is associated with respiratory responses to acute psychological stress

BACKGROUND

Acute psychological stress induces respiratory responses, and stress-induced respiratory changes can be used to non-invasively reflect metabolic regulation. Respiratory and cardiovascular responses to stress are both driven by sympathetic mechanisms. Higher volumes of sedentary behaviour and lower volumes of physical activity are associated with elevated sympathetic tone and larger cardiovascular responses to stress. The aim of this study was to test whether these associations translate to measures of respiratory stress reactivity.

METHODS

Daily hours of sedentary behaviour (thigh-mounted activPAL) and moderate-to-vigorous physical activity (MVPA; wrist-mounted ActiGraph) were assessed across seven days. Breath-by-breath respiratory (e.g., breathing frequency [BF], end-tidal carbon dioxide partial pressure [PetCO₂], carbon dioxide output [V̇CO₂] and respiratory exchange ratio [RER]) responses to an 8-min Paced Auditory Serial Addition Test were then measured using a Cortex MetaLyzer3B.

RESULTS

Healthy participants (N = 61, mean age ± SD = 25.7 ± 8.9 years) recorded high volumes of sedentary behaviour (9.96 ± 1.48 h/day) and MVPA (1.70 ± 0.71 h/day). In adjusted models (with the inclusion of sedentary behaviour, MVPA, and other a priori selected covariates) hours of daily sedentary behaviour were associated with baseline to stress changes in BF (Β = 0.695, 95% CI = 0.281 - 1.109, p = .014), V_T (Β = -0.042, 95% CI = -0.058 - -0.026, p = .014), PetCO₂ (Β = -0.537, 95% CI = -0.829 - -0.245, p = .014), V̇CO₂ (Β = -0.008, 95% CI = -0.014 - -0.003, p = .030), and RER (Β = -0.013, 95% CI = -0.021 - -0.005, p = .022). Daily hours of MVPA were not linked with respiratory responses to stress.

DISCUSSION

Sedentary behaviour, but not MVPA, is associated with respiratory stress reactivity. Future work should untangle the underlying mechanisms of these findings and explore the consequences for cardiometabolic disease.

Testing motivational and self-regulatory mechanisms of action on device-measured physical activity in the context of a weight loss maintenance digital intervention: A secondary analysis of the NoHoW trial

BACKGROUND

To date, few digital behavior change interventions for weight loss maintenance focusing on long-term physical activity promotion have used a sound intervention design grounded on a logic model underpinned by behavior change theories. The current study is a secondary analysis of the weight loss maintenance NoHoW trial and investigated putative mediators of device-measured long-term physical activity levels (six to 12 months) in the context of a digital intervention.

METHODS

A subsample of 766 participants (Age = 46.2 ± 11.4 years; 69.1% female; original NoHoW sample: 1627 participants) completed all questionnaires on motivational and self-regulatory variables and had all device-measured physical activity data available for zero, six and 12 months. We examined the direct and indirect effects of Virtual Care Climate on post intervention changes in moderate-to-vigorous physical activity and number of steps (six to 12 months) through changes in the theory-driven motivational and self-regulatory mechanisms of action during the intervention period (zero to six months), as conceptualized in the logic model.

RESULTS

Model 1 tested the mediation processes on Steps and presented a poor fit to the data. Model 2 tested mediation processes on moderate-to-vigorous physical activity and presented poor fit to the data. Simplified models were also tested considering the autonomous motivation and the controlled motivation variables independently. These changes yielded good results and both models presented very good fit to the data for both outcome variables. Percentage of explained variance was negligible for all models. No direct or indirect effects were found from Virtual Care Climate to long term change in outcomes. Indirect effects occurred only between the sequential paths of the theory-driven mediators.

CONCLUSION

This was one of the first attempts to test a serial mediation model considering psychological mechanisms of change and device-measured physical activity in a 12-month longitudinal trial. The model explained a small proportion of variance in post intervention changes in physical activity. We found different pathways of influence on theory-driven motivational and self-regulatory mechanisms but limited evidence that these constructs impacted on actual behavior change. New approaches to test these relationships are needed. Challenges and several alternatives are discussed.

TRIAL REGISTRATION

ISRCTN Registry, ISRCTN88405328. Registered December 16, 2016, https://www.isrctn.com/ISRCTN88405328.

A Physical Activity Intervention in Older African Americans: The PAACE Pilot Randomized Controlled Trial

INTRODUCTION

Older African Americans have low levels of physical activity, which increases the risk of aging-related health conditions. This study aimed to determine the effectiveness of, and satisfaction with, a community-engaged physical activity intervention targeting older African Americans.

METHODS

Fifty-six older African Americans were randomized to a successful aging group (SAG) or a physical activity group (PAG) for 12 wk. The PAG consisted of two weekly group exercise sessions and two to three home-based exercise sessions per week. The SAG consisted of weekly group educational sessions related to various aspects of healthy aging. Physical activity was measured by ActiGraph accelerometers and the Community Healthy Activities Model Program for Seniors Activities Questionnaire in both groups, and a Fitbit was continuously worn by PAG participants.

RESULTS

Group session attendance was 93% and 86% in the PAG and SAG, respectively. The increase in ActiGraph-derived steps per day (1085.3 ± 265.6 vs 34.7 ± 274.3; P = 0.008) and daily minutes of moderate-to-vigorous physical activity (MVPA; 6.2 ± 1.6 vs 0.3 ± 1.7; d = 0.68; P = 0.01), and self-reported Community Healthy Activities Model Program for Seniors Activities Questionnaire MVPA bouts (3.5 ± 0.77 vs 0.33 ± 0.79; P < 0.001) were significantly greater within the PAG than the SAG. ActiGraph-derived minutes of daily MVPA and steps as well as Fitbit-derived steps per day were significantly greater on days when PAG participants engaged in group sessions compared with days when they self-reported home-based exercise or no exercise ( P < 0.016). Participants in both study arms reported high levels of satisfaction (>4- on 5-point Likert scale).

CONCLUSIONS

The intervention increased physical activity, was well attended, and resulted in high satisfaction. Future studies should assess long-term sustainability in this population.

Influence of upper limb activity on the step count and accuracy of sleep time of a wristband-type physical activity tracker

Background

A wristband-type consumer physical activity tracker (PAT) is commonly used in rehabilitation to assess an individual’s physical activity. However, under the free-living setting, the wristband-type PAT tends to overestimate step counts when compared with the research-standard criterion. Also, daily rhythm characteristics, such as sleep time, are difficult to monitor accurately based solely on self-reporting.

Purpose

To identify the conditions measured as step counts by a wristband-type consumer PAT when using the upper limbs in daily living, and the measurement accuracy of the sleeping time estimated from the wristband-type PAT.

Methods

Forty participants (20 females, mean age 32.65 ± 9.52 years) were enrolled in two experiments in this study. In Experiment 1, we measured the influence of upper limbs activity (movement speed and distance) on step counts of wristband-type and waist holder-type PAT in two upper limb tasks. In Experiment 2, we verified the measurement accuracy of two sleep times by wristband-type PAT using a self-reported survey for 3 days.

Results

The results of Experiment 1 revealed that the step counts using wristband-type PAT were influenced by upper limbs activity depending on movement distance (F (1, 19) = 31.705, p < 0.001) but not speed (F (1, 19) = 2.669, p < 0.117). Whereas, there was no relationship between step counts and upper limb activity in waist holder-type PAT. The results of Experiment 2 showed that the sleep times of wristband-type and self-report had a strong correlation (coefficient value = 0.93, p < 0.001).

Conclusions

This PAT is useful for capturing changes in the amount of physical activity and the daily rhythm within the individual. It can be expected to be used for rehabilitation support centered on upper limb activity and daily rhythm.

Sedentary behaviour is associated with heightened cardiovascular, inflammatory and cortisol reactivity to acute psychological stress

BACKGROUND

Sedentary behaviour is a risk factor for cardiovascular disease (CVD), but the underlying mechanisms remain unclear. Exaggerated psychobiological responses to acute psychological stress increase CVD risk. Sedentary behaviour is associated with characteristics that can predict large psychobiological stress response patterns (e.g., elevated resting blood pressure and systemic inflammation), but it is currently unknown whether sedentary behaviour and stress reactivity are directly linked. The aim of this study was to examine associations between device-assessed sedentary behaviour and measures of stress reactivity.

METHODS

Sixty-one healthy adults wore an activPAL (thigh) and ActiGraph (wrist) for seven days to measure habitual levels of sedentary behaviour (mean ± SD = 9.96 ± 1.48 h/day) and moderate-to-vigorous physical activity (mean ± SD = 101.82 ± 42.92 min/day). Participants then underwent stress reactivity testing, where beat-to-beat cardiovascular (e.g., blood pressure, total peripheral resistance), inflammatory (plasma interleukin-6, leukocytes) and salivary cortisol measurements were taken in response to an 8-minute socially evaluative Paced Auditory Serial Addition Test.

RESULTS

Higher volumes of daily sedentary behaviour were associated with larger stress responses for diastolic blood pressure (Β=1.264, 95%CI=0.537-1.990, p = .005), total peripheral resistance (Β=40.563, 95%CI=19.310-61.812, p < .001), interleukin-6 (Β=0.219, 95%CI=0.109-0.329, p < .001) and cortisol (Β=1.844, 95%CI=1.139-2.549, p < .001). These findings emerged independent of a priori determined covariates, including daily levels of moderate-to-vigorous physical activity and adiposity.

DISCUSSION

Exaggerated stress reactivity is characteristic of high sedentary behaviour and could be a novel mechanism linking sedentary behaviour with CVD. Future work should examine the impact of reducing sedentary behaviour on measures of stress reactivity, as this may have clinical relevance for preventing CVD.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Comparative Analysis of Physical Activity Detected via an External Accelerometer and Cardiac Implantable Electronic Devices

Background

Physical activity (PA) has become an important health issue for decades. Cardiovascular implantable electronic devices (CIEDs) have built-in PA-recording functions. We aimed to compare PA measurements using an external accelerometer (ActiGraph GT3X+) and internal accelerometers (Abbott, Biotronik, and Medtronic CIEDs).

Methods

This was a prospective, single-center observational study. The device-measured 7-day average PA was collected, and GT3X+ -measured 7-day average PA was used as the gold-standard, including all daily observations of activity. Pearson’s correlation coefficients were used to compare the correlations between GT3X+ -measured and CIED-measured PA. Bland-Altman plots were used to analyze measurement agreement, and intraclass correlation coefficients were used to analyze reliability.

Results

In total, 720 patients treated with CIEDs were surveyed between November 2020 and April 2021, 60 of them were analyzed after patient screening by our protocol. Each manufacturer included 20 patients for the final analysis. The CIED-measured PAs of Abbott, Biotronik, and Medtronic were 3.0 ± 1.5, 2.6 ± 1.8, and 3.8 ± 2.5 h per day, respectively; the GT3X+ -measured PAs were 6.9 ± 2.8, 6.0 ± 2.4, and 6.4 ± 2.5 h per day, respectively. Moderate and significant correlations were found in patients using Abbott, Biotronik, and Medtronic CIEDs (r = 0.534, p = 0.015; r = 0.465, p = 0.039; r = 0.677, p = 0.001, respectively). Bland-Altman plots and intraclass correlation coefficients both showed a significant correlation and reliability between the average PA measured by GT3X+ and CIEDs (hours per day).

Conclusion

Although the PA recording function of CIEDs includes a single-axis accelerometer, it has a moderate correlation compared with the triaxial accelerometer of the GT3X+. However, CIEDs seem to underestimate PA for 3–4 h compared to the GT3X+.

Comparison of Wrist- and Hip-Worn Activity Monitors When Meeting Step Guidelines

Introduction

Physical activity (PA) guidelines aimed at accumulating 10,000 steps per day have become increasingly common with the advent of wristband PA monitors. However, accumulated steps measured with wristband PA monitors may not be equal to steps measured with validated, hip-worn pedometers. Consequently, evaluating and developing guidelines for step counts using wristband PA monitors for the general population is needed. We compared step counts accumulated with hip-worn pedometers with those accumulated with wrist-worn activity monitors during 1) treadmill exercise, 2) treadmill walking, and 3) activities of daily living (ADL) to determine their accuracy in meeting step count guidelines (ie, 10,000 steps/d).

Methods

Eighty-six adults (aged 18–65 y; body mass index, 19–45 kg/m²) completed 30 minutes of treadmill exercise while simultaneously using a hip-worn pedometer and wrist-worn PA monitor. Remaining steps needed to reach 10,000 steps (ie, 10,000 steps minus the number of pedometer steps recorded from treadmill exercise = remainder) were completed via treadmill walking or ADL. Steps were recorded for both devices after treadmill exercise, treadmill walking, and ADL for both devices.

Results

Fewer steps were accumulated via wrist-worn PA monitors than via hip-worn pedometers during treadmill exercise (3,552 [SD, 63] steps vs 3,790 [SD, 55] steps, P < .01) and treadmill walking (5,877 [SD, 83] steps vs 6,243 [SD, 49] steps, P < .01). More steps were accumulated via wrist-worn PA monitors than hip-worn pedometers during ADL (7,695 [SD, 207] steps vs 6,309 [SD, 57] steps, P < .01). Consequently, total steps were significantly higher for wristband PA monitors than hip-worn pedometers (11,247 [SD, 210] steps vs 10,099 [SD, 39] steps; P < .01).

Conclusion

The widely used 10,000-step recommendation may not be accurate for all users of all activity monitors, given the discrepancy in daily step count among wrist-worn and hip-worn devices. Having a more accurate indication of number of steps taken per day based on the device used could have positive effects on health.

A meta-analysis of Fitbit devices: same company, different models, different validity evidence

Fitbit devices are among the most commonly used physical activity devices used by the general public. Multiple studies have examined the validity evidence of Fitbit devices of estimating energy expenditure during physical activity compared to criterion references. However, the literature lacks objective, summary validity evidence that supports the use of various models of Fitbit devices. Therefore, this study aims (a) to examine the validity evidence among the various models of Fitbit devices and (b) to investigate the influence of several device factors on the validity evidence of Fitbit models using meta-analysis. A total of 402 articles were identified through five databases. Upon review of the articles, 29 studies were included in the meta-analysis. Seven different moderator variables, including Fitbit model, device placement, type of device, heart rate capability, release year of devices, activity types and sedentary activity, were identified and included in the meta-analysis to examine their impact on the validity evidence of Fitbit devices. The summarised validity coefficient of energy expenditure during physical activity estimated by Fitbit devices and measured by criterion references was r=.64 (k = 29, 95% CI [.59, .69], p<.001). Fitbit model was not found to be a significant factor impacting validity evidence of Fitbit devices, but heart rate capability, activity types and sedentary activity were found to be significant factors impacting validity evidence. This study found that not all Fitbit models have a similar ability in estimating energy expenditure during physical activity. Continued research is needed in examining the validity evidence of Fitbit devices, especially considering some factors may affect the validity evidence in measuring energy expenditure during physical activity.

Physical activity analysis for the elderly person in free-living conditions

A growing amount of people are beginning to monitor themselves with the rapid emergence of a wide variety of cost-effective personal sensing instruments. To measure different facets of personal life, innovation helps people better understand their lifestyles, enhance their work quality, or maximize various health factors, allowing free-living. Although vast amounts of raw information on the provisioning and physiological parameters have been obtained much more straightforward, making use of all the information remains a significant task. The article introduces the Physical Activity Analysis Framework (PAAF) for the Elderly Person in Free-Living Conditions. In the framework, the acceleration signals split into overlapped windows and derive information in each frame’s frequency domain. The framework’s sensors sense the activity and evaluate a profound learning structure dependent on each window’s progressive networks. The proposed IoT model has multiple layers separately connected with each sensor, and the critical element integrates the outputs of all sensors for the classification of physical activity. In longer cycles, the model combines the window decision with a substantial increase in its efficiency. The model in the research has been evaluated using labelled free-living pilot data. Eventually, discover the use of the proposed models from a broader lifestyle intervention analysis in unlabeled, free-living data. The results show that the proposed model performs well for both labelled and unlabeled data. The experimental analyses of an older person in living conditions with their daily activities to be monitored via IoT system as Meditation effect analysis ratio is 86.6%, Physical activity ratio is 87.12%, Physical disability ratio is 87.1%, Exercise satisfaction ratio is 85.05%, and Self-efficacy ratio is 93.5%.

Data from Digital Health Devices Informs Ideal Cardiovascular Health

Ideal cardiovascular health is associated with a decrease in adverse cardiovascular events. The My Research Legacy study examined ideal cardiovascular health using the Life's Simple 7 survey and data from digital health devices. We hypothesized that digital devices provide a more objective view of overall cardiovascular health status than self-reported measures. Therefore, we analyzed weight and activity data recorded by digital devices to recalculate the Life's Simple 7 Health Score. All study participants (n = 1561) answered the survey, while a subgroup (n = 390) provided data from digital devices. Individuals with digital devices had a lower body mass index (BMI) and higher weekly minutes of vigorous exercise than participants without digital devices (p < 0.01). Baseline Health Scores were higher in individuals with digital devices compared to those without (7.0 ± 1.6 vs. 6.6 ± 1.6, p < 0.01). Data from digital devices reveal both increases and decreases in measured vs. self-reported BMI (p < 0.04) and weekly minutes of moderate and vigorous exercise activity (p < 0.01). Using these data, a significant difference was found between the recalculated and the self-reported Life's Simple 7 Health Score (p < 0.05). These findings suggest that incorporation of digital health devices should be considered as part of a precision medicinal approach to assessing ideal cardiovascular health.

Hair Cortisol Concentration, Weight Loss Maintenance and Body Weight Variability: A Prospective Study Based on Data From the European NoHoW Trial

Several cross-sectional studies have shown hair cortisol concentration to be associated with adiposity, but the relationship between hair cortisol concentration and longitudinal changes in measures of adiposity are largely unknown. We included 786 adults from the NoHoW trial, who had achieved a successful weight loss of ≥5% and had a body mass index of ≥25 kg/m² prior to losing weight. Hair cortisol concentration (pg/mg hair) was measured at baseline and after 12 months. Body weight and body fat percentage were measured at baseline, 6-month, 12-month and 18-month visits. Participants weighed themselves at home ≥2 weekly using a Wi-Fi scale for the 18-month study duration, from which body weight variability was estimated using linear and non-linear approaches. Regression models were conducted to examine log hair cortisol concentration and change in log hair cortisol concentration as predictors of changes in body weight, change in body fat percentage and body weight variability. After adjustment for lifestyle and demographic factors, no associations between baseline log hair cortisol concentration and outcome measures were observed. Similar results were seen when analysing the association between 12-month concurrent development in log hair cortisol concentration and outcomes. However, an initial 12-month increase in log hair cortisol concentration was associated with a higher subsequent body weight variability between month 12 and 18, based on deviations from a nonlinear trend (β: 0.02% per unit increase in log hair cortisol concentration [95% CI: 0.00, 0.04]; P=0.016). Our data suggest that an association between hair cortisol concentration and subsequent change in body weight or body fat percentage is absent or marginal, but that an increase in hair cortisol concentration during a 12-month weight loss maintenance effort may predict a slightly higher subsequent 6-months body weight variability.

CLINICAL TRIAL REGISTRATION

ISRCTN registry, identifier ISRCTN88405328.