Improving Physical Activity and Body Composition in a Medical Workplace Using Brief Goal Setting

Next generation smartwatches to estimate whole body composition using bioimpedance analysis: accuracy and precision in a diverse multiethnic sample

BACKGROUND

Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of devices are unknown.

OBJECTIVES

This study evaluated smart watches with integrated bioimpedance (BIA) sensors for their ability to measure and monitor change in body composition.

DESIGN

Participants recruited across body mass indexes received duplicate body composition measures using two wearable smart watch (W-BIA) models in sitting and standing positions and multiple versions of each watch were used to evaluate inter- and intra-model precision. Duplicate laboratory-grade octapolar bioimpedance (8-BIA) and criterion dual-energy X-ray absorptiometry (DXA) scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor change in body composition.

RESULTS

Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (p < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA, p > 0.05, CCC = 0.97). FFM was less precise on the watches than DXA (CV = 0.7%, RMSE = 0.4 kg versus CV = 1.3%, RMSE = 0.7 kg for W-BIA), requiring more repeat measures to equal the same confidence in body composition change over time as DXA.

CONCLUSIONS

After systematic correction, smart watch BIA devices are capable of stable, reliable and accurate body composition with precision comparable but lower than laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems such as the home, training centers, and geographically remote locations.

The Effect of Wearable Tracking Devices on Cardiorespiratory Fitness Among Inactive Adults: Crossover Study

Background

Modern lifestyle is associated with a high prevalence of physical inactivity.

Objective

This study aims to investigate the effect of a wearable tracking device on cardiorespiratory fitness among inactive adults and to explore if personal characteristics and health outcomes can predict adoption of the device.

Methods

In total, 62 inactive adults were recruited for this study. A control period (4 weeks) was followed by an intervention period (8 weeks) where participants were instructed to register and follow their physical activity (PA) behavior on a wrist-worn tracking device. Data collected included estimated cardiorespiratory fitness, body composition, blood pressure, perceived stress levels, and self-reported adoption of using the tracking device.

Results

In total, 50 participants completed the study (mean age 48, SD 13 years, 84% women). Relative to the control period, participants increased cardiorespiratory fitness by 1.52 mL/kg/minute (95% CI 0.82-2.22; P<.001), self-reported PA by 140 minutes per week (95% CI 93.3-187.1; P<.001), daily step count by 982 (95% CI 492-1471; P<.001), and participants’ fat percentage decreased by 0.48% (95% CI –0.84 to –0.13; P=.009). No difference was observed in blood pressure (systolic: 95% CI –2.16 to 3.57, P=.63; diastolic: 95% CI –0.70 to 2.55; P=.27) or perceived stress (95% CI –0.86 to 1.78; P=.49). No associations were found between adoption of the wearable tracking device and age, gender, personality, or education. However, participants with a low perceived stress at baseline were more likely to rate the use of a wearable tracking device highly motivating.

Conclusions

Tracking health behavior using a wearable tracking device increases PA resulting in an improved cardiorespiratory fitness among inactive adults.

Lifestyle Modification Using a Wearable Biometric Ring and Guided Feedback Improve Sleep and Exercise Behaviors: A 12-Month Randomized, Placebo-Controlled Study

Purpose: Wearable biometric monitoring devices (WBMD) show promise as a cutting edge means to improve health and prevent disease through increasing accountability. By regularly providing real-time quantitative data regarding activity, sleep quality, and recovery, users may become more aware of the impact that their lifestyle has on their health. The purpose of this study was to examine the efficacy of a biometric tracking ring on improving sleep quality and increasing physical fitness over a one-year period. Methods: Fifty-six participants received a biometric tracking ring and were placed in one of two groups. One group received a 3-month interactive behavioral modification intervention (INT) that was delivered virtually via a smartphone app with guided text message feedback (GTF). The other received a 3-month non-directive wellness education control (CON). After three months, the INT group was divided into a long-term feedback group (LT-GTF) that continued to receive GTF for another nine months or short-term feedback group (ST-GTF) that stopped receiving GTF. Weight, body composition, and VO2max were assessed at baseline, 3months, and 12months for all participants and additionally at 6 and 9months for the ST-GTF and LT-GTF groups. To establish baseline measurements, sleep and physical activity data were collected daily over a 30-day period. Daily measurements were also conducted throughout the 12-month duration of the study. Results: Over the first 3months, the INT group had significant (p<0.001) improvements in sleep onset latency, daily step count, % time jogging, VO2max, body fat percentage, and heart rate variability (rMSSD HRV) compared to the CON group. Over the next 9months, the LT-GTF group continued to improve significantly (p<0.001) in sleep onset latency, daily step count, % time jogging, VO2max, and rMSSD HRV. The ST-GTF group neither improved nor regressed over the latter 9months except for a small increase in sleep latency. Conclusion: Using a WBMD concomitantly with personalized education, encouragement, and feedback, elicits greater change than using a WBMD alone. Additionally, the improvements achieved from a short duration of personalized coaching are largely maintained with the continued use of a WBMD.

Wearable activity trackers for promoting physical activity: A systematic meta-analytic review

PURPOSE

Although wearable activity trackers hold a promise of nudging people toward a more active lifestyle, current research reveals inconsistent findings regarding the effectiveness of them. The objectives of this paper are two-fold: (1) to synthesize evidence on the effects of wearable activity trackers for improving physical activities, and (2) to identify potential moderators of effect size.

METHODS

A systematic meta-analytic review was conducted. Forty-eight eligible papers based on forty-four distinct trials were identified through a systematic literature search process. Two authors independently extracted information from each study based on predefined data fields. Random-effects meta-analysis, subgroup analysis, and meta-regression analysis were employed.

RESULTS

First, interventions with wearable activity trackers significantly increased daily steps and weekly moderate-to-vigorous physical activity but had no impact on light physical activity or sedentary behavior. Second, daily steps and weekly moderate-to-vigorous physical activity were associated with participants' characteristics (i.e., gender, age, medical condition, and baseline physical activity level) and intervention features (i.e., sensors, modes of expert support, and intervention duration). The identified factors explained 53 % of the total variance for weekly moderate-to-vigorous physical activity.

CONCLUSIONS

The use of wearable activity trackers effectively improves conscious exercise behavior, including daily steps and weekly moderate-to-vigorous physical activity, but not effective for modifying habitual behavior, such as light physical activity and sedentary behavior. We also explicitly show that the extent to which the interventions with wearable activity trackers help users is contingent on the type of users and the design and delivery of interventions. Future studies are called to validate the findings and to offer theoretical explanations.

The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults

Background

Step-count monitors (pedometers, body-worn trackers and smartphone applications) can increase walking, helping to tackle physical inactivity. We aimed to assess the effect of step-count monitors on physical activity (PA) in randomised controlled trials (RCTs) amongst community-dwelling adults; including longer-term effects, differences between step-count monitors, and between intervention components.

Methods

Systematic literature searches in seven databases identified RCTs in healthy adults, or those at risk of disease, published between January 2000–April 2020. Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcome was mean differences (MD) with 95% confidence intervals (CI) in steps at follow-up between treatment and control groups. Our preferred outcome measure was from studies with follow-up steps adjusted for baseline steps (change studies); but we also included studies reporting follow-up differences only (end-point studies). Multivariate-meta-analysis used random-effect estimates at different time-points for change studies only. Meta-regression compared effects of different step-count monitors and intervention components amongst all studies at ≤4 months.

Results

Of 12,491 records identified, 70 RCTs (at generally low risk of bias) were included, with 57 trials (16,355 participants) included in meta-analyses: 32 provided change from baseline data; 25 provided end-point only. Multivariate meta-analysis of the 32 change studies demonstrated step-counts favoured intervention groups: MD of 1126 steps/day 95%CI [787, 1466] at ≤4 months, 1050 steps/day [602, 1498] at 6 months, 464 steps/day [301, 626] at 1 year, 121 steps/day [− 64, 306] at 2 years and 434 steps/day [191, 676] at 3–4 years. Meta-regression of the 57 trials at ≤4 months demonstrated in mutually-adjusted analyses that: end-point were similar to change studies (+ 257 steps/day [− 417, 931]); body-worn trackers/smartphone applications were less effective than pedometers (− 834 steps/day [− 1542, − 126]); and interventions providing additional counselling/incentives were not better than those without (− 812 steps/day [− 1503, − 122]).

Conclusions

Step-count monitoring leads to short and long-term step-count increases, with no evidence that either body-worn trackers/smartphone applications, or additional counselling/incentives offer further benefit over simpler pedometer-based interventions. Simple step-count monitoring interventions should be prioritised to address the public health physical inactivity challenge.

Systematic review registration

PROSPERO number CRD42017075810.