Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis

Positive Affect and Physical Activity Associations in Women and Their Spouses

BACKGROUND

Only 21% of U.S. women meet the recommended physical activity guidelines, placing them at increased risk for long-term conditions such as heart disease and diabetes. Physical activity is influenced by individual and interpersonal factors (e.g., romantic partners). Individual factors, such as positive affect, are associated with lower mortality risk and improved health behaviors.

OBJECTIVES

This secondary data analysis, guided by Fredrickson's Broaden and Build Theory, aims to examine the relationship between positive affect of married women (n = 115 couples) and their physical activity behavior on the same- and next- day, while also considering their spouses' positive affect.

METHODS

Two population average models assessed the relationship of calm and happy (positive affect) to physical activity. Physical activity was assessed as the sum of the minutes of moderate-to-vigorous physical activity (MVPA) over the prior 24 hours. Covariates of age, baseline activity frequency, education, marital quality, and race/ethnicity were also included.

RESULTS

Women's happiness (β = 0.15, p < .005), not calmness (β = -0.03, p = .60), was found to have a significant association with same-day MVPA. Spouses' happiness (β = 0.11, p = .045) was significantly associated with women's next-day MVPA while their calmness (β = -0.04, p = .44) was not.

CONCLUSIONS

The results of this study support that incorporating positive affect could be valuable for improving physical activity behaviors. Spouse reports provide additional context to consider in physical activity promotion research.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mechanisms of an App-Based Physical Activity Intervention and Maintenance in Community-Dwelling Women: Mediation Analyses of a Randomized Controlled Trial

BACKGROUND

Understanding the mechanism of interventions that increase physical activity (PA) is critical to developing robust intervention strategies.

AIMS

This study aims to examine the mediation effects of hypothesized changes in self-efficacy, social support, and barriers on daily changes in accelerometer-measured steps and the duration of moderate to vigorous PA over 3-month intervention and 6-month maintenance periods with a mobile phone-based PA education program.

METHODS

Data were analyzed for a total of 210 physically inactive women who were randomized. The mean (SD) age was 52.4 (11.0) years. The framework of Baron and Kenny and the Sobel test were used to evaluate the proportion of the treatment effect explained by mediation factors.

RESULTS

Postintervention PA changes were mediated by a reduction in self-efficacy and barriers and an increase in social support from friends during the intervention and maintenance periods (P ≤ .05). However, social support from family was significant only during the intervention, but not the maintenance (P = .90). Barriers to PA had the largest mediation effect on the intervention, explaining 13% to 16% of the 3-month intervention effect and 14% to 19% of the 6-month maintenance effect on daily steps and duration of moderate to vigorous PA minutes (P ≤ .05).

CONCLUSIONS

Incorporating strategies for overcoming PA barriers and promoting social support for PA is important for the design of interventions for physically inactive women. However, a reduction in self-efficacy was observed in the intervention group at 3 and 9 months as compared with the control group. This unexpected finding requires further investigation.

A Novel Approach to Assess Weekly Self-efficacy for Meeting Personalized Physical Activity Goals Via a Cellphone: 12-Week Longitudinal Study

BACKGROUND

Despite the health benefits of engaging in regular physical activity (PA), the majority of American adults do not meet the PA guidelines for aerobic and muscle-strengthening activities. Self-efficacy, the belief that one can execute specific actions, has been suggested to be a strong determinant of PA behaviors. With the increasing availability of digital technologies, collecting longitudinal real-time self-efficacy and PA data has become feasible. However, evidence in longitudinal real-time assessment of self-efficacy in relation to objectively measured PA is scarce.

OBJECTIVE

This study aimed to examine a novel approach to measure individuals' real-time weekly self-efficacy in response to their personalized PA goals and performance over the 12-week intervention period in community-dwelling women who were not meeting PA guidelines.

METHODS

In this secondary data analysis, 140 women who received a 12-week PA intervention were asked to report their real-time weekly self-efficacy via a study mobile app. PA (daily step counts) was measured by an accelerometer every day for 12 weeks. Participants rated their self-efficacy on meeting PA goals (ranging from "not confident" to "very confident") at the end of each week via a mobile app. We used a logistic mixed model to examine the association between weekly self-efficacy and weekly step goal success, controlling for age, BMI, self-reported White race, having a college education or higher, being married, and being employed.

RESULTS

The mean age was 52.7 (SD 11.5, range 25-68) years. Descriptive analyses showed the dynamics of real-time weekly self-efficacy on meeting PA goals and weekly step goal success. The majority (74.4%) of participants reported being confident in the first week, whereas less than half of them (46.4%) reported confidence in the final week of the intervention. Participants who met weekly step goals were 4.41 times more likely to be confident about achieving the following week's step goals than those who did not meet weekly step goals (adjusted odds ratio 4.41; 95% CI 2.59-7.50; P<.001). Additional analysis revealed that participants who were confident about meeting the following week's step goals were 2.07 times more likely to meet their weekly step goals in the following week (adjusted odds ratio 2.07; 95% CI 1.16-3.70; P=.01). The significant bidirectional association between real-time self-efficacy and weekly step goal success was confirmed in a series of sensitivity analyses.

CONCLUSIONS

This study demonstrates the potential utility of a novel approach to examine self-efficacy in real time for analysis of self-efficacy in conjunction with objectively measured PA. Discovering the dynamic patterns and changes in weekly self-efficacy on meeting PA goals may aid in designing a personalized PA intervention. Evaluation of this novel approach in an RCT is warranted.

Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia

The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dyslipidemia. The participants were 701 adults (305 males) aged 18−60 years undergoing continuous measurement of PA with Actigraph GT3X+ accelerometers for at least 3 days. A trajectory analysis was applied based on moderate-to-vigorous intensity physical activity (MVPA) accumulated values over every period per day. The association between PA and dyslipidemia was estimated using a logistic regression model. Four distinct PA trajectory groups in the population were identified (continued low, stable and moderate, late increasing, and early increasing). Specifically, the “moderate and stable group” was associated with a decreased rate of high TG (p < 0.05) and the “moderate and stable group” and “late increasing group” were associated with decreased rates of low HDL-C (p < 0.05). In conclusion, there were four activity trajectory groups in this population and the continued low PA trajectory was associated with a high prevalent rate of an abnormal lipid profile, and continued and moderate activity or late afternoon increasing activity might have lower HDL-C distribution.

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant's needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

Data-driven Interpretable Policy Construction for Personalized Mobile Health

To promote healthy behaviors, many mobile health applications provide message-based interventions, such as tips, motivational messages, or suggestions for healthy activities. Ideally, the intervention policies should be carefully designed so that users obtain the benefits without being overwhelmed by overly frequent messages. As part of the HeartSteps physical-activity intervention, users receive messages intended to disrupt sedentary behavior. HeartSteps uses an algorithm to uniformly spread out the daily message budget over time, but does not attempt to maximize treatment effects. This limitation motivates constructing a policy to optimize the message delivery decisions for more effective treatments. Moreover, the learned policy needs to be interpretable to enable behavioral scientists to examine it and to inform future theorizing. We address this problem by learning an effective and interpretable policy that reduces sedentary behavior. We propose Optimal Policy Trees + (OPT+), an innovative batch off-policy learning method, that combines a personalized threshold learning and an extension of Optimal Policy Trees under a budget-constrained setting. We implement and test the method using data collected in HeartSteps V2/V3. Computational results demonstrate a significant reduction in sedentary behavior with a lower delivery budget. OPT+ produces a highly interpretable and stable output decision tree thus enabling theoretical insights to guide future research.

Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach

BACKGROUND

Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of the lack of a method to identify features of long sequences next to mutation bases, the understanding of how flanking sequences influence mutation signatures is limited.

METHODS

We constructed a long short-term memory-self organizing map (LSTM-SOM) unsupervised neural network. By extracting mutated sequence features via LSTM and clustering similar features with the SOM, single-base substitutions in The Cancer Genome Atlas database were clustered according to both their mutation site and flanking sequences. The relationship between mutation sequence signatures and clinical features was then analyzed. Finally, we clustered patients into different classes according to the composition of the mutation sequence signatures by the K-means method and then studied the differences in clinical features and survival between classes.

RESULTS

Ten classes of mutant sequence signatures (mutation blots, MBs) were obtained from 2,141,527 single-base substitutions via LSTM-SOM machine learning approach. Different features in mutation bases and flanking sequences were revealed among MBs. MBs reflect both the site and pathological features of cancers. MBs were related to clinical features, including age, sex, and cancer stage. The class of an MB in a given gene was associated with survival. Finally, patients were clustered into 7 classes according to the MB composition. Significant differences in survival and clinical features were observed among different patient classes.

CONCLUSIONS

We provided a method for analyzing the characteristics of mutant sequences. Result of this study showed that flanking sequences, together with mutation bases, shape the signatures of SBSs. MBs were shown related to clinical features and survival of cancer patients. Composition of MBs is a feasible predictive factor of clinical prognosis. Further study of the mechanism of MBs related to cancer characteristics is suggested.

A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy

With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collection and release of trajectory data will raise serious privacy concerns for users. If users’ privacy is not protected enough, they will refuse to share their trajectory data. In this paper, a new trajectory privacy protection method based on random sampling differential privacy (TPRSDP), which can provide more security protection, is proposed. Compared with other methods, it takes less time to run this method. Experiments are conducted on two real world datasets to validate the proposed scheme, and the results are compared with others in terms of running time and information loss. The performance of the scheme with different parameter values is verified. The setting of the new scheme parameters is discussed in detail, and some valuable suggestions are given.

Temporal physical activity patterns are associated with obesity in U.S. adults

Few attempts have been made to incorporate multiple aspects of physical activity (PA) to classify patterns linked with health. Temporal PA patterns integrating time and activity counts were created to determine their association with health status. Accelerometry data from the National Health and Nutrition Examination Survey 2003-2006 was used to pattern PA counts and time of activity from 1999 adults with one weekday of activity. Dynamic time warping and kernel k-means clustering partitioned 4 participant clusters representing temporal PA patterns. Multivariate regression models determined associations between clusters and health status indicators and obesity, type 2 diabetes, and metabolic syndrome. Cluster 1 with a temporal PA pattern of the lowest activity counts reaching 4.8e4 cph from 6:00-23:00 was associated with higher body mass index (BMI) (β = 2.5 ± 0.6 kg/m2, 95% CI: 1.0, 4.1), higher waist circumference (WC) (β = 6.4 ± 1.3 cm, 95% CI: 2.8, 10.0), and higher odds of obesity (OR: 2.4; 95% CI: 1.3, 4.4) compared with Cluster 3 with activity counts reaching 9.6e4-1.2e5 cph between 16:00-21:00. Cluster 1 was also associated with higher BMI (β = 1.5 ± 0.5 kg/m2, 95% CI: 0.1, 2.8) and WC (β = 3.6 ± 1.3 cm, 95% CI: 0.1, 7.0) compared to Cluster 4 with activity counts reaching 9.6e4 cph between 8:00-11:00. A Temporal PA pattern with the lowest PA counts had significantly higher mean BMI and WC compared to temporal PA patterns of higher activity counts performed early (8:00-11:00) or late (16:00-21:00) throughout the day. Temporal PA patterns appear to meaningfully link to health status.

Differences in objectively measured daily physical activity patterns related to depressive symptoms in community dwelling women - mPED trial

Physical activity (PA) is an effective depression treatment. However, knowledge on how variation in day-to-day PA relates to depression in women is lacking. The purposes of this study were to 1) compare overall objectively measured baseline daily steps and duration of moderate to vigorous PA (MVPA) and 2) examine differences in steps and MVPA on days of the week between women aged 25–65 years, who were physically inactive, with high and low depressive symptoms, enrolled in a run-in period of the mobile phone based physical activity education (mPED) trial. The Center for Epidemiological Studies Depression Scale was used to categorize low/high depressive symptom groups. We used linear mixed-effects models to examine the associations between steps and MVPA and depression-status overall and by day of the week, adjusting for selected demographic variables and their interactions with day of the week. 274 women were included in the final analysis, of which 58 had high depressive symptoms. Overall physical activity levels did not differ. However, day of the week modified the associations of depression with MVPA (p = 0.015) and daily steps (p = 0.08). Women with high depression were characterized by reduced activity at the end of the week (Posthoc: Friday: 791 fewer steps, 95% CI: 73–1509, p = 0.03; 8.8 lower MVPA, 95% CI: 2.16–15.5, p = 0.0098) compared to women with low depression, who showed increased activity. Day of the week might be an important target for personalization of physical activity interventions. Future work should evaluate potential causes of daily activity alterations in depression in women.

Diurnal patterns of sedentary behavior and changes in physical function over time among older women: a prospective cohort study

BACKGROUND

Sedentary behavior (SB) is linked to negative health outcomes in older adults. Most studies use summary values, e.g., total sedentary minutes/day. Diurnal timing of SB accumulation may further elucidate SB-health associations.

METHODS

Six thousand two hundred four US women (mean age = 79 ± 7; 50% White, 34% African-American) wore accelerometers for 7-days at baseline, yielding 41,356 person-days with > 600 min/day of data. Annual follow-up assessments of health, including physical functioning, were collected from participants for 6 years. A novel two-phase clustering procedure discriminated participants' diurnal SB patterns: phase I grouped day-level SB trajectories using longitudinal k-means; phase II determined diurnal SB patterns based on proportion of phase I trajectories using hierarchical clustering. Mixed models tested associations between SB patterns and longitudinal physical functioning, adjusted for covariates including total sedentary time. Effect modification by moderate-vigorous-physical activity (MVPA) was tested.

RESULTS

Four diurnal SB patterns were identified: p1 = high-SB-throughout-the-day; p2 = moderate-SB-with-lower-morning-SB; p3 = moderate-SB-with-higher-morning-SB; p4 = low-SB-throughout-the-day. High MVPA mitigated physical functioning decline and correlated with better baseline and 6-year trajectory of physical functioning across patterns. In low MVPA, p2 had worse 6-year physical functioning decline compared to p1 and p4. In high MVPA, p2 had similar 6-year physical functioning decline compared to p1, p3, and p4.

CONCLUSIONS

In a large cohort of older women, diurnal SB patterns were associated with rates of physical functioning decline, independent of total sedentary time. In particular, we identified a specific diurnal SB subtype defined by less SB earlier and more SB later in the day, which had the steepest decline in physical functioning among participants with low baseline MVPA. Thus, diurnal timing of SB, complementary to total sedentary time and MVPA, may offer additional insights into associations between SB and physical health, and provide physicians with early warning of patients at high-risk of physical function decline.

Applying machine learning to predict future adherence to physical activity programs

BACKGROUND

Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.

METHODS

We use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.

RESULTS

we had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.

CONCLUSIONS

DiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01280812 Registered on January 21, 2011.

How to Classify Super-Utilizers: A Methodological Review of Super-Utilizer Criteria Applied to the Utah Medicaid Population, 2016-2017

A limited number of patients, commonly termed super-utilizers, account for the bulk of health care expenditures. Multiple criteria for identifying super-utilizers exist, but no standard methodology is available for determining which criteria should be used for a specific population. Application is often arbitrary, and poorly aligned super-utilizer criteria might result in misallocation of resources and diminished effects of interventions. This study sought to apply an innovative, data-driven approach to classify super-utilizers among Utah Medicaid beneficiaries. The authors conducted a literature review of research methods to catalogue applied super-utilizer criteria. The most commonly used criteria were applied to Utah Medicaid beneficiaries enrolled during July 1, 2016-June 30, 2017, using their previous 12 months of claims data (N = 309,921). The k-medoids algorithm cluster analysis was used to find groups of beneficiaries with similar characteristic based on criteria from the literature. In all, 180 super-utilizer criteria were identified in the literature, 21 of which met the inclusion criteria. When these criteria were applied to Utah Medicaid data, 5 distinct subpopulation clusters were found: non-super-utilizers (n = 163,118), beneficiaries with multiple chronic or mental health conditions (n = 68,054), beneficiaries with a single chronic health condition (n = 43,939), emergency department super-utilizers with chronic or mental health conditions (n = 7809), and beneficiaries with uncomplicated hospitalizations (n = 27,001). This study demonstrates how cluster analysis can aid in selecting characteristics from the literature that systematically differentiate super-utilizer groups from other beneficiaries. This methodology might be useful to health care systems for identifying super-utilizers within their patient populations.

Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife

Physical activity (PA) and sedentary time (SED) are associated with the risk of cardiovascular disease (CVD), but the temporal patterns of these behaviors most beneficial for cardiovascular health remain unknown. We aimed to identify the intensity and temporal patterns of PA and SED measured continuously by an accelerometer and their relationship with CVD risk. At the age of 46 years, 4582 members (1916 men; 2666 women) of the Northern Finland Birth Cohort 1966 study underwent continuous measurement of PA with Polar Active (Polar Electro, Finland) accelerometers for one week. X-means clustering was applied based on 10 min average MET (metabolic equivalent) values during the measurement period. Ten-year risk of CVD was estimated using the Framingham risk model. Most of the participants had low risk for CVD. Four distinct PA clusters were identified that were well differentiable by the intensity and temporal patterns of activity (inactive, evening active, moderately active, very active). A significant difference in 10-year CVD risk across the clusters was found in men (p = 0.028) and women (p < 0.001). Higher levels of HDL cholesterol were found in more active clusters compared to less active clusters (p < 0.001) in both genders. In women total cholesterol was lower in the moderately active cluster compared to the inactive and evening active clusters (p = 0.001). Four distinct PA clusters were recognized based on accelerometer data and X-means clustering. A significant difference in CVD risk across the clusters was found in both genders. These results can be used in developing and promoting CVD prevention strategies.

Short- and Long-term Effects of a Mobile Phone App in Conjunction With Brief In-Person Counseling on Physical Activity Among Physically Inactive Women: The mPED Randomized Clinical Trial

IMPORTANCE

Mobile phone applications (apps) and activity trackers allow researchers to remotely deliver an intervention and monitor physical activity but have not been rigorously evaluated for longer periods.

OBJECTIVE

To determine whether a mobile phone-based physical activity education app, in conjunction with brief in-person counseling, increases and then maintains levels of physical activity.

DESIGN, SETTING, AND PARTICIPANTS

In this parallel randomized clinical trial, community-dwelling physically inactive women recruited between May 2011 and April 2014 were randomized in equal proportions into the control (n = 69), regular (n = 71), and plus (n = 70) groups. Data were analyzed using intention to treat from September 16, 2016, through June 30, 2018.

INTERVENTIONS

The regular and plus groups were instructed to use the app on their mobile phone and an accelerometer every day for 3 months and attend brief in-person counseling. During the 6-month maintenance period, the plus group continued to use the app and accelerometer, while the regular group stopped using the app but continued using the accelerometer. The control group used the accelerometer throughout.

MAIN OUTCOMES AND MEASURES

The primary and secondary outcomes were daily accelerometer-measured total steps and time spent in moderate to vigorous physical activity (MVPA).

RESULTS

The 210 participants had a mean (SD) age of 52.4 (11.0) years. At baseline, the mean (SD) daily total steps by accelerometer in the control, regular, and plus groups were 5384 (2920), 5063 (2526), and 5837 (3235), respectively. During the 3-month intervention period, daily steps and MVPA increased in the combined regular and plus groups compared with the control group (between-group differences, 2060 steps per day; 95% CI, 1296-2825 steps per day; P < .001 and 18.2 min/d MVPA; 95% CI, 10.9-25.4 min/d MVPA; P < .001). During the subsequent 6-month maintenance period, mean activity level remained higher in the combined plus and regular groups than among controls (between-group difference, 1360 steps per day; 95% CI, 694-2026 steps per day; P <. 001), but trends in total daily steps and MVPA were similar in the plus and regular groups.

CONCLUSIONS AND RELEVANCE

In this trial, the intervention groups substantially increased their physical activity. However, use of both the app and accelerometer for an additional 6 months after the initial 3-month intervention did not help to maintain increases in physical activity compared with continued use of the accelerometer alone.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01280812.