BeWell24: development and process evaluation of a smartphone "app" to improve sleep, sedentary, and active behaviors in US Veterans with increased metabolic risk

StandUPTV: Preparation and optimization phases of a mHealth intervention to reduce sedentary screen time in adults

Recreational sedentary screen time (rSST) is the most prevalent sedentary behavior for adults outside of work, school, and sleep, and is strongly linked to poor health. StandUPTV is a mHealth trial that uses the Multiphase Optimization Strategy (MOST) framework to develop and evaluate the efficacy of three theory-based strategies for reducing rSST among adults. This paper describes the preparation and optimization phases of StandUPTV within the MOST framework. We identified three candidate components based on previous literature: (a) rSST electronic lockout (LOCKOUT), which restricts rSST through electronic means; (b) adaptive prompts (TEXT), which provides adaptive prompts based on rSST behaviors; and (c) earning rSST through increased moderate-vigorous physical activity (MVPA) participation (EARN). We also describe the mHealth iterative design process and the selection of an optimization objective. Finally, we describe the protocol of the optimization randomized controlled trial using a 23 factorial experimental design. We will enroll 240 individuals aged 23-64 y who engage in >3 h/day of rSST. All participants will receive a target to reduce rSST by 50% and be randomized to one of 8 combinations representing all components and component levels: LOCKOUT (yes vs. no), TEXT (yes vs. no), and EARN (yes vs. no). Results will support the selection of the components for the intervention package that meet the optimization objective and are acceptable to participants. The optimized intervention will be tested in a future evaluation randomized trial to examine reductions in rSST on health outcomes among adults.

Potential associations between behavior change techniques and engagement with mobile health apps: a systematic review

Introduction

Lack of engagement is a common challenge for digital health interventions. To achieve their potential, it is necessary to understand how best to support users' engagement with interventions and target health behaviors. The aim of this systematic review was to identify the behavioral theories and behavior change techniques being incorporated into mobile health apps and how they are associated with the different components of engagement.

Methods

The review was structured using the PRISMA and PICOS frameworks and searched six databases in July 2022: PubMed, Embase, CINAHL, APA PsycArticles, ScienceDirect, and Web of Science. Risk of bias was evaluated using the Cochrane Collaboration Risk of Bias 2 and the Mixed Methods Appraisal Tools.

Analysis

A descriptive analysis provided an overview of study and app characteristics and evidence for potential associations between Behavior Change Techniques (BCTs) and engagement was examined.

Results

The final analysis included 28 studies. Six BCTs were repeatedly associated with user engagement: goal setting, self-monitoring of behavior, feedback on behavior, prompts/cues, rewards, and social support. There was insufficient data reported to examine associations with specific components of engagement, but the analysis indicated that the different components were being captured by various measures.

Conclusion

This review provides further evidence supporting the use of common BCTs in mobile health apps. To enable developers to leverage BCTs and other app features to optimize engagement in specific contexts and individual characteristics, we need a better understanding of how BCTs are associated with different components of engagement.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022312596.

Formative Development of ClockWork for the Postpartum Period: A Theory-Based Intervention to Harness the Circadian Timing System to Address Cardiometabolic Health-Related Behaviors

Individuals with body mass index (BMI) ≥ 25 kg/m2 before pregnancy have greater difficulty losing the weight gained during pregnancy, and this postpartum weight retention predicts higher risk for cardiometabolic disease. The postpartum period involves substantial disruptions in circadian rhythms, including rhythms related to eating, physical activity, sleep, and light/dark exposure, each of which are linked to obesity and cardiometabolic disease in non-pregnant adult humans and animals. We posit that a multi-component, circadian timing system-based behavioral intervention that uses digital tools-ClockWork-will be feasible and acceptable to postpartum individuals and help promote weight- and cardiometabolic health-related behaviors. We provide data from stakeholder interviews with postpartum individuals (pre-pregnancy BMI ≥ 25; n = 7), which were conducted to obtain feedback on and improve the relevance and utility of digital self-monitoring tools for health behaviors and weight during the postpartum period. Participants perceived the ClockWork intervention and digital monitoring app to be helpful for management of postpartum weight-related health behaviors. They provided specific recommendations for increasing the feasibility intervention goals and improving app features for monitoring behaviors. Personalized, easily accessible interventions are needed to promote gestational weight loss after delivery; addressing circadian behaviors is an essential component of such interventions. Future studies will evaluate the efficacy of the ClockWork intervention and associated digital tools for improving cardiometabolic health-related behaviors linked to the circadian timing system during the postpartum period.

Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling

BACKGROUND

Sleep disorders are a global challenge, affecting a quarter of the global population. Mobile health (mHealth) sleep apps are a potential solution, but 25% of users stop using them after a single use. User satisfaction had a significant impact on continued use intention.

OBJECTIVE

This China-US comparison study aimed to mine the topics discussed in user-generated reviews of mHealth sleep apps, assess the effects of the topics on user satisfaction and dissatisfaction with these apps, and provide suggestions for improving users' intentions to continue using mHealth sleep apps.

METHODS

An unsupervised clustering technique was used to identify the topics discussed in user reviews of mHealth sleep apps. On the basis of the two-factor theory, the Tobit model was used to explore the effect of each topic on user satisfaction and dissatisfaction, and differences in the effects were analyzed using the Wald test.

RESULTS

A total of 488,071 user reviews of 10 mainstream sleep apps were collected, including 267,589 (54.8%) American user reviews and 220,482 (45.2%) Chinese user reviews. The user satisfaction rates of sleep apps were poor (China: 56.58% vs the United States: 45.87%). We identified 14 topics in the user-generated reviews for each country. In the Chinese data, 13 topics had a significant effect on the positive deviation (PD) and negative deviation (ND) of user satisfaction. The 2 variables (PD and ND) were defined by the difference between the user rating and the overall rating of the app in the app store. Among these topics, the app's sound recording function (β=1.026; P=.004) had the largest positive effect on the PD of user satisfaction, and the topic with the largest positive effect on the ND of user satisfaction was the sleep improvement effect of the app (β=1.185; P<.001). In the American data, all 14 topics had a significant effect on the PD and ND of user satisfaction. Among these, the topic with the largest positive effect on the ND of user satisfaction was the app's sleep promotion effect (β=1.389; P<.001), whereas the app's sleep improvement effect (β=1.168; P<.001) had the largest positive effect on the PD of user satisfaction. The Wald test showed that there were significant differences in the PD and ND models of user satisfaction in both countries (all P<.05), indicating that the influencing factors of user satisfaction with mHealth sleep apps were asymmetrical. Using the China-US comparison, hygiene factors (ie, stability, compatibility, cost, and sleep monitoring function) and 2 motivation factors (ie, sleep suggestion function and sleep promotion effects) of sleep apps were identified.

CONCLUSIONS

By distinguishing between the hygiene and motivation factors, the use of sleep apps in the real world can be effectively promoted.

A Proof-of-Concept Pilot Test of a Behavioral Intervention to Improve Adherence to Dietary Recommendations for Cancer Prevention

OBJECTIVES

Prevention programs that can help adults improve the quality of their diets to reduce cancer risk are needed. This Phase IIa study prospectively tested a mHealth intervention designed to improve adherence to dietary quality guidelines for cancer prevention.

METHODS

All participants (N = 62) received nutrition education and a self-regulation skills curriculum, with a primary target of changing grocery shopping behavior. Using a randomized, factorial design, the study varied whether each of the following 4 components were added to the 20-week intervention: (1) location-triggered app messaging, delivered when individuals arrived at grocery stores, (2) reflections on benefits of change, delivered with extra coaching time and tailored app messages, (3) coach monitoring, in which food purchases were digitally monitored by a coach, and (4) involvement of a household member in the intervention.

RESULTS

Benchmarks were successfully met for recruitment, retention, and treatment acceptability. Across conditions, there were significant reductions in highly processed food intake (P < .001, η2 = .48), red and processed meat intake (P < .001, η2 = .20), and sugar-sweetened beverage intake (P = .008, η2 = .13) from pre-to post-treatment. Analyses examining whether each intervention component influenced change across time found that participants who received coach monitoring increased their intake of fruits, vegetables, and fiber, whereas those with no coach monitoring had less improvement (P = .01, η2 = .14). The improvement in red and processed meat was stronger among participants with household support ON, at a marginally significant level, than those with household support OFF (P = .056, η2 = .07).

CONCLUSION

This study showed feasibility, acceptability, and preliminary signals of efficacy of a remotely delivered intervention to facilitate adherence to dietary guidelines for cancer prevention and that coach monitoring and household support may be especially effective strategies. A fully powered clinical trial is warranted to test an optimized version of the intervention that includes nutrition education, self-regulation skills training, coach monitoring, and household member involvement.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04947150.

The preparation phase in the multiphase optimization strategy (MOST): a systematic review and introduction of a reporting checklist

Multicomponent behavioral interventions developed using the multiphase optimization strategy (MOST) framework offer important advantages over alternative intervention development models by focusing on outcomes within constraints relevant for effective dissemination. MOST consists of three phases: preparation, optimization, and evaluation. The preparation phase is critical to establishing the foundation for the optimization and evaluation phases; thus, detailed reporting is critical to enhancing rigor and reproducibility. A systematic review of published research using the MOST framework was conducted. A structured framework was used to describe and summarize the use of MOST terminology (i.e., preparation phase and optimization objective) and the presentation of preparation work, the conceptual model, and the optimization. Fifty-eight articles were reviewed and the majority focused on either describing the methodology or presenting results of an optimization trial (n = 38, 66%). Although almost all articles identified intervention components (96%), there was considerable variability in the degree to which authors fully described other elements of MOST. In particular, there was less consistency in use of MOST terminology. Reporting on the MOST preparation phase is varied, and there is a need for increased focus on explicit articulation of key design elements and rationale of the preparation phase. The proposed checklist for reporting MOST studies would significantly advance the use of this emerging methodology and improve implementation and dissemination of MOST. Accurate reporting is essential to reproducibility and rigor of scientific trials as it ensures future research fully understands not only the methodology, but the rationale for intervention and optimization decisions.

Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review

BACKGROUND

Traditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear.

OBJECTIVE

This review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible.

METHODS

Databases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults; either developed or evaluated integrated psychological theory with dynamic theories; used smartphones for the intervention delivery; the interventions were adaptive or just-in-time adaptive; included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs; and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results.

RESULTS

A total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA; 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study; P=.08), increased light PA (1 study; P=.002), walking steps (2 studies; P=.06 and P<.001), walking time (1 study; P=.02), moderate-to-vigorous PA (2 studies; P=.08 and P=.81), and nonwalking exercise time (1 study; P=.31). RCT studies showed increased walking steps (1 study; P=.003) and walking time (1 study; P=.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone's GPS, and 3 studies used wearable activity trackers.

CONCLUSIONS

To our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model-based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB.

TRIAL REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.

Mobile health apps to reduce sedentary behavior: a scoping review

Given the continued increase in mobile health applications (apps) aimed at healthcare and the recognition of sedentary behavior (SB) as a public health problem, the goal of this scoping review study was to summarize the effects of interventions based on mobile health apps designed to reduce SB in adults, with a specific focus on SB. The electronic databases PubMed, PsycINFO, SportDISCUS, Web of Science, and manual searches in reference lists were conducted on papers published up to September 2020. Nine out of the 897 studies researched were included and composed the descriptive synthesis. The investigations found in the present study showed a decrease in time spent on television viewing and in total time spent sitting, as well as an increase in the number of SB breaks after interventions based on mobile health apps. In conclusion, despite the growing interest in intervention programs in SB, only nine studies have used smartphone apps as a strategy to reduce SB in adults. Mobile health apps were proved to be effective in SB reduction, as assessed by different parameters, and should be encouraged. However, further studies are needed to verify the long-term effects of the utilization of such applications.

The "Worktivity" mHealth intervention to reduce sedentary behaviour in the workplace: a feasibility cluster randomised controlled pilot study

Background

Office work generally consists of high amounts of sedentary behaviour (SB) which has been associated with negative health consequences. We developed the “WorktivIty” mobile app to help office workers reduce their SB through self-monitoring and feedback on sedentary time, prompts to break sedentary time, and educational facts. The aim of this paper is to report the feasibility of delivering the Worktivity intervention to desk-based office workers in the workplace setting and describe methodological considerations for a future trial.

Methods

We conducted a three-arm feasibility cluster randomised controlled pilot study over an 8-week period with full time-desk based employees. Clustered randomisation was to one of three groups: Worktivity mobile app (MA; n = 20), Worktivity mobile app plus SSWD (MA+SSWD; n = 20), or Control (C; n = 16). Feasibility was assessed using measures of recruitment and retention, intervention engagement, intervention delivery, completion rates and usable data, adverse events, and acceptability.

Results

Recruitment of companies to participate in this study was challenging (8% of those contacted), but retention of individual participants within the recruited groups was high (81% C, 90% MA + SSWD, 95% MA). Office workers’ engagement with the app was moderate (on average 59%). Intervention delivery was partially compromised due to diminishing user engagement and technical issues related to educational fact delivery. Sufficient amounts of useable data were collected, however either missing or unusable data were observed with activPAL™, with data loss increasing at each follow up time point. No serious adverse events were identified during the study. The majority of participants agreed that the intervention could be implemented within the workplace setting (65% MA; 72% MA + SSWD) but overall satisfaction with the intervention was modest (58% MA; 39% MA + SSWD).

Conclusions

The findings suggest that, in principle, it is feasible to implement a mobile app-based intervention in the workplace setting however the Worktivity intervention requires further technical refinements before moving to effectiveness trials. Challenges relating to the initial recruitment of workplaces and maintaining user engagement with the mHealth intervention over time need to be addressed prior to future large-scale implementation. Further research is needed to identify how best to overcome these challenges.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11473-6.

A Mobile Health Behavior Intervention to Reduce Pain and Improve Health in Older Adults With Obesity and Chronic Pain: The MORPH Pilot Trial

Chronic, multisite pain is a common phenomenon in aging and is associated with a host of negative health outcomes. It is a complex and multifaceted condition that may be exacerbated by weight gain and long periods of inactivity. Unfortunately, older adults suffering from chronic pain have unique barriers limiting access to center-based behavior change interventions. The MORPH study first adapted and iteratively refined an evidence-based group-mediated intervention for delivery in the home via mHealth tools (a smartphone app, teleconferencing software, wearable activity monitor, smart weight scale). This was followed by a pilot randomized controlled trial (RCT) meant to assess feasibility of the MORPH intervention, and to examine initial effects on physical function, pain, weight, and sedentary behavior. We recruited low-active and obese older adults with multisite pain to partake in a series of N-of-1 refinement studies (N = 5 total) or a 12-week pilot RCT delivered largely in the home (N = 28 assigned to active intervention or wait-list control). The refinement phase identified several key technological (e.g., selection of a new smart weight scale) and user interface (e.g., clarification of in-app phrasing) modifications that were made before initiating the RCT phase. Analyses of covariance, controlling for baseline values, sex, and age indicated effects favoring the intervention across all domains of interest: there was a substantially clinically meaningful difference in short physical performance battery scores (0.63 points, η² = 0.08), a moderate-to-large difference in PROMIS pain intensity scores (5.52 points, η² = 0.12), a large difference in body weight (2.90 kg, η² = 0.207), and a moderate effect on adjusted ActivPAL-assessed sedentary time (64.90 min, η² = 0.07) with a small effect on steps (297.7 steps, η² = 0.01). These results suggest a largely-home delivered movement and weight loss program for older adults with pain is feasible and recommendations are provided for future programs of this nature.

Clinical Trial Registration:ClinicalTrials.gov, Identifier: NCT03377634.

Characterizing the US Population by Patterns of Mobile Health Use for Health and Behavioral Tracking: Analysis of the National Cancer Institute's Health Information National Trends Survey Data

BACKGROUND

Multiple types of mobile health (mHealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. However, despite their availability, some individuals do not own, do not realize they own, or own but do not use these technologies. Others may use mHealth devices, but their use varies in tracking health, behaviors, and goals. Examining patterns of mHealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mHealth interventions for diverse audiences.

OBJECTIVE

The aim of this study was to identify patterns of mHealth use for health and behavioral tracking in the US adult population and to characterize the population according to those patterns.

METHODS

We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6789) to characterize respondents according to 5 mutually exclusive reported patterns of mHealth use for health and behavioral tracking: (1) mHealth nonowners and nonusers report not owning or using devices to track health, behaviors, or goals; (2) supertrackers track health or behaviors and goals using a smartphone or tablet plus other devices (eg, Fitbit); (3) app trackers use only a smartphone or tablet; (4) device trackers use only nonsmartphone or nontablet devices and do not track goals; and (5) nontrackers report having smartphone or tablet health apps but do not track health, behaviors, or goals.

RESULTS

Being in the mHealth nonowners and nonusers category (vs all mHealth owners and users) is associated with males, older age, lower income, and not being a health information seeker. Among mHealth owners and users, characteristics of device trackers and supertrackers were most distinctive. Compared with supertrackers, device trackers have higher odds of being male (odds ratio [OR] 2.22, 95% CI 1.55-3.19), older age (vs 18-34 years; 50-64 years: OR 2.83, 95% CI 1.52-5.30; 65+ years: OR 6.28, 95% CI 3.35-11.79), have an annual household income of US $20,000 to US $49,999 (vs US $75,000+: OR 2.31, 95% CI 1.36-3.91), and have a chronic condition (OR 1.69, 95% CI 1.14-2.49). Device trackers also have higher odds of not being health information seekers than supertrackers (OR 2.98, 95% CI 1.66-5.33).

CONCLUSIONS

Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mHealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to those of mHealth nonowners and nonusers. Our results suggest patterns of mHealth use may inform how to target mHealth interventions to enhance reach and facilitate healthy behaviors.

Your Personal Motivator is with You: A Systematic Review of Mobile Phone Applications Aiming at Increasing Physical Activity

BACKGROUND

Literature shows mixed evidence about the power of mobile phone applications to foster physical activity. A systematic integration that offers insights into which mobile phone application techniques can or cannot foster physical activity is lacking, as is a theoretical integration of current research.

OBJECTIVES

We performed a systematic review guided by a theoretical framework focusing on effects that certain mobile phone application techniques have on physical activity, to improve our understanding of what techniques are more or less effective.

METHODS

We identified articles by searching EBSCO Business Source Complete, Science Direct, PsycINFO, Springer, PLoS ONE, Taylor and Francis, IEEE, Social Science Citation Index, Science Citation Index Expanded, PUBMED, MEDLINE, and Google Scholar. We considered articles if (1) they referred to the use of mobile phone applications to promote physical activity; (2) their methodological approach allowed one to derive appropriate results (e.g., intervention-based approach, observational study); (3) they were published in peer-reviewed journals or conference proceedings; and (4) they were written in English. The literature search resulted in 41 usable studies. Meta-synthesis and vote counting were applied to analyze these studies.

RESULTS

Based on the ratio of supportive versus non-supportive evidence in both the qualitative and the quantitative studies, we propose the following descending rank order for the effectiveness of application techniques to foster physical activity. This is tentative in nature because the current overall small body of literature made coming to definite conclusions difficult: (1) feedback, (2) goal setting and its sub-forms, (3) competition, social sharing with familiar users in both segregated and social network groups, and (4) social sharing with strangers in segregated groups, reward, and social sharing with strangers in social network groups. Rewards in particular provided mixed results, and social sharing with strangers in segregated and social network groups seemed rather ineffective but may work under special conditions that need to be identified in additional research. One limitation of our study was that our results are mostly derived from qualitative studies, since quantitative studies are underrepresented in the field.

CONCLUSION

Several mobile phone application techniques were identified that have the potential to foster physical activity, whereas others were identified that are unlikely to increase physical activity. Major avenues for future research include more theoretical development and more quantitative studies, among others.

Rationale, design, and development of SleepWell24: A smartphone application to promote adherence to positive airway pressure therapy among patients with obstructive sleep apnea

BACKGROUND

Positive airway pressure (PAP) therapy is the gold standard treatment for obstructive sleep apnea (OSA), a chronic disorder that affects 6-13% of the adult population. However, adherence to PAP therapy is challenging, and current approaches to improve adherence have limited efficacy and scalability.

METHODS/DESIGN

To promote PAP adherence, we developed SleepWell24, a multicomponent, evidence-based smartphone application that delivers objective biofeedback concerning PAP use and sleep/physical activity patterns via cloud-based PAP machine and wearable sensor data, and behavior change strategies and troubleshooting of PAP therapy interface use. This randomized controlled trial will evaluate the feasibility, acceptability, and initial efficacy of SleepWell24 compared to a usual care control condition during the first 60 days of PAP therapy among patients newly diagnosed with OSA.

DISCUSSION

SleepWell24 is an innovative, multi-component behavior change intervention, designed as a self-management approach to addressing the psychosocial determinants of adherence to PAP therapy among new users. The results will guide lengthier future trials that assess numerous patient-centered and clinical outcomes.

A Pain eHealth Platform for Engaging Obese, Older Adults with Chronic Low Back Pain in Nonpharmacological Pain Treatments: Protocol for a Pilot Feasibility Study

Background

Low back pain is a costly healthcare problem and the leading cause of disability among adults in the United States. Primary care providers urgently need effective ways to deliver evidence-based, nonpharmacological therapies for chronic low back pain. Guidelines published by several government and national organizations have recommended nonpharmacological and nonopioid pharmacological therapies for low back pain.

Objective

The Pain eHealth Platform (PEP) pilot trial aims to test the feasibility of a highly innovative intervention that (1) uses an electronic health record (EHR) query to systematically identify a phenotype of obese, older adults with chronic low back pain who may benefit from Web-based behavioral treatments; (2) delivers highly tailored messages to eligible older adults with chronic low back pain via the patient portal; (3) links affected patients to a Web app that provides education on the efficacy of evidence-based, nonpharmacological, behavioral pain treatments; and (4) directs patients to existing Web-based health treatment tools.

Methods

Using a three-step modified Delphi method, an expert panel of primary care providers will define a low back pain phenotype for an EHR query. Using the defined low back pain phenotype, an EHR query will be created to identify patients who may benefit from the PEP. Up to 15 patients with low back pain will be interviewed to refine the tailored messaging, esthetics, and content of the patient-facing Web app within the PEP. Up to 10 primary care providers will be interviewed to better understand the facilitators and barriers to implementing the PEP, given their clinic workflow. We will assess the feasibility of the PEP in a single-arm pragmatic pilot study in which secure patient portal invitations containing a hyperlink to the PEP Web app are sent to 1000 patients. The primary outcome of the study is usability as measured by the System Usability Scale.

Results

Qualitative interviews with primary care providers were completed in April 2019. Qualitative interviews with patients will begin in December 2019.

Conclusions

The PEP will leverage informatics and the patient portal to deliver evidence-based nonpharmacological treatment information to adults with chronic low back pain. Results from this study may help inform the development of Web-based health platforms for other pain and chronic health conditions.

International Registered Report Identifier (IRRID)

DERR1-10.2196/14525

Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study

Background

There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features.

Objective

This study aimed to examine adult users’ preferences for techniques and features in mobile apps for 24-hour movement behaviors.

Methods

A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample t tests were used to examine associations and differences between and within users by the type of health domain and users’ behavioral intention and adoption.

Results

Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users’ intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (P=.03), information on behavior health outcome (P=.048), and feedback (P=.04) and incorporate social support (P=.048) to help those who are further removed from healthy sleep. A virtual coach (P<.001) and video modeling (P=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (P=.03). Social comparison and support features are not high on users’ agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method.

Conclusions

The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines.

Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations

The multiphase optimization strategy (MOST) is an increasingly popular framework to prepare, optimize, and evaluate multicomponent behavioral health interventions. Within this framework, it is common to use a factorial trial to assemble an optimized multicomponent intervention by simultaneously testing several intervention components. With the possibility of a large number of conditions (unique combinations of components) and a goal to balance conditions on both sample size (for statistical efficiency) and baseline covariates (for internal validity), such trials face additional randomization challenges compared to the standard two-arm trial. The purpose of the current paper is to compare and contrast potential randomization methods for factorial trials in the context of MOST and to provide guidance for the reporting of those methods. We describe the principles, advantages, and disadvantages of several randomization methods in the context of factorial trials. We then provide examples to examine current practice in the MOST-related literature and provide recommendations for reporting of randomization. We identify two key randomization decisions for MOST-related factorial trials: (i) whether to randomize to components or conditions and (ii) whether to use restricted randomization techniques, such as stratification, permuted blocks, and minimization. We also provide a checklist to assist researchers in ensuring complete reporting of randomization methods used. As more investigators use factorial trials within the MOST framework for assembling optimized multicomponent behavioral interventions, appropriate implementation and rigorous reporting of randomization procedures will be essential for ensuring the efficiency and validity of the results.

Efficacy of an m-Health Physical Activity and Sleep Health Intervention for Adults: A Randomized Waitlist-Controlled Trial

INTRODUCTION

Interventions that improve both physical activity and sleep quality may be more effective in improving overall health. The purpose of the Synergy Study is to test the efficacy of a mobile health combined behavior intervention targeting physical activity and sleep quality.

STUDY DESIGN

Randomized, waitlist-controlled trial.

SETTING/PARTICIPANTS

This study had an app-based delivery mode, Australia-wide. The participants were 160 adults who reported insufficient physical activity and poor sleep quality in an eligibility survey.

INTERVENTION

The intervention was a mobile app providing educational resources, goal setting, self-monitoring, and feedback strategies. It included 12 weeks of personalized support including weekly reports, tool sheets, and prompts.

MAIN OUTCOME MEASURES

Outcomes were assessed at baseline, 3 months (primary), and 6 months (secondary endpoint). Self-reported minutes of moderate-to-vigorous intensity physical activity and sleep quality were co-primary outcomes. Resistance training; sitting time; sleep hygiene; sleep timing variability; insomnia severity; daytime sleepiness; quality of life; and depression, anxiety, and stress symptoms were secondary outcomes. Data were collected between June 2017 and February 2018 and analyzed in August 2018.

RESULTS

At 3 months, between-group differences in moderate-to-vigorous intensity physical activity were not statistically significant (p=0.139). Significantly more participants in the intervention group engaged in ≥2 days/week (p=0.004) of resistance training. The intervention group reported better overall sleep quality (p=0.009), subjective sleep quality (p=0.017), sleep onset latency (p=0.013), waketime variability (p=0.018), sleep hygiene (p=0.027), insomnia severity (p=0.002), and lower stress symptoms (p=0.032) relative to waitlist controls. At 6 months, group differences were maintained for sleep hygiene (p=0.048), insomnia severity (p=0.002), and stress symptoms (p=0.006). Differences were observed for bedtime variability (p=0.023), sleepiness (p<0.001), daytime dysfunction (p=0.039), and anxiety symptoms (p=0.003) at 6 months, but not 3 months.

CONCLUSIONS

This remotely delivered intervention did not produce statistically significant between-group differences in minutes of moderate-to-vigorous intensity physical activity. Significant short-term differences in resistance training and short- and medium-term differences in sleep health in favor of the intervention were observed.

TRIAL REGISTRATION

This study is registered at anzctr.org.au ACTRN12617000376347.

Mobile Health Interventions for Physical Activity, Sedentary Behavior, and Sleep in Adults Aged 50 Years and Older: A Systematic Literature Review

We provide a systematic review of interventions utilizing mobile technology to alter physical activity, sedentary behavior, and sleep among adults aged 50 years and older. A systematic search identified 52 relevant articles (randomized control trial [RCT], quasi-experimental, pre/post single-group design). Of 50 trials assessing physical activity, 17 out of 29 RCTs and 13 out of 21 trials assessed for pre/post changes only supported the effectiveness of mobile interventions to improve physical activity, and 9 studies (five out of 10 RCTs and all four pre/post studies) out of 14 reduced sedentary behavior. Only two of five interventions improved sleep (one out of two RCTs and one out of three pre/post studies). Text messaging was the most frequently used intervention (60% of all studies) but was usually used in combination with other components (79% of hybrid interventions included SMS, plus either web or app components). Although more high-quality RCTs are needed, there is evidence supporting the effectiveness of mHealth approaches in those aged 50 years and older.

Participatory Methods to Engage Health Service Users in the Development of Electronic Health Resources: Systematic Review

Background

When health service providers (HSP) plan to develop electronic health (eHealth) resources for health service users (HSU), the latter’s involvement is essential. Typically, however, HSP, HSU, and technology developers engaged to produce the resources lack expertise in participatory design methodologies suited to the eHealth context. Furthermore, it can be difficult to identify an established method to use, or determine how to work stepwise through any particular process.

Objective

We sought to summarize the evidence about participatory methods and frameworks used to engage HSU in the development of eHealth resources from the beginning of the design process.

Methods

We searched for studies reporting participatory processes in initial development of eHealth resources from 2006 to 2016 in 9 bibliographic databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Emcare, Cochrane Library, Web of Science, ACM Guide to Computing Literature, and IEEE Xplore. From 15,117 records initially screened on title and abstract for relevance to eHealth and early participatory design, 603 studies were assessed for eligibility on full text. The remaining 90 studies were rated by 2 reviewers using the Mixed Methods Appraisal Tool Version 2011 (Pluye et al; MMAT) and analyzed with respect to health area, purpose, technology type, and country of study. The 30 studies scoring 90% or higher on MMAT were included in a detailed qualitative synthesis.

Results

Of the 90 MMAT-rated studies, the highest reported (1) health areas were cancer and mental disorders, (2) eHealth technologies were websites and mobile apps, (3) targeted populations were youth and women, and (4) countries of study were the United States, the United Kingdom, and the Netherlands. Of the top 30 studies the highest reported participatory frameworks were User-Centered Design, Participatory Action Research Framework, and the Center for eHealth Research and Disease Management (CeHRes) Roadmap, and the highest reported model underpinning development and engagement was Social Cognitive Theory. Of the 30 studies, 4 reported on all the 5 stages of the CeHRes Roadmap.

Conclusions

The top 30 studies yielded 24 participatory frameworks. Many studies referred to using participatory design methods without reference to a framework. The application of a structured framework such as the CeHRes Roadmap and a model such as Social Cognitive Theory creates a foundation for a well-designed eHealth initiative that ensures clarity and enables replication across participatory design projects. The framework and model need to be clearly articulated and address issues that include resource availability, responsiveness to change, and the criteria for good practice. This review creates an information resource for future eHealth developers, to guide the design of their eHealth resource with a framework that can support further evaluation and development.

Trial Registration

PROSPERO CRD42017053838; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=53838

Experiences of mobile health in promoting physical activity: A qualitative systematic review and meta-ethnography

OBJECTIVE

Despite evidence supporting physical activity in primary and secondary prevention, many individuals do not meet recommended levels. Mobile health is a field with a growing evidence base and is proposed as a convenient method for delivering health interventions. Despite qualitative exploration of stakeholder perspectives, there is a lack of synthesis to inform evidence-based design. This study aims to resolve this by identifying and synthesising qualitative research on the experience of using mobile health applications to promote physical activity.

METHOD

A systematic review focused on qualitative research, mobile health and physical activity was conducted in October 2017 using CINAHL, ERIC, EMBASE, MEDLINE and PsycINFO databases. The protocol was registered with the Prospero database (Registration: CRD42018080610). Results were synthesised as a meta-ethnography.

RESULTS

Fifteen studies were included, covering a variety of populations, including people with diabetes, obesity, and serious mental illness. Five themes emerged: (a) personal factors and the experience of using mobile health, (b) mobile health and changes in thinking that support physical activity, (c) the experience of mobile health features, including prompts, goal setting and gamification, (d) the experience of personalised mobile health and physical activity, (e) technical and user issues in mobile health and their effect on experience.

CONCLUSION

Personal factors and features of the device influenced the experience of using mobile health to support physical activity. The two mechanisms through which mobile health use facilitated physical activity were strengthening of motivation and changes in self-awareness and strategising. Experiences were not entirely unproblematic as technical issues and adverse effects related to self-monitoring were noted. This synthesis provides insight into the experience of mobile health and is useful for researchers and healthcare practitioners interested in designing user-informed mobile health interventions for promoting physical activity.

Creating a Theoretically Grounded, Gamified Health App: Lessons From Developing the Cigbreak Smoking Cessation Mobile Phone Game

Background

Gaming techniques are increasingly recognized as effective methods for changing behavior and increasing user engagement with mobile phone apps. The rapid uptake of mobile phone games provides an unprecedented opportunity to reach large numbers of people and to influence a wide range of health-related behaviors. However, digital interventions are still nascent in the field of health care, and optimum gamified methods of achieving health behavior change are still being investigated. There is currently a lack of worked methodologies that app developers and health care professionals can follow to facilitate theoretically informed design of gamified health apps.

Objective

This study aimed to present a series of steps undertaken during the development of Cigbreak, a gamified smoking cessation health app.

Methods

A systematic and iterative approach was adopted by (1) forming an expert multidisciplinary design team, (2) defining the problem and establishing user preferences, (3) incorporating the evidence base, (4) integrating gamification, (5) adding behavior change techniques, (6) forming a logic model, and (7) user testing. A total of 10 focus groups were conducted with 73 smokers.

Results

Users found the app an engaging and motivating way to gain smoking cessation advice and a helpful distraction from smoking; 84% (62/73) of smokers said they would play again and recommend it to a friend.

Conclusions

A dedicated gamified app to promote smoking cessation has the potential to modify smoking behavior and to deliver effective smoking cessation advice. Iterative, collaborative development using evidence-based behavior change techniques and gamification may help to make the game engaging and potentially effective. Gamified health apps developed in this way may have the potential to provide effective and low-cost health interventions in a wide range of clinical settings.

The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices

BACKGROUND

Sleep disorders have a prevalence of up to 50% and are commonly diagnosed using polysomnography. However, polysomnography requires trained staff and specific equipment in a laboratory setting, which are expensive and limited resources are available. Mobile and wearable devices such as fitness wristbands can perform limited sleep monitoring but are not evaluated well. Here, the development and evaluation of a mobile application to record and synchronize data from consumer-grade sensors suitable for sleep monitoring is presented and evaluated for data collection capability in a clinical trial.

METHODS

Wearable and ambient consumer-grade sensors were selected to mimic the functionalities of clinical sleep laboratories. Then, a modular application was developed for recording, processing and visualizing the sensor data. A validation was performed in three phases: (1) sensor functionalities were evaluated, (2) self-experiments were performed in full-night experiments, and (3) the application was tested for usability in a clinical trial on primary snoring.

RESULTS

The evaluation of the sensors indicated their suitability for assessing basic sleep characteristics. Additionally, the application successfully recorded full-night sleep. The collected data was of sufficient quality to detect and measure body movements, cardiac activity, snoring and brightness. The ongoing clinical trial phase showed the successful deployment of the application by medical professionals.

CONCLUSION

The proposed software demonstrated a strong potential for medical usage. With low costs, it can be proposed for screening, long-term monitoring or in resource-austere environments. However, further validations are needed, in particular the comparison to a clinical sleep laboratory.

A Mobile Health Intervention to Reduce Pain and Improve Health (MORPH) in Older Adults With Obesity: Protocol for the MORPH Trial

BACKGROUND

Chronic pain is a complex, age-related health issue that affects both physical functioning and quality of life. Because the impact of chronic pain is worsened by obesity and inactivity, nonpharmacological interventions that promote movement, reduce sitting, and aid in weight loss are needed to help manage pain symptoms among older adults with chronic pain.

OBJECTIVE

The Mobile Intervention to Reduce Pain and Improve Health (MORPH) pilot trial aims to develop and test the feasibility and acceptability of a novel, patient-centered intervention to reduce chronic pain and improve physical functioning in older adults, leveraging the combination of telecoaching and individually adaptive mHealth tools to decrease both body mass and sedentary behavior.

METHODS

MORPH comprises 2 phases, including a 1-year iterative development phase, and a 1-year pilot randomized controlled trial (RCT). During the development phase, representative participants will engage in one-on-one structured interviews and a 1-week field test. The resulting feedback will be used to guide the development of the finalized MORPH intervention package. During the second phase, the finalized intervention will be tested in a pilot RCT (N=30) in which older adult participants with chronic pain and obesity will be assigned to receive the 12-week MORPH intervention or to a waitlist control. Primary outcomes include self-reported pain symptoms and physical function.

RESULTS

Phase 1 recruitment is ongoing as of December 2017.

CONCLUSIONS

The MORPH intervention brings together a strong body of evidence using group-based behavioral intervention designs with contemporary mHealth principles, allowing for intervention when and where it matters the most. Given the ubiquity of smartphone devices and the popularity of consumer activity and weight monitors, the results of this study may serve to inform the development of scalable, socially driven behavioral pain management interventions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03377634; https://clinicaltrials.gov/ct2/show/NCT03377634 (Archived by WebCite at http://www.webcitation.org/6yj0J5Pan).

REGISTERED REPORT IDENTIFIER

RR1-10.2196/9712.

MÉTODOS PARA DESENVOLVIMENTO DE APLICATIVOS MÓVEIS EM SAÚDE: REVISÃO INTEGRATIVA DA LITERATURA

RESUMO Objetivo: identificar nas publicações nacionais e internacionais indexadas nas bases de dados os principais métodos adotados pelos pesquisadores para o desenvolvimento de aplicativos móveis em saúde. Método: revisão integrativa da literatura de estudos publicados nas bases de dados MEDLINE/PubMed, Scopus, Web of Science, CINAHL e SciELO, no período de 2012 a 2016. Foram selecionados para análise 21 artigos. Resultados: os principais métodos para desenvolvimento de aplicativos móveis na área da saúde descritos nos artigos foram: design instrucional sistemático, design instrucional contextualizado, design centrado no usuário e ciclo de vida de desenvolvimento de sistemas. Conclusão: independentemente do método de desenvolvimento escolhido, as etapas devem ser bem definidas e estruturadas, a fim de que o aplicativo móvel desenvolvido seja útil ao usuário final.

Effectiveness of the mHealth technology in improvement of healthy behaviors in an elderly population-a systematic review

Demographic changes in the 21st century, increased population of the elderly and high prevalence of related diseases call for new healthcare strategies that can change the behavior and lifestyle of elderly individuals. Innovative information and communication technology, such as mobile health (mHealth), can play a significant role. The present study was conducted aiming to assess the effectiveness of mHealth in improving health behaviors among an elderly population. This paper presents a systematic review involving a search of PubMed, Web of Science (ISI), Scopus, Science Direct and Embase databases from [2012-2016]. Our search resulted after initial evaluations 12 articles. Inclusion criteria mostly revolved around interventional studies, other studies were excluded because of their methodology, non-elderly target groups and irrelevant to the subject. Findings showed that mHealth can improve care, self-management, self-efficacy, behavior promotion (quality of sleep, diet, physical activity mental health) and medication adherence. The mHealth technology has proven effective for disease prevention, lifestyle changes, management of cardiovascular disease and diabetes, and is a suitable tool for elderly people. In conclusion, it seems that mHealth can facilitate behavioral changes; although, further research is necessary in this regard.

Mobile Phone Interventions for Sleep Disorders and Sleep Quality: Systematic Review

Background

Although mobile health technologies have been developed for interventions to improve sleep disorders and sleep quality, evidence of their effectiveness remains limited.

Objective

A systematic literature review was performed to determine the effectiveness of mobile technology interventions for improving sleep disorders and sleep quality.

Methods

Four electronic databases (EBSCOhost, PubMed/Medline, Scopus, and Web of Science) were searched for articles on mobile technology and sleep interventions published between January 1983 and December 2016. Studies were eligible for inclusion if they met the following criteria: (1) written in English, (2) adequate details on study design, (3) focus on sleep intervention research, (4) sleep index measurement outcome provided, and (5) publication in peer-reviewed journals.

Results

An initial sample of 2679 English-language papers were retrieved from five electronic databases. After screening and review, 16 eligible studies were evaluated to examine the impact of mobile phone interventions on sleep disorders and sleep quality. These included one case study, three pre-post studies, and 12 randomized controlled trials. The studies were categorized as (1) conventional mobile phone support and (2) utilizing mobile phone apps. Based on the results of sleep outcome measurements, 88% (14/16) studies showed that mobile phone interventions have the capability to attenuate sleep disorders and to enhance sleep quality, regardless of intervention type. In addition, mobile phone intervention methods (either alternatively or as an auxiliary) provide better sleep solutions in comparison with other recognized treatments (eg, cognitive behavioral therapy for insomnia).

Conclusions

We found evidence to support the use of mobile phone interventions to address sleep disorders and to improve sleep quality. Our findings suggest that mobile phone technologies can be effective for future sleep intervention research.

Validation of a Smartphone App for the Assessment of Sedentary and Active Behaviors

BACKGROUND

Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors.

OBJECTIVE

This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA).

METHODS

Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute).

RESULTS

Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA.

CONCLUSIONS

Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors.

Replacing sedentary time with sleep, light, or moderate-to-vigorous physical activity: effects on self-regulation and executive functioning

Recent attention has highlighted the importance of reducing sedentary time for maintaining health and quality of life. However, it is unclear how changing sedentary behavior may influence executive functions and self-regulatory strategy use, which are vital for the long-term maintenance of a health behavior regimen. The purpose of this cross-sectional study is to examine the estimated self-regulatory and executive functioning effects of substituting 30 min of sedentary behavior with 30 min of light activity, moderate-to-vigorous physical activity (MVPA), or sleep in a sample of older adults. This study reports baseline data collected from low-active healthy older adults (N = 247, mean age 65.4 ± 4.6 years) recruited to participate in a 6 month randomized controlled exercise trial examining the effects of various modes of exercise on brain health and function. Each participant completed assessments of physical activity self-regulatory strategy use (i.e., self-monitoring, goal-setting, social support, reinforcement, time management, and relapse prevention) and executive functioning. Physical activity and sedentary behaviors were measured using accelerometers during waking hours for seven consecutive days at each time point. Isotemporal substitution analyses were conducted to examine the effect on self-regulation and executive functioning should an individual substitute sedentary time with light activity, MVPA, or sleep. The substitution of sedentary time with both sleep and MVPA influenced both self-regulatory strategy use and executive functioning. Sleep was associated with greater self-monitoring (B = .23, p = .02), goal-setting (B = .32, p < .01), and social support (B = .18, p = .01) behaviors. Substitution of sedentary time with MVPA was associated with higher accuracy on 2-item (B = .03, p = .01) and 3-item (B = .02, p = .04) spatial working memory tasks, and with faster reaction times on single (B = -23.12, p = .03) and mixed-repeated task-switching blocks (B = -27.06, p = .04). Substitution of sedentary time with sleep was associated with marginally faster reaction time on mixed-repeated task-switching blocks (B = -12.20, p = .07) and faster reaction time on mixed-switch blocks (B = 17.21, p = .05), as well as reduced global reaction time switch cost (B = -16.86, p = .01). Substitution for light intensity physical activity did not produce significant effects. By replacing sedentary time with sleep and MVPA, individuals may bolster several important domains of self-regulatory behavior and executive functioning. This has important implications for the design of long-lasting health behavior interventions. Trial Registration clinicaltrials.gov identifier NCT00438347.

Behavioral Periodicity Detection from 24 h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life

Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data. GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for 64.4 ± 26.2 (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline. Periodograms were constructed to determine patterns emergent from the accelerometer data. Periodicity strength was calculated using circular autocorrelations for time-lagged windows. The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants. Periodicity strength was most consistently associated with LDL-cholesterol (r's = 0.40–0.79, P's < 0.05) and triglycerides (r's = 0.68–0.86, P's < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms. Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes.