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

Deceptively Simple yet Profoundly Impactful: Text Messaging Interventions to Support Health

This paper examines the use of text message (SMS) interventions for health-related behavioral support. It first outlines the historical progress in SMS intervention research publications and the variety of funds from US government agencies. A narrative review follows, highlighting the effectiveness of SMS interventions in key health areas, such as physical activity, diet and weight loss, mental health, and substance use, based on published meta-analyses. It then outlines advantages of text messaging compared to other digital modalities, including the real-time capability to collect information and deliver microdoses of intervention support. Crucial design elements are proposed to optimize effectiveness and longitudinal engagement across communication strategies, psychological foundations, and behavior change tactics. We then discuss advanced functionalities, such as the potential for generative artificial intelligence to improve user interaction. Finally, major challenges to implementation are highlighted, including the absence of a dedicated commercial platform, privacy and security concerns with SMS technology, difficulties integrating SMS interventions with medical informatics systems, and concerns about user engagement. Proposed solutions aim to facilitate the broader application and effectiveness of SMS interventions. Our hope is that these insights can assist researchers and practitioners in using SMS interventions to improve health outcomes and reducing disparities.

Predicting Goal Attainment in Process-Oriented Behavioral Interventions Using a Data-Driven System Identification Approach

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk, thus emphasizing the importance of a personalized approach for optimized behavioral interventions.

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial

Introduction

Forensic psychiatric patients receive treatment to address their violent and aggressive behavior with the aim of facilitating their safe reintegration into society. On average, these treatments are effective, but the magnitude of effect sizes tends to be small, even when considering more recent advancements in digital mental health innovations. Recent research indicates that wearable technology has positive effects on the physical and mental health of the general population, and may thus also be of use in forensic psychiatry, both for patients and staff members. Several applications and use cases of wearable technology hold promise, particularly for patients with mild intellectual disability or borderline intellectual functioning, as these devices are thought to be user-friendly and provide continuous daily feedback.

Method

In the current randomized crossover trial, we addressed several limitations from previous research and compared the (continuous) usability and acceptance of four selected wearable devices. Each device was worn for one week by staff members and patients, amounting to a total of four weeks. Two of the devices were general purpose fitness trackers, while the other two devices used custom made applications designed for bio-cueing and for providing insights into physiological reactivity to daily stressors and events.

Results

Our findings indicated significant differences in usability, acceptance and continuous use between devices. The highest usability scores were obtained for the two fitness trackers (Fitbit and Garmin) compared to the two devices employing custom made applications (Sense-IT and E4 dashboard). The results showed similar outcomes for patients and staff members.

Discussion

None of the devices obtained usability scores that would justify recommendation for future use considering international standards; a finding that raises concerns about the adaptation and uptake of wearable technology in the context of forensic psychiatry. We suggest that improvements in gamification and motivational aspects of wearable technology might be helpful to tackle several challenges related to wearable technology.

Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation

BACKGROUND

Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device-based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings.

OBJECTIVE

The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression.

METHODS

A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression.

RESULTS

The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64).

CONCLUSIONS

This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.

A Comprehensive Review of Behavior Change Techniques in Wearables and IoT: Implications for Health and Well-Being

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

The Use of Mobile Technologies to Promote Physical Activity and Reduce Sedentary Behaviors in the Middle East and North Africa Region: Systematic Review and Meta-Analysis

BACKGROUND

The Middle East and North Africa (MENA) region faces unique challenges in promoting physical activity and reducing sedentary behaviors, as the prevalence of insufficient physical activity is higher than the global average. Mobile technologies present a promising approach to delivering behavioral interventions; however, little is known about the effectiveness and user perspectives on these technologies in the MENA region.

OBJECTIVE

This study aims to evaluate the effectiveness of mobile interventions targeting physical activity and sedentary behaviors in the MENA region and explore users' perspectives on these interventions as well as any other outcomes that might influence users' adoption and use of mobile technologies (eg, appropriateness and cultural fit).

METHODS

A systematic search of 5 databases (MEDLINE, Embase, CINAHL, Scopus, and Global Index Medicus) was performed. Any primary studies (participants of all ages regardless of medical condition) conducted in the MENA region that investigated the use of mobile technologies and reported any measures of physical activity, sedentary behaviors, or user perceptions were included. We conducted a narrative synthesis of all studies and a meta-analysis of randomized controlled trials (RCTs). The Cochrane risk-of-bias tool was used to assess the quality of the included RCTs; quality assessment of the rest of the included studies was completed using the relevant Joanna Briggs Institute critical appraisal tools.

RESULTS

In total, 27 articles describing 22 interventions (n=10, 37% RCTs) and 4 (15%) nonexperimental studies were included (n=6141, 46% women). Half (11/22, 50%) of the interventions included mobile apps, whereas the other half examined SMS. The main app functions were goal setting and self-monitoring of activity, whereas SMS interventions were primarily used to deliver educational content. Users in experimental studies described several benefits of the interventions (eg, gaining knowledge and receiving reminders to be active). Engagement with the interventions was poorly reported; few studies (8/27, 30%) examined users' perspectives on the appropriateness or cultural fit of the interventions. Nonexperimental studies examined users' perspectives on mobile apps and fitness trackers, reporting several barriers to their use, such as perceived lack of usefulness, loss of interest, and technical issues. The meta-analysis of RCTs showed a positive effect of mobile interventions on physical activity outcomes (standardized mean difference=0.45, 95% CI 0.17-0.73); several sensitivity analyses showed similar results. The trim-and-fill method showed possible publication bias. Only 20% (2/10) of the RCTs measured sedentary behaviors; both reported positive changes.

CONCLUSIONS

The use of mobile interventions for physical activity and sedentary behaviors in the MENA region is in its early stages, with preliminary evidence of effectiveness. Policy makers and researchers should invest in high-quality studies to evaluate long-term effectiveness, intervention engagement, and implementation outcomes, which can inform the design of culturally and socially appropriate interventions for countries in the MENA region.

TRIAL REGISTRATION

PROSPERO CRD42023392699; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=392699.

Wearable Devices to Improve Physical Activity and Reduce Sedentary Behaviour: An Umbrella Review

BACKGROUND

Several systematic reviews (SRs), with and without meta-analyses, have investigated the use of wearable devices to improve physical activity, and there is a need for frequent and updated syntheses on the topic.

OBJECTIVE

We aimed to evaluate whether using wearable devices increased physical activity and reduced sedentary behaviour in adults.

METHODS

We conducted an umbrella review searching PubMed, Cumulative Index to Nursing and Allied Health Literature, the Cochrane Library, MedRxiv, Rxiv and bioRxiv databases up to February 5th, 2023. We included all SRs that evaluated the efficacy of interventions when wearable devices were used to measure physical activity in adults aged over 18 years. The primary outcomes were physical activity and sedentary behaviour measured as the number of steps per day, minutes of moderate to vigorous physical activity (MVPA) per week, and minutes of sedentary behaviour (SB) per day. We assessed the methodological quality of each SR using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR 2) and the certainty of evidence of each outcome measure using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations). We interpreted the results using a decision-making framework examining the clinical relevance and the concordances or discordances of the SR effect size.

RESULTS

Fifty-one SRs were included, of which 38 included meta-analyses (302 unique primary studies). Of the included SRs, 72.5% were rated as 'critically low methodological quality'. Overall, with a slight overlap of primary studies (corrected cover area: 3.87% for steps per day, 3.12% for MVPA, 4.06% for SB) and low-to-moderate certainty of the evidence, the use of WDs may increase PA by a median of 1,312.23 (IQR 627-1854) steps per day and 57.8 (IQR 37.7 to 107.3) minutes per week of MVPA. Uncertainty is present for PA in pathologies and older adults subgroups and for SB in mixed and older adults subgroups (large confidence intervals).

CONCLUSIONS

Our findings suggest that the use of WDs may increase physical activity in middle-aged adults. Further studies are needed to investigate the effects of using WDs on specific subgroups (such as pathologies and older adults) in different follow-up lengths, and the role of other intervention components.

A control system model of capability-opportunity-motivation and behaviour (COM-B) framework for sedentary and physical activity behaviours

Objective

Theoretical frameworks are essential for understanding behaviour change, yet their current use is inadequate to capture the complexity of human behaviour such as physical activity. Real-time and big data analytics can assist in the development of more testable and dynamic models of current theories. To transform current behavioural theories into more dynamic models, it is recommended that researchers adopt principles such as control systems engineering. In this article, we aim to describe a control system model of capability-opportunity-motivation and behaviour (COM-B) framework for reducing sedentary behaviour (SB) and increasing physical activity (PA) in adults.

Methods

The COM-B model is explained in terms of control systems. Examples of effective behaviour change techniques (BCTs) (e.g. goal setting, problem-solving and social support) for reducing SB and increasing PA were mapped to the COM-B model for illustration.

Result

A fluid analogy of the COM-B system is presented.

Conclusions

The proposed integrated model will enable empirical testing of individual behaviour change components (i.e. BCTs) and contribute to the optimisation of digital behaviour change interventions.

Day-of-the-Week and Time-of-the-Day Patterns of Sedentary Behavior in the Hispanic Community Health Study/Study of Latinos

PURPOSE

Existing sedentary behavior interventions have largely achieved mixed results. Conventionally, interventions have attempted to reduce sedentary behavior using a full-day approach. An alternative strategy may be to target specific periods during the day and/or week. This study examined the day-of-the-week (Monday to Sunday) and time-of-the-day patterns (3-and 6-h periods) of sedentary behavior among U.S. Hispanics/Latinos adults.

METHODS

Participants ( n = 12,241) from the Hispanic Community Health Study/Study of Latinos, a multisite community-based prospective cohort study of Hispanic/Latino adults, were studied. Sedentary behavior was assessed for 1 wk using a hip-mounted accelerometer through total sedentary time, sedentary time in bouts ≥60 min, and total number of sedentary breaks. The temporal patterns of sedentary behavior metrics were evaluated using linear mixed effect models accounting for the Hispanic Community Health Study/Study of Latinos complex survey design.

RESULTS

There were statistically significant variations in temporal patterns across day-of-the-week and time-of-the-day periods for all three metrics ( P < 0.001). Adults were more sedentary on weekends than on weekdays and most sedentary on Sundays. The time-of-the-day patterns had a U-curve pattern wherein adults were most sedentary late at night, became less sedentary throughout the day, reached peak activeness around noon, then gradually became more sedentary into the evening. These patterns were largely robust across seasonality and most sociodemographic characteristics, including age, employment status, work shift schedule, acculturation, and field center.

CONCLUSIONS

Our findings suggest that early mornings, evenings, and weekends were the more sedentary periods in this cohort of Hispanic/Latino adults, characterized by higher volumes of sedentary time, higher volumes of time in prolonged sedentary bouts, and fewer number of sedentary breaks than other time periods, highlighting important windows of opportunity to reduce sedentary behavior.

Evaluation of Exercise Mobile Applications for Adults with Cardiovascular Disease Risk Factors

OBJECTIVE

To conduct a systematic review to determine if there are exercise mobile applications (apps) that can produce evidence-based, individualized exercise plans.

MATERIALS AND METHODS

We searched the Apple Store and Google Play for exercise apps with terms related to exercise and health. Exercise apps were eligible if they: (1) had a ≥4 out of 5 overall rating with ≥1000 reviews; (2) were free to download; and (3) were not gender specific. Exercise apps were evaluated via the evidence-based exercise prescription (ExRx) standards of the American College of Sports Medicine (ACSM) and American Heart Association. For the exercise app evaluation criteria, an app was included if it (1) was evidence-based; (2) contained a preparticipation health screening protocol; (3) built a cardiovascular disease (CVD) risk factor profile; (4) prioritized one chronic disease or health condition to focus on; (5) framed the exercise plan by the frequency, intensity, time, and type principle (FITT) of ExRx; and (6) specified special considerations.

RESULTS

Of the 531 potentially qualifying apps, 219 qualified. The qualifying apps were rarely evidenced-based (0.5%) or had a preparticipation screening protocol (3.7%). Only 27.7% built CVD risk factor profiles. Most apps (64.8%) focused on body image and/or athletic performance. Only 4.3% focused on chronic diseases or health conditions, while the remainder (34.5%) did not disclose a focus. No app framed the exercise plans by the FITT of ExRx. Only 1.4% of the apps specified special considerations.

CONCLUSION

There are no commercially available exercise apps that generate an evidence-based, individualized exercise plan for adults with CVD risk factors.

System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity

The application of control systems principles in behavioral medicine includes developing interventions that can be individualized to promote healthy behaviors, such as sustained engagement in adequate levels of physical activity (PA). This paper presents the use of system identification and control engineering methods in the design of behavioral interventions through the novel formalism of a control-optimization trial (COT). The multiple stages of a COT, from experimental design in system identification through controller implementation, are illustrated using participant data from Just Walk, an intervention to promote walking behavior in sedentary adults. ARX models for individual participants are estimated using multiple estimation and validation data combinations, with the model leading to the best performance over a weighted norm being selected. This model serves as the internal model in a hybrid MPC controller formulated with three degree-of-freedom (3DoF) tuning that properly balances the requirements of physical activity interventions. Its performance in a realistic closed-loop setting is evaluated via simulation. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial YourMove.

Prediction of cardiac autonomic neuropathy using a machine learning model in patients with diabetes

Background

Cardiac autonomic neuropathy (CAN) is a diabetes-related complication with increasing prevalence and remains challenging to detect in clinical settings. Machine learning (ML) approaches have the potential to predict CAN using clinical data. In this study, we aimed to develop and evaluate the performance of an ML model to predict early CAN occurrence in patients with diabetes.

Methods

We used the diabetes complications screening research initiative data set containing 200 CAN-related tests on more than 2000 participants with type 2 diabetes in Australia. Data were collected on peripheral nerve functions, Ewing's tests, blood biochemistry, demographics, and medical history. The ML model was validated using 10-fold cross-validation, of which 90% were used in training the model and the remaining 10% was used in evaluating the performance of the model. Predictive accuracy was assessed by area under the receiver operating curve, and sensitivity, specificity, positive predictive value, and negative predictive value.

Results

Of the 237 patients included, 105 were diagnosed with an early stage of CAN while the remaining 132 were healthy. The ML model showed outstanding performance for CAN prediction with receiver operating characteristic curve of 0.962 [95% confidence interval (CI) = 0.939-0.984], 87.34% accuracy, and 87.12% sensitivity. There was a significant and positive association between the ML model and CAN occurrence (p < 0.001).

Conclusion

Our ML model has the potential to detect CAN at an early stage using Ewing's tests. This model might be useful for healthcare providers for predicting the occurrence of CAN in patients with diabetes, monitoring the progression, and providing timely intervention.

Leisure-Time and Transport-Related Physical Activity and the Risk of Mortality: The CRONICAS Cohort Study

BACKGROUND

The long-term health association of the leisure-time and transport-related physical activity domains of the International Physical Activity Questionnaire have not been established in Latin American settings. The authors aimed to quantify the 7-year all-cause mortality risk associated with levels of leisure-time and transport-related physical activity.

METHODS

Ongoing prospective cohort study conducted in 4 sites in Peru. People ≥35 years were randomly selected from the general population in each study site. The exposures were leisure-time and transport-related physical activity (International Physical Activity Questionnaire) at baseline. The outcome was all-cause mortality based on information retrieved from national records. Cox regression and sensitivity analyses were conducted.

RESULTS

There were 3601 people (mean age 55.8 y, 51.5% women). Greater levels of physical activity were associated with lower risk of all-cause mortality, an observation confirmed in sensitivity analyses. Compared with those with low levels of physical activity, leisure-time (≥500 metabolic equivalent of task minutes per week) and transport-related (500-1499 and ≥1500 metabolic equivalent of task minutes per week) physical activity were associated with 70% (95% confidence interval, 3%-90%), 43% (95% confidence interval, 18%-61%), and 42% (95% confidence interval, 8%-63%) lower all-cause mortality, respectively.

CONCLUSIONS

Greater levels of leisure-time and transport-related physical activity were associated with a strong reduction in the risk of all-cause mortality across different geographical sites.

What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review

Background: Lifestyle Medicine (LM) aims to address six main behavioral domains: diet/nutrition, substance use (SU), physical activity (PA), social relationships, stress management, and sleep. Digital Health Interventions (DHIs) have been used to improve these domains. However, there is no consensus on how to measure lifestyle and its intermediate outcomes aside from measuring each behavior separately. We aimed to describe (1) the most frequent lifestyle domains addressed by DHIs, (2) the most frequent outcomes used to measure lifestyle changes, and (3) the most frequent DHI delivery methods. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) Extension for Scoping Reviews. A literature search was conducted using MEDLINE, Cochrane Library, EMBASE, and Web of Science for publications since 2010. We included systematic reviews and meta-analyses of clinical trials using DHI to promote health, behavioral, or lifestyle change. Results: Overall, 954 records were identified, and 72 systematic reviews were included. Of those, 35 conducted meta-analyses, 58 addressed diet/nutrition, and 60 focused on PA. Only one systematic review evaluated all six lifestyle domains simultaneously; 1 systematic review evaluated five lifestyle domains; 5 systematic reviews evaluated 4 lifestyle domains; 14 systematic reviews evaluated 3 lifestyle domains; and the remaining 52 systematic reviews evaluated only one or two domains. The most frequently evaluated domains were diet/nutrition and PA. The most frequent DHI delivery methods were smartphone apps and websites. Discussion: The concept of lifestyle is still unclear and fragmented, making it hard to evaluate the complex interconnections of unhealthy behaviors, and their impact on health. Clarifying this concept, refining its operationalization, and defining the reporting guidelines should be considered as the current research priorities. DHIs have the potential to improve lifestyle at primary, secondary, and tertiary levels of prevention-but most of them are targeting clinical populations. Although important advances have been made to evaluate DHIs, some of their characteristics, such as the rate at which they become obsolete, will require innovative research designs to evaluate long-term outcomes in health.