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

Smartwatch-Based Tailored Gamification and User Modeling for Motivating Physical Exercise: Experimental Study With the Maximum Difference Scaling Segmentation Method

BACKGROUND

Smartwatch-based gamification holds great promise for enhancing fitness apps and promoting physical exercise; however, empirical evidence on its effectiveness remains inconclusive, partly due to "one-size-fits-all" design approaches that overlook individual differences. While the emerging research area of tailored gamification calls for more accurate user modeling and better customization of game elements, existing studies have relied primarily on rating scale-based measures and correlational analyses with methodological limitations.

OBJECTIVE

This study aimed to improve smartwatch-based gamification through an innovative user modeling approach to better motivate physical exercise among different user groups with tailored solutions. It incorporated both individual preferences and needs for game elements into the user segmentation process and used the maximum difference scaling (MaxDiff) technique, which can overcome the limitations of traditional methods.

METHODS

With data collected from 2 MaxDiff experiments involving 378 smartwatch users and latent class statistical models, the relative power of each of the 16 popular game elements was examined in terms of what users liked and what motivated them to exercise based on which distinct user segments were identified. Prediction models were also proposed for quickly classifying future users into the right segments to provide them with tailored gamification solutions on smartwatch fitness apps.

RESULTS

We identified 3 segments of smartwatch users based on their preferences for gamification. More importantly, we uncovered 4 segments motivated by goals, immersive experiences, rewards, or social comparison. Such user heterogeneity confirmed the susceptibility of the effects of gamification and indicated the necessity of accurately matching gamified solutions with user characteristics to better change health behaviors through different mechanisms for different targets. Important differences were also observed between the 2 sets of user segments (ie, those based on preferences for game elements vs those based on the motivational effects of the elements), indicating the gap between what people enjoy using on smartwatches and what can motivate them for physical exercise engagement.

CONCLUSIONS

To our knowledge, this study is the first to investigate MaxDiff-based user segmentation for tailored gamification on smartwatches promoting physical exercise and contributes to a detailed understanding of preferences for, and the effectiveness of, different game elements among different groups of smartwatch users. As existing tailored gamification studies continue to explore ways of user modeling with mostly surveys and questionnaires, this study supported the adoption of MaxDiff experiments as an alternative method to better capture user heterogeneity in the health domain and inform the design of tailored solutions for more application types beyond smartphones.

Symptoms of Depression, Physical Activity, and Sedentary Time: Within-Person Relations From Age 6 to 18 in a Birth Cohort

OBJECTIVE

To examine the within-person relations between objectively measured physical activity and clinically assessed symptoms of depressive disorders from childhood to late adolescence.

METHOD

Seven waves of biennially collected data from a Norwegian birth-cohort (n = 873; 53% girls) followed from age 6 to 18 years were used. Accelerometers were used to assess total physical activity, moderate-to vigorous physical activity, and sedentary time, and symptoms of depression were assessed by semi-structured psychiatric interviews. Mediators (athletic self-esteem, body image, sports participation) were captured by questionnaires. Random intercept cross-lagged panel models were estimated to test the within-person relations and their potential mediators.

RESULTS

From ages 14 to 16 and 16 to 18 years, significant within-person relations were found. Decreased levels of total physical activity (PA) and moderate-to-vigorous physical activity (MVPA) predicted more symptoms of depression (PA: 14-16 years: β = -0.08; 16-18 years: β = -0.09; MVPA: 14-16 years: β = -0.10; 16-18 years: β = -0.07). These relations were not evident at earlier ages. From ages 10 to 12 and 14 to 16 years, an increased number of depressive symptoms predicted decreased levels of physical activity (PA: 10-12 years: β = -0.10; 14-16 years: β = -0.14: MVPA: 10-12 years: β = -0.10; 14-16 years: β = -0.17). We found no evidence for within-person relations between sedentary time and depressive symptoms, and no significant sex differences or mediations.

CONCLUSION

Individuals who increase their physical activity levels from middle to late adolescence are less likely to develop symptoms of depression compared to what they otherwise would. Thus, in this age period, physical activity may protect against depressive symptoms.

Digital Physical Activity and Sedentary Behavior Interventions for Community-Living Adults: Umbrella Review

BACKGROUND

Digital interventions hold significant potential for improving physical activity (PA) and reducing sedentary behavior (SB) in adults. Despite increasing interest, there remain surprising gaps in the current knowledge of how best to deliver these interventions, including incorporating appropriate theoretical frameworks and behavior change techniques. Following numerous systematic reviews, there is now significant potential for umbrella reviews to provide an overview of the current evidence.

OBJECTIVE

This umbrella review aimed to explore digital PA and SB interventions for community-living adults across effectiveness, key components, and methodological quality.

METHODS

This review followed the Joanna Briggs Institute framework for umbrella reviews. Key search terms were developed iteratively, incorporating physical and sedentary activity alongside digital interventions. We searched 7 online databases (Web of Science Core Collection, CINAHL, APA PsycINFO, Inspec, the Cochrane Library, MEDLINE [Ovid], and PROSPERO) alongside gray literature databases. Information was extracted and tabulated from each included article on intervention effectiveness, key components, and content acknowledging both the digital and human elements. The study quality was appraised using A Measurement Tool to Assess systematic Reviews 2 (AMSTAR 2). The corrected covered area method was used to assess the overlap of primary studies included in the systematic reviews. All relevant research findings were extracted and reported.

RESULTS

Search terms identified 330 articles, of which 5 (1.5%) met the inclusion criteria. The most common PA outcomes identified were daily steps, moderate-to-vigorous PA, total PA, and PA change. Reviews with meta-analysis reported that digital interventions improved multiple PA outcomes (daily steps, moderate-to-vigorous PA time, and total PA time). However, findings from the remaining systematic reviews were mixed. Similarly, the findings for SB were contrasting. Regarding intervention components, monitor- and sensor-only intervention delivery methods were most frequently implemented. Eleven theoretical frameworks were identified, with social cognitive theory being the most prominent theory. In total, 28 different behavior change techniques were reported, with goal setting, self-monitoring, feedback, and social support being the most frequently used. All 5 systematic reviews were of low or critically low quality, each incorporating unique primary studies (corrected covered area=0%).

CONCLUSIONS

This umbrella review highlights the potential of digital interventions to increase PA and reduce SB among community-living adults. However, the disparate nature of current academic knowledge means potentially efficacious research may not realistically translate to real work impact. Our review identified a lack of consensus around outcomes and components at both individual (eg, difficult to collate and compare findings) and multiple study (poor reported quality of systematic reviews) levels. Collective, concerted action is required to standardize outcomes and improve systematic review reporting to optimize future learning around digital interventions to increase PA and reduce SB in community-living adults, including traditionally overlooked populations, like informal carers.

TRIAL REGISTRATION

PROSPERO CRD42023450773; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023450773.

Fine-tuning Large Language Models in Behavioral Psychology for Scalable Physical Activity Coaching

Personalized, smartphone-based coaching improves physical activity but relies on static, human-crafted messages. We introduce My Heart Counts (MHC)-Coach, a large language model fine-tuned on the Transtheoretical Model of Change. MHC-Coach generates messages tailored to an individual's psychology (their "stage of change"), providing personalized support to foster long-term physical activity behavior change. To evaluate MHC-Coach's efficacy, 632 participants compared human-expert and MHC-Coach text-based interventions encouraging physical activity. Among messages matched to an individual's stage of change, 68.0% (N=430) preferred MHC-Coach-generated messages (P < 0.001). Blinded behavioral science experts (N=2) rated MHC-Coach messages higher than human-expert messages for perceived effectiveness (4.4 vs. 2.8) and Transtheoretical Model alignment (4.1 vs. 3.5) on a 5-point Likert scale. This work demonstrates how language models can operationalize behavioral science frameworks for personalized health coaching, promoting long-term physical activity and potentially reducing cardiovascular disease risk at scale.

A Scoping Review of Physical Activity Interventions Among Sexual Minority Adults: A Call to Action for Future Research

Physical activity (PA) has a variety of well-established benefits for physical and mental health. However, there are PA disparities based on numerous sociodemographic characteristics, including sexual orientation. Mapping of PA interventions tailored to sexual minorities (ie, nonheterosexual) is currently absent from the literature.

PURPOSE

The purpose of this scoping review was to summarize the existing interventions focused on promoting PA among sexual minority (SM) adults.

METHODS

A search strategy was developed in consultation with a research librarian for PubMed, Web of Science, SportDiscus, and CINAHL. The scoping review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. Randomized and nonrandomized interventions that focused on increasing PA among SM adults were included. Data on study design, sample demographics, purpose of research, methodology, intervention components, theory-based constructs used, PA measures, and outcomes of the intervention were extracted.

RESULTS

Our search yielded 7289 articles, with 26 articles requiring full-text review. Four interventions were identified that focused on increasing PA among SM adults, with 2 focusing on lesbian/bisexual women, 1 focusing on lesbian/gay cancer survivors, and 1 focusing on men who have sex with men. Two of the 4 studies were successful at increasing PA, with varying psychosocial theories (ie, social cognitive theory and theory of reasoned action) and intervention strategies (ie, PA counseling, group meetings, and pedometers).

CONCLUSION

Findings of this review demonstrate a clear lack of PA interventions targeting SM adults, limiting the available knowledge required for preventionists, practitioners, and health care professionals to effectively promote PA through behavioral modification among this group.

Multivariable analysis for predicting lower limb muscular strength with a hip-joint exoskeleton

Introduction

Advancements in exercise science have highlighted the importance of accurate muscular strength assessments for optimizing performance and preventing injuries.

Methods

We propose a novel approach to measuring muscular strength in young, healthy individuals using Bot Fit, a hip-joint exoskeleton, during resistance exercises. In this study, we introduced performance metrics to evaluate exercise performance during both short and extended durations of three resistance exercises: squats, knee-ups, and reverse lunges. These metrics, derived from the robot's motor signals and sEMG data, include initial exercise speed, the number of repetitions, and muscle engagement. We compared these metrics against baseline muscular strength, measured using standard fitness equipment such as one-repetition maximum (1RM) and isometric contraction tests, conducted with 30 participants aged 23 to 30 years.

Results

Our results revealed that initial exercise speed and the number of repetitions were significant predictors of baseline muscular strength. Using statistical multivariable analysis, we developed a highly accurate model ( R = 0.884 , adj.R 2 = 0.753 , p-value < 0.001 ) and an efficient model (with all models achieving R > 0.87 ) with strong explanatory power.

Conclusion

This model, focusing on a single exercise (squat) and a key performance metric (initial speed), accurately represents the muscular strength of Bot Fit users across all three exercises. This study expands the application of hip-joint exoskeleton robots, enabling efficient estimation of lower limb muscle strength through resistance exercises with Bot Fit.

Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Wearable devices have the potential to advance health care by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep outcomes for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may affect those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment.

Wearable technology in vascular surgery: Current applications and future perspectives

The COVID-19 pandemic exposed the vulnerabilities of global health care systems, underscoring the need for innovative solutions to meet the demands of an aging population, workforce shortages, and rising physician burnout. In recent years, wearable technology has helped segue various medical specialties into the digital era, yet its adoption in vascular surgery remains limited. This article explores the applications of wearable devices in vascular surgery and explores their potential outlets, such as enhancing primary and secondary prevention, optimizing perioperative care, and supporting surgical training. The integration of artificial intelligence and machine learning with wearable technology further expands its applications, enabling predictive analytics, personalized care, and remote monitoring. Despite the promising prospects, challenges such as regulatory complexities, data security, and interoperability must be addressed. As the digital health movement unfolds, wearable technology could play a pivotal role in reshaping vascular surgery while offering cost-effective, accessible, and patient-centered care.

The Best of Two Worlds to Promote Healthy Cognitive Aging: Definition and Classification Approach of Hybrid Physical Training Interventions

A healthy lifestyle can be an important prerequisite to prevent or at least delay the onset of dementia. However, the large number of physically inactive adults underscores the need for developing and evaluating intervention approaches aimed at improving adherence to a physically active lifestyle. In this regard, hybrid physical training, which usually combines center- and home-based physical exercise sessions and has proven successful in rehabilitative settings, could offer a promising approach to preserving cognitive health in the aging population. Despite its potential, research in this area is limited as hybrid physical training interventions have been underused in promoting healthy cognitive aging. Furthermore, the absence of a universally accepted definition or a classification framework for hybrid physical training interventions poses a challenge to future progress in this direction. To address this gap, this article informs the reader about hybrid physical training by providing a definition and classification approach of different types, discussing their specific advantages and disadvantages, and offering recommendations for future research. Specifically, we focus on applying digital technologies to deliver home-based exercises, as their use holds significant potential for reaching underserved and marginalized groups, such as older adults with mobility impairments living in rural areas.

Age Unplugged: A Brief Narrative Review on the Intersection of Digital Tools, Sedentary and Physical Activity Behaviors in Community-Dwelling Older Adults

This brief narrative review assesses how digital technologies-such as wearables, mobile health apps, and various digital tools such as computers, game consoles, tablets, smartphones, and extended reality systems-can influence sedentary and physical activity behaviors among community-dwelling older adults. Each section highlights the central role of these technologies in promoting active aging through increased motivation, engagement and customized experiences. It underlines the critical importance of functionality, usability and adaptability of devices and confirms the effectiveness of digital interventions in increasing physical activity and reducing sedentary behavior. The sustainable impact of these technologies needs to be further investigated, with a focus on adapting digital health strategies to the specific needs of older people. The research advocates an interdisciplinary approach and points out that such collaborations are essential for the development of accessible, effective and ethical solutions. This perspective emphasizes the potential of digital tools to improve the health and well-being of the aging population and recommends their strategic integration into health promotion and policy making.

Combining various acupuncture therapies with multimodal analgesia to enhance postoperative pain management following total knee arthroplasty: a network meta-analysis of randomized controlled trials

OBJECTIVE

This study aims to evaluate the efficacy and safety of various acupuncture treatments in conjunction with multimodal analgesia (MA) for managing postoperative pain and improving knee function in patients undergoing total knee arthroplasty (TKA), based on the findings from clinical research indicating the potential benefits of acupuncture-related therapies in this context.

METHODS

We searched Web of Science, PubMed, SCI-hub, Embase, Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wanfang Data, and Chinese Scientific Journal Database (VIP) to collect randomized controlled trials of acupuncture-related therapies for post-TKA pain. After independent screening and data extraction, the quality of the included literature was evaluated. The potential for bias in the studies incorporated in the analysis was assessed according to the guidelines outlined in the Cochrane Handbook 5.1. Network meta-analysis (NMA) was conducted using RevMan 5.4 and Stata 16.0 software, with primary outcome measures including visual analog scale (VAS), pain pressure threshold (PPT), hospital for special surgery knee score (HSS), and knee joint range of motion (ROM). Furthermore, the interventions were ranked based on the SUCRA value.

RESULTS

We conducted an analysis of 41 qualifying studies encompassing 3,003 patients, examining the efficacy of four acupuncture therapies (acupuncture ACU, electroacupuncture EA, transcutaneous electrical acupoint stimulation TEAS, and auricular acupoint therapy AAT) in conjunction with multimodal analgesia (MA) and MA alone. The VAS results showed no significant difference in efficacy among the five interventions for VAS-3 score. However, TEAS+MA (SMD: 0.67; 95%CI: 0.01, 1.32) was more effective than MA alone for VAS-7 score. There was no significant difference in PPT score among the three interventions. ACU + MA (SMD: 6.45; 95%CI: 3.30, 9.60), EA + MA (SMD: 4.89; 95%CI: 1.46, 8.32), and TEAS+MA (SMD: 5.31; 95%CI: 0.85, 9.78) were found to be more effective than MA alone for HSS score. For ROM score, ACU + MA was more efficacious than EA + MA, TEAS+MA, and AAT + MA, MA. Regarding the incidence of postoperative adverse reactions, nausea and vomiting were more prevalent after using only MA. Additionally, the incidence of postoperative dizziness and drowsiness following ACU + MA (OR = 4.98; 95%CI: 1.01, 24.42) was observed to be higher compared to that after AAT + MA intervention. Similarly, the occurrence of dizziness and drowsiness after MA was found to be significantly higher compared to the following interventions: TEAS+MA (OR = 0.36; 95%CI: 0.18, 0.70) and AAT + MA (OR = 0.20; 95%CI: 0.08, 0.50). The SUCRA ranking indicated that ACU + MA, EA + MA, TEAS+MA, and AAT + MA displayed superior SUCRA scores for each outcome index, respectively.

CONCLUSION

For the clinical treatment of post-TKA pain, acupuncture-related therapies can be selected as a complementary and alternative therapy. EA + MA and TEAS+MA demonstrate superior efficacy in alleviating postoperative pain among TKA patients. ACU + MA is the optimal choice for promoting postoperative knee joint function recovery in TKA patients. AAT + MA is recommended for preventing postoperative adverse reactions.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/, identifier (CRD42023492859).

Concurrent Validity of Four Activity Monitors in Older Adults

Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere-Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere-Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms.