Fitness Bands Research Articles

Understanding Goal Setting and Behavior Change Mechanics in an Older Adult Sitting Reduction Intervention.

We explored intervention fidelity, participant satisfaction, and the goals and reminder strategies participants chose to reduce sitting. Mixed methods approach leveraging data collected during study coaching and fidelity monitoring. A successful 6-month randomized controlled trial of a sedentary behavior (SB) intervention for adults ≥60years in Washington, USA. N = 283 (140 intervention, 143 attention control); mean age 69, 66% women, 69% Non-Hispanic White. Theory-based SB reduction intervention structured around phone-based health coaching and goal setting. Attention control received equal coaching on non-SB health topics. Coaches tracked all participant goals, and 8% of visits were randomly observed and fidelity coded using a structured template. Participants completed a satisfaction questionnaire at study end. Goals data were qualitatively grouped by reminder strategy and topic. Fidelity and satisfaction data were summarized and compared by study arm using two-sided paired t-tests. Both participants' satisfaction (>90% satisfied, between-group P = .195) and coach fidelity to intervention content and techniques were high (96% sessions set SMART goals, P = .343) across both arms. Intervention participants primarily set goals leveraging outward (e.g., fitness band prompts) and habit (e.g., adding standing to a daily meal) reminder strategies highly tailored to individual preferences and lifestyle. Participants' SB-related goals varied widely, suggesting tailored intervention approaches are important to change sitting behavior, particularly for older adults with chronic conditions.

E-Healthcare System for Stroke Management Using IoT and Machine Learning

Machine learning is one of the hottest topics in e-healthcare and has a big social impact. It is challenging to build and develop an efficient and successful e-healthcare system without machine learning. Machine learning's primary function is to analyse healthcare data, which is gathered from various sources and can be both homogeneous and heterogeneous. Therefore, creating an effective machine learning algorithm that functions with both homogeneous and heterogeneous data is a difficult task. On the other hand, it is noted that a sizable number of health-related products, such as fitness bands, smart watches, and sensors, are being developed, and the majority of people use these items to track their own health. Each person's health data is also gathered by these devices, and a machine learning algorithm is integrated into the data to identify any strange patterns or behaviours. These gadgets capture any aberrant behaviour or pattern, and an alarm message is sent to the individual. Thus, machine learning (ML) presents a promising technique for cutting healthcare device costs and describing doctor-patient relationships at a higher level.

Flame temperature and emissivity distribution measurement of solid propellant based on clustering analysis of multispectral radiation images.

To measure the combustion parameters of a solid propellant, this Letter researches the fitting method for flame temperature and emissivity based on multispectral images and proposes the particle swarm optimization-K-means (PSO-K-means) clustering optimization algorithm of a flame multispectral image. Considering the difference in flame radiation characteristics in different regions, the flame multispectral image is clustered, and spectra in different regions are analyzed and selected in different fitting bands to inverse temperature and emissivity. On this basis, the method is applied to measure solid propellant combustion parameters with different formulations. The measurement shows that the flame temperature is between 1700 and 2100 K, and the emissivity is concentrated in 0.1-0.5. Compared with temperature measurements obtained from tungsten-rhenium thermocouples, the relative deviation of multispectral imaging thermometry is less than 5%. The distribution characteristics of solid propellant combustion parameters with different formulations were analyzed, which provided important data support for evaluating combustion conditions and optimizing solid propellant formulations.

Interface modeling analysis using density functional theory in highly reliable Pt/HfO2/TaOx/Ta self-rectifying memristor

The self-rectifying memristor (SRM) is a promising device prototype for high-density three-dimensional (3D) integration and high-efficiency in-memory computing (IMC) by virtue of its ability to effectively suppress sneak current, simple device structure, and low energy consumption. Theoretically understanding the intrinsic mechanisms of SRM is a matter of concern. Here, we fabricated a Ta/TaOx/HfO2/Pt-stacked SRM exhibiting >103 on/off ratio, rectification ratio, and nonlinearity. The SRM can be repeatedly programmed by more than 106 pulses and demonstrates robust retention and high scalability (∼59 Mbit). A reasonable interface model for this SRM is established based on first-principles calculations. Using self-energy corrected density function theory, we calculate the barrier heights at each interface. Detailed I–V curve fitting and energy band analysis are performed and computationally verified to explain the intrinsic reasons for resistive switching, self-rectifying, and nonlinear behaviors. The work may advance the development of SRM prototype to enable energy-efficient 3D IMC.

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.

Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain. This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events. Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%). Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability. Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

In the ever-evolving realm of technology, the identification of human activities using intelligent devices such as smartwatches, fitness bands, and smartphones has emerged as a crucial area of study. These devices, equipped with inertial sensors, gather a wealth of data and provide insights into users' movements and behaviors. These data not only serve practical purposes, but also hold significant implications for domains such as healthcare and fitness tracking. Traditionally, these devices have been employed to monitor various health metrics such as step counts, calorie expenditure, and real-time blood pressure monitoring. However, recent research has shifted its focus to leveraging the data collected by these sensors for user authentication purposes. This innovative approach involves the utilization of Machine Learning (ML) models to analyze the routine data captured by sensors in smart devices employing ML algorithms, which can recognize and authenticate users based on their unique movement patterns and behaviors. This introduces a paradigm shift from traditional one-time authentication methods to continuous authentication, adding an extra layer of security to protect users against potential threats. Continuous authentication offers several advantages over its conventional counterparts. First, it enhances security by constantly verifying a user's identity through their interaction with the device, thereby mitigating the risk of unauthorized access. Second, it provides a seamless and nonintrusive user experience, eliminating the need for repetitive authentication prompts. Moreover, it offers robust protection against various threats such as identity theft, unauthorized access, and device tampering. The application of continuous authentication extends beyond individual devices and encompasses interconnected systems and networks. This holistic approach ensures a comprehensive security across digital platforms and services. The experiments demonstrate that the logistic regression model achieves an accuracy of 82.32% on the test dataset, highlighting its robustness for binary classification tasks. Additionally, the random forest model outperforms with a 92.18% accuracy, emphasizing its superior capability in handling complex feature interactions. In the study, the sequential neural network achieved an accuracy of 92% on the HAR dataset, outperforming traditional machine learning models by a significant margin. The model also demonstrated robust generalization capabilities with a minimal drop in performance across various cross-validation folds.

Hydrophobic Electron‐Transport Layer for Efficient Tin‐Based Perovskite Solar Cells

AbstractWith excellent biocompatibility, a narrow bandgap, and long thermal carrier lifetime, tin‐based perovskite solar cells (TPSCs) are promising within the solar technology. The fullerene derivative indene‐C60 bisadduct (ICBA) is recognized as the most efficient electron‐transport material for TPSCs, thanks to its suitable band structure. Nevertheless, the limited electron transport capability and susceptibility to moisture penetration of ICBA have hindered the progress of TPSCs. Herein, the study proposes a new hydrophobic electron‐transport layer (ETL) comprising a surface‐anchored non‐fullerene n‐type semiconducting polymer layer, poly (naphthalene diimide‐alt‐dithiophenebezothiadiazole) (PNDI‐BT), in conjunction with ICBA. PNDI‐BT exhibits high electron mobilities, a fitting band structure, robust interaction with Sn‐based perovskites, and exceptional moisture resistance, effectively addressing the shortcomings of ICBA. Consequently, this innovative hydrophobic ETL of PNDI‐BT/ICBA enhances electron transport and protects Sn‐based perovskites from moisture. As a result, the inverted TPSCs with the new hydrophobic ETL achieve an impressive efficiency of 13.90%, surpassing TPSCs with the ICBA layer (12.75%). Moreover, even after 1000 h of storage in ambient atmosphere, the encapsulated TPSC maintains a remarkable 81% of its initial efficiency. This comprehensive study seamlessly integrates the synthesis of non‐fullerene n‐type semiconducting polymer and device fabrication, providing valuable insights into designing cutting‐edge ETL structure for inverted TPSCs.

Observational study protocol for an arrhythmia notification feature

IntroductionScreening for atrial fibrillation (AF) in the general population may help identify individuals at risk, enabling further assessment of risk factors and institution of appropriate treatment. Algorithms deployed on wearable...

MWIoTAuth: Multi-wearable data-driven implicit IoT authentication

Due to the advancement of wearables and the Internet of Things (IoT), end-users are using a range of wearables, such as smartwatches, fitness bands, and smart glasses, to receive a range of services, e.g., bank transactions and access several physical objects, e.g., smart cars/homes. While wearables collect various information, e.g., physiological and behavioral data of an IoT end-user, to provide different services, market-wearables often have no authentication or have knowledge-based authentications. This limitation brings additional security challenges. However, similar types of data, e.g., heart rate (source: both Wellue and Fitbit), or different types of data, e.g., oxygen saturation values (source: Wellue) and calorie burn (source: Fitbit), obtained from multiple IoT-connected wearables could contain complementary information (due to positional variation of different wearables on different body parts), which can be helpful in uniquely identifying a user. Therefore, in this work, we propose an implicit IoT end-user authentication (mWIoTAuth) approach utilizing data from two market wearables, i.e., Wellue (providing heart rate and oxygen saturation values) and Fitbit (providing heart rate, calorie burn, and step count). From our detailed analysis of a 2-phase study conducted with two separate cohorts of 40 subjects wearing Wellue and Fitbit in their daily life for continuous 8 h, we find that models developed from two wearables have 5%–14% higher accuracy and F1 score compared to the single-wearable models. These findings show the promise to develop multi-wearable implicit authentication for end-users to secure the IoT world accessible via wearables.

Integration of real-world data into clinical data for better personalised treatment of breast-cancer patients

REBECCA is 4-year project funded under the European Union’s Horizon 2020 research and innovation programme. The project aims to tap into the potential of Real World Data to provide personalised treatment and care to breast cancer patients or cancer survivors. The ultimate goals of REBECCA are to advance clinical research by moving beyond randomised controlled trials and to improve post-treatment in breast cancer to increase patients’ quality of life. 
 REBECCA stands for REsearch on BrEast Cancer induced chronic conditions supported by Causal Analysis of multi-source data. The project is built around two axes: 1) clinical research by analysing data to better understand how treatment affects quality of life, and 2) patient management to improve interventions and care choices at the individual level. 
 Currently, randomised controlled trials (RCT) are the gold standard in clinical research. However, there are several limitations of using RCTs for complex chronic conditions such as breast cancer as patients may suffer from different conditions that are influenced by various variables. Moreover, RCTs usually only include a small population sample and researchers do not have control over the independent variables. REBECCA aims to move beyond RCTs by using real-world, observational data. 
 Real-World Data in REBECCA is collected through sensor and log data retrieved via electronic health records and innovative technical tools developed by the project consortium such as mobile apps and wearable devices (e.g. smartwatches or fitness bands). Patients’ real-life behaviour can thus be monitored, including their physical activity, eating habits, sleep, local environment and information related to their online interaction. Once collected, the real-world data is then processed to extract indicators, which are consequently used to infer causal relationships with PROMS and complex chronic conditions. The causal modelling then indicates causalities and sheds light on confounders.
 Causal modelling will enable health managers and practitioners to measure the safety and effectiveness of breast cancer treatments and measure their impact on the quality of life after breast cancer treatment, to improve clinical outcomes and PROMs, and to help develop personalised recommendations and treatments based on the data collected by each patient, thus increasing the value for patients. 
 The proposed oral poster session aims to demonstrate through evidence how innovative digital tools collecting patients’ data from informal life settings in a non-invasive manner can be integrated to clinical data gathered in formal care settings and how these different sets of data together can support the provision of personalised treatment and care for breast cancer patients. This integrated care method also highlights the benefits of detailed patient monitoring and individualised patient lifestyle consultations in patient care workflows. "

Machine learning-based mapping of band gaps for metal halide perovskites

Recently, perovskite solar cells (PSCs) have received great attentions as the most promising candidate for the next generation solar cells; where the halide perovskite with ABX3 stoichiometry are playing as key components. As a key relevant parameter for various applications of perovskites, the band gap can be modified and optimized by tuning their compositions. In order to enhance the screening efficiency of perovskites with appropriate band gap range for particular applications, this work developed a mapping strategy for band gap range classification through data-driven selected features. This work demonstrates that the proposed features, and further the developed band gap maps, are able to offer a useful initial guiding principle for screenings of potential halide perovskites with fitting band gap ranges and provide opportunities for their compositional design.

In vitro assessment of inflammatory skin potential of poly(methyl methacrylate) at non-cytotoxic concentrations.

Poly(methyl methacrylate) (PMMA) is considered an attractive substrate material for fabricating wearable skin sensors such as fitness bands and microfluidic devices. Despite its widespread use, inflammatory and allergic responses have been attributed to the use of this material. Therefore, the main objective of this study was to obtain a comprehensive understanding of potential biological effects triggered by PMMA at non-cytotoxic concentrations using in vitro models of NIH3T3 fibroblasts and reconstructed human epidermis (RhE). It was hypothesized that concentrations that do not reduce cell viability are sufficient to activate pathways of inflammatory processes in the skin. The study included cytotoxicity, cell metabolism, cytokine quantification, histopathological, and gene expression analyses. The NIH3T3 cell line was used as a testbed for screening cell toxicity levels associated with the concentration of PMMA with different molecular weights (MWs) (i.e., MW ~5,000 and ~15,000 g/mol). The lower MW of PMMA had a half-maximal inhibitory concentration (IC50 ) value of 5.7 mg/cm2 , indicating greater detrimental effects than the higher MW (IC50 = 14.0 mg/cm2 ). Non-cytotoxic concentrations of 3.0 mg/cm2 for MW ~15,000 g/mol and 0.9 mg/cm2 for MW ~5,000 g/mol) induced negative metabolic changes in NIH3T3 cells. Cell viability was severely reduced to 7% after the exposure to degradation by-products generated after thermal and photodegradation degradation of PMMA. PMMA at non-cytotoxic concentrations still induced overexpression of pro-inflammatory cytokines, chemokines, and growth factors (IL1B, CXCL10, CCL5, IL1R1, IL7, IL17A, VEGFA, FGF2, IFNG, IL15) on the RhE model. The inflammatory response was also supported by histopathological and gene expression analyses of PMMA-treated RhE, indicating tissue damage and gene overexpression. Results suggested that non-cytotoxic concentrations of PMMA (3.0 to 5.6 mg/cm2 for MW ~15,000 g/mol and 0.9 to 2.1 mg/cm2 for MW ~5,000 g/mol) were sufficient to negatively alter NIH3T3 cells metabolism and activate inflammatory events in the RhE skin.

An Examination of WHOOP Fitness Bands in College Athletics

Wearable technology is novice and beneficial in its ability to retrieve real-time data with minimal hindrance to the performance and mobility of its users. WHOOP fitness band capture physiological metrics and turns them into three metrics: strain, sleep, and recovery. These metrics allow for personalized and monitoring of training metrics to help guide workload. WHOOP bands were distributed to 56 division II collegiate athletes participating in three sports: softball (15), women's lacrosse (15), and baseball (25). Women’s Lacrosse indicated a negative correlation between the hours of sleep players received the day before a game and their respiratory rate during their game (r = -0.55) and a positive correlation between the hours of exercise the day before a game and the respiratory between hours of sleep the day before and respiratory rate the day of the game (r = -0.46) and a moderate negative correlation between hours of exercise the day before a game and the respiratory rate the day of the game (r = -0.55). Baseball showed indicated a negative correlation between sleep quality the night before the game and duration of exercise the day of the game (r = -0.61). In our examination of performance metrics for three Division II athletic teams using WHOOP fitness strap, it was found that variables differed between teams. Coaches can use WHOOP fitness bands to assist in training and recovering by monitoring pre-game training load, intensity, and sleep.

Unlock digital health promotion in LMICs to benefit the youth.

As digital technologies such as smartphones and fitness bands become more ubiquitous, individuals can engage in self-monitoring and self-care, gaining greater control over their health trajectories along the life-course. These technologies appeal particularly to young people, who are more familiar with digital devices. How this digital transformation facilitates health promotion is therefore a topic of animated debate. However, most research to date focuses on the promise and peril of digital health promotion (DHP) in high-income settings, while DHP in low- and middle-income countries (LMICs) remain largely unexplored. This narrative review aims to fill this gap by critically examining key ethical challenges of implementing DHP in LMICs, with a focus on young people. In the existing literature, we identified potential impediments as well as enabling conditions. Aspects to consider in unlocking the potential of DHP include (1) addressing the digital divide and structural injustice in data-related practices; (2) engaging the target population and responding to their specific needs given their economic, cultural, and social contexts; (3) monitoring the quality and impact of DHP over time; and (4) improving responsible technology governance and its implementation. Addressing these concerns could result in meaningful health benefits for populations lacking access to more conventional healthcare resources.

Efficiency of highintensity magnetic therapy in combination with kinesiotherapy on a balancing platform in patients with myogenic cervicalgia in the subacute period of rehabilitation

In recent years, a physical inactivity of able-bodied people has spread, in particular, due to an increase in the time they spend online both due to the development of digital technologies and due to forced isolation due to viral infection, military operations, etc. Therefore, myogenic cervicalgia in people with hypotonia of the muscles of the axial skeleton is becoming more and more common. All this determines the need to improve the physical therapy of people with myogenic cervicalgia based on the principle of an integrated approach. It is possible to assume an increase in the effectiveness of kinesiotherapy on simple simulators in combination with the analgesic effect of physiotherapy. Hence, the use of physical exercises on a balancing platform with free weights to increase muscle tone, eliminate pain and restore proprioception and coordination should be effective in the process of physical rehabilitation of such patients. The purpose of the research was to study the effectiveness of therapeutic exercises on a balancing platform with gymnastics of the muscles cervical spine stabilizers in combination with high-intensity magnetic therapy in patients with myogenic cervicalgia. The study lasted 2 months. 42 patients (25 women and 17 men) aged 25–45 years, who were divided into two groups: main (MG) and control (CG), 21 people in each group. High-intensity magnetotherapy was performed in MG before therapeutic exercises, in contrast to CG patients. The patients performed therapeutic gymnastics on the balancing platform Stein Massage Balance Platform with free weights (with dumbbells and fitness bands) in combination with exercises for the muscles of the stabilizers of the cervical spine (long muscles of the neck and head, anterior and lateral rectus muscles of the head, and also anterior, middle and posterior scalene muscles). According to the results of the study, we concluded that the effectiveness of kinesiotherapy on a balancing platform and exercises for the stabilizer muscles in patients with myogenic cervicalgia was confirmed, but high-intensity magnetic therapy before therapeutic exercises promotes more active regression of symptoms. Keywords: physical therapy, pain in neck muscles, proprioception, coordination.

Harnessing personal smart tools for enhanced STEM education: exploring IoT integration

In the realm of STEM education, various computer-based methodologies have been employed to invigorate student motivation, personalize learning experiences, and elevate the overall quality of education. However, the integration of Internet of Things (IoT) and smart tools for measuring parameters during educational research processes has garnered insufficient attention thus far. This neglect becomes all the more pronounced in light of the burgeoning popularity of personal smartwatches and bands among individuals. In this study, we develop innovative techniques for incorporating personal smart tools into STEM classes and research endeavors. By employing the Colmi Land 1, Xiaomi Mi Band, Samsung Smart Fitness Band, and Xiaomi Mi Smart Scale as testbeds, we conduct a comprehensive evaluation using the As Is – To Be Business Process Model and Notation method. The findings substantiate that our proposed methods exhibit superior efficacy compared to conventional educational processes. Moreover, this paper represents a pioneering effort in describing and offering ready-to-use techniques for utilizing personalized smart tools to measure parameters during experiments within educational contexts.

Efficacy of Bluetooth-Based Data Collection for Road Traffic Analysis and Visualization Using Big Data Analytics

Effective management of daily road traffic is a huge challenge for traffic personnel. Urban traffic management has come a long way from manual control to artificial intelligence techniques. Still real-time adaptive traffic control is an unfulfilled dream due to lack of low cost and easy to install traffic sensor with real-time communication capability. With increasing number of on-board Bluetooth devices in new generation automobiles, these devices can act as sensors to convey the traffic information indirectly. This paper presents the efficacy of road-side Bluetooth scanners for traffic data collection and big-data analytics to process the collected data to extract traffic parameters. Extracted information and analysis are presented through visualizations and tables. All data analytics and visualizations are carried out off-line in R Studio environment. Reliability aspects of the collected and processed data are also investigated. Higher speed of traffic in one direction owing to the geometry of the road is also established through data analysis. Increased penetration of smart phones and fitness bands in day to day use is also established through the device type of the data collected. The results of this work can be used for regular data collection compared to the traditional road surveys carried out annually or bi-annually. It is also found that compared to previous studies published in the literature, the device penetration rate and sample size found in this study are quite high and very encouraging. This is a novel work in literature, which would be quite useful for effective road traffic management in future.

Security Risks and User Perception towards Adopting Wearable Internet of Medical Things

The Wearable Internet of Medical Things (WIoMT) is a collective term for all wearable medical devices connected to the internet to facilitate the collection and sharing of health data such as blood pressure, heart rate, oxygen level, and more. Standard wearable devices include smartwatches and fitness bands. This evolving phenomenon due to the IoT has become prevalent in managing health and poses severe security and privacy risks to personal information. For better implementation, performance, adoption, and secured wearable medical devices, observing users' perception is crucial. This study examined users' perspectives of trust in the WIoMT while also exploring the associated security risks. Data analysed from 189 participants indicated a significant variance (R2 = 0.553) on intention to use WIoMT devices, which was determined by the significant predictors (95% Confidence Interval; p < 0.05) perceived usefulness, perceived ease of use, and perceived security and privacy. These were found to have important consequences, with WIoMT users intending to use the devices based on the trust factors of usefulness, easy to use, and security and privacy features. Further outcomes of the study identified how users' security matters while adopting the WIoMT and provided implications for the healthcare industry to ensure regulated devices that secure confidential data.

The Feasibility of a Text-Messaging Intervention Promoting Physical Activity in Shift Workers: A Process Evaluation

Workplace health promotion programs (WHPPs) can improve shift workers' physical activity. The purpose of this paper is to present the process evaluation of a text messaging health promotion intervention for mining shift workers during a 24-day shift cycle. Data collected from intervention participants with a logbook (n = 25) throughout the intervention, exit interviews (n = 7) and online surveys (n = 17) examined the WHPP using the RE-AIM (Reach, Efficacy, Adoption, Implementation and Maintenance) framework. The program reached 66% of workers across three departments, with 15% of participants dropping out. The program showed the potential to be adopted if the recruitment strategies are improved to reach more employees, especially when involving work managers for recruitment. A few changes were made to the program, and participant adherence was high. Facilitators to adopt and implement the health promotion program included the use of text messaging to improve physical activity, feedback on behaviour, and providing incentives. Work-related fatigue was reported as a barrier to implementing the program. Participants reported that they would recommend the program to other workers and use the Mi fitness band to continue monitoring and improving their health behaviour. This study showed that shift workers were optimistic about health promotion. Allowing for long-term evaluation and involving the company management to determine scale-up should be considered for future programs.

It’s Cool to be Healthy! The Effect of Perceived Coolness on the Adoption of Fitness Bands and Health Behaviour

Contemporary technology success is frequently associated with the competitive advantage of being cool. A fitness band is one of the smart wearable devices promoting health behaviours, which is one of the cool lifestyle trends in modern societies. Although past research established the profound effects of coolness on user technology acceptance, the influencing role in fostering health behaviour remained obscure. To bridge the existing literature gap, the current study aims to examine the perception of coolness as a higher-order construct with multiple dimensions, namely originality, attractiveness, and sub-cultural appeals, by investigating the direct effect on fitness band adoption and indirect influence on users’ health behaviour. An online survey was conducted on 280 fitness band users, and the data was subsequently analysed via the Partial Least Squares-Structural Equation Modeling (PLS-SEM). The study results demonstrated that the perceived coolness of fitness bands significantly affects users’ device adoption levels, which subsequently influence personal health behaviour. This study thus contributes to health communication research by testing the coolness concept and developing the diffusioninnovation framework from current human-computer interaction literature. The findings would guide future developers of fitness bands to emphasise the coolness functions for higher degrees of adoption and positive impact on society.