How Self-tracking and the Quantified Self Promote Health and Well-being: Systematic Review

Exploring the role of personal quantification in alleviating generalized anxiety disorder among Chinese PhD students: A cross-sectional study

Personal quantification plays a crucial role in preserving individual mental health. However, in previous research, its effectiveness in alleviating generalized anxiety disorder (GAD) has not been conclusively established. This study explores the impact of personal quantification on GAD among PhD students. The research data was obtained through questionnaires distributed to 308 PhD students across universities in China. Among these students, 118 anxiety-free participants were excluded, yielding valuable data from 190 students with GADs. We employed Python programming language and SPSS software for the empirical analysis. The results illustrated that personal quantification significantly and negatively impacted GAD (β = -0.148, P = .002), concurrently producing a significantly positive effect on self-efficacy (β = 0.359, P < .001). Further analysis showed that through 5000 sampling iterations and a 95% confidence level, self-efficacy significantly reduced certain symptoms of GAD (β = -0.1183; P = .026; 95% Cl: -0.2222 to -0.0144). Moreover, when the coefficient of self-efficacy was significantly negative, the impact of personal quantification on GAD remained statistically significant (β = -0.1056; P = .033; 95% Cl: -0.2025 to -0.0087). The findings indicated that personal quantification has a significant role in alleviating GAD among PhD students, which is partly mediated through self-efficacy. This study contributes valuable insights to the nonpharmacological alleviation of GAD in Chinese PhD students.

Teleneurology and Artificial Intelligence in Clinical Practice

ABSTRACT

As teleheath becomes integrated into the practice of medicine, it is important to understand the benefits, limitations, and variety of applications. Telestroke was an early example of teleneurology that arose from a need for urgent access to neurologists for time-sensitive treatments for stroke. It made a scarce resource widely available via video conferencing technologies. Additionally, applications such as outpatient video visits, electronic consultation (e-consult), and wearable devices developed in neurology, as well. Telehealth dramatically increased during the COVID-19 pandemic when offices were closed and hospitals were overwhelmed; a multitude of both outpatient and inpatient programs developed and matured during this time. It is helpful to explore what has been learned regarding the quality of telehealth, disparities in care, and how artificial intelligence can interact with medical practices in the teleneurology context.

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

BACKGROUND

Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

OBJECTIVE

This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

METHODS

A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

RESULTS

The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

CONCLUSIONS

PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/39389.

A Taxonomy for Health Information Systems

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.

Modelling the significance of value-belief-norm framework to predict mass adoption potentials of internet of things-enabled wearable fitness devices

Self-health monitoring technologies have become increasingly popular in averting unanticipated health complications. However, the adoption rate of such technologies in developing countries is surprisingly low. Furthermore, empirical studies on the application of the value-belief-norm (VBN) model to elucidate intention to use IoT-enabled wearable fitness devices (IoT-enabled WFDs) are scarce. This study aimed to expand the VBN model by integrating health values, health consciousness, health knowledge-seeking, and social norms as influencing constructs. The proposed holistic framework was empirically tested to examine these constructs on users' decision-making process of adopting IoT-enabled WFDs. A web-based survey involving 866 adults in China aged 18-30 years was conducted. The gathered data were analysed using partial least squares-structural equation modeling. The results revealed the significant influence of health consciousness and health knowledge-seeking on personal health beliefs, as well as the favourable impact of personal health beliefs on personal norms and awareness of consequences. The results further demonstrated the substantial influence of awareness of consequences and ascription of responsibilities on personal norms. Besides that, personal norms and societal norms were found to have strong influence on the intention to adopt IoT-enabled WFDs, which was revealed to have significant influence on the actual usage. This study's findings offer novel theoretical insights on the behavioural characteristics of adopting IoT-enabled WFDs and serve as a practical guideline for industry experts and marketers to establish appropriate marketing strategies to support the IoT-enabled wearable sector. The findings also benefit policymakers in their efforts of developing strategies that emphasise the unique benefits of self-healthcare monitoring to encourage active lifestyle and decrease obesity and overweight-related health risks.

Co-design personal sleep health technology for and with university students

University students often experience sleep disturbances and disorders. Personal digital technologies present a great opportunity for sleep health promotion targeting this population. However, studies that engage university students in designing and implementing digital sleep health technologies are scarce. This study sought to understand how we could build digital sleep health technologies that meet the needs of university students through a co-design process. We conducted three co-design workshops with 51 university students to identify design opportunities and to generate features for sleep health apps through workshop activities. The generated ideas were organized using the stage-based model of self-tracking so that our findings could be well-situated within the context of personal health informatics. Our findings contribute new design opportunities for sleep health technologies targeting university students along the dimensions of sleep environment optimization, online community, gamification, generative AI, materializing sleep with learning, and personalization.

Telehealth Education in Allied Health Care and Nursing: Web-Based Cross-Sectional Survey of Students' Perceived Knowledge, Skills, Attitudes, and Experience

BACKGROUND

The COVID-19 pandemic has highlighted the growing relevance of telehealth in health care. Assessing health care and nursing students' telehealth competencies is crucial for its successful integration into education and practice.

OBJECTIVE

We aimed to assess students' perceived telehealth knowledge, skills, attitudes, and experiences. In addition, we aimed to examine students' preferences for telehealth content and teaching methods within their curricula.

METHODS

We conducted a cross-sectional web-based study in May 2022. A project-specific questionnaire, developed and refined through iterative feedback and face-validity testing, addressed topics such as demographics, personal perceptions, and professional experience with telehealth and solicited input on potential telehealth course content. Statistical analyses were conducted on surveys with at least a 50% completion rate, including descriptive statistics of categorical variables, graphical representation of results, and Kruskal Wallis tests for central tendencies in subgroup analyses.

RESULTS

A total of 261 students from 7 bachelor's and 4 master's health care and nursing programs participated in the study. Most students expressed interest in telehealth (180/261, 69% very or rather interested) and recognized its importance in their education (215/261, 82.4% very or rather important). However, most participants reported limited knowledge of telehealth applications concerning their profession (only 7/261, 2.7% stated profound knowledge) and limited active telehealth experience with various telehealth applications (between 18/261, 6.9% and 63/261, 24.1%). Statistically significant differences were found between study programs regarding telehealth interest (P=.005), knowledge (P<.001), perceived importance in education (P<.001), and perceived relevance after the pandemic (P=.004). Practical training with devices, software, and apps and telehealth case examples with various patient groups were perceived as most important for integration in future curricula. Most students preferred both interdisciplinary and program-specific courses.

CONCLUSIONS

This study emphasizes the need to integrate telehealth into health care education curricula, as students state positive telehealth attitudes but seem to be not adequately prepared for its implementation. To optimally prepare future health professionals for the increasing role of telehealth in practice, the results of this study can be considered when designing telehealth curricula.

The impact of self-monitoring physical and mental health via an mHealth application on postpartum weight retention: Data from the INTER-ACT RCT

Background

Postpartum weight retention (PPWR) has many health risks. Digital self-monitoring of weight can potentially make postpartum weight management easier. We aim to test to what extent the self-monitoring of weight, steps and mental health through an mHealth application increases postpartum weight loss and reduces the odds of substantial PPWR (≥5 kg).

Methods

Participants were mothers in the intervention arm of the INTER-ACT multicenter randomized controlled trial (RCT), an inter-pregnancy lifestyle intervention among mothers with excessive gestational weight gain. Participants (n=288) had access to an mHealth application to log their weight, steps and mental health between 6 weeks and 6 months postpartum. A linear multiple regression model and a logistic regression model were run to test to what extent self-monitoring via the app increases postpartum weight loss and reduces the risk of substantial PPWR.

Results

Women who logged their weight more often lost more weight (B=0.03, β=0.26, CIB =[0.01,0.05], P<0.01), and had reduced odds of substantive PPWR (OR=0.99, CIOR =[0.98, 0.999], P<.05). Mental health logging reduced the odds of substantive PPWR (OR=0.98, CIOR =[0.97, 1.00], P<0.05), but was unrelated to the amount of weight loss. Steps logging was unrelated to either weight loss or substantive PPWR.

Conclusion

Mothers with excessive gestational weight gain can benefit from app-based lifestyle interventions to reduce PPWR by self-monitoring their weight. More attention to mental health in PPWR interventions is needed.

Backfire effects of performance quantification on stress and disidentification: The role of metadehumanization in organizations, sport, and social networks

Quantification, that is, the shaping of human environments in numerical terms, is so widespread in contemporary societies that it has contaminated almost all spheres of human life. We explore the links between performance quantification and individuals' feelings of being treated in a dehumanized way, that is, metadehumanization. We present an integrative research that assessed the relationships between performance quantification, metadehumanization, and on two of metadehumanization's consequences, that is, stress and disidentification, in three contexts, that is, organizations, sport, and social networks. In addition, we test the moderating roles of two individual variables, that is, competitiveness and tender-mindedness, in this model. In three samples (Ns = 204, 300, 297, for Samples A, B, and C, respectively), we show a mediation effect of metadehumanization on the links between performance quantification and stress and disidentification that holds despite of contextual variations. Unexpectedly, our two moderated mediation hypotheses did not hold or showed inconsistent effects across samples.

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care

BACKGROUND

Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.

OBJECTIVE

The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.

METHOD

A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.

RESULTS

The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.

CONCLUSION

HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

The Efficacy of an mHealth App in Facilitating Weight Loss Among Japanese Fitness Center Members: Regression Analysis Study

BACKGROUND

Self-tracking smartphone apps have emerged as promising tools to encourage healthy behaviors. In this longitudinal study, we used gym use data from members of a major fitness club that operates gyms throughout Japan from January 2014 to December 2019.

OBJECTIVE

Our objective was to assess the extent to which a health and fitness self-tracking mobile app introduced to gym members on January 1, 2018, contributed to their weight loss. The app allows users to input information regarding diet, sleep, weight, and gym exercise so that they can receive personalized feedback from an artificial intelligence chatbot to improve their health behaviors.

METHODS

We used linear regression to quantify the association between app use and weight loss. The primary outcome of the study was the weight loss achieved by each gym user, which was calculated as the difference between their initial and final weights in kilograms, as recorded in the app. Individuals who did not attend the gym or failed to use the mobile app at least twice during the study period were excluded from the analysis. The model accounted for age, gender, distance between the gym and the member's residence, average weekly number of times a member used the gym, user's gym membership length in weeks, average weekly number of times a member input information into the app, and the number of weeks that the app was used at least once.

RESULTS

Data from 26,589 participants were analyzed. Statistically significant associations were detected between weight loss and 2 metrics related to app use: the average weekly frequency of use and the total number of weeks in which the app was used at least once. One input per week was found to be associated with a loss of 62.1 (95% CI 53.8-70.5) g, and 1 week of app use was associated with 21.7 (95% CI 20.5-22.9) g of weight loss from the day of the first input to that of the final input to the app. Furthermore, the average number of times that a member used the gym weekly was also shown to be statistically significantly associated with weight loss: 1 use per week was associated with 255.5 (95% CI 228.5-282.6) g of weight loss.

CONCLUSIONS

This empirical study demonstrated a significant association between weight loss among gym members and not only the frequency of weekly gym use but also the use of a health and fitness self-tracking app. However, further work is needed to examine the mechanisms through which mobile apps affect health behaviors and to identify the specific app features that are most effective in promoting weight loss.

Personal Health Tracking: A Paradigm Shift in the Self-Care Models in Nursing

The rapidly evolving digital health landscape necessitates updates to existing self-care models in nursing. This viewpoint paper revisits and evaluates prevalent models, recognizing their comprehensive exploration of self-care concepts while also identifying a gap in the incorporation of personal informatics. It underscores the missing link of human-technology interplay, an essential aspect in understanding self-care practices within digital generations. The author delineates the role of personal health tracking in self-care and the achievement of desired health outcomes. Based on these insights, the author proposes a refined, digitized self-care model that incorporates mobile health (mHealth) technologies and self-tracking behaviors. The paper concludes by advocating the application of this model for future mHealth nursing interventions, providing a framework for facilitating patient self-care and improving health and well-being in the era of digital health.

A review of the utilization of digital health technologies for college students with disabilities

OBJECTIVES

The purpose of this study was to understand current research on the utilization of mobile health (mHealth) technologies for college students with disabilities.

METHODS

We conducted a bibliometric analysis to understand the longitudinal research trends and dominant topics in mHealth research for college students. Next, we performed a scoping review to gain a more in-depth understanding of the current research on the use of mobile technologies for college students with disabilities.

RESULTS

Despite the increasing number of publications on the development of mobile health applications and mHealth interventions for college students, we found only five studies on disabilities. Most previous studies discussed mental health problems, and we could not find any research utilizing mHealth technologies for college students with physical disabilities.

CONCLUSION

Due to a lack of scientific evidence on the digitalized self-care of college students with disabilities, future studies focusing on this minority population are needed.

Liquid Health. Medicine in the age of surveillance capitalism

Digital health technologies transform practices, roles, and relationships in medicine. New possibilities for a ubiquitous and constant data collection and the processing of data in real-time enable more personalized health services. These technologies might also allow users to actively participate in health practices, thus potentially changing the role of patients from passive receivers of healthcare to active agents. The crucial driving force of this transformation is the implementation of data-intensive surveillance and monitoring as well as self-monitoring technologies. Some commentators use terms like revolution, democratization, and empowerment to describe the aforementioned transformation process in medicine. The public debate as well as most of the ethical discourse on digital health tends to focus on the technologies themselves, mostly ignoring the economic framework of their design and implementation. Analyzing the transformation process connected to digital health technologies needs an epistemic lens that also considers said economic framework, which I argue is surveillance capitalism. This paper introduces the concept of liquid health as such an epistemic lens. Liquid health is based on Zygmunt Bauman's framing of modernity as a process of liquefaction that dissolves traditional norms and standards, roles, and relations. By using liquid health as an epistemic lens, I aim to show how digital health technologies reshape concepts of health and illness, change the scope of the medical domain, and liquify roles and relationships that surround health and healthcare. The basic hypothesis is that although digital health technologies can lead to personalization of treatment and empowerment of users, their economic framework of surveillance capitalism may undermine these very goals. Using liquid health as a concept allows us to better understand and describe practices of health and healthcare that are shaped by digital technologies and the specific economic practices they are inseparably attached to.

The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health

Mental health symptoms are commonly discovered in primary care. Yet, these settings are not set up to provide psychological treatment. Digital interventions can play a crucial role in stepped care management of patients' symptoms where patients are offered a low intensity intervention, and treatment evolves to incorporate providers if needed. Though digital interventions often use smartphone and wearable sensor data, little is known about patients' desires to use these data to manage mental health symptoms. In 10 interviews with patients with symptoms of depression and anxiety, we explored their: symptom self-management, current and desired use of sensor data, and comfort sharing such data with providers. Findings support the use digital interventions to manage mental health, yet they also highlight a misalignment in patient needs and current efforts to use sensors. We outline considerations for future research, including extending design thinking to wraparound services that may be necessary to truly reduce healthcare burden.

Wearable Biosensors in Congenital Heart Disease: Needs to Advance the Field

Traditional measures of clinical status and physiology have generally been based in health care settings, episodic, short in duration, and performed at rest. Wearable biosensors provide an opportunity to obtain continuous non-invasive physiologic data from patients with congenital heart disease (CHD) in the real-world setting, over longer durations, and across varying levels of activity. However, there are significant technical limitations to the use of wearable biosensors in CHD. Here, we review current applications of wearable biosensors in CHD; how clinical and research uses of wearable biosensors must consider various CHD physiologies; the technical challenges in developing wearable biosensors for CHD; and special considerations for digital biomarkers in CHD.

User types, psycho-social effects and societal trends related to the use of consumer health technologies

Objective

The term consumer health technologies we use in this paper refers to fitness and health apps, wearables and other self-tracking devices that collect health-related data. Our paper aims to bridge the gap between the growing literature base of sociological research and ethical reflection on the (non-intended) effects of consumer health technology use on the psycho-social level, such as stress, responsibilization or a loss of intuitive sense for signs of health or illness. Special consideration should be given to vulnerable individuals, as the positive and negative effects of consumer health technology use may be unequally distributed. This perspective may help to guide policymaking and the responsible development of consumer health technologies.

Methods

Using a narrative review approach, we refer to empirical and theoretical studies dealing with user types and effects related to the use of consumer health technologies. We provide an overview of consumer health technology user typologies and evidence of the unintended psycho-social effects of consumer health technology use. On this basis, we propose a user typology that may serve as a future tool for ethical reflection on negative side effects.

Results

Evidence of the potential negative side effects of consumer health technology use, as presented in the literature, is inconclusive due to the high diversity of consumer health technology users and the way they use consumer health technologies. Our proposed user typology aims to more comprehensively document the diversity of users by incorporating the way in which users identify with and use their self-tracked data, attitudes towards the new technology and social interactions via consumer health technologies, and the purpose and self-determinedness of consumer health technology use.

Conclusions

More systematic and quantitative empirical research on the effects of consumer health technology use in diverse settings and with diverse user types is necessary to inform public health policy. In addition to evidence-based certification of medical consumer health technologies, more practical and flexible ways to protect users from side effects may have to be developed and adopted, especially regarding the increasing number of non-medical consumer health technologies.

The social grounds of self-tracking in insurance: A mixed-method approach to adoption and use

Scholars have explored the role of self-tracking in mediating people's values, perceptions, and practices. But little is known about its institutionalised forms, although it is becoming a routine component of health policies and insurance programs. Furthermore, the role of structural elements such as sociodemographic variables, socialisations, and trajectories has been neglected. Using both quantitative (n  =  818) and qualitative (n  =  44) data gathered from users and non-users of an insurance program's self-tracking intervention, and drawing from Bourdieu's theoretical framework, we highlight the impact of users' social background on the adoption and use of the technology. We show that older, poorer, and less educated individual are less likely to adopt the technology, and describe four prototypical categories of users, the meritocrats, the litigants, the scrutinisers and the good-intentioned. Each category displays different reasons and ways to use the technology that are grounded in users' socialisations and life trajectories. Results suggest that too much emphasis may have been put on self-tracking's transformative powers and not enough on its reproductive inertia, with important consequences for both scholars, designers, and public health stakeholders.

Factors related to moderate exercise during COVID-19 for overweight and obese individuals: A secondary analysis of HINTS data

Objective

COVID-19 has exacerbated pre-existing rates of overweight and obesity in the United States. mHealth technologies are gaining in popularity for its potential to reduce obesity, if facilitated by patient-centered communication. This study explores predictors of overweight and obese individuals' exercise levels during COVID-19.

Methods

2191 respondents who visited a doctor in the past year and self-reported being overweight were selected from the 2020 Health Information National Trends Survey (HINTS). Respondents reported their physical activity, beliefs about obesity, health tracking behaviors, and communication with providers during the pandemic. Structural equation modeling was used to explore connections among the variables.

Results

Patient-provider (e-)communication was significantly associated with changes in people's obesity-related beliefs and mHealth tracking usage, predicting moderate exercise during the pandemic.

Conclusion

The findings illustrate the need for patient-centered communication encounters to include discussions on mHealth technologies and accessible methods of engaging in physical activity.Innovation: This study examined secondary data provided by overweight and obese individuals from the early days of the COVID-19 pandemic; this population may benefit from targeted health interventions using mHealth technologies. Our findings suggest that healthcare providers should engage patients through mHealth technology and seek to improve digital health literacy to progress physical activity nationwide.

Football Fandom as a Platform for Digital Health Promotion and Behaviour Change: A Mobile App Case Study

Background: The last decade has seen a dramatic shift toward the study of fitness surveillance, thanks in part to the emergence of mobile health (mHealth) apps that allow users to track their health through a variety of data-driven insights. This study examines the adoption trends and community mediation of the mobile fitness application ‘FanFit’, a platform aimed at promoting physical activity among sports fans by creating a fitness app branded to their favourite team for health promotion. Objective: Our study looked at the impact of a specially designed mobile app (FanFit) as a digital health intervention for initiating and maintaining physical activity as part of football club membership. Our analysis indicates that app users will adopt healthier behaviours as a result of the app’s sense of fan community and behaviour change. Methods: The findings reported here are based on an implementation of the FanFit app and, in particular, on those who participated in a more in-depth study (n = 30). These participants were Rangers FC supporters with a mix of genders (n = 19 males and n = 11 females). Focus groups and interviews were conducted with participants to ascertain users’ perspectives on the most effective methods for nudging users toward adopting and maintaining a pattern of fitness behaviours. Results: The findings show that the user community was interested in fitness and wanted to live a ‘healthy lifestyle,’ which was augmented and fuelled by the app’s competitive architecture design. Furthermore, the data reveal a new fan-health discourse about a person’s developing wants, talents, and identities as embodied beings. Conclusions: We have developed and presented valid links between the use of sports club apps and health programmes. The app could be useful for sports programmes and club providers looking for mHealth applications that provide community support through fan discourse with opportunities for both male and female fans.

Self-management interventions for people with Parkinson’s Disease: A scoping review (Preprint)

Background

Objective

Methods

Results

Conclusions

Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial

Personal wellness data collected using wearable devices is a valuable resource, potentially containing knowledge that goes beyond what the device and its the associated software application can tell the user. However, extracting such knowledge from the data requires expertise that an average user cannot be expected to have. To overcome this problem, the data owner could collaborate with a data analysis expert; for such a collaboration to succeed, the collaborators need to be able to find one another, communicate with one another and share datasets and analysis results with one another. In this paper we presents a process model for such collaborations, a domain ontology and software system developed to support the process, and the results of a user trial demonstrating collaborative analysis of sleep data. Unlike existing collaborative data analytics tools, the process and software have been specifically designed with the non-expert data owner in mind, enabling them to control their data and protect their privacy by selecting the data to be shared on a case-by-case basis. Theoretical analysis and empirical results suggest that the process and its implementation are valid as a proof of concept.

Research on the Connotation and Dimension of Consumers’ Quantified-Self Consciousness

Quantified-self practice has penetrated into people’s daily life. Academic circles have begun to study it, but at present, scholars have not raised quantified-self practice to the level of consciousness. In order to explore the structural connotation of quantified-self consciousness and then provide management reference for enterprises offering quantified-self services, this study conducted in-depth interviews with self-trackers with the method of grounded theory. The conceptual model of quantified-self consciousness is formed through step-by-step coding, and the theoretical saturation is tested by reserving original sentences and crawling relevant online comments. The model shows that quantified-self consciousness can be divided into three dimensions: individual thinking, social projection, and data sensitivity.