Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study

"An excellent servant but a terrible master": Understanding the value of wearables for self-management in people with cystic fibrosis and their healthcare providers - A qualitative study

BACKGROUND

Wearables hold potential to improve chronic disease self-management in conditions like cystic fibrosis (CF) through remote monitoring, early detection of illness and motivation. Little is known about the acceptability and sustainability of integrating wearables into routine care from the perspectives of people with CF (pwCF) and their treating clinicians.

METHODS

A cross-sectional qualitative study involving semi-structured interviews with adult pwCF and focus groups comprising members of a CF multidisciplinary team (MDT) were conducted at a specialist CF centre in Australia. A phenomenological orientation underpinned the study. Inductive thematic analysis was performed using the Framework method. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.

RESULTS

Nine pwCF and eight members of a CF MDT, representing six clinical disciplines, participated in the study. Eight themes were inductively generated from the data, of which four were identified from each group. PwCF valued wearables for providing real-time data to motivate healthy behaviours and support shared goal-setting with healthcare providers. Wearables did not influence adherence to CF-specific self-management practices and had some hardware limitations. Members of the CF MDT recognised potential benefits of remote monitoring and shared goal-setting, but advised caution regarding data accuracy, generating patient anxiety in certain personality traits, and lack of evidence supporting use in CF self-management.

CONCLUSIONS

Perspectives on integrating wearables into CF care were cautiously optimistic, with emerging risks related to patient anxiety and lack of evidence moderating acceptance.

Patients with Heart Failure: Internet Use and Mobile Health Perceptions

BACKGROUND

 Heart failure is a complex clinical syndrome noted on approximately one in eight death certificates in the United States. Vital to reducing complications of heart failure and preventing hospital readmissions is adherence to heart failure self-care routines. Mobile health offers promising opportunities for enhancing self-care behaviors by facilitating tracking and timely reminders.

OBJECTIVES

 We sought to investigate three characteristics of heart failure patients with respect to their heart failure self-care behaviors: (1) internet use to search for heart failure information; (2) familiarity with mobile health apps and devices; and (3) perceptions of using activity trackers or smartwatches to aid in their heart failure self-care.

METHODS

 Forty-nine heart failure patients were asked about their internet and mobile health usage. The structured interview included questions adapted from the Health Information National Trends Survey.

RESULTS

 Over 50% of the patients had utilized the internet to search for heart failure information in the past 12 months, experience using health-related apps, and thoughts that an activity tracker or smartwatch could help them manage heart failure. Qualitative analysis of the interviews revealed six themes: trust in their physicians, alternatives to mobile health apps, lack of need for mobile health devices, financial barriers to activity tracker and smartwatch ownership, benefits of tracking and reminders, and uncertainty of their potential due to lack of knowledge.

CONCLUSION

 Trust in their physicians was a major factor for heart failure patients who reported not searching for health information on the internet. While those who used mobile health technologies found them useful, patients who did not use them were generally unaware of or unknowledgeable about them. Considering patients' preferences for recommendations from their physicians and tendency to search for heart failure information including treatment and management options, patient-provider discussions about mobile health may improve patient knowledge and impact their usage.

Data work and practices in healthcare: A scoping review

CONTEXT

In healthcare, digitization has been widespread and profound, entailing a deluge of data. This has spurred ambitions for healthcare to become data-driven to improve efficiency and quality, and within medicine itself to improve diagnosing and treating diseases. The generation and processing of data requires human intervention and work, though this is often not acknowledged.

PURPOSE

The paper investigates who, where, by which means, and for which purposes data work is conducted which is crucial for healthcare managers and policy makers if ambitions to become data-driven are to succeed. To guide further research, it also provides an overview of existing research on data work and practices.

METHODS

We conducted a scoping review based on a search for papers including the terms healthcare or health care combined with at least one of the following terms: data work, data worker*, data practice*, data practitioner* in Scopus and Web of Science. 74 papers on data work or practices in healthcare were included.

ANALYSIS

The 74 papers were coded and analyzed regarding the following themes: the kind of data workers and practitioners, organizational settings, involved technologies, purposes, data work tasks, theories and concepts, and definitions of data work and practice.

RESULTS

Data work is pervasive in healthcare and conducted by various professions and people and in various contexts. The field researching data work and practices is emerging, with publications spread across multiple venues. and there is a need for more precise definitions of data work. Further, data work and practices are useful concepts that have enabled the exploration of those efforts and tasks in detail.

CONCLUSION

The research on data work and practices in healthcare is emerging and promising. We call for more research to consolidate the field and to better understand and support the work needed for healthcare to become data-driven.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.

Delineating and clarifying the concept of self-care monitoring: a concept analysis

AIM

To delineate and clarify the meaning of the concept of self-care monitoring from a patient perspective.

METHODS

A systematic search was performed in the databases ASSIA, CINAHL, PsycInfo, and PubMed (January 2016-September 2021). A selection of 46 peer-reviewed articles was included in the study and analysed using Rodgers' Evolutionary Method for Concept Analysis.

RESULTS

The following four attributes were identified: Tracking symptoms, signs, and actions, Paying attention, Being confident, and Needing routines, creating a descriptive definition: "Self-care monitoring is an activity that means a person has to pay attention and be confident and needs routines for tracking symptoms, signs, and action." The antecedents of the concept were shown to be Increased knowledge, Wish for independence, and Commitment. The concepts' consequences were identified as Increased interaction, Perceived burden, and Enhanced well-being.

CONCLUSIONS

This concept analysis provides extensive understanding of self-care monitoring from a patient perspective. It was shown that the concept occurs when a person practices self-care monitoring at home either with or without devices. A descriptive definition was constructed and presented with exemplars to encourage practice of the concept in various healthcare settings and could be of relevance to people with chronic illnesses or other long-term conditions.

Intelligent Health: Progress and Benefit of Artificial Intelligence in Sensing-Based Monitoring and Disease Diagnosis

Technology has progressed and allows people to go further in multiple fields related to social issues. Medicine cannot be the exception, especially nowadays, when the COVID-19 pandemic has accelerated the use of technology to continue living meaningfully, but mainly in giving consideration to people who remain confined at home with health issues. Our research question is: how can artificial intelligence (AI) translated into technological devices be used to identify health issues, improve people's health, or prevent severe patient damage? Our work hypothesis is that technology has improved so much during the last decades that Medicine cannot remain apart from this progress. It must integrate technology into treatments so proper communication between intelligent devices and human bodies could better prevent health issues and even correct those already manifested. Consequently, we will answer: what has been the progress of Medicine using intelligent sensor-based devices? Which of those devices are the most used in medical practices? Which is the most benefited population, and what do physicians currently use this technology for? Could sensor-based monitoring and disease diagnosis represent a difference in how the medical praxis takes place nowadays, favouring prevention as opposed to healing?

Use of wearables for monitoring cardiometabolic health: A systematic review

INTRODUCTION

Cardiometabolic disorders (CMD) such as hyperglycemia, obesity, hypertension, and dyslipidemia are the leading causes of mortality and significant public health concerns worldwide. With the advances in wireless technology, wearables have become popular for health promotion, but its impact on cardiometabolic health is not well understood.

PURPOSE

A systematic literature review aimed to describe the features of wearables used for monitoring cardiometabolic health and identify the impact of using wearables on those cardiometabolic health indicators.

METHODS

A systematic search of PubMed, CINAHL, Academic Search Complete, and Science and Technology Collection databases was performed using keywords related to CMD risk indicators and wearables. The wearables were limited to sensors for blood pressure (BP), heart rate (HR), electrocardiogram (ECG), glucose, and cholesterol.

INCLUDED STUDIES

1) were published from 2016 to March 2021 in English, 2) focused on wearables external to the body, and 3) examined wearable use by individuals in daily life (not by health care providers). Protocol, technical, and non-empirical studies were excluded.

RESULTS

Out of 53 studies, the types of wearables used were smartwatches (45.3%), patches (34.0%), chest straps (22.6%), wristbands (13.2%), and others (9.4%). HR (58.5%), glucose (28.3%), and ECG (26.4%) were the predominant indicators. No studies tracked BP or cholesterol. Additional features of wearables included physical activity, respiration, sleep, diet, and symptom monitoring. Twenty-two studies primarily focused on the use of wearables and reported direct impacts on cardiometabolic indicators; seven studies used wearables as part of a multi-modality approach and presented outcomes affected by a primary intervention but measured through CMD-sensor wearables; and 24 validated the precision and usability of CMD-sensor wearables.

CONCLUSION

The impact of wearables on cardiometabolic indicators varied across the studies, indicating the need for further research. However, this body of literature highlights the potential of wearables to promote cardiometabolic health.

Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors

BACKGROUND

Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care.

OBJECTIVE

This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives.

METHODS

We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook.

RESULTS

We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors.

CONCLUSIONS

A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.

Older Veterans' Experiences of a Multicomponent Telehealth Program: Qualitative Program Evaluation Study

BACKGROUND

There are 8.8 million American veterans aged >65 years. Older veterans often have multiple health conditions that increase their risk of social isolation and loneliness, disability, adverse health events (eg, hospitalization and death), mental illness, and heavy health care use. This population also exhibits low levels of physical function and daily physical activity, which are factors that can negatively influence health. Importantly, these are modifiable risk factors that are amenable to physical therapy intervention. We used a working model based on the dynamic biopsychosocial framework and social cognitive theory to conceptualize the multifactorial needs of older veterans with multiple health conditions and develop a novel, 4-component telehealth program to address their complex needs.

OBJECTIVE

This study aims to describe veterans' experiences of a multicomponent telehealth program and identify opportunities for quality and process improvement. We conducted qualitative interviews with telehealth program participants to collect their feedback on this novel program; explore their experience of program components; and document perceived outcomes and the impact on their daily life, relationships, and quality of life.

METHODS

As part of a multimethod program evaluation, semistructured interviews were conducted with key informants who completed ≥8 weeks of the 12-week multicomponent telehealth program for veterans aged ≥50 years with at least 3 medical comorbidities. Interviews were audio recorded and transcribed. Data were analyzed by a team of 2 coders using a directed content analysis approach and Dedoose software was used to assist with data analysis.

RESULTS

Of the 21 individuals enrolled in the program, 15 (71%) met the inclusion criteria for interviews. All 15 individuals completed 1-hour interviews. A total of 6 main conceptual domains were identified: technology, social networks, therapeutic relationship, patient attributes, access, and feasibility. Themes associated with each domain detail participant experiences of the telehealth program. Key informants also provided feedback related to different components of the program, leading to adaptations for the biobehavioral intervention, group sessions (transition from individual to group sessions and group session dynamics), and technology supports.

CONCLUSIONS

Findings from this program evaluation identified quality and process improvements, which were made before rigorously testing the intervention in a larger population through a randomized controlled trial. The findings may inform adaptations of similar programs in different contexts. Further research is needed to develop a deeper understanding of how program components influence social health and longer-term behavior change.

Social and ethical implications of data and technology use on farms: a qualitative study of Swedish dairy and pig farmers

Introduction

Livestock farmers are being increasingly encouraged to adopt digital health technologies on their farms. Digital innovations may have unintended consequences, but there tends to be a pro-innovation bias in previous literature. This has led to a movement towards "responsible innovation," an approach that questions the social and ethical challenges of research and innovation. This paper explores the social and ethical issues of data and technologies on Swedish dairy and pig farms from a critical perspective.

Methods

Six focus groups were conducted with thirteen dairy and thirteen pig farmers. The data were analysed using reflexive thematic analysis and a digital critical health lens, which focuses on concepts of identity and power.

Results and discussion

The analysis generated four themes: extending the self, sense of agency, quantifying animals, and managing human labour. The findings suggest that technologies can change and form the identities of farmers, their workers, and animals by increasing the visibility of behaviours and bodies through data collection. Technologies can also facilitate techniques of power such as conforming to norms, hierarchical surveillance, and segregation of populations based on data. There were many contradictions in the way that technology was used on farms which suggests that farmers cannot be dichotomised into those who are opposed to and those that support adoption of technologies. Emotions and morality played an important role in the way animals were managed and technologies were used by farmers. Thus, when developing innovations, we need to consider users' feelings and attachments towards the technologies. Technologies have different impacts on farmers and farm workers which suggests that we need to ensure that we understand the perspectives of multiple user groups when developing innovations, including those that might be least empowered.

‘The plural of silo is not ecosystem’: Qualitative study on the role of innovation ecosystems in supporting ‘Internet of Things’ applications in health and care

Background

Internet of Things (IoT) innovations such as wearables and sensors promise improved health outcomes and service efficiencies. Yet, most applications remain experimental with little routine use in health and care settings. We sought to examine the multiple interacting influences on IoT implementation, spread and scale-up, including the role of regional innovation ‘ecosystems’ and the impact of the COVID-19 context.

Methods

Qualitative study involving 20 participants with clinical, entrepreneurial and broader innovation experience in 18 in-depth interviews, focusing primarily on heart monitoring and assistive technology applications. Data analysis was informed by the NASSS (non-adoption, abandonment, scale-up, spread, sustainability) framework.

Results

Interviewees discussed multiple tensions and trade-offs, including lack of organisational capacity for routine IoT use, limited ability to receive and interpret data, complex procurement and governance processes, and risk of health disparities and inequalities without system support and funding. Although the pandemic highlighted opportunities for IoT use, it was unclear whether these would be sustained, with framings of innovation as ‘disruption’ coming at odds with immediate needs in healthcare settings. Even in an ‘ecosystem’ with strong presence of academic and research institutions, support was viewed as limited, with impressions of siloed working, conflicting agendas, fragmentation and lack of collaboration opportunities.

Conclusions

IoT development, implementation and roll-out require support from multiple ecosystem actors to be able to articulate a value proposition beyond experimental or small-scale applications. In contexts where clinical, academic and commercial worlds collide, sustained effort is needed to align needs, priorities and motives, and to strengthen potential for good value IoT innovation.

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.

Naturalizing digital and quality of life in chronic diseases: Systematic review to research perspective into technological advancing and personalized medicine

The increasing of chronic diseases and related health management are the main clinical and public health challenges. The long-term nature and the need for continuous monitoring in chronic disease management gave rise to early technological innovations (mobile Health) to improve care management plans, therapeutic adherence, and psychological support to the patient. This review aimed to map the literature on the impact of the use of wearable device on quality of life in patients with chronic diseases. We performed a systematic search of MEDLINE through PubMed, Web of Science, and Scopus of all scientific literature published until January 2022. Based on inclusion and exclusion criteria, a total of 10 papers were included. This review pointed out the relevant focus on the use of wearable device in chronic disease patients highlighting the wearable device impact on several domains including quality of life, Self-Efficacy, Self-Management, and feelings on patients with chronic diseases. The available scientific literature related to the impact of the use of wearable device on quality of life and psychological features in patients with chronic diseases, general underline a need to develop professional healthcare guidelines and tailored intervention on patients with a chronic condition, using mobile Health solutions and trying to fill the lack of knowledge about the topic.

Better self-care through co-care? A latent profile analysis of primary care patients' experiences of e-health-supported chronic care management

Background

Efficient self-care of chronic conditions requires that an individual's resources be optimally combined with healthcare's resources, sometimes supported by e-health services (i.e., co-care). This calls for a system perspective of self-care to determine to what extent it involves demanding or unnecessary tasks and whether role clarity, needs support, and goal orientation are sufficient. This study aims to explore typical configurations of how the co-care system is experienced by individuals with chronic conditions who used an e-health service supporting self-monitoring and digital communication with primary care.

Method

We performed a latent profile analysis using questionnaire data from two waves (7 months apart) involving 180 of 308 eligible patients who pilot-tested an e-health service for co-care at a Swedish primary care center. The five subscales of the Distribution of Co-Care Activities (DoCCA) scale were used to create profiles at Time 1 (T1) and Time 2 (T2). Profiles were described based on sociodemographic variables (age, gender, education level, and health condition) and compared based on exogenous variables (self-rated health, satisfaction with healthcare, self-efficacy in self-care, and perceptions of the e-health service).

Results

We identified four typical configurations of co-care experiences at T1: strained, neutral, supportive, and optimal. Patients with optimal and supportive profiles had higher self-rated health, self-efficacy in self-care, and satisfaction with healthcare than patients with strained and neutral profiles. Slightly more than half transitioned to a similar or more positive profile at T2, for which we identified five profiles: unsupportive, strained, neutral, supportive, and optimal. Patients with optimal and supportive profiles at T2 had higher self-efficacy in self-care and satisfaction with healthcare than the other profiles. The optimal profiles also had higher self-rated health than all other profiles. Members of the optimal and supportive profiles perceived the effectiveness of the e-health service as more positive than the unsupportive and strained profile members.

Discussion

Primary care patients' co-care profiles were primarily distinguished by their experiences of needs support, goal orientation, and role clarity. Patients with more positive co-care experiences also reported higher self-rated health, self-efficacy in self-care, and satisfaction with healthcare, as well as more positive experiences of the e-health service.

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7−14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1−8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk.

Barriers to and Facilitators of Using a One Button Tracker and Web-Based Data Analytics Tool for Personal Science: Exploratory Study

Background

Individuals’ self-tracking of subjectively experienced phenomena related to health can be challenging, as current options for instrumentation often involve too much effort in the moment or rely on retrospective self-reporting, which is likely to impair accuracy and compliance.

Objective

This study aims to assess the usability and perceived usefulness of low-effort, in-the-moment self-tracking using simple instrumentation and to establish the amount of support needed when using this approach.

Methods

In this exploratory study, the One Button Tracker—a press-button device that records time stamps and durations of button presses—was used for self-tracking. A total of 13 employees of an academic medical center chose a personal research question and used the One Button Tracker to actively track specific subjectively experienced phenomena for 2 to 4 weeks. To assess usability and usefulness, we combined qualitative data from semistructured interviews with quantitative results from the System Usability Scale.

Results

In total, 29 barriers and 15 facilitators for using the One Button Tracker were found. Ease of use was the most frequently mentioned facilitator. The One Button Tracker’s usability received a median System Usability Scale score of 75.0 (IQR 42.50), which is considered as good usability. Participants experienced effects such as an increased awareness of the tracked phenomenon, a confirmation of personal knowledge, a gain of insight, and behavior change. Support and guidance during all stages of the self-tracking process were judged as valuable.

Conclusions

The low-effort, in-the-moment self-tracking of subjectively experienced phenomena has been shown to support personal knowledge gain and health behavior change for people with an interest in health promotion. After addressing barriers and formally validating the collected data, self-tracking devices may well be helpful for additional user types or health questions.

New Opportunities to Advance the Field of Sports Nutrition

Sports nutrition is a relatively new discipline; with ~100 published papers/year in the 1990s to ~3,500+ papers/year today. Historically, sports nutrition research was primarily initiated by university-based exercise physiologists who developed new methodologies that could be impacted by nutrition interventions (e.g., carbohydrate/fat oxidation by whole body calorimetry and muscle glycogen by muscle biopsies). Application of these methods in seminal studies helped develop current sports nutrition guidelines as compiled in several expert consensus statements. Despite this wealth of knowledge, a limitation of the current evidence is the lack of appropriate intervention studies (e.g., randomized controlled clinical trials) in elite athlete populations that are ecologically valid (e.g., in real-life training and competition settings). Over the last decade, there has been an explosion of sports science technologies, methodologies, and innovations. Some of these recent advances are field-based, thus, providing the opportunity to accelerate the application of ecologically valid personalized sports nutrition interventions. Conversely, the acceleration of novel technologies and commercial solutions, especially in the field of biotechnology and software/app development, has far outstripped the scientific communities' ability to validate the effectiveness and utility of the vast majority of these new commercial technologies. This mini-review will highlight historical and present innovations with particular focus on technological innovations in sports nutrition that are expected to advance the field into the future. Indeed, the development and sharing of more “big data,” integrating field-based measurements, resulting in more ecologically valid evidence for efficacy and personalized prescriptions, are all future key opportunities to further advance the field of sports nutrition.

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial

Background

Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions.

Methods

The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement.

Results

Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements.

Conclusions

The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

Impact of using wearable devices on psychological Distress: Analysis of the health information national Trends survey

AIM

This study explores the possible impact of wearables on psychological distress and their implications on designs.

METHOD

The study conceptualizes and tests two exploratory models by analyzing the US-based Health Information National Trends Survey of 2019 and 2020. Six variants from the databases were used in the study as predictors. We used models 4 and 6 of the Hayes PROCESS macros to test our conceptual parallel and sequential mediation models, respectively.

RESULTS

The finding indicates significant and negative indirect effects of 'Use of wearable device' on 'Psychological distress.' In parallel mediation models, 'self-care' and 'health perception' were noted to be significant mediators. Wearable devices were associated with improved 'Health perception,' 'Self-care,' and longer 'workout duration,', which in turn helped reduce 'psychological distress' (better mental health). The sequential mediation model captured the indirect effect of 'Use of wearable device' on 'Psychological distress' when sequentially mediated by 'workout duration,' 'BMI,' 'self-care,' and 'health perception' in the given order.

CONCLUSION

As the adoption of digital wearables is increasing due to their growing potential to augment physiological and psychosocial health, it is critical that these technologies are designed to address the needs of users from diverse backgrounds (race, education level, age).

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Associations Among Wearable Activity Tracker Use, Exercise Motivation, and Physical Activity in a Cohort of Cancer Survivors: Secondary Data Analysis of the Health Information National Trends Survey

Background

Cancer survivors who meet physical activity (PA) recommendations (≥150 minutes of moderate-to-vigorous physical activity [MVPA] per week) experience better health outcomes. With the growing availability of wearable activity trackers (WATs), it may be easier to track PA. However, it is unknown what motivates survivors to use these devices.

Objective

The aim of this study is to investigate the associations among motivations for exercise, previous WAT use for tracking a health goal or activity, and meeting the recommended amount of PA among a cohort of cancer survivors.

Methods

Data on WAT users who reported having a previous cancer diagnosis were analyzed from the National Cancer Institute’s Health Information National Trends Survey 5 Cycle 3. All survivors with complete information on demographics, exercise motivations (internal guilt, external pressure, physical appearance, and exercise enjoyment), previous WAT use (yes or no), and minutes of MVPA per week (N=608) were included. Multivariate logistic regression models were used to test these associations. A separate cluster analysis was conducted to identify the profiles of exercise motivation that were associated with reporting WAT use.

Results

The mean age of the cohort was 66.9 years (SD 12.1). The majority were non-Hispanic White (473/608, 78.8%) and female (322/608, 54.9%), and skin cancer was the most commonly reported diagnosed cancer (154/608, 27.8%). Survivors who reported using WATs to track a health goal or activity were 1.6 times more likely to meet MVPA recommendations than those who did not use WATs (odds ratio [OR] 1.65, 95% CI 1.03-2.65; P=.04). When exercise motivations were assessed independently, survivors who reported not feeling any internal guilt as an exercise motivation were 73% less likely to report having used a WAT than those who felt any internal guilt (OR 0.27, 95% CI 0.14-0.54; P<.001). A total of 3 distinct motivational profiles emerged from the cluster analysis. WAT users had an increased probability of membership in profile 3, which was characterized as being strongly motivated to exercise by internal guilt, physical appearance, and exercise enjoyment (OR 4.5, 95% CI 2.1-9.7; P<.001).

Conclusions

Among this cohort, survivors who reported using WATs to track a health goal or activity were significantly more likely to report meeting PA recommendations. Survivors who reported feeling internal guilt as an exercise motivation were significantly more likely to report using WATs to track a health goal or activity. When examining clusters of motivation, survivors who reported previous WAT use were more likely to report being motivated to exercise by a mix of intrinsic and extrinsic motivations, including internal guilt, exercise enjoyment, and physical appearance. Given the health benefits of PA for cancer survivors, technology-focused interventions that use WATs and target exercise motivation may aid in cancer survivors meeting the level of recommended PA.

Potentials and barriers of using digital tools for collecting daily measurements in multiple sclerosis research

Objective

Digital tools offer new ways of collecting outcome data in intervention research. Little is known about the potentials and barriers of using such tools for outcome measurement in multiple sclerosis trials. This study aimed to examine reporting adherence and barriers experienced by people with multiple sclerosis in an intervention study using three different digital tools for outcome measurement.

Methods

This was a mixed-methods study conducted in the context of a randomized controlled trial. Data collected during the randomized controlled trial were analysed to assess reporting adherence. Twenty-three semi-structured, in-depth interviews were conducted to investigate randomized controlled trial participants’ experiences.

Results

Reporting adherence was high for all three measurement tools, but lower in the control group. Four main barriers were defined: (1) the self-monitoring aspect and repeated tests imbedded in the digital tools affected participants’ behavior during the randomized controlled trial. (2) Self-monitoring caused some participants to worry more about their health. (3) Passively collected data did not always correspond with participants’ own experiences, which caused them to question the validity of the collected data. (4) Daily reporting using different digital tools placed a significant burden on participants.

Conclusion

The study indicates a high reporting adherence using digital tools among people with multiple sclerosis. However, future studies should carefully consider the overall burden imposed on participants when taking this approach. Measures should be taken to avoid the potential unintended effects of the self-monitoring and gamification aspects of using digital tools. These measures could include passive monitoring, reducing the frequency of reporting and blinding participants to their own data.

Predictors of Walking Activity in Patients With Systolic Heart Failure Equipped With a Step Counter: Randomized Controlled Trial

Background

Physical activity has been shown to decrease cardiovascular mortality and morbidity. Walking, a simple physical activity which is an integral part of daily life, is a feasible and safe activity for patients with heart failure (HF). A step counter, measuring daily walking activity, might be a motivational factor for increased activity.

Objective

The aim of this study was to examine the association between walking activity and demographical and clinical data of patients with HF, and whether these associations could be used as predictors of walking activity.

Methods

A total of 65 patients with HF from the Future Patient Telerehabilitation (FPT) program were included in this study. The patients monitored their daily activity using a Fitbit step counter for 1 year. This monitoring allowed for continuous and safe data transmission of self-monitored activity data.

Results

A higher walking activity was associated with younger age, lower New York Heart Association (NYHA) classification, and higher ejection fraction (EF). There was a statistically significant correlation between the number of daily steps and NYHA classification at baseline (P=.01), between the increase in daily steps and EF at baseline (P<.001), and between the increase in daily steps and improvement in EF (P=.005). The patients’ demographic, clinical, and activity data could predict 81% of the variation in daily steps.

Conclusions

This study demonstrated an association between demographic, clinical, and activity data for patients with HF that could predict daily steps. A step counter can thus be a useful tool to help patients monitor their own physical activity.

Trial Registration

ClinicalTrials.gov NCT03388918; https://clinicaltrials.gov/ct2/show/NCT03388918

International Registered Report Identifier (IRRID)

RR2-10.2196/14517