Sedentary behavior-based user life-log monitoring for wellness services

© Springer International Publishing Switzerland 2016. Ubiquitous computing and smart gadgets have revolutionized the selfquantification in tracking and logging activities for improving daily life and inducing healthy behavior. Life-log monitoring is the process of monitoring the daily life routines of user in an efficient manner in terms of time and amount of activities. The effective utilization of life-log monitoring is to correctly identify and intimate user unhealthy activities in a timely manner. For monitoring lifelog, the knowledge of sedentary behavior first need to be formulated by the domain expert in the form of unhealthy situations, these situations are used as the monitoring unit. In this study we proposed a method for automatically monitoring users’ unhealthy situations in the domain of sedentary behavior with prolonged activities. The proposed method simultaneously filters out multiple sedentary activities of users simultaneously while ignoring the activities having no situations. The results depict that the monitoring method intimates the stakeholder with delay less than the monitoring interval cycle

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

CoachAI: A Conversational Agent Assisted Health Coaching Platform

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot system that bridges the remote human coach and the users. This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one month validation study on physical activity, healthy diet and stress management

A journey from Cure to Care- Wellness management for healthy lifestyle: Diabetes management a case study

Smart ubiquitous computing has a vital role to avoid and indicate the preventable lifestyle-based chronic diseases. It is focusing to adopt a healthy lifestyle by converging science and technology in this digital world for improving health and quality of life. From the last decade, the development of wellness applications has supported personalization and self-quantification. These applications facilitate the users through activity tracking and monitoring, based on the raw sensory data to adopt healthy behavior. The challenge of behavior change is not only to indicate the issues but also provides step-by-step coaching and guidance at real time. The realization of behavior change theories through digital technology has revolutionized the lifestyle change in a systematic and measurable manner. We have proposed a methodology to understand the behavior for generating just-in-time intervention for adopting a healthy lifestyle. Wellness platform based behavior analysis is performed using unbiased life-log and questionnaire for qualitative assessment of behavior. Behavior stage wise intervention is provided to adapt behavior for enhancing the quality of life and boost the socio-economic conditions. Personalized education is provided to understand the importance of healthy behavior and motivate the users, whereas just-in-time context-based recommendations have supported the stage-wise adaptation of unhealthy behavior. These capabilities require status evaluation of the activities and an efficient way to portray the comprehensive index of lifestyle habits. The real focus is to correlate the primarily linked habits in appropriate proportion through healthy behavior index (HBI) for personalized wellness support services. The healthy behavior index and behavior change theories through smart technologies

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations

Background: Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. Methods: We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting diferent phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fdelity eCoach prototype. In design workshop 2, we focused on maturing the low-fdelity solution design and development for the visualization of continuous and discrete data, artifcial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. Results: The iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an efective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. Conclusions: The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working.publishedVersio

Managing obesity through mobile phone applications: a state-of-the-art review from a user-centred design perspective

Evidence has shown that the trend of increasing obesity rates has continued in the last decade. Mobile phone applications, benefiting from their ubiquity, have been increasingly used to address this issue. In order to increase the applications’ acceptance and success, a design and development process that focuses on users, such as User-Centred Design, is necessary. This paper reviews reported studies that concern the design and development of mobile phone applications to prevent obesity, and analyses them from a User-Centred Design perspective. Based on the review results, strengths and weaknesses of the existing studies were identified. Identified strengths included: evidence of the inclusion of multidisciplinary skills and perspectives; user involvement in studies; and the adoption of iterative design practices. Weaknesses included the lack of specificity in the selection of end-users and inconsistent evaluation protocols. The review was concluded by outlining issues and research areas that need to be addressed in the future, including: greater understanding of the effectiveness of sharing data between peers; privacy; and guidelines for designing for behavioural change through mobile phone applications

Fresh start: a group-based intervention to promote physical activity among college freshman

Master of ScienceDepartment of KinesiologyEmily MaileyPhysical activity levels tend to decline as students transition from high school to college, and freshmen college women may be particularly susceptible to physical activity barriers. It is possible that providing physical activity resources and support via text messages could assist freshmen women in increasing their physical activity levels. The primary purpose of this study was to evaluate the effects of a mobile group-based intervention for freshmen female college students on physical activity and sedentary behavior. In addition, we examined intervention effects on social support, enjoyment, and stress in this population. Freshmen females (n=30) were recruited to participate in a 9-week intervention that involved wearing a physical activity monitor for three individual weeks (week 0, week 5, and week 9) and receiving tailored weekly messages via GroupMe. Participants were randomly assigned to groups of 6-7 participants, and each group was moderated by one research assistant. GroupMe discussions were specifically formatted to provide physical activity social support, promote physical activity enjoyment, enhance knowledge about benefits of physical activity, suggest ways to decrease sedentary behavior, and increase awareness of various physical activity resources on campus, such as the recreational center. Outcomes were assessed at baseline and post-intervention. Additionally, follow-up focus group sessions were conducted during the fall semester of the participants’ sophomore year to gain further feedback about the intervention. We hypothesized that students would demonstrate increases in physical activity, enjoyment, and social support, and decreases in sedentary behavior and stress after participating in the intervention. Results revealed no significant changes in physical activity or sedentary behavior based on objective data from the activPALs. A Wilcoxon Signed-Rank Test of self-reported physical activity and sedentary behavior (International Physical Activity Questionnaire) indicated increases in self-reported sitting time from baseline to post-intervention (Z=-2.654, p<0.008). There were no significant changes in enjoyment, social support, or stress from baseline to post-intervention. A total of 10 participants attended a follow-up focus group session. Key recommendations included incorporating more face to face interaction, a change of topics within the messages to focus on more nutrition and exercise and or guided exercises, and running the intervention during the fall semester rather than the spring. Aspects of the program that participants liked the best included the feedback of activity provided by the activPAL, the idea of using GroupMe for the program, and the length of the program. Overall, results did not align with our hypotheses, but the intervention results and feedback from participants will help with intervention refinement. Future studies should continue to seek creative ways to promote physical activity in this population, with an overall purpose of sustaining physical activity habits beyond the intervention

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

The Effects of a Mobile Fitness Application on Weight Management and Physical Acticity Amongst University Students

The prevalence of obesity, and obesity related diseases throughout America, specifically in regard to the college student population has steadily climbed over the course of the last forty years, due largely in part to the increase in sedentary lifestyle behaviors, amongst other factors (Swanson, 2016). Physical activity has been widely recognized as a valid means of combatting obesity and weight gain while promoting health related quality of life (Swanson, 2016). Therefore, implementing strategies aimed at increasing physical fitness in attempt to control weight management is imperative to promoting improved health outcomes. The purpose of this evidence-based project was to examine the effects that the mobile fitness application “My Fitness Pal” had on weight management and prevalence of physical activity amongst university students. Theoretically, the project was designed with aid of the Health Belief Model to promote self-efficacy of participants and motivate them to engage in physical activity to achieve health benefits. A convenience sample of undergraduate students was drawn from a Midwestern Lutheran University. Participants provided baseline information regarding their physical activity and bodyweight measurement and then were presented with an educational intervention that promoted the use of a mobile fitness application, along with customized exercise tips and resources available on the university campus to encourage them to participate and log physical activity. In order to ascertain the effectiveness of the exercise promotion intervention, paired sample t-tests will be utilized to compare participant’s caloric expenditure, BMI, and exercise self-efficacy scores both pre and post intervention. The software program Intellectus Statistics will be utilized to complete all statistical analysis with a statistical significance level set at p \u3c 0.5. Implications for practice will be further discussed