Technology-Based Innovations to Foster Personalized Healthy Lifestyles and Well-Being: A Targeted Review.

BACKGROUND: New community-based arrangements and novel technologies can empower individuals to be active participants in their health maintenance, enabling people to control and self-regulate their health and wellness and make better health- and lifestyle-related decisions. Mobile sensing technology and health systems responsive to individual profiles combined with cloud computing can expand innovation for new types of interoperable services that are consumer-oriented and community-based. This could fuel a paradigm shift in the way health care can be, or should be, provided and received, while lessening the burden on exhausted health and social care systems. OBJECTIVE: Our goal is to identify and discuss the main scientific and engineering challenges that need to be successfully addressed in delivering state-of-the-art, ubiquitous eHealth and mHealth services, including citizen-centered wellness management services, and reposition their role and potential within a broader context of diverse sociotechnical drivers, agents, and stakeholders. METHODS: We review the state-of-the-art relevant to the development and implementation of eHealth and mHealth services in critical domains. We identify and discuss scientific, engineering, and implementation-related challenges that need to be overcome to move research, development, and the market forward. RESULTS: Several important advances have been identified in the fields of systems for personalized health monitoring, such as smartphone platforms and intelligent ubiquitous services. Sensors embedded in smartphones and clothes are making the unobtrusive recognition of physical activity, behavior, and lifestyle possible, and thus the deployment of platforms for health assistance and citizen empowerment. Similarly, significant advances are observed in the domain of infrastructure supporting services. Still, many technical problems remain to be solved, combined with no less challenging issues related to security, privacy, trust, and organizational dynamics. CONCLUSIONS: Delivering innovative ubiquitous eHealth and mHealth services, including citizen-centered wellness and lifestyle management services, goes well beyond the development of technical solutions. For the large-scale information and communication technology-supported adoption of healthier lifestyles to take place, crucial innovations are needed in the process of making and deploying usable empowering end-user services that are trusted and user-acceptable. Such innovations require multidomain, multilevel, transdisciplinary work, grounded in theory but driven by citizens' and health care professionals' needs, expectations, and capabilities and matched by business ability to bring innovation to the market.

Using music to change mood while driving.

This study investigated whether gradual or abrupt music change towards more calming music is most effective in calming drivers during high-demand driving situations. Twenty-eight participants were subjected to two types of music change (gradual, abrupt) in a within-subject design. First, a relatively happy mood was induced with personally selected music during an eight-minute simulated high-demand drive. The drive then continued and the mood was changed either gradually or abruptly. Subjective results showed successful music mood induction irrespective of gradual or abrupt changes. The results further showed lower skin conductance (less arousal) and more facial corrugator muscle tension (more sadness) during the abrupt music change. Fewer accidents occurred during the abrupt music mood change. To conclude, the results support the abrupt way of changing music type to down-regulate one's mood: during high-demand driving, abrupt changes in music led to more physiological calmness and improved driving performance, and were thus safer and more effective. PRACTITIONER SUMMARY: The current study shows that during high-demand drives, drivers are calmed more effectively using abrupt music changes compared to gradual music changes. This is illustrated by reductions in physiological arousal and improved driving behaviour. Hence, in-car music presentation can be used as a tool to improve driver's mood and behaviour.

Measuring Free-Living Physical Activity With Three Commercially Available Activity Monitors for Telemonitoring Purposes: Validation Study.

BACKGROUND: Remote monitoring of physical activity in patients with chronic conditions could be useful to offer care professionals real-time assessment of their patient's daily activity pattern to adjust appropriate treatment. However, the validity of commercially available activity trackers that can be used for telemonitoring purposes is limited. OBJECTIVE: The purpose of this study was to test usability and determine the validity of 3 consumer-level activity trackers as a measure of free-living activity. METHODS: A usability evaluation (study 1) and validation study (study 2) were conducted. In study 1, 10 individuals wore one activity tracker for a period of 30 days and filled in a questionnaire on ease of use and wearability. In study 2, we validated three selected activity trackers (Apple Watch, Misfit Shine, and iHealth Edge) and a fourth pedometer (Yamax Digiwalker) against the reference standard (Actigraph GT3X) in 30 healthy participants for 72 hours. Outcome measures were 95% limits of agreement (LoA) and bias (Bland-Altman analysis). Furthermore, median absolute differences (MAD) were calculated. Correction for bias was estimated and validated using leave-one-out cross validation. RESULTS: Usability evaluation of study 1 showed that iHealth Edge and Apple Watch were more comfortable to wear as compared with the Misfit Flash. Therefore, the Misfit Flash was replaced by Misfit Shine in study 2. During study 2, the total number of steps of the reference standard was 21,527 (interquartile range, IQR 17,475-24,809). Bias and LoA for number of steps from the Apple Watch and iHealth Edge were 968 (IQR -5478 to 7414) and 2021 (IQR -4994 to 9036) steps. For Misfit Shine and Yamax Digiwalker, bias was -1874 and 2004, both with wide LoA of (13,869 to 10,121) and (-10,932 to 14,940) steps, respectively. The Apple Watch noted the smallest MAD of 7.7% with the Actigraph, whereas the Yamax Digiwalker noted the highest MAD (20.3%). After leave-one-out cross validation, accuracy estimates of MAD of the iHealth Edge and Misfit Shine were within acceptable limits with 10.7% and 11.3%, respectively. CONCLUSIONS: Overall, the Apple Watch and iHealth Edge were positively evaluated after wearing. Validity varied widely between devices, with the Apple Watch being the most accurate and Yamax Digiwalker the least accurate for step count in free-living conditions. The iHealth Edge underestimates number of steps but can be considered reliable for activity monitoring after correction for bias. Misfit Shine overestimated number of steps and cannot be considered suitable for step count because of the low agreement. Future studies should focus on the added value of remotely monitoring activity patterns over time in chronic patients.

Evaluating an mHealth App for Health and Well-Being at Work: Mixed-Method Qualitative Study.

BACKGROUND: To improve workers' health and well-being, workplace interventions have been developed, but utilization and reach are unsatisfactory, and effects are small. In recent years, new approaches such as mobile health (mHealth) apps are being developed, but the evidence base is poor. Research is needed to examine its potential and to assess when, where, and for whom mHealth is efficacious in the occupational setting. To develop interventions for workers that actually will be adopted, insight into user satisfaction and technology acceptance is necessary. For this purpose, various qualitative evaluation methods are available. OBJECTIVE: The objectives of this study were to gain insight into (1) the opinions and experiences of employees and experts on drivers and barriers using an mHealth app in the working context and (2) the added value of three different qualitative methods that are available to evaluate mHealth apps in a working context: interviews with employees, focus groups with employees, and a focus group with experts. METHODS: Employees of a high-tech company and experts were asked to use an mHealth app for at least 3 weeks before participating in a qualitative evaluation. Twenty-two employees participated in interviews, 15 employees participated in three focus groups, and 6 experts participated in one focus group. Two researchers independently coded, categorized, and analyzed all quotes yielded from these evaluation methods with a codebook using constructs from user satisfaction and technology acceptance theories. RESULTS: Interviewing employees yielded 785 quotes, focus groups with employees yielded 266 quotes, and the focus group with experts yielded 132 quotes. Overall, participants muted enthusiasm about the app. Combined results from the three evaluation methods showed drivers and barriers for technology, user characteristics, context, privacy, and autonomy. A comparison between the three qualitative methods showed that issues revealed by experts only slightly overlapped with those expressed by employees. In addition, it was seen that the type of evaluation yielded different results. CONCLUSIONS: Findings from this study provide the following recommendations for organizations that are planning to provide mHealth apps to their workers and for developers of mHealth apps: (1) system performance influences adoption and adherence, (2) relevancy and benefits of the mHealth app should be clear to the user and should address users' characteristics, (3) app should take into account the work context, and (4) employees should be alerted to their right to privacy and use of personal data. Furthermore, a qualitative evaluation of mHealth apps in a work setting might benefit from combining more than one method. Factors to consider when selecting a qualitative research method are the design, development stage, and implementation of the app; the working context in which it is being used; employees' mental models; practicability; resources; and skills required of experts and users.

Emotional sweating across the body: comparing 16 different skin conductance measurement locations.

Skin conductance (SC) is one of the most commonly used measures in psychophysiological studies involving emotional arousal and is traditionally measured at the fingers or the palms (i.e., the palmar locations) of the hand. Palmar skin conductance recording positions are, however, not always preferred for ambulatory recordings in real-life situations. This study quantifies the responsiveness and similarity with the finger of 16 different recording positions of skin conductance while watching emotional film fragments. Findings indicated foot, fingers and shoulders being most responsive, whereas arm, back, armpit, and thighbone were least responsive. The measurements at the foot were most similar with those of the finger. In contrast, arm, back, and armpit traces differed most from the finger trace. Taken together, foot and shoulders are the best alternatives to the finger for ambulatory measurement of skin conductance to reflect emotional arousal. These findings can help new applications using skin conductance, like automated emotion measurements, to come to fruition.