Perceptions and effectiveness of episodic future thinking as digital micro-interventions based on mobile health technology.

Objective: Delay discounting denotes the tendency for humans to favor short-term immediate benefits over long-term future benefits. Episodic future thinking (EFT) is an intervention that addresses this tendency by having a person mentally "pre-experience" a future event to increase the perceived value of future benefits. This study explores the feasibility of using mobile health (mHealth) technology to deliver EFT micro-interventions. Micro-interventions are small, focused interventions aiming to achieve goals while matching users' often limited willingness or capacity to engage with interventions. We aim to explore whether EFT delivered as digital micro-interventions can reduce delay discounting, the users' perceptions, and if there are differences between regular EFT and goal-oriented EFT (gEFT), a variant where goals are embedded into future events. Method: A randomized study was conducted with 208 participants allocated to either gEFT, EFT, or a control group for a 21-day study. Results: Results indicate intervention groups when combined achieved a significant reduction of Δlogk=-.80 in delay discounting (p=.017) compared to the control. When split into gEFT and EFT separately only the reduction of Δlogk=.96 in EFT delay discounting was significant (p=.045). We further explore and discuss thematic user perceptions. Conclusions: Overall, user perceptions indicate gEFT may be slightly better for use in micro-interventions. However, perceptions also indicate that audio-based EFT micro-interventions were not always preferable to users, with findings suggesting that future EFT micro-interventions should be delivered using different forms of multimedia based on user preference and context and supported by other micro-interventions to maintain interest.

Exploring Patient Needs and Designing Concepts for Digitally Supported Health Solutions in Managing Type 2 Diabetes: Cocreation Study.

BACKGROUND: Self-management of the progressive disease type 2 diabetes mellitus (T2DM) becomes part of the daily life of patients starting from the time of diagnosis. However, despite the availability of technical innovations, the uptake of digital solutions remains low. One reason that has been reported is that digital solutions often focus purely on clinical factors that may not align with the patient's perspective. OBJECTIVE: The aim of this study was to develop digital solutions that address the needs of patients with T2DM, designed from the user's perspective. The goal was to address the patients' expressed real-world needs by having the users themselves choose the scope and format of the solutions. METHODS: Using participatory methods, we conducted 3 cocreation workshops in collaboration with the Danish Diabetes Association, with 20 persons with T2DM and 11 stakeholders across workshops: user experience designers, researchers, and diabetes experts including a diabetes nurse. The overall structure of the 3 workshops was aligned with the 4 phases of the double diamond: initially discovering and mapping out key experienced issues, followed by a workshop on thematic mapping and definition of key concepts, and succeeded by an exploration and development of 2 prototypes. Subsequently, high-fidelity interactive prototypes were refined as part of the delivery phase, in which 7 formative usability tests were conducted. RESULTS: The workshops mapped experiential topics over time from prediagnosis to the current state, resulting in a detailed exploration and understanding of 6 themes related to and based on the experiences of patients with T2DM: diabetes care, diabetes knowledge, glucose monitoring, diet, physical activity, and social aspects of diabetes. Two prototypes were developed by the participants to address some of their expressed needs over time related to the 6 themes: an activity-based continuous glucose monitoring app and a web-based guide to diabetes. Both prototypes emphasize periods of structured self-measurements of blood glucose to support evolving needs for self-exploration through distinct phases of learning, active use, and supporting use. Periods of low or intermittent use may thus not reflect a failure of design in a traditional sense but rather be a sign of evolving needs over time. CONCLUSIONS: Our results indicate that the needs of patients with T2DM differ between individuals and change over time. As a result, the suggested digitally supported empowering health prototypes can be personalized to support self-exploration, individual preference in long-term management, and changing needs over time. Despite individuals experiencing different journeys with diabetes, users perceive the self-measurement of blood glucose as a universally useful tool to empower everyday decision-making.

Reproducibility of Mesopic and Photopic Pupil Sizes in Myopic Children Using a Dedicated Pupillometer with Human-Assisted or Automated Reading.

This study aimed to investigate the reproducibility of pupil size measurements over time and between reading methods when comparing human-assisted reading to automated reading. Pupillary data were analyzed on a subset of myopic children enrolled in a multicenter randomized clinical trial on myopia control with low-dose atropine. Pupil size measurements were obtained prior to randomization at two time points (screening and baseline visits) using a dedicated pupillometer under mesopic and photopic conditions. A customized algorithm was built to perform automated readings, allowing comparisons between human-assisted and automated readings. Reproducibility analyses followed the principles of Bland and Altman and included the calculation of the mean difference between measurements and limits of agreement (LOA). We included 43 children. Mean (standard deviation) age was 9.8 (1.7) years and 25 (58%) children were girls. Using human-assisted readings, reproducibility over time showed mesopic mean difference of 0.02 mm with LOA from -0.87 mm to 0.91 mm, whereas photopic mean difference was -0.01 mm with LOA from -0.25 mm to 0.23 mm. Reproducibility between human-assisted and automated readings was also higher under photopic conditions, with mean difference of 0.03 mm and LOA from -0.03 mm to 0.10 mm at screening and mean difference of 0.03 mm and LOA from -0.06 mm to 0.12 mm at baseline. Using a dedicated pupillometer, we found that examinations performed under photopic conditions demonstrated higher reproducibility over time and between reading methods. We speculate whether mesopic measurements are sufficiently reproducible to be monitored over time. Furthermore, photopic measurements may be of greater relevance when evaluating the side effects of atropine treatment, such as photophobia.

Baseline pupil size encodes task-related information and modulates the task-evoked response in a speech-in-noise task.

Pupillometry data are commonly reported relative to a baseline value recorded in a controlled pre-task condition. In this study, the influence of the experimental design and the preparatory processing related to task difficulty on the baseline pupil size was investigated during a speech intelligibility in noise paradigm. Furthermore, the relationship between the baseline pupil size and the temporal dynamics of the pupil response was assessed. The analysis revealed strong effects of block presentation order, within-block sentence order and task difficulty on the baseline values. An interaction between signal-to-noise ratio and block order was found, indicating that baseline values reflect listener expectations arising from the order in which the different blocks were presented. Furthermore, the baseline pupil size was found to affect the slope, delay and curvature of the pupillary response as well as the peak pupil dilation. This suggests that baseline correction might be sufficient when reporting pupillometry results in terms of mean pupil dilation only, but not when a more complex characterization of the temporal dynamics of the response is considered. By clarifying which factors affect baseline pupil size and how baseline values interact with the task-evoked response, the results from the present study can contribute to a better interpretation of the pupillary response as a marker of cognitive processing.

What's Up With These Conversational Health Agents? From Users' Critiques to Implications for Design.

Recent advancements in speech recognition technology in combination with increased access to smart speaker devices are expanding conversational interactions to ever-new areas of our lives - including our health and wellbeing. Prior human-computer interaction research suggests that Conversational Agents (CAs) have the potential to support a variety of health-related outcomes, due in part to their intuitive and engaging nature. Realizing this potential requires however developing a rich understanding of users' needs and experiences in relation to these still-emerging technologies. To inform the design of CAs for health and wellbeing, we analyze 2741 critical reviews of 485 Alexa health and fitness Skills using an automated topic modeling approach; identifying 15 subjects of criticism across four key areas of design (functionality, reliability, usability, pleasurability). Based on these findings, we discuss implications for the design of engaging CAs to support health and wellbeing.

Smooth-pursuit performance during Eye-typing from Memory indicates Mental Fatigue.

Mental fatigue is known to occur as a result of activities related to e.g. transportation, health-care, military operations and numerous other cognitive demanding tasks. Gaze tracking has wide-ranging applications, with the technology becoming more compact and processing power reducing. Though numerous techniques have been applied to measure mental fatigue using gaze tracking, smooth-pursuit movement, a natural eye movement generated when following a moving object with gaze, has not been explored with relation to mental fatigue. In this paper, we report the results from a smooth-pursuit movement based eye-typing experiment with varying task difficulty to generate cognitive load, performed in the morning and afternoon by 36 participants. We have investigated the effects of time-on-task and time of day on mental fatigue using self-reported questionnaires and smooth-pursuit performance, extracted from the gaze data. The self-reported mental fatigue increased due to time-on-task, but the time of day did not have an effect. The results illustrate that smooth-pursuit movement performance declined with time-on-task, with increased error in the gaze position and an inability to match the speed of the moving object. The findings exhibit the feasibility of mental fatigue detection using smoothpursuit movements during an eye-interactive task of eye-typing.

Mental fatigue prediction during eye-typing.

Mental fatigue is a common problem associated with neurological disorders. Until now, there has not been a method to assess mental fatigue on a continuous scale. Camera-based eye-typing is commonly used for communication by people with severe neurological disorders. We designed a working memory-based eye-typing experiment with 18 healthy participants, and obtained eye-tracking and typing performance data in addition to their subjective scores on perceived effort for every sentence typed and mental fatigue, to create a model of mental fatigue for eye-typing. The features of the model were the eye-based blink frequency, eye height and baseline-related pupil diameter. We predicted subjective ratings of mental fatigue on a six-point Likert scale, using random forest regression, with 22% lower mean absolute error than using simulations. When additionally including task difficulty (i.e. the difficulty of the sentences typed) as a feature, the variance explained by the model increased by 9%. This indicates that task difficulty plays an important role in modelling mental fatigue. The results demonstrate the feasibility of objective and non-intrusive measurement of fatigue on a continuous scale.

Integrated personalized diabetes management goes Europe: A multi-disciplinary approach to innovating type 2 diabetes care in Europe.

Type 2 diabetes mellitus represents a multi-dimensional challenge for European and global societies alike. Building on an iterative six-step disease management process that leverages feedback loops and utilizes commodity digital tools, the PDM-ProValue study program demonstrated that integrated personalized diabetes management, or iPDM, can improve the standard of care for persons living with diabetes in a sustainable way. The novel "iPDM Goes Europe" consortium strives to advance iPDM adoption by (1) implementing the concept in a value-based healthcare setting for the treatment of persons living with type 2 diabetes, (2) providing tools to assess the patient's physical and mental health status, and (3) exploring new avenues to take advantage of emerging big data resources.

Pupillary measurement during an assembly task.

We conducted an empirical study of 57 children using a printed Booklet and a digital Tablet instruction for LEGO® construction while they wore a head-mounted gaze tracker. Booklets caused a particularly strong pupil dilation when encountered as the first media. Subjective responses confirmed the booklet to be more difficult to use. The children who were least productive and asked for assistance more often had a significantly different pupil pattern than the rest. Our findings suggest that it is possible to collect pupil size data in unconstrained work scenarios, providing insight to task effort and difficulties.

A gaze interactive assembly instruction with pupillometric recording.

This paper presents a study of a gaze interactive digital assembly instruction that provides concurrent logging of pupil data in a realistic task setting. The instruction allows hands-free gaze dwells as a substitute for finger clicks, and supports image rotation as well as image zooming by head movements. A user study in two LEGO toy stores with 72 children showed it to be immediately usable by 64 of them. Data logging of view-times and pupil dilations was possible for 59 participants. On average, the children spent half of the time attending to the instruction (S.D. 10.9%). The recorded pupil size showed a decrease throughout the building process, except when the child had to back-step: a regression was found to be followed by a pupil dilation. The main contribution of this study is to demonstrate gaze-tracking technology capable of supporting both robust interaction and concurrent, non-intrusive recording of gaze- and pupil data in-the-wild. Previous research has found pupil dilation to be associated with changes in task effort. However, other factors like fatigue, head motion, or ambient light may also have an impact. The final section summarizes our approach to this complexity of real-task pupil data collection and makes suggestions for how future applications may utilize pupil information.

SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events.

We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.