In today's connected society, many people rely on mHealth and self-tracking (ST) technology to help them break their sedentary lifestyle and stay fit. However, there is scarce evidence of such technological interventions' effectiveness, and there are no standardized methods to evaluate the short-and long-term impact of such technologies on people's physical activity and health. This work aims to help ST and HCI practitioners and researchers by empowering them with systematic guidelines and an extensible framework for constructing such technological interventions. This survey and the proposed design and evaluation framework aim to contribute to health behavior change and user engagement sustainability. To this end, we conduct a literature review of 117 papers between 2008 and 2020, which identifies the core ST HCI design methods and their efficacy, as well as and the most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review's findings, we propose the PAST SELF end-to-end framework to facilitate the classification, design, and evaluation of ST technology. PAST SELF systematically organizes common methods and guidelines from existing works in ubiquitous ST research. Hence, it has potential applications in industrial and scientific settings and can be utilized by practitioners and researchers alike. CCS Concepts: • Human-centered computing → HCI design and evaluation methods; Ubiquitous and mobile computing design and evaluation methods; • General and reference → Surveys and overviews; • Applied computing → Consumer health.

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It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions. To achieve this, we experiment with numerous machine learning and deep learning paradigms as a robust benchmark for physical activity prediction tasks. To train our models, we utilize, "MyHeart Counts", an open, large-scale dataset collected in-the-wild from thousands of users. We also propose a prescriptive framework for self-tracking aggregated data preprocessing, to facilitate data wrangling of real-world, noisy data. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error, proving the feasibility of the physical activity prediction task, and paving the way for future research.

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral and psychological patterns due to challenges in collecting and releasing such datasets, including waning user engagement or privacy considerations. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n = 71 participants. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71 M rows of data. The participants contributed their data through validated surveys, ecological momentary assessments, and a Fitbit Sense smartwatch and consented to make these data available to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data will open novel research opportunities and potential applications in multiple disciplines.

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