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As self-tracking has evolved from a niche movement to a mass-market phenomenon, it has become possible for people to track a broad range of activities and vital parameters over years, even decades. The associated opportunities, as well as the challenges, have had very little research attention so far. With the phenomenon of long-term tracking becoming widespread and important, we have identified its key characteristics by drawing on work from UbiComp, HCI, and health informatics. We identify important differences between long- and short-term tracking, and discuss consequences for the tracking process. Going beyond previous models for short-term tracking, we now present a model for long-term tracking, integrating its distinctive characteristics in purposeful and incidental tracking. Finally, we present major topics for future research.

© 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery. Rhythm is the most basic skill for people learning to dance. Beginners need practice but also close coaching and constant feedback. However, in most dance classes teachers often find challenging to provide attention to each student. A possible solution to this problem would be to automate the provision of feedback to students by objectively assessing rhythm from their movement data. But how effective would a fully automated approach be compared to dance experts in evaluating dance performance? We conducted a study aimed at exploring this by ?easuring' dance rhythm from accelerometer data streams and contrasting the algorithm results with expert human judgement.We developed RiMoDe, an algorithm that tracks bodily rhythmic skills, and gathered a dataset that includes 282 independent evaluations made by expert dance teachers on 94 dance exercises performed by 7 dance students. Our findings revealed major gaps between a purely algorithmic approach and how experts evaluate dance rhythm.We identified 6 themes that are important when assessing rhythm. We discuss how these themes should be considered and incorporated into future systems aimed at supporting people learning to dance.

Automation of dietary assessment can reduce limitations of established methodologies, by alleviating participant and researcher burden. Designed as a research tool, the electronic Dietary Intake Assessment (e-DIA) is a food record in mobile phone application format. The present study aimed to examine the relative validity of the e-DIA with the 24-h recall method to estimate intake of food groups. A sample of eighty university students aged 19–24 years recorded 5 d of e-DIA and 3 d of recall within this 5-d period. The three matching days of dietary data were used for analysis. Food intake data were disaggregated and apportioned to one of eight food groups. Median intakes of food groups were similar between the methods, and strong correlations were found (mean: 0·79, range: 0·69–0·88). Cross-classification by tertiles produced a high level of exact agreement (mean: 71 %, range: 65–75 %), and weightedκvalues were moderate to good (range: 0·54–0·71). Although mean differences (e-DIA–recall) were small (range: –13 to 23 g), limits of agreement (LOA) were relatively large (e.g. for vegetables, mean difference: –4 g, LOA: –159 to 151 g). The Bland–Altman plots showed robust agreement, with minimum bias. This analysis supports the use of e-DIA as an alternative to the repeated 24-h recall method for ranking individuals’ food group intake.

BACKGROUND: The electronic Dietary Intake Assessment (e-DIA), a digital entry food record mobile phone app, was developed to measure energy and nutrient intake prospectively. This can be used in monitoring population intakes or intervention studies in young adults. OBJECTIVE: The objective was to assess the relative validity of e-DIA as a dietary assessment tool for energy and nutrient intakes using the 24-hour dietary recall as a reference method. METHODS: University students aged 19 to 24 years recorded their food and drink intake on the e-DIA for five days consecutively and completed 24-hour dietary recalls on three random days during this 5-day study period. Mean differences in energy, macro-, and micronutrient intakes were evaluated between the methods using paired t tests or Wilcoxon signed-rank tests, and correlation coefficients were calculated on unadjusted, energy-adjusted, and deattenuated values. Bland-Altman plots and cross-classification into quartiles were used to assess agreement between the two methods. RESULTS: Eighty participants completed the study (38% male). No significant differences were found between the two methods for mean intakes of energy or nutrients. Deattenuated correlation coefficients ranged from 0.55 to 0.79 (mean 0.68). Bland-Altman plots showed wide limits of agreement between the methods but without obvious bias. Cross-classification into same or adjacent quartiles ranged from 75% to 93% (mean 85%). CONCLUSIONS: The e-DIA shows potential as a dietary intake assessment tool at a group level with good ranking agreement for energy and all nutrients., published_or_final_version