Using a Mobile Phone App to Analyze the Relationship Between Planned and Performed Physical Activity in University Students: Observational Study

Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population

OBJECTIVES

Smartphones are increasingly used to collect real-time information on time-varying exposures. We developed and deployed an application (app) to evaluate the feasibility of using smartphones to collect real-time information on intermittent agricultural activities and to characterize agricultural task variability in a longitudinal study of farmers.

METHODS

We recruited 19 male farmers, aged 50-60 years, to report their farming activities on 24 randomly selected days over 6 months using the Life in a Day app. Eligibility criteria include personal use of an iOS or Android smartphone and >4 h of farming activities at least two days per week. We developed a study-specific database of 350 farming tasks that were provided in the app; 152 were linked to questions that were asked when the activity ended. We report eligibility, study compliance, number of activities, duration of activities by day and task, and responses to the follow-up questions.

RESULTS

Of the 143 farmers we reached out to for this study, 16 were not reached by phone or refused to answer eligibility questions, 69 were ineligible (limited smartphone use and/or farming time), 58 met study criteria, and 19 agreed to participate. Refusals were mostly related to uneasiness with the app and/or time commitment (32 of 39). Participation declined gradually over time, with 11 farmers reporting activities through the 24-week study period. We obtained data on 279 days (median 554 min/day; median 18 days per farmer) and 1,321 activities (median 61 min/activity; median 3 activities per day per farmer). The activities were predominantly related to animals (36%), transportation (12%), and equipment (10%). Planting crops and yard work had the longest median durations; short-duration tasks included fueling trucks, collecting/storing eggs, and tree work. Time period-specific variability was observed; for example, crop-related activities were reported for an average of 204 min/day during planting but only 28 min/day during pre-planting and 110 min/day during the growing period. We obtained additional information for 485 (37%) activities; the most frequently asked questions were related to "feed animals" (231 activities) and "operate fuel-powered vehicle (transportation)" (120 activities).

CONCLUSIONS

Our study demonstrated feasibility and good compliance in collecting longitudinal activity data over 6 months using smartphones in a relatively homogeneous population of farmers. We captured most of the farming day and observed substantial heterogeneity in activities, highlighting the need for individual activity data when characterizing exposure in farmers. We also identified several areas for improvement. In addition, future evaluations should include more diverse populations.

Test-retest of the Subjective Visual Vertical Test performed using a mobile application with the smartphone anchored to a turntable

PURPOSE

The alterations of the Subjective visual vertical test are related to vestibular pathology. Our previously validated method to distinguish between healthy and pathological individuals measures the deviation from the Subjective visual vertical using a mobile application installed on a smartphone fixed to a turntable anchored to the wall. The aim of this study was evaluating the intra-observer reliability of our method in individuals with or without vestibular pathology.

METHODS

Participants were recruited consecutively. In each individual two measurements with an interval of 2 h were made. Both tests were performed by the same examiner. A total of 91 patients were included in this study, of which 25 were healthy and 66 diseased. Intra-observer reliability was evaluated using the intraclass correlation coefficient (ICC). To assess the clinical accuracy of the measurement, we calculated the standard error of the measurement (SEM) and the minimum detectable change (MDC) with a 95% confidence interval.

RESULTS

Intra-observer reliability was excellent with an ICC 0.95 (0.92-0.97) in the whole sample, in healthy patients 0.91 (0.80-0.96) and in pathological patients 0.92 (0.87-0.95). The SEM was calculated to be 0.59 for the whole sample (0.26 in the "healthy" group, and 0.67 in the pathological group). Likewise, the sample's MDC was 1.16, being 0.52 and 1.36 for the healthy and the pathological group, respectively.

CONCLUSIONS

Considering the results, our method presents an excellent intraobserver reliability. Furthermore, changes in deviation greater than 0.52 in healthy individuals and 1.36 in pathological individuals can be considered a real change in deviation.