Validity of pervasive computing based continuous physical activity assessment in community-dwelling old and oldest-old

Narayan Schütz, Hugo Saner, Beatrice Rudin, Angela Botros, Bruno Pais, Valérie Santschi, Philipp Buluschek, Daniel Gatica-Perez, Prabitha Urwyler, Laura Marchal-Crespo, René M Müri, Tobias Nef, Narayan Schütz, Hugo Saner, Beatrice Rudin, Angela Botros, Bruno Pais, Valérie Santschi, Philipp Buluschek, Daniel Gatica-Perez, Prabitha Urwyler, Laura Marchal-Crespo, René M Müri, Tobias Nef

Abstract

In older adults, physical activity is crucial for healthy aging and associated with numerous health indicators and outcomes. Regular assessments of physical activity can help detect early health-related changes and manage physical activity targeted interventions. The quantification of physical activity, however, is difficult as commonly used self-reported measures are biased and rather unprecise point in time measurements. Modern alternatives are commonly based on wearable technologies which are accurate but suffer from usability and compliance issues. In this study, we assessed the potential of an unobtrusive ambient-sensor based system for continuous, long-term physical activity quantification. Towards this goal, we analysed one year of longitudinal sensor- and medical-records stemming from thirteen community-dwelling old and oldest old subjects. Based on the sensor data the daily number of room-transitions as well as the raw sensor activity were calculated. We did find the number of room-transitions, and to some degree also the raw sensor activity, to capture numerous known associations of physical activity with cognitive, well-being and motor health indicators and outcomes. The results of this study indicate that such low-cost unobtrusive ambient-sensor systems can provide an adequate approximation of older adults' overall physical activity, sufficient to capture relevant associations with health indicators and outcomes.

Conflict of interest statement

Dr. Philipp Buluschek is employed by DomoSafety S.A., which is the manufacturer of the displayed sensor system. The remaining authors declare no potential conflict of interest.

Figures

Figure 1
Figure 1
Visual Correlation Matrix of the four sensor-derived physical activity metrics and the clinical assessments). Shown is a visual representationh of the respective correlations as measured by the Spearman’s rank correlation coefficients (ρ) based on an α = 0.05i. The sensor-derived physical activity metrics (rows) represent the mean and the coefficient of variation (CV) of the daily measurements over the whole monitoring duration. The size as well as colour-intensity signal the correlation strength, where red means a strong positive and blue a strong negative correlation. aTimed Up & Go (TUG) (Counting = while additionally counting backwards from 100; Cup = while holding a full cup of water). bGeriatric Depression Scale (GDS). cTinetti Performance-oriented mobility assessment (POMA). dMontreal cognitive assessment (MoCA). eKnee extensor strength (Knee). fHip flexor strength (Hip). gVisual analogue scale: measuring perceived health based on the EQ-5D-3L system (EQ-VAS). hcreated using the R package “corrplot” i*<0.05; **<0.01.
Figure 2
Figure 2
Room-transition heatmaps: comparison between healthy subject and subject with health issues. Shows two heatmaps comparing five months of physical activity measured by the number of room-transitions. One example of a healthy participant (participant 9) with a rather active lifestyle (upper) and the other one of a subject (participant 11) which developed severe and eventually fatal health issues (lower). Note the increased number of transitions throughout the second last week of the subject with health issues, distinctly showing the influence of visits from nurses, family and friends. (more intense colour signifies more room-transitions).
Figure 3
Figure 3
Comparison of first and last measurements of multiple assessments in a healthy subject and one with rapidly declining health. Shows a case of rapid declining health (participant 11) and compares it with data from a healthy reference subject (participant 9). As such, the average in room-transitions of the first and last recorded month is displayed (left). In a similar manner, the handgrip strength (middle) and timed up and go (TUG) times (right) of the first and last assessments are shown. In all cases, for the subject with health issues there was a decrease in metrics (less room-transitions, less handgrip-strength, longer TUG times), while the healthy subject did not exhibit negative changes.
Figure 4
Figure 4
Exemplary apartment layout and PIR-sensor placement. Gives a broad idea of the kind of apartments we monitored and where the sensors where placed.

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