Impact of information technology supported serious leisure gardening on the wellbeing of older adults: The Turntable project

OBJECTIVE

The study presented in this paper aimed to assess the effect of an Information Technology enabled community gardening program for older adults, developed by an international consortium.

METHODS

We have executed a quantitative, pre- and post-test field trial with older adult volunteers to test the proposed programme in two European countries, Italy and Belgium (n=98). We used standardized and ad hoc questionnaires to measure changes in the volunteers' mental and psychological state during the trial. The statistical data analysis sought for differences in the pre- and post-test values of the key scores related to the perceived quality of life and benefits of gardening via paired-samples t-tests, and also tried to identify the important factors of significant changes via logistic regression.

RESULTS

We found significant improvements in the perceived benefits of gardening and also in the scores computed from the WHO Quality of Life instruments, especially in the social sub-domains. The improvements were associated with the country, age, marital state and education of the volunteers. Higher age or being widow, divorced or single increased the odds of a significant improvement in the scores in more than one sub-domains.

CONCLUSION

Though the two trial settings were different in some aspects, the observed significant improvements generally confirmed the positive effects of gardening concerning the perceived quality of life and benefits of gardening.

Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

Background

Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices.

Methods

Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed.

Results

The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively.

Conclusion

The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00911-6.