A range of studies in gerontology and geriatrics have looked at changes in positive affect (PA) and negative affect (NA) from pre-test to post-test as an indicator of any given intervention’s effectiveness in improving well-being. However, this method of data collection ignores potential non-linear trajectories of affect during an intervention. This study aimed to examine potential non-linear trajectories of affect in older adults who were enrolled in a study that investigated the effects of a language learning intervention on cognitive function and psychosocial well-being. Participants completed a digital version of the Positive and Negative Affect Schedule (PANAS) weekly over a period of 13 weeks during a language course. A total of 556 observations were collected out of 572 invitations to complete the questionnaire (97% completion rate). Results were analyzed with Generalized Additive Mixed Models (GAMMs), which can take into account non-linear patterns. Our sample included 44 older adults aged ≥65 ( M ± SD = 71 ± 4.4) recruited into three groups: (1) 18 older adults with (past) depression, (2) 12 older adults with cognitive impairment and (3) 14 control participants without depression or cognitive impairment. Our analysis found a clear non-linear change of positive affect over time in participants with (past) depression. We also found a significant linear reduction in negative affect in participants with (past) depression. Our study underscores the utility of collecting multiple data points per participant over time, and then analyzing those with methods that allow for ‘wiggly’ data. Based on our results, we speculate that this approach may be especially relevant for older adults with current or past depression. • Older adults in the control group show stable patterns over time • Older adults with (past) depression show non-linear changes in positive affect • Older adults with cognitive impairment show non-linear changes in negative affect • Those with (past) depression show a linear decrease in experienced negative affect • Generalized Additive Mixed Modeling is useful for modeling this non-linear change
Brouwer et al. (Sun,) studied this question.