A behavioral approach to identifying sources of happiness and unhappiness among individuals with profound multiple disabilities.

Five adults with profound multiple disabilities lived in a state hospital. Staff wanted to know which daily activities made them happy or unhappy.

The team filmed each person during ten routine activities like music, meals, and chores. They counted clear signs: smiles, laughs, bright eyes for happiness. Scowls, turned-away body, closed eyes marked unhappiness.

Two observers scored the same tapes. Their counts matched almost perfectly. The code was simple enough for anyone to learn in one shift.

Every person showed a different pattern. One man smiled most during drum time. One woman frowned most during tooth brushing.

The numbers were steady across days. Happy signs stayed high for favorite activities. Unhappy signs stayed high for disliked ones.

The data gave staff a quick map of each resident’s joy and distress. No guessing, just counts.

Nijs et al. (2016) later used a team of raters instead of one. They still got great reliability, showing the idea scales up.

Cashon et al. (2013) warn that short samples can mislead if behavior jumps around. W et al. used many short clips and still found steady patterns, likely because happiness signs are less variable than problem behavior.

Fabbretti et al. (1997) used the same setting and population but looked at why problem behavior happened. W et al. flip the lens: they track positive affect, giving a fuller picture of quality of life.

You now have a five-minute tool to measure happiness in clients who can’t speak. Film, count smiles, pick activities that win. Re-check every quarter to catch new likes or dislikes. Use the data to justify more music, art, or movement time in the day-hab schedule.