A journal club presentation: Objective: To quantify the consumption of chocolates in a hospital ward environment. A study published in the BMJ on 14th Dec 2013.

1. Survival time of chocolates
in hospital wards
Dr. George Koshy Vilanilam
Journal Club

2. Objective
To quantify the consumption of chocolates in a
hospital ward environment

3. Introduction
Reduced risks for the
development of cardio-metabolic
disorders (BMJ 2011)
Buitrago-Lopez A, Sanderson J, Johnson L, Warnakula S, Wood A, Di Angelantonio E, et al. Chocolate consumption and
cardiometabolic disorders: systematic review and meta-analysis. BMJ 2011;343:d4488.

4. Methods and design
❖ Multi-centre !
❖ Across 3 UK sites and 4 wards!
❖ Prospective !
❖ Time frame: Last week of August 2013!
❖ Covert observational study

5. Acute & General Medicine Ward

6. Haematology and Oncology wards

7. General Surgery Ward

8. Participants
!

9. Participants
350 g box of Quality Street chocolates (Nestlé, Switzerland)
350 g box of Roses chocolates (Cadbury, United Kingdom)
30 -35 chocolates on an
average in each box

10. Method
10 am

11. Method

12. Methods
❖ Observation period: Minimum two hours to maximum of 4 hours
❖ Before leaving: Number of leftover chocolates
❖ Covert and off-duty

13. Outcomes
❖ Primary outcome:
Median survival time of
a chocolate!
❖ Secondary outcome:
Mean time taken for a
box of chocolates to be
opened when placed in
the ward

14. Statistical analysis
❖Estimated that a total of at least 210 chocolates would be needed to
provide 80% power (P<0.05, two sided) to detect a 50% change in
the hazard ratio between groups (Quality Street and Roses)
❖Assumed a median survival of 60 minutes for an individual
chocolate
❖Follow up period: 4 hours maximum
❖Assuming a 350 g box of chocolates contained 30 chocolates
❖Estimated that they needed eight boxes = approx. 240 chocolates
❖The leftover ones: Lost to follow-up

15. Statistical analysis
❖ Primary outcome: Kaplan-Meier survival analysis and Cox
regression.!
❖ Mathematical model testing and selection: Using
independent variable of survival time and dependant
variable of proportion of chocolates remaining. Highest R2
value and an F statistic with a two sided significance level
of <0.05 was chosen as the best fit.!
❖ Survival half life : Time taken until 50% of the chocolates
remained.!
❖ Preference: Sample t-test with null hypothesis test value of
0.5 = equal preference.

16. Results
❖ Total chocolates in the boxes finally: 258!
❖ Total consumed: 191 !
❖ Percentage eaten: 74% !
❖ Remainder: Lost to follow-up!
!
❖ Mean observation period: 254 minutes (4 hours 14 mins)!
❖ No adverse events

17. Results
Regression curve fitting suggested that the rate of emptying of a
box of chocolates was best explained by an exponential decay
curve with equation Cp=e−λt; where Cp is the proportion of
chocolates remaining, t is the time in minutes, and λ is the decay
constant, which was −0.007 for this model !
(model R2=0.844, P<0.001).

18. Results

19. Results

20. Results
❖ The median survival time of a chocolate was 51 minutes (39 to 63)
❖ An exponential decay model best fitted these findings (model R2=0.844,
P<0.001), with a survival half life (time taken for 50% of the chocolates to
be eaten) of 99 minutes
❖ Mean time taken to open a box of chocolates from first appearance on the
ward was 12 minutes (95% confidence interval 0 to 24)
❖ Quality Street chocolates survived longer than Roses chocolates (hazard
ratio for survival of Roses v Quality Street 0.70, 95% confidence interval
0.53 to 0.93, P=0.014).
❖ Higher Percentages of chocolates were consumed by healthcare assistants
(28%) and nurses (28%), followed by doctors (15%).

21. Pros
❖ Leading chocolate brands
❖ Multiple sites
❖ Multiple specialties
❖ Ensuring that all observers were familiar with the study protocol
❖ Bias was minimised by using a similar start time

22. !
❖The trial was conducted during Ramadan
❖Limited sample size
❖Differences in consumption habits were noted between ward and
specialty. But not significant due to small sample size.
❖A larger study powered to detect differences in these endpoints
would be interesting.
Limitations
The observers
were all doctors

23. Limitations
1. To avoid potential contamination bias, the trial should
better have been designed as a cluster randomized
study.
Hundley V, Cheyne H, Bland JM ,Styles M , Barnett CA. So you want to conduct a cluster
randomized controlled trial? Journal of Evaluation in Clinical Practice 2010;16:632–638.

24. Limitations
2. The location and display of the two boxes in each of the
four test areas is critical!
Considering the heavy workload of many hospital
employees it is likely that the box closest to the entrance
door of the test facility will be emptied at the fastest rate.
Dijksterhuis A, Smith PK, Baaren RB van, Wigboldus DHJ. The unconscious consumer: effects
of environment on consumer behavior. Journal of Consumer Psychology 2005;15:193-202.

25. Limitations
3. The “loss to follow-up” rate of the chocolates was high
(26%). The main reason for this was that observers
prematurely ended the continuous observation of the test
boxes. This was a clear violation of the protocol which
writes“The observers will record data until both chocolate
boxes have emptied”. This suggests that the trial was
executed in a suboptimal way.

26. Conclusion
What is already known on the topic:!
!
Few quantitative data are available on chocolate survival in a hospital
environment and on healthcare professionals’ chocolate consumption,
although anecdotal evidence is abundant!
!
What this study adds: !
!
1. Suggests that the survival time of a chocolate is short, at under an hour.!
2. the initial rate of chocolate consumption from a box is rapid but slows with
time!
3. An exponential decay model best fitted this trend!
4. Taken as whole groups, the highest percentages of chocolates were
consumed by healthcare assistants and nurses, followed by doctors!

27. Or are these more up your street?
Can roses really grow on you?