FES-I

The Falls Efficacy Scale-International (FES-I)

Measuring: Measure of fear of falling that assesses both easy and difficult physical activities and social activities and is suitable for use in a range of languages and cultural contexts.

Number of Items: 16

Item	Question Wording	TILDA Variable
1	Cleaning the house.	SCQFalls1
2	Getting dressed or undressed.	SCQFalls2
3	Preparing simple meal.	SCQFalls3
4	Taking a bath or shower.	SCQFalls4
5	Going to the shop.	SCQFalls5
6	Getting in or out of a chair.	SCQFalls6
7	Going up or down stairs.	SCQFalls7
8	Walking around in the neighbourhood.	SCQFalls8
9	Reaching for something above your head or on the ground.	SCQFalls9
10	Going to answer the telephone before it stops ringing.	SCQFalls10
11	Walking on a slippery surface.	SCQFalls11
12	Visiting a friend or relative.	SCQFalls12
13	Walking in a place with crowds.	SCQFalls13
14	Walking on an uneven surface.	SCQFalls14
15	Walking up or down a slope.	SCQFalls15
16	Going out to a social event.	SCQFalls16

Scoring Method:

4-point Likert Scale.

1 = Not at all concerned

2 = Somewhat concerned

3 = Fairly concerned

4 = Very concerned

A cumulative score is generated with higher scores representing greater fear of falling (16-64).

Citation: Yardley, L., Beyer, N., Hauer, K., Kempen, G., Piot-Ziegler, C., & Todd, C. (2005). Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age and ageing, 34(6), 614–619. https://doi.org/10.1093/ageing/afi196

Example TILDA Papers:

Hartley, P., Forsyth, F., O'Halloran, A., Kenny, R. A., & Romero-Ortuno, R. (2023). Eight-year longitudinal falls trajectories and associations with modifiable risk factors: evidence from The Irish Longitudinal Study on Ageing (TILDA). Age and ageing, 52(3): 1-8. https://doi.org/10.1093/ageing/afad037

Code

forvalues i = 1/16 {
    clonevar FESI`i' = SCQFalls`i'   
}

mvdecode FESI1-FESI16, ///
    mv(-99=. \ -812=. \ -823=. \ -834=.)


foreach var of varlist FESI1-FESI16 {
    
    replace `var' = . if !inrange(`var', 1, 4)
    
}


egen FESI_nvalid = rownonmiss(FESI1-FESI16)

egen FESI_sum = rowtotal(FESI1-FESI16)


gen FESIscore = .


* All 16 items completed
replace FESIscore = FESI_sum ///
    if FESI_nvalid == 16


* 1-4 items missing:
* prorate according to official FES-I instructions
replace FESIscore = ceil((FESI_sum / FESI_nvalid) * 16) ///
    if inrange(FESI_nvalid, 12, 15)


* 5 or more items missing:
* score remains missing
replace FESIscore = . ///
    if FESI_nvalid < 12

label variable FESIscore ///
    "Falls Efficacy Scale - International total score (16-64)"

notes FESIscore : ///
    Higher scores indicate greater concern about falling.

notes FESIscore : ///
    If <=4 items missing, completed-item mean is multiplied by 16 ///
    and rounded upward; >=5 missing items results in a missing score.


summarize FESIscore, detail

tab FESI_nvalid, missing

assert inrange(FESIscore, 16, 64) ///
    if !missing(FESIscore)


drop FESI1-FESI16 FESI_nvalid FESI_sum

Download Stata .do file

# ---------------------------------------------------------------
# 1. Create scored copies of the 16 FES-I items
# ---------------------------------------------------------------

fesi_source <- paste0("SCQFalls", 1:16)
fesi_items  <- paste0("FESI", 1:16)

data[fesi_items] <- data[fesi_source]


# ---------------------------------------------------------------
# 2. Recode TILDA special missing values
# ---------------------------------------------------------------

special_missing <- c(-99, -812, -823, -834)

for (v in fesi_items) {
  data[[v]][data[[v]] %in% special_missing] <- NA_real_
}


# ---------------------------------------------------------------
# 3. Restrict items to valid FES-I responses
#    1 = Not at all concerned
#    2 = Somewhat concerned
#    3 = Fairly concerned
#    4 = Very concerned
# ---------------------------------------------------------------

for (v in fesi_items) {
  data[[v]][!is.na(data[[v]]) &
            !(data[[v]] >= 1 & data[[v]] <= 4)] <- NA_real_
}


# ---------------------------------------------------------------
# 4. Count valid items and calculate observed sum
# ---------------------------------------------------------------

data$FESI_nvalid <- rowSums(!is.na(data[fesi_items]))

data$FESI_sum <- rowSums(data[fesi_items], na.rm = TRUE)


# ---------------------------------------------------------------
# 5. Calculate official FES-I total score
# ---------------------------------------------------------------

data$FESIscore <- NA_real_


# All 16 items completed
complete <- data$FESI_nvalid == 16

data$FESIscore[complete] <- data$FESI_sum[complete]


# 1-4 items missing:
# prorate according to official FES-I instructions
prorate <- data$FESI_nvalid >= 12 & data$FESI_nvalid <= 15

data$FESIscore[prorate] <- ceiling(
  (data$FESI_sum[prorate] / data$FESI_nvalid[prorate]) * 16
)


# 5 or more items missing:
# score remains missing
data$FESIscore[data$FESI_nvalid < 12] <- NA_real_


# ---------------------------------------------------------------
# 6. Labels / notes
# ---------------------------------------------------------------

attr(data$FESIscore, "label") <-
  "Falls Efficacy Scale - International total score (16-64)"

attr(data$FESIscore, "note") <-
  paste(
    "Higher scores indicate greater concern about falling.",
    "If <=4 items are missing, the completed-item mean is multiplied",
    "by 16 and rounded upward; >=5 missing items results in a missing score."
  )


# ---------------------------------------------------------------
# 7. Checks
# ---------------------------------------------------------------

summary(data$FESIscore)

table(data$FESI_nvalid, useNA = "ifany")

stopifnot(
  all(
    is.na(data$FESIscore) |
      (data$FESIscore >= 16 & data$FESIscore <= 64)
  )
)


# ---------------------------------------------------------------
# 8. Drop temporary scoring variables if not required
# ---------------------------------------------------------------

data[fesi_items] <- NULL
data$FESI_nvalid <- NULL
data$FESI_sum <- NULL

Download R file

* ---------------------------------------------------------------.
* 1. Create scored copies of the 16 FES-I items.
* ---------------------------------------------------------------.

DO REPEAT src =
  SCQFalls1 SCQFalls2 SCQFalls3 SCQFalls4
  SCQFalls5 SCQFalls6 SCQFalls7 SCQFalls8
  SCQFalls9 SCQFalls10 SCQFalls11 SCQFalls12
  SCQFalls13 SCQFalls14 SCQFalls15 SCQFalls16
 /dst =
  FESI1 FESI2 FESI3 FESI4
  FESI5 FESI6 FESI7 FESI8
  FESI9 FESI10 FESI11 FESI12
  FESI13 FESI14 FESI15 FESI16.

  COMPUTE dst = src.
END REPEAT.


* ---------------------------------------------------------------.
* 2. Recode TILDA special missing values.
* ---------------------------------------------------------------.

RECODE
  FESI1 TO FESI16
  (-99=SYSMIS)
  (-812=SYSMIS)
  (-823=SYSMIS)
  (-834=SYSMIS).


* ---------------------------------------------------------------.
* 3. Restrict items to valid FES-I responses.
*    1 = Not at all concerned.
*    2 = Somewhat concerned.
*    3 = Fairly concerned.
*    4 = Very concerned.
* ---------------------------------------------------------------.

DO REPEAT v = FESI1 TO FESI16.
  IF (NOT MISSING(v) AND NOT RANGE(v,1,4)) v = $SYSMIS.
END REPEAT.


* ---------------------------------------------------------------.
* 4. Count valid items and calculate observed sum.
* ---------------------------------------------------------------.

COMPUTE FESI_nvalid = NVALID(
  FESI1, FESI2, FESI3, FESI4,
  FESI5, FESI6, FESI7, FESI8,
  FESI9, FESI10, FESI11, FESI12,
  FESI13, FESI14, FESI15, FESI16
).

COMPUTE FESI_sum = SUM(
  FESI1, FESI2, FESI3, FESI4,
  FESI5, FESI6, FESI7, FESI8,
  FESI9, FESI10, FESI11, FESI12,
  FESI13, FESI14, FESI15, FESI16
).


* ---------------------------------------------------------------.
* 5. Calculate official FES-I total score.
* ---------------------------------------------------------------.

COMPUTE FESIscore = $SYSMIS.


* All 16 items completed.
IF (FESI_nvalid = 16) FESIscore = FESI_sum.


* 1-4 items missing:
* prorate according to official FES-I instructions.
IF (RANGE(FESI_nvalid,12,15))
  FESIscore = TRUNC(((FESI_sum / FESI_nvalid) * 16) + .999999999).


* 5 or more items missing:
* FESIscore remains system-missing.


* ---------------------------------------------------------------.
* 6. Labels.
* ---------------------------------------------------------------.

VARIABLE LABELS FESIscore
  "Falls Efficacy Scale - International total score (16-64)".


* ---------------------------------------------------------------.
* 7. Checks.
* ---------------------------------------------------------------.

DESCRIPTIVES VARIABLES=FESIscore
  /STATISTICS=MEAN STDDEV MIN MAX.

FREQUENCIES VARIABLES=FESI_nvalid
  /MISSING=INCLUDE.


* ---------------------------------------------------------------.
* 8. Drop temporary scoring variables if not required.
* ---------------------------------------------------------------.

DELETE VARIABLES
  FESI1 TO FESI16
  FESI_nvalid
  FESI_sum.

EXECUTE.

Download SPSS .sps file