ISSN 0030- 6096

O S A K A
C I T Y

Vol.

69

No.1

M E D I C A L

2023

J O U R N A L

V o l . 6 9 ,N o . 12 0 2 3

PUBLISHED BYOSAKA CITY MEDICAL ASSOCIATIONOSAKA JAPANOsaka City Medical Journal
Vo l . 6 9 , N o . 1 ,Ju n e 2 0 2 3

CONTENTS
Endocardial Bipolar Voltage Predicts the Need for Additional Ablation

page

Inside the Left Atrial Posterior Wall
TOMOYA YANAGISHITA, TOMOTAKA YOSHIYAMA, RYOTA KAWAI,NATSUMI TOYODA, KENICHI NAKATSUJI, YASUHIRO IZUMIYA,AYUMI SHINTANI, and DAIJU FUKUDA ･･････････････････････････････････････････････････････････････

1

Relationship between Work Life Balance and Temperament among
Japanese Workers
YOKO NAKAMICHI, YASUHIKO DEGUCHI, SHINICHI IWASAKI,YUTARO OKAWA, YUKI UESAKA, and KOKI INOUE ･･････････････････････････････････････････････ 11
The Comorbidity of Autism Spectrum Disorder Slows Body Mass Index
Gain in Adult-onset Avoidant/Restrictive Food Intake Disorder
TAKUMI MATSUZUKA, SAORI MIYAMOTO, TOMOKO HARADA,TSUNEO YAMAUCHI, MIHOKO HONDA, NAOKI OHARA,AKIHIRO MUI, and KOKI INOUE ･･････････････････････････････････････････････････････････････････････ 21
Effectiveness of Financial Incentives for a Pedometer-based
Walking Promotion Program
ERIKO KOMIYA, SATSUKI TANIUCHI, MASATSUGU SHIBA,AYUMI SHINTANI, and HIROAKI NAKAMURA ･･････････････････････････････････････････････････････ 31
Clinical Significance of the AID Classification in Diabetic Foot Ulcers:
Awareness of Arteriopathy, Bacterial Infection, and Foot Deformity
SHINOBU AYABE, OSAMI KAWARADA, and HISASHI MOTOMURA ･････････････････････････････ 41

Osaka City Med. J. Vol. 69, 1-9, 2023

Endocardial Bipolar Voltage Predicts the Needfor Additional Ablation Inside the Left Atrial Posterior Wall
TOMOYA YANAGISHITA1), TOMOTAKA YOSHIYAMA2), RYOTA KAWAI3), NATSUMI TOYODA2),KENICHI NAKATSUJI2), YASUHIRO IZUMIYA2), AYUMI SHINTANI3), and DAIJU FUKUDA2)
Department of Cardiovascular Medicine1),
Osaka City University Graduate School of Medicine; andDepartment of Cardiovascular Medicine2) and Medical Statistics3),
Osaka Metropolitan University Graduate School of Medicine

Abstract
Background
Several reports have shown that prognosis of patients with non-paroxysmal atrial fibrillation, whowere treated with pulmonary vein isolation with left atrial posterior wall isolation may be superior tothose who were treated with pulmonary vein isolation alone. In patients undergoing posterior wall
isolation, additional ablation inside the posterior wall is a predictor of posterior wall isolation
reconduction in the long term. This study aimed to identify predictors of the need for additional
ablation inside the posterior wall.
Methods
In total, 77 patients with non-paroxysmal atrial fibrillation who underwent first-time pulmonaryvein isolation with left atrial posterior wall isolation were retrospectively examined. All patients
underwent placement of a linear lesion in the left atrial roof and f loor. Patients were grouped
according to need for additional ablation inside the posterior wall.
Results
First-pass posterior wall isolation was achieved in 32 patients (42%). Three required additional
gap ablation and 42 required ablation inside the posterior wall. Complete posterior wall isolation was
eventually achieved in all. In the multivariate analysis, maximum voltage (3.68±1.94 mV vs 2.38±
1.49 mV; p＝0.02) and mean voltage (1.94±1.03 vs 1.28±0.91 mV; p＝0.03) at the roof line weresignificantly higher in patients with an additional ablation.
Conclusions
High endocardial bipolar voltage at the left atrial roof line was an independent predictor of
additional ablation inside the posterior wall. Therefore, we think that when obtaining left atrial
posterior wall first-pass isolation, it is necessary to note whether there is a high-voltage region in the
Received August 3, 2022; accepted November 21, 2022.
Correspondence to: Tomotaka Yoshiyama, MD, PhD.
Department of Cardiovascular Medicine, Osaka Metropolitan University Graduate School of Medicine,
1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
Tel: +81-6-6645-3801; Fax: +81-6-6646-6215
E-mail: tomotaka0324@gmail.com-1-

Yanagishita et alleft atrial roof.
Key Words: ‌Atrial fibrillation; Catheter ablation; Left atrial posterior wall isolation;
Left atrial roof line; Left atrial f loor line

Introduction
Pulmonary vein isolation (PVI) via catheter ablation is a standard treatment for atrial fibrillation(AF). However, PVI alone is insufficient for many patients with non-paroxysmal AF and an additional
procedure may be required. Additional procedures that may improve catheter ablation effectivenessinclude linear ablation of the left atrial (LA) roof and mitral isthmus1), complex fractionated atrial
electrogram ablation2), low-voltage area ablation3), and LA posterior wall isolation (PWI)4).
The LA posterior wall is important in initiation and maintenance of AF5-9). PVI with LA PWI may
be superior to PVI alone in the treatment of non-paroxysmal AF10). However, achieving PWI via roof
and f loor linear ablation combined with gap ablation is difficult. Further ablation inside the posteriorwall is frequently necessary.
Successful first-pass isolation is associated with treatment durability and lower risk ofrecurrence 11,12). Performing additional ablation inside the posterior wall is a predictor of PWI
reconduction in the long term13). Therefore, first-pass blockage across the roof and f loor lines andavoiding additional ablation inside the posterior wall are considered important when performing
PWI.
Indicators of acute reconduction after PVI include ablation index (AI), which incorporates catheter
tip contact force (CF), power, and radiofrequency (RF) time into its calculation; force-time integral(FTI); and LA voltage14,15). This study aimed to identify predictors of the need for additional ablation
inside the posterior wall in patients undergoing PWI.

Methods
Study patients
We conducted a retrospective cohort study at Osaka Metropolitan University Hospital thatexamined patients with non-paroxysmal AF who underwent catheter ablation for the first time
between June 2018 and July 2020. Patients who had undergone a previous LA catheter ablation
procedure were excluded. Non-paroxysmal AF was defined as continuous AF with episode duration＞7 days16).

Catheter ablation procedure
All patients underwent AI-guided catheter ablation using a three-dimensional navigation system(CARTO 3; Biosense Webster, Irvine, CA, USA). An LA voltage map was created during high rightatrial pacing in sinus rhythm. If the patient was in AF, sinus rhythm was restored before mapping
via low-energy (10 to 30 J) internal cardioversion achieved with a BeeAT catheter (Japan Lifeline,
Tokyo, Japan) placed in the coronary sinus. Bipolar signals, high-pass filtered at 30 Hz and low-passfiltered at 500 Hz, were acquired using a multipolar multispline catheter with 2-mm interelectrodespacing (PENTARAY NAV; Biosense Webster). Ablation was performed using a 3.5-mm tip-irrigatedcatheter with CF sensor (Navistar Thermocool SmartTouch™ Surround Flow, Biosense Webster).
After confirming completion of bilateral PVI, linear lesions were created at the roof and the f loor. Alllesions were created at a target AI ≧550 for the roof line, ≧450 for the f loor line, and ≧400 for theesophageal segments. Inter-lesion distance target was 2 to 4 mm. Each lesion was created using 35-2-Voltage Predicts the Need for Additional Ablation

Figure 1. Posterior wall isolation techniques. Group A comprised patients in whom isolation was successfully
achieved by (a) a single ablation line on the roof and f loor alone or with (b) additional gap ablation. (c) In group B
patients, isolation required additional ablation inside the posterior wall.

W at the roof and 30 W at the f loor. Parameter settings in the VisiTag module of the CARTO 3
system were catheter stability (3 mm for 3 s) and minimum CF (50% of time ＞5g). An esophageal
temperature monitoring catheter (SensiTherm; St. Jude Medical Inc., Saint Paul, MN, USA) was used.
RF energy delivery was immediately stopped if the temperature exceeded 41ºC. Completion of PWI
was confirmed by the disappearance of atrial sharp potentials within the isolated posterior area andthe lack of LA excitation with high output stimulation in the posterior wall. If isolation was notachieved after performing single linear lesions at the roof and f loor, additional ablation was
performed along the lesions based on further mapping. If isolation was still not achieved, additional
ablation inside the posterior wall was performed with reference to the earliest activation until
isolation was achieved. The procedure was evaluated using the bipolar voltage at a point closest to
the ablation point. If necessary, cavotricuspid isthmus ablation, superior vena cava isolation, andpremature atrial contraction/non-pulmonary vein triggers were performed at the operatorʼs
discretion.
Data and outcomes
Study patients were divided into two groups according to how the posterior wall was isolated (Fig.
1). In group A, isolation was achieved by a single ablation line or a single line with additional gap
ablation; in group B, isolation required additional ablation inside the posterior wall. Clinical,
laboratory, and echocardiographic data and patient comorbidities and medications were collectedfrom the medical records. Characteristics of catheter ablation procedures were exported and analyzed
off line. For each roof or f loor line, number of RF lesions, line length, and RF time were measured.
For each RF tag, mean values were calculated for FTI and AI. Mean and minimum values were
calculated for RF power, impedance drop, CF, and voltage.
Statistical analysis
Continuous variables are expressed as means with standard deviation and were compared using
the Mann-Whitney U test. Categorical variables are expressed as numbers with percentage and were-3-

Yanagishita et alcompared using Fisherʼs exact test. Multivariable predictors of LA posterior wall ablation were
examined using logistic regression analysis with adjustment for patientʼs age. All p-values were twotailed, with p ＜0.05 indicating statistical significance. All statistical analyses were performed using
JMP software version 10.0.0 (SAS Institute, Cary, NC, USA) and R statistical software version 4.0.2(R Foundation for Statistical Computing).
Ethical statement
Institutional ethics committee approval was obtained. All patients provided informed consent.
This study was approved by the Ethical Committee of Osaka City University Graduate School of
Medicine (approval number: 4264).

Results
Patient and procedural characteristics
During the study period, 117 patients with non-paroxysmal AF underwent initial RF catheter
ablation. PWI was not performed in 18 because of advanced patient age or prolonged proceduraltime. Ninety-nine patients underwent PVI with additional PWI. Among these, superior vena cava
isolation was performed in two patients, premature atrial contraction/non-pulmonary vein trigger
ablation in seven, and linear ablation of the anterior left atrium in one. Twenty patients in whom
pulmonary vein-left atrium voltage maps were not obtained because of immediate recurrence of AF
after cardioversion were excluded. Two patients in whom PWI was discontinued at the operatorʼs
discretion were also excluded. Therefore, 77 patients were included for analysis (Fig. 2).
Ablation outcomes
We took on average 2359±734 points for the voltage map of the entire LA, and used the bipolar
voltage at the point nearest to the ablation point for our data. PVI was successful in all patients. For
PWI, first-pass block across roof and f loor lines was observed in 32 patients (42%); two required
additional ablation at the roof line and one at the roof and f loor line. Therefore, 35 patients

Figure 2. Study f low chart.

-4-Voltage Predicts the Need for Additional Ablation
Table 1. Patient characteristics
Total number(n＝77)

Group A
(n＝35)

Group B
(n＝42)

p-value

Age, years

65.3±10.768.2±8.3

62.9±11.8

0.04

Male, n (%)
BMI
Duration of AF (months)

57 (74.0)
24.2±3.817.9±22.7

24 (68.6)
24.0±4.0

33 (78.6)
24.5±3.7

0.32
0.42

18.9±18.9

16.9±25.9

0.59

CHA2DS2VASc-score

2.6±1.5

2.7±1.5

2.5±1.6

0.69

27 (35.1)
54 (70.1)
9 (11.7)
11 (14.3)
10 (13.0)

11 (31.4)
24 (68.6)
5 (14.3)
3 (8.6)
4 (11.4)

16 (38.1)
30 (71.4)4 (9.5)8 (19.1)
6 (14.3)

0.54
0.76
0.52
0.19
0.71

Comorbid disease
Congestive Heart failure, n (%)
Hypertension, n (%)
Diabetes mellitus, n (%)
History of stroke or TIA, n (%)
Vascular disease, n (%)
Echocardiographic parameters
Left atrial diameter (mm)Left atrial volume index (mL/m2)
Left ventricular EF (%)
Moderate or severe MR, n (%)
Moderate or severe TR, n (%)
Laboratory dataeGFR (mL/min/1.73 m2)
BNP (pg/mL)
Antiarrhythmic agents, n (%)

44.4±6.0

44.6±6.3

44.1±5.80.8140.1±13.0
50.9±11.4
56 (72.7)
48 (62.3)

40.6±16.3
51.1±10.8
25 (71.4)
24 (68.6)

39.7±9.550.8±12.1
31 (73.8)
24 (57.1)

0.84
1.0
0.82
0.30

70.6±75.959.8±16.3

79.6±101.4

0.25

140.3±160.8

180.5±220.6
4 (11.4)

106.8±71.0
6 (14.3)

0.11
0.71

10 (13.0)＊

Data are presented as mean±standard deviation or n (%).
BMI, body mass index; AF, atrial fibrillation; TIA, transient ischemic attack; EF, ejection fraction; MR, mitral
regurgitation; TR, tricuspid regurgitation; eGFR, estimated glomerular filtration rate; and BNP, brain natriuretic
peptide.

comprised group A. Group B, who required additional ablation inside the LA posterior wall,comprised 42 patients. Complete PWI was achieved in all.
Follow-up
During the average follow-up period of 823 ±300 days, 24 patients (31%) experienced atrial
arrhythmia recurrence (10 patients with atrial tachycardia and 14 patient s with AF). There was no
significant difference in the recurrence rate between group A and B [12 patients (34.3%) vs 12
patients (28.6%) p＝0.59].
Group comparisonsPatient characteristics are summarized in Table 1. Overall, mean age was 65±11 years and 74%
of patients were men. Mean CHA2DS2 -VASc score was 2.6±1.5. Mean LA dimension and left
ventricular ejection fraction were 44.4±6 mm and 50.9%±11.4%, respectively.
Mean age was significantly younger in group B (62.9±11.8 years vs 68.2±11.8 years; p＝0.04).
Sex, body mass index, CHA 2 DS 2 -VASc score, brain natriuretic peptide concentration, andechocardiographic measurement parameters did not significantly differ between the groups.
Ablation parameters according to group are shown in Table 2. Maximum voltage (3.68±1.94 mV
vs 2.38±1.49 mV; p＜0.01; Fig. 3) and mean voltage (1.94±1.03 vs 1.28±0.91 mV; p＜0.01; Fig. 3) atthe roof line were significantly higher in group B. At the f loor line, although maximum voltage wassignificantly higher in group B (4.47±2.81 mV vs 3.21±1.89 mV; p＝0.02), mean voltage did not-5-

Yanagishita et al

Figure 3. (a) Left atrial roof maximum voltage in groups A and B. (b) Left atrial roof mean voltage in groups A and B.

significantly differ between group B (2.25±1.43 mV) and group A (1.62±0.92 mV). Other indicators,
such as power, impedance drop, AI, FTI, CF, length line, and maximum inter-lesion distance, did not
significantly differ between groups.
Predictors of additional ablation inside the LA posterior wall
In the multivariate analysis with age adjustment, mean and maximum voltage at the roof line
were independent predictors of additional ablation inside the posterior wall (Odds Ratio [95%confidence interval] ＝ 1.97 [1.07-3.61] and 1.52 [1.08-2.13]) (Table 2). Receiver operating
characteristic curve analysis showed cutoff values for mean voltage at the roof line 1.29 mV (areaunder the curve, 0.71; sensitivity, 79%; specificity, 60%) and maximum voltage at the roof line of 3.08
mV (area under the curve, 0.70; sensitivity, 67%; specificity, 69%).

Discussion
The major findings of this study are as follows: (1) Complete first-pass PWI was achieved via
blockage across roof and f loor lines in 32 patients (42%) and three (4%) required additional gap
ablation near the initial roof or f loor line. (2) Additional ablation inside the posterior wall was neededto achieve PWI in 42 patients (55%). (3) Additional ablation inside the LA posterior wall achieved
complete PWI in all patients. (4) In multivariate analysis, mean and maximum voltage at the roof
line were independent predictors of additional ablation inside the posterior wall. To our knowledge,
this is the first study to show that endocardial bipolar voltage is a useful predictor of additional
ablation inside the posterior wall during PWI.
The effect of adding PWI to PVI when treating non-paroxysmal AF is controversial. The
multicenter international randomized Substrate and Trigger Ablation for Reduction of Atrial
Fibrillation Trial Part Ⅱ (STAR AF Ⅱ) demonstrated that adding complex fractionated atrial
electrocardiogram ablation or linear ablation to PVI did not improve outcomes compared to PVI alone
in patients with non-paroxysmal AF 17). However, the linear ablation arm of this trial did notincorporate LA PWI. Pathologic remodeling in the LA posterior wall appears to be involved in AF-6-Voltage Predicts the Need for Additional Ablation
Table 2. Comparisons of ablation parameters between the two study groups
Age adjusted
p-valueGroup A
(n＝35)

Group B
(n＝42)

Number of RF lesions

8.0±2.98.0±2.3

0.76

0.63

Min. power (W)31.4±3.931.4±3.7

0.92

0.58

Mean power (W)32.6±3.4

32.4±3.5

0.82

0.61

Min. impedance drop (Ω)

4.3±2.5

4.0±2.4

0.82

0.51

Mean impedance drop (Ω)

8.8±2.4

8.0±2.3

0.13

0.12

Min. contact force (g)9.3±5.3

9.1±3.7

0.92

0.74

Mean contact force (g)22.8±6.0

21.9±4.0

0.54

0.33

Mean force-time integral

500.3±97.7

504.5±98.6

0.84

0.51

Mean ablation index

501.4±57.3

501.4±59.0

0.93

Mean voltage (mV)

1.28±0.91

1.94±1.03

0.002

＊
＊

Procedural characteristics

p-value

Roof line

0.42
0.03

Max. voltage (mV)

2.38±1.49

3.68±1.94

0.003

Line length (mm)Max. interlesion distance (mm)21.2±8.85.1±1.1

21.0±5.8

0.65

0.78

5.3±1.0

0.25

0.35

Total RF time (s)251.8±143.5

268.8±146.0

0.71

0.60

Number of RF lesions
Min. power (W)

7.9±2.2
28.1±2.5

9.0±3.4

0.16

0.08

28.8±2.4

0.25

0.07

Mean power (W)30.4±2.430.6±2.9

0.59

0.53

Min. impedance drop (Ω)

4.0±2.2

4.3±2.6

0.72

0.60

Mean impedance drop (Ω)

8.8±2.1

9.0±2.1

0.72

0.84

Min. contact force (g)7.1±4.3

7.3±4.2

0.87

0.54

Mean contact force (g)20.7±4.4

20.6±3.9

0.87

0.91

Mean force-time integral

338.3±62.4

Mean ablation index
Mean voltage (mV)

421.4±19.0
1.62±0.92

345.3±47.7
426.5±21.1

0.38
0.26

0.40
0.05

2.25±1.43

0.09

Max. voltage (mV)

3.21±1.89

4.47±2.81

0.02Line length (mm)23.6±6.6

26.9±9.2

0.17

0.10

Max. interlesion distance (mm)5.5±0.85.4±1.0

0.42

0.48

283.5±174.3

289.6±150.5

0.62

0.67

0.02

＊

＊

Floor line

Total RF time (s)

0.19
＊

0.16

Data are presented as mean±standard deviation unless otherwise indicated.
RF, radiofrequency; Min, minimum; and Max, maximum.

pathogenesis and more aggressive treatment of this region correlates with improved ablation
outcomes18). Thiyagarajah et al reported that PWI can be achieved in most patients undergoing AF
ablation; the risk of complications is low and the 12-month rate of freedom from atrial arrhythmia is
acceptable19). Another study reported that PWI in addition to PVI plus linear lesions was efficient andcaused no deterioration in LA pump function in patients with non-paroxysmal AF10).
However, PWI durability is not high20). Tamura et al reported that ablation inside the posteriorwall is a predictor of PWI reconduction and aiming to complete line ablation without ablating theinside of the posterior wall may lead to improved durability13). Therefore, our study aimed to identify
predictors of additional ablation inside the posterior wall.
Among the various parameters examined, maximum and mean LA roof voltage were theindependent predictor. We hypothesize that high LA roof voltage indicates a thick roof wall, which-7-

Yanagishita et alcauses difficulty with completing the roof block line; therefore, these patients require additional
ablation inside the posterior wall. Although there are only few reports investigating the relationships
between wall thickness and LA voltage, there have been reports showing that the thicker LA walls
have higher LA voltages14).
Other reasons may also explain why completing the roof block line is challenging. Pambrun et alreported that the atrial myocardium between the superior pulmonary veins is thicker andconsistently contains adipose tissue separating the septopulmonary bundle from the septoatrial
bundle. They also suggested that myocardial thickness and fat interposition may explain difficulties
in creating a transmural lesion during roof line ablation21). Yokokawa et al reported that a left sinus
node artery may act as an epicardial heat sink, preventing adequate heating of the LA roof during
linear ablation22). These studies demonstrate that epicardial conduction across the roof line is related
to difficulties with linear ablation of the LA roof and achieving a completely transmural lesion isimportant for roof line ablation. In addition to the report that it is difficult to create a roof line due tothe various factors mentioned above, there is also a report that the durability of the roof line waslower than that of the f loor line, which indicates our hypothesis13). In our study, additional ablation
inside the posterior wall was necessary to complete PWI in patients with high roof voltage; therefore,
avoiding high-voltage areas when creating a roof line might increase the PWI success rate. Furthermore,
it has been reported that additional ablation inside the posterior wall is associated with poor durabilityof treatment13). Therefore, in patients with high roof voltage, durability might be improved by increasingstability of the ablation catheter, setting a higher AI, or shortening the inter-lesion distance. Other
than procedural parameters, patients in group B were significantly younger than those in group A. It
has been reported that patients who have low voltage areas, defined as bipolar voltage of ≦0.5 mV in
LA, was significantly older than patients who did not have low voltage areas 23). Therefore, thesignificant difference in age between the groups may affect the interpretation of the present results.
In the present multivariate analysis with age adjustment, we found that mean and maximum voltage
at the roof line were independent predictors of additional resection within the posterior wall.
This study had several limitations. First, the AI criteria (≧550 in the roof line and ≧400 in thef loor line) and inter-lesion distance (2-4 mm) were selected arbitrarily based on our experience tominimize risks of complications and dislocation, which could have affected data interpretation.
Future studies should investigate these variables in patients undergoing PWI. Second, we did nothave LA wall thickness data from imaging studies, as our study was retrospective in design. However,previous studies have shown a direct and positive correlation between LA wall thickness and LA
voltage. Third, we were unable to prove that incomplete roof block was a direct cause of the need for
additional ablation inside the posterior wall. Fourth, bipolar voltage does not simply ref lect
myocardium wall thickness. It can also ref lect various factors such as conduction direction;
depending on the conduction direction, it may be possible that we have underestimated the voltage.
In conclusion, when performing PWI, high endocardial bipolar voltage at the LA roof line is anindependent predictor of additional ablation inside the posterior wall. Therefore, we believe thatwhen obtaining left atrial posterior wall first-pass isolation, it is necessary to note whether there is ahigh-voltage region in the left atrial roof.
Acknowledgements
All authors have no COI to declare regarding the present study.
-8-Voltage Predicts the Need for Additional Ablation

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based on the isolation process. J Interv Card Electrophysiol 2022;65:45-51.
14. Nagashima K, Watanabe I, Okumura Y, et al. High-voltage zones within the pulmonary vein antra: major
determinants of acute pulmonary vein reconnections after atrial fibrillation ablation. J Interv Card
Electrophysiol 2017;49:137-145.
15. Wakamatsu Y, Nagashima K, Watanabe I, et al. The modified ablation index: a novel determinant of acutepulmonary vein reconnections after pulmonary vein isolation. J Interv Card Electrophysiol 2019;55:277-285.
16. Calkins H, Hindricks G, Cappato R, et al. 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus
statement on catheter and surgical ablation of atrial fibrillation. Heart Rhythm 2017;14:e275-e444.
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Med 2015;372:1812-1822.
18. Segerson NM, Daccarett M, Badger TJ, et al. Magnetic resonance imaging-confirmed ablative debulking of theleft atrial posterior wall and septum for treatment of persistent atrial fibrillation: rationale and initial
experience. J Cardiovasc Electrophysiol 2010;21:126-132.
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atrial fibrillation ablation: a systematic review and meta-analysis. Circ Arrhythm Electrophysiol 2019;12:
e007005.
20. Markman TM, Hyman MC, Kumareswaran R, et al. Durability of posterior wall isolation after catheter
ablation among patients with recurrent atrial fibrillation. Heart Rhythm 2020;17:1740-1744.
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considerations and clinical implications for roof line completion. Heart Rhythm 2021;18:349-357.
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block at the left atrial roof in patients with persistent atrial fibrillation. Heart Rhythm 2012;9:1395-1402.
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the ZAQ score. J Cardiovasc Electrophysiol 2020;31:895-902.

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Osaka City Med. J. Vol. 69, 11-19, 2023

Relationship between Work Life Balance and Temperament among
Japanese Workers
YOKO NAKAMICHI1), YASUHIKO DEGUCHI2), SHINICHI IWASAKI2), YUTARO OKAWA1),
YUKI UESAKA1), and KOKI INOUE2)
Department of Neuropsychiatry1), Osaka City University Graduate School of Medicine; andDepartment of Neuropsychiatry2), Osaka Metropolitan University Graduate School of Medicine

Abstract
Background
Work life balance is an important issue for working people. To understand the impact of work andfamily, both positive and negative inf luences should be considered. Although the relationshipbetween individual differences and work life balance has been studied, no study has examined therelationship between temperament and work life balance. Therefore, this study aimed to clarify the
relationship between temperament and work-family spillover.
Methods
This web-based, cross-sectional study was conducted among 839 eligible Japanese workers. We
used the Japanese version of the Survey Work-Home Interaction -NijmeGen to measure the four
aspects of work-family spillover (positive/negative, work to family/family to work). Furthermore, thefull version of the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-autoquestionnaire version was used to evaluate the five temperaments. Hierarchical multiple linear
regression analysis was used.
Results
Cyclothymic, irritable, and anxious temperaments indicated significantly higher scores of work tofamily negative spillover. Cyclothymic and irritable temperaments also indicated significantly higherscores and depressive temperament indicated a significantly lower score of family to work negative
spillover. Hyperthymic temperament indicated a significantly higher score, and irritabletemperament indicated a significantly lower score of work to family and family to work positive
spillover.
Conclusions
To promote better work life balance, it is important to recognize workersʼ temperament forthemselves and workplace.
Key Words: ‌Work life balance; Temperament; Work family spillover; Self-care; SWING
Received August 24, 2022; accepted November 29, 2022.
Correspondence to: Yoko Nakamichi, MD.
Department of Neuropsychiatry, Osaka Metropolitan University Graduate School of Medicine,
1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
Tel: +81-6-6645-3821; Fax: +81-6-6636-0439
E-mail: yoko.nakamichi.4@gmail.com- 11 -

Nakamichi et al

Introduction
Recently, changes in the industrial structure and diversification of work styles have made itdifficult to clearly distinguish between working and non-working time. Therefore, work life balance(WLB) has become an important issue1). Workers are expected to spend time both at work and infamily matters, such as housework, nursing and childcare. Because both are equally important, it is
crucial to strike a balance between work and family. Concepts related to WLB and the impact ofwork and family life have been studied extensively. Work-family conf lict (WFC) arises when the
demands of work and family roles cannot be reconciled2). WFC has been shown to be related withvarious health issues, such as higher burnout3), depression, poor physical health and heavy alcoholuse4). In contrast, work-family enrichment (WFE), a concept with a positive view on the relationship,
implies that experiences in one role enrich and strengthen the other5). WFE has shown to be relatedwith positive health effects, such as lower depression6) and burnout and higher life satisfaction3).
It is necessary to consider the negative and positive aspects of spillover, as well as its direction(family to work, work to family) to understand their impact7). Negative spillover between work andfamily has been considered as a chronic stressor, and may cause a physiological stress response8).
Previous studies showed that it was associated with poor physical and mental health, self-reported
musculoskeletal pains9), possibility of obesity10), depression symptoms11) and anxiety and emotionalstrain6). Positive spillover from work to family and family to work were associated with better
physical and mental health and fewer chronic diseases and better mental health and well-being,respectively10).
Recently, as a factor affecting WLB, the relationship between individual differences and WLB has
been studied. As antecedents of WFC, personality traits, such as negative affect and neuroticism,
were found to have strong predictive validity than many work and non-work stressors and support
factors12). A meta-analysis examined the relationship between the Five-Factor Model of Personality(FFM) and negative and positive forms of work and non-work spillover and found that each
personality variable was related to both directions of spillover for all personality variables and forms.
Furthermore, extraversion, agreeableness, and conscientiousness were negatively related to andneuroticism was positively related to work to family negative spillover. Meanwhile, extraversion,agreeableness, conscientiousness and openness to experience were positively related to work to family
positive spillover13). Personality is inf luenced by the environment and develops during the maturation
process14), while temperaments are stable across oneʼs lifespan15). Temperament is believed to be thebiological basis for personality development14). Hagop Akiskal developed the concept of five emotionaltemperaments by adding anxiety type to the four basic states proposed by Kraepeli16). The fivetemperaments are depressive, irritable, anxious, hyperthymic, and cyclothymic. Temperament
involves an inherent bias in how a person views and acts and causes the person to be more prone tocertain behaviors17). Therefore, we considered that temperament impacted workersʼ WLB. However,limited research on the relationship between temperament, as an individual factor, and WLB has
been conducted. This study clarified the relationship between temperament and work-family
spillover.

Methods
An online, cross-sectional survey was conducted in Japan through an research company,
Macromill, Inc. Japan, on 16-17 December 202018). The inclusion criteria were: lived in Japan, were
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Work Life Balance and Temperament in Japan

employed, and aged between 20-65. The objective was to recruit approximately 1000 Japanese
workers from various employment types from the approximately 10 million people registered with
Macromill, Inc. Participants were informed their voluntary participation. An informed consent form
was completed by all participants and were assured that the researcher would not have access topersonal information (e.g., name, telephone number, address of house) from Macromill, Inc. All
participants received Macromill Points. Macromill Point is an original point service of Macromill, Inc.
and the participants can exchange these points for prizes or cash.
We gathered a total of 1070 workers and excluded participants with at least one missing entry.
The final analytic sample included 839 eligible participants. This study was conducted in accordancewith the Declaration of Helsinki and its future amendments. The study design was approved by the
Ethical Committee of Osaka City University (Authorization Number 4245). All data were stored only
in our database, and the participantsʼ employers or institutions did not have access to the data or
knew who had participated.
The Survey Work-Home Interaction-NijmeGen (SWING) was developed in 2005 and measured
work-home interaction7). The reliability and validity of the Japanese version have been confirmed19).
We used the Japanese version of the SWING (SWING-J) to measure four aspects of work-family
spillover: (1) work to family negative spillover (WFNS): eight items, (2) family to work negative
spillover (FWNS): four items, (3) work to family positive spillover (WFPS): five items, and (4) family
to work positive spillover (FWPS): five items. The responses were rated on a 4-point Likert scale,
which ranged from 0 (never) to 3 (always). The scores for each item were totalled, and a higher scoreindicated greater spillover. The Cronbachʼs αʼs for the present sample were 0.874, 0.805, 0.756 and0.740 for WFNS, FWNS, WFPS, and FWPS, respectively.
Temperaments were assessed by the full version of the Temperament Evaluation of Memphis,
Pisa, Paris, and San Diego-auto questionnaire version (TEMPS-A) developed by Akiskal et al20). The
reliability and validity of the Japanese version have been confirmed21). The TEMPS-A was a yes-no
self-reporting questionnaire that assessed temperaments. The instrument consisted 110 items andwas divided into five types of temperaments: depressive, cyclothymic, hyperthymic, irritable, andanxious. Higher scores suggested a greater magnitude of the temperament. A stability of thetemperaments by the TEMPS-A has already been shown, regardless of the temperament type, gender,or age15).
We collected their demographic information, which included age, gender, marital status, family
income, number of children who lived together, and number of people who required care who lived
together. Participants also reported information on work-related variables: position classification,
type of employment, work pattern and frequency of working from home.
We conducted Spearmanʼs correlation analysis of the TEMPS-A and SWING-J scores. To examine
the independent inf luence of temperament on work-life balance, we performed a hierarchical multiple
linear regression using the TEMPS-A and SWING-J scores, while adjusting for the effects ofdemographic variables and work-related variables. Statistical significance was set at p ＜0.05. We
used the SPSS version 28.0 software (SPSS Inc., Chicago, IL) for statistical analyses.

Results
Table 1 shows the subjectsʼ characteristics and the TEMPS-A and SWING-J scores. The mean age
was 46.5±10.5 years. The participants consisted of 638 (76.0%) males and 201 (24.0%) females. Of- 13 -

Nakamichi et al
Table 1. Participants’ characteristics. Total (N=839)
Range
Age (years)
20-29
30-39
40-49
50-59
≧60
GenderMale
FemaleMarital status
Unmarried
MarriedFamily income (million yen)

Mean (SD)n (%)

46.5 (10.5)
59 (7.0)163 (19.4)
264 (31.5)
255 (30.4)
98 (11.7)
638 (76.0)
201 (24.0)
292 (34.8)
547 (65.2)＜4

184 (21.9)≧4655 (78.1)

Children living together
No
Yes
People requiring care living together
No
Yes
Position classification
Non-manager
Manager
Type of employment
RegularTemporaryWork pattern
DaytimeShift
Working at homeNo
Yes
Temperaments
Depressive
Cyclothymic
Hyperthymic
Irritable
Anxious
Spillover
WFNS
FWNS
WFPS
FWPS

439 (52.3)
400 (47.7)800 (95.4)
39 (4.6)465 (55.4)
374 (44.6)
689 (82.1)
150 (17.9)705 (84.0)
134 (16.0)
608 (72.5)
231 (27.5)
0-20
0-21
0-20
0-21
0-26

9.0 (4.1)
6.8 (5.0)
6.7 (4.4)
5.8 (4.3)
7.5 (6.2)

0-24
0-12
0-15
0-15

6.4 (4.8)
2.3 (2.4)
5.7 (3.1)
6.1 (3.1)

SD, standard deviation; WFNS, work to family negative spillover; FWNS, family to work negative spillover; WFPS,
work to family positive spillover; and FWPS, family to work positive spillover.

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Work Life Balance and Temperament in Japan
Table 2. Correlation between the temperaments and spillovers
WFNS

FWNS

WFPS

FWPS

0.270

＊＊

−0.022−0.024

0.461

＊＊

0.080

＊

0.079

＊

＊＊

0.146

＊＊

0.343

＊＊

0.381

＊＊

＊＊

0.450

＊＊

−0.043−0.0120.408＊＊−0.034−0.039＊＊Depressive
Cyclothymic

0.289
＊＊
0.430

Hyperthymic

0.204

Irritable
Anxious

0.396
＊＊
0.384

p＜0.01, ＊p＜0.05.
WFNS, work to family negative spillover; FWNS, family to work negative spillover; WFPS, work to family positive
spillover; and FWPS, family to work positive spillover.
＊＊Table 3. The temperament effects for WFNS, FWNS, WFPS, and FWPSWFNSStep 1β

Step 2β

Step 2β

Step 1β

Step 2
β

0.031

0.034

−0.044−0.081＊−0.067

0.114

0.007

−0.005−0.019−0.038

Family income(million yen)

0.009

0.02

−0.023−0.012Presence of children
living together

0.145

Living with a person who
required care

0.006

Gender

−0.023

Marital status

Position classification
Type of employment

＊＊

0.12

＊＊

−0.017

0.059

0.061

−0.039−0.069＊＊

Step 2β−0.087−0.109＊＊FWPS

Step 1β＊＊＊−0.183＊＊

Step 1β

WFPS−0.171Age (years)

＊＊＊

FWNS

0.011

0.053

＊＊

0.072

0.100

＊

0.062

0.097

＊

0.081

0.107

＊

0.088

−0.022−0.057

0.006

0.081

0.05

0.088

＊＊

0.104

0.068

＊

0.046

0.067

0.059

0.022

0.013

−0.003

0.001

0.078

0.048

0.057

0.025

−0.001−0.034

0.031

0.036

0.024

0.027

0.025

0.011

0.004

−0.007Work pattern

0.063

0.052

0.051

0.042

0.004

Working at home

0.038

0.04

0.027

0.032

0.049

0.038

Depressive−0.0850.01

Cyclothymic

0.195

Hyperthymic

0.032−0.0330.126−0.008

＊＊＊

0.286

＊

−0.031＊＊＊−0.01−0.033

＊

0.054

−0.01
−0.007

0.371

＊＊＊

0.393

Irritable

0.105

Anxious
R

0.155

＊＊

0.257a0.485b0.225a0.517b0.206a0.398b0.197a0.413b

R2

0.066

0.235

0.051

0.268

0.042

0.158

0.039

0.171

0.066

0.169

0.051

0.217

0.042

0.116

0.039

R2

Change score

F

5.839

Adjusted R2

0.55

＊＊＊

16.868＊＊＊0.221−0.148

0.045

4.424

＊＊＊

0.039

＊＊＊

＊＊

＊＊＊

＊

0.269

＊＊＊

＊

−0.111

0.046

＊＊＊

＊＊＊

20.0443.667

10.305

0.254

0.031

0.143

＊

0.005

3.34

＊＊＊

0.027

0.132
11.289＊＊＊0.156

p＜0.05,p＜0.01,p＜0.001.
WFNS, work to family negative spillover; FWNS, family to work negative spillover; WFPS, work to family positive
spillover; and FWPS, family to work positive spillover.
＊

＊＊

＊＊＊

these, 292 (34.8%) were unmarried and 547 (65.2%) were married. The number of participants withfamily incomes over 4 million yen was 655 (78.1%). Furthermore, 400 (47.7%) participants lived withchildren and 39 (4.6%) lived with people who required nursing care. The participants consisted of 465
non-managers (55.4%) and 374 (44.6%) managers, 689 (82.1%) regular and 150 (17.9%) temporary
workers, and 705 (84.0%) daytime and 134 (16.0%) shift workers. Of these, 608 (72.5%) did not workat home or telecommute at all.
- 15 -

Nakamichi et al

Spearmanʼs correlations between the temperaments and work-home interaction according to the
SWING-J is shown in Table 2. The results were interpreted based on Guilfordʼs rule of thumb.
Depressive, irritable and anxious temperaments scores were positively correlated with WFNS and
FWNS. Cyclothymic and hyperthymic temperaments scores were positively correlated with WFNS,FWNS, WFPS, and FWPS.
Table 3 shows the results of the hierarchical multiple linear regression analysis of the TEMPS-A
and SWING-J scores. Step 1 shows that age and the presence of children living together hadsignificant associations with WFNS scores. Step 2 shows that the temperament variables explained
the additional 16.9% of the variance (F＝16.87, p ＜0.001). Cyclothymic, irritable and anxious
temperaments indicated significantly high scores of WFNS. Step 1 shows that age, gender, thepresence of children living together and living with a person who required care had significant
associations with FWNS scores. Step 2 shows that the temperament variables explained the
additional 21.7% of the variance (F ＝20.04, p＜0.001). Cyclothymic and irritable temperaments
indicated significantly higher scores and depressive temperament indicated a significant lower scoreof FWNS. Step 1 shows that gender and marital status had significant associations with WFPS
scores. Step 2 shows that the temperament variables explained the additional 11.6% of the variance(F＝10.31, p＜0.001). Hyperthymic temperament indicated a significantly higher score, and irritabletemperament indicated a significant lower score of WFPS. Step 1 shows that gender, marital status
and the presence of children living together had significant associations with FWPS scores. Step 2
shows that the temperament variables explained the additional 13.2% of the variance (F＝11.29, p＜
0.001). Hyperthymic temperament indicated a significantly higher score, and irritable temperament
indicated a significantly lower scores of FWPS.

Discussion
We investigated the effects of temperament on work-family interaction after adjusting forsociodemographic and work-related factors. We used the TEMPS-A and SWING-J to evaluate thetemperaments and consider the balance for both work and family life, respectively. The association
between spillover and temperaments was discussed.
Temperaments and negative spillovers
Higher scores of cyclothymic and irritable temperaments were associated with higher WFNS and
FWNS. Cyclothymic temperament was characterized by being unstable in mood, energy, socialization,and self-esteem and unevenly gifted and dilettante, whereas perceptive, compassionate, andromantic20). Cyclothymic temperament was associated with agitation following a stressful event23) andrisk of depressive symptoms 24). Irritable temperament was the darkest, characterized by being
sceptical and critical (considered intellectual virtues), and individuals tended to be moody,complaining, jealous, angry and violent20). Previous study regarding the relationship between
occupational stress and temperaments found that this temperament was associated with role andinterpersonal conf lict 25). Furthermore, irritable temperament was noted to be vulnerable tooccupational stress25). In addition, work stress was cited as an antecedent of WFNS8). Workers withirritable temperament may be more likely to experience conf licts in various aspects. A previous study
showed that cyclothymic and irritable temperaments were positively related to negative affect, risky
behavior and restlessness, and negatively related to positive affect and preference for being withsomeone. Furthermore, individuals with high cyclothymic and irritable temperament were more- 16 -

Work Life Balance and Temperament in Japan

likely to perceive situations as stressful and experience higher levels of negative emotions toward
stressful environments22). Workers with these temperaments are easily irritated and stressed both athome and work, and may not be able to relieve their feelings, which may interfere with both sides.
Therefore, workers with these temperaments may be more likely to experience high WFNS and
FWNS. These could be associated with the higher WFNS and FWNS results in this study. Higherscore of anxious temperament was associated with higher WFNS in this study. Anxious temperament
was characterized by worry, vigilance and tension for external dangers20). It was associated with therisk of depressive symptoms24) and role and interpersonal conf lict25), noted to be vulnerable tooccupational stress25). Workers may be more likely to experience conf licts in various aspects andtherefore workers with anxious temperament may be more likely to experience higher WFNS. In thisstudy, although workers with anxious temperament were prone their family life being interrupted bywork demand even when they were not at work, there was no indication that their work life was more
likely to be affected by family demands. Family-related stressors such as family distress and family
support have been identified as antecedents of FWNS26). Anxious temperaments are vulnerable tooccupational stress25), but may not be vulnerable to family-related stress, therefore workers with
anxious temperament may not be associated with higher WFNS. Higher score of depressive
temperament was associated with lower FWNS. Depressive temperament was characterized by atendency to be tied to daily life, self-blaming, shy, unassertive, sensitive to criticism, but self-denying,willing to work for someone else rather than be the boss20). Even if they have some problems at homeor with friends, they may tend to over-adapt and behave in the workplace.
Temperaments and positive spillovers
Higher score of irritable temperament was associated with lower WFPS and FWPS. It was
associated with spillover in all four directions, with higher and lower levels for negative and positive
spillover, respectively. Workers with irritable temperament may find it difficult to use developed
work skills and roles at home and vice versa.
In contrast, higher score of hyperthymic temperament was not associated with negative spillover.
However, it was associated with higher WFPS and FWPS. It is associated with many positive traits,
such as cheerfulness, fun-loving, outgoing, joking, optimistic, self-confident, full of ideas, eloquent,active, no fatigue despite short sleep and leadership preference. However, it is also associated with alow likelihood of admitting to being single-minded, risk-taking and meddlesome20). This was generally
consistent with previous studies in which extraversion and experiential openness were particularly
strong predictors of positive spillover13). Hyperthymic temperament has been shown to be protective
against mental disorders and occupational stress10,25,27). In previous study, cyclothymic, depressive,
irritable and anxious temperaments showed significant correlations between cognitive complaints
and depressive symptoms, while hyperthymic temperament did not28). Hyperthymic temperament is
positively correlated with happiness and elation, and individuals have a positive outlook on daily
life22). Workers may use their experiences and demands at work or home more effectively in anothersituation using their ideas.
The strengths and limitations are discussed. This was the first study on the relationship between
temperament and work to family spillover using two measures with reliability and validity. Ourfindings help workers understand the effect of temperaments on work-family positive or negative
spillover, which could lead to a better WLB. However, this study has some limitations. First, we could
not determine the causal relationship between temperaments and work-family spillover due to the- 17 -

Nakamichi et al

cross-sectional design. However, considering the stability of temperament, we believe they affected
spillovers. Second, we did not explore the interaction effects between positive and negative spillover
and work to family and family to work spillover. Future studies should consider these effects. Third,
this study was a small, short-term, web-based survey conducted in Japan. Hence, sample bias may
have inf luence the result and replicability cannot be confirmed. Furthermore, differences in thenumber of children and people who required care, and their ages were not examined. Furthermore,
the survey was conducted during the COVID-19 pandemic, and it was possible that the participantsʼ
working conditions and family life were different from normal conditions. Future longitudinal studies
with a larger sample and detailed questionnaire should be conducted.
Our study suggested that a hyperthymic temperament was more likely, while irritable andcyclothymic temperaments were less likely, to achieve better WLB for Japanese workers due to higher
positive spillover. Our findings may help workers better manage their work and family life byunderstanding their own temperament and recognizing their susceptibility to negative and positive
inf luences. A previous study showed that increased time in moderate leisure-time physical activity
buffered the association between increased negative spillover and poorer health 29). Moreover yoga
and massage therapy have been demonstrated to reduce occupational stress in previous studies withhealthcare workers30) and stretching programs promoted health in desk workers 31). Encouraging
workers to have short exercise routines may benefit their health and job performance32). It may bedesirable for workers with large negative spillover to have exercise habit and engage in moderate
exercise to maintain their health, do yoga, get massage therapy, stretch during breaks and walk on
the way to work. It may also be important for workers, especially with irritable and cyclothymic
temperaments, to promote help-seeking behavior at the workplace and adjust workload if they haveany family problems, and seek help for their family life when the workload is heavy or stressful.
Recognition of workersʼ temperaments by supervisors, co-workers and family might lead to great
support both at work and home.
Acknowledgements
All authors have no COI to declare regarding the present study.
We would like to thank all the participants for their contributions.
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during the COVID-19 pandemic, and depressive and anxiety symptoms among non-healthcare workers inJapan: a cross-sectional study. Int J Environ Res Public Health 2022;19:983.
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24. Tei-Tominaga M, Akiyama T, Sakai Y. Effect of affective temperaments assessed by the TEMPS-A on therelationship between work-related stressors and depressive symptoms among workers in their twenties toforties in Japan. Depress Res Treat 2012;2012:469384.
25. Deguchi Y, Iwasaki S, Konishi A, et al. The usefulness of assessing and identifying workersʼ temperaments andtheir effects on occupational stress in the workplace. PLoS One 2016;11:e0156339.
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Osaka City Med. J. Vol. 69, 21-30, 2023

The Comorbidity of Autism Spectrum Disorder Slows Body Mass
Index Gain in Adult-onset Avoidant/Restrictive
Food Intake Disorder
TAKUMI MATSUZUKA1), SAORI MIYAMOTO2), TOMOKO HARADA2), TSUNEO YAMAUCHI2), MIHOKO HONDA1),NAOKI OHARA1), AKIHIRO MUI1), and KOKI INOUE2)
Department of Neuropsychiatry1), Osaka City University Graduate School of Medicine; andDepartment of Neuropsychiatry2), Osaka Metropolitan University Graduate School of Medicine

Abstract
Background
The Diagnostic and Statistical Manual of Mental Disorders, 5th edition, expanded the criteria foravoidant/restrictive food intake disorder (ARFID) to apply to adults as well as children. Therefore,
previous studies of ARFID focused on childhood-onset, and none examined the clinical features andprognosis of adult-onset only. Comorbidity of autism spectrum disorder (ASD) and ARFID is common
in children, but the prevalence of such comorbidity in adult-onset ARFID is unclear. This study
examined the characteristics of adult-onset ARFID with ASD comorbidity and its weight course
during treatment.
Methods
This study was a retrospective chart review of 34 patients with ARFID with onset after the age of
18. The participants were divided into two groups according to ASD comorbidity, and their clinicalcharacteristics and body mass index (BMI) gain over one year from treatment initiation werecompared.
Results
ASD comorbidity was present in 44.1% of the sample. ASD characteristics were associated withonset and background psychopathology. The group with comorbidity exhibited significantly higher
values than the group without comorbidity for ineffectiveness (p＝0.030) and interoceptive awareness(p＝0.040) on the Eating Disorder Inventory. Further, ASD comorbidity had a significantly negative
impact on BMI gain during treatment (p＝0.014). The slopes were 0.11 and 0.19 for the group withand without comorbidity, respectively.
Conclusions
Approximately half of the adult-onset ARFID group had ASD comorbidity, which was a factor inpoor prognosis. When treating adult-onset ARFID, therapists should consider the possibility ofReceived September 1, 2022; accepted November 29, 2022.
Correspondence to: Saori Miyamoto, MD, PhD.
Department of Neuropsychiatry, Osaka Metropolitan University Graduate School of Medicine,
1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
Tel: +81-6-6645-3821; Fax: +81-6-6636-0439
E-mail: s21753l@omu.ac.jp- 21 -

Matsuzuka et alcomorbidity and diagnose appropriately, as special interventions may be needed based on ASD
characteristics.
Key Words: ‌Avoidant/restrictive food intake disorder; Autism spectrum disorder;
Adult-onset; Prognosis; Outpatient

Introduction
Avoidant/restrictive food intake disorder (ARFID) is similar to anorexia nervosa (AN),characterized by poor food intake, significant weight loss, and social dysfunction. However, ARFID
differs from AN in that the former does not have a core eating disorder pathology, such as a drive forthinness, fear of gaining weight/becoming fat, or body image disturbances. Although ARFID usuallyoccurs in infancy or early childhood1), adult onset has been observed in clinical situations2). Adults
with ARFID were diagnosed with eating disorder not otherwise specified, a residual diagnostic
criterion, according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSMⅣ)3). Revisions to the DSM-51) expanded the diagnostic criteria for “infantile or early childhood
feeding disorder” in the DSM-Ⅳ to apply to any age group; thus, adult ARFID, which was previously
considered eating disorder not otherwise specified, came into focus. Studies of children with feeding
disorders have reported ARFID prevalence ranging from 1.5%-64%4-10). A survey of patients aged 1540 years with eating disorders, including adult cases, reported that ARFID accounted for 9.2% of theclinical sample 11). Previous studies have not focused on whether the onset of ARFID occurs in
adulthood, even if it is diagnosed in adulthood. To the best of our knowledge, there are no existing
reports of ARFID that were limited to adult onset (AO); most reports on its clinical features andprognosis focused on childhood-onset cases5,6,12,13).
Childhood-onset ARFID was reported to be comorbid with autism spectrum disorder (ASD) in 3%13% of cases7,14). ASD is a neurodevelopmental condition associated with restricted, repetitive, orstereotypical behaviors or interests, as well as impairments in social communication and social
reciprocity1). In a prognostic study of all eating disorders, the presence of autistic traits was found to
be a negative predictor of treatment outcomes for eating disorders15,16). Furthermore, Bourne et alstated that ASD comorbidity with childhood-onset ARFID is a significant health risk17). However, no
studies have evaluated whether ASD comorbid with AO-ARFID leads to a poorer prognosis.
As weight regain is an essential goal of treatment, it is of great significance to examine the factorsthat inf luence weight change when considering prognosis. In this study, we examined thecharacteristics of eating behavior and psychopathology, clinical background, and treatment-inducedchanges in the body mass index (BMI) of AO-ARFID patients, to assess the effect of ASD comorbidity
on the course of treatment.

Methods
Participants
A retrospective chart review was conducted of a cohort of patients with eating disorders who madetheir first visit to the outpatient clinic specializing in eating disorders at the Department of
Neuropsychiatry, Osaka Metropolitan University Hospital (former Osaka City University Hospital)
between January 1, 2014, and December 31, 2021. After the initial visit, a comprehensive clinicalmanagement process was conducted, based on supportive psychotherapy, one of the standard
treatments for eating disorders, that encourages patients to self-manage their eating behavior- 22 -

Comorbid ASD Slows BMI Gain in Adult-onset ARFID

problems through empathy for the patient while monitoring the patientsʼ physical condition. Patientswith ARFID who met the DSM-5 criteria and were 18 years old and older at onset age of ARFID were
included in the study. Patients were excluded who discontinued after the first visit or continued
seeing their doctor but did not track their weight during the treatment process.
Study variables at entry and during treatment
At the initial visit, a psychiatrist with extensive clinical experience in eating disorders performeda pre-treatment evaluation of the patients. Information from the patients and their family members
was also collected at the time of this visit, including medical history related to eating behavior anddevelopmental characteristics in childhood, factors related to the development of eating disorders,current eating behavior, presence of a drive for thinness and a fear of gaining weight or becoming fat,
attitudes toward weight and body shape, and the presence of comorbid psychiatric or physicalillnesses. We measured height and weight and calculated BMI at the time of the first visit. The ageof onset and duration of illness were assessed based on the individualʼs medical history. Age of onset
was defined as the age at which eating behavior abnormalities started, along with weight loss.
Patientsʼ weight was recorded for up to one year from the first visit, or less if the treatment was
terminated due to improvement of underweight status or improvement of life obstacles. The
frequency of visits and weight measurements varied among patients; thus, monthly measurements
were used for this study.
Diagnosis
At the initial visit, the psychiatrist made a tentative diagnosis of ARFID and ASD according to theDSM-5 diagnostic criteria, based on the information collected from the patients and their families.
Several psychiatrists reviewed the subsequent progress of the patients and made a final diagnosis.
In the DSM-51), ARFID is characterized by significant weight loss, significant nutritional deficiency,
dependence on enteral feeding or oral nutritional supplements, and marked interference withpsychosocial functioning (Criterion A). However, this diagnosis is applied only if the disturbance is notcaused by food unavailability or cultural practices (Criterion B), the patient has no body image disorder(Criterion C), or the condition cannot be explained by medical or psychiatric illness (Criterion D).
Procedure
We classified AO-ARFID patients using the three subtypes of ARFID based on eating behavior
characteristics proposed by Norris et al18), who classified ARFID into three subtypes: (1) ARFIDaversive subtype (aversion to the physical changes associated with food ingestion, such as vomiting,abdominal pain, choking), (2) ARFID-limited intake subtype (overall lack of interest in eating food),and (3) ARFID-limited variety subtype (avoidance due to sensory sensitivities, such as picky eating or
preoccupation with food texture)18). We classified patients with multiple subtypes into one of the threesubtypes by identifying the predominant factor at ARFID onset18).
We administered the Eating Disorder Inventory (EDI)19) during the initial consultation to identify
psychopathological features. The EDI is a widely used multidimensional inventory consisting of 64
items that comprise eight subscales, three of which measure eating disorder pathology (drive forthinness, bulimia, and body dissatisfaction), and five that measure psychopathology commonly
associated with, but not unique to, eating disorders (ineffectiveness, perfectionism, interpersonal
distrust, interoceptive awareness and maturity fears). Participants selected whether each item
applied to them as “always”, “usually”, “often”, “sometimes”, “rarely”, or “never”. These six responses
were coded into four categories between 0 and 3, with the most symptomatic response receiving a- 23 -

Matsuzuka et alscore of 3, the second highest a score of 2, the next highest a score of 1, and the remaining receiving ascore of 0. The scores for all items on each scale were summed to obtain a total score. The validity
and reliability of the Japanese version of the scale has been documented previously20).
Statistical analyses
Clinical and psychopathological variables were compared in the AO-ARFID group, classified by
ASD comorbidities. Continuous variables were subjected to the Shapiro-Wilk normality test andMann-Whitney U test and reported as median and first-third quartiles. Categorical variables were
subjected to the chi-squared test or Fisherʼs exact test and reported as frequencies and proportions.
Weight gain in patients with eating disorder does not occur at a constant pace during treatment.
Therefore, an analysis of BMI change associated with AO-ARFID treatment was conducted using alinear mixed model assuming a random intercept because of its f lexibility in accommodating different
numbers of BMI measurements among participants and its ability to capture overall trends rather
than temporal points. The independent variable was BMI, which was measured at one-monthintervals from the start of treatment. The model was set up with a random intercept among
participants. Duration from the start of treatment was used as a covariate (Duration). To examine
the impact of ASD comorbidity (ASD) on duration after treatment initiation, the model included theinteraction between duration and ASD comorbidity (Duration×ASD). The categorical variable of ASD
comorbidity was dummy coded (ASD＝0, without ASD comorbidity; ASD＝1, with ASD comorbidity).
A p value of less than 0.05 was considered statistically significant.
Most analyses were conducted using IBM SPSS Statistics for Mac, ver.26 (SPSS Japan, Tokyo,
Japan). The linear mixed model analysis was conducted using EZR (Saitama Medical Center, Jichi
Medical University, Saitama, Japan), a graphical user interface for R 4.1.2 (The R Foundation for
Statistical Computing)21).
Ethical statement
This study was reviewed and approved by the Osaka City Graduate School Ethics Committee(2020-106).

Results
During the study period, 927 patients made their first visit to the outpatient clinic specializing ineating disorders, of whom, 72 (7.8%) were ARFID and 44 were AO-ARFID patients (including twomale). Of the 44 patients, 10 did not meet the inclusion criteria and were excluded; thus, 34 patients(32 female and 2 male) comprised the final sample for this study. There were 15 and 19 patients
respectively in the groups with and without ASD comorbidity, of whom 6 and 5 patients, respectively,
terminated treatment in less than a year.
Table 1 shows the clinical characteristics and background of AO-ARFID participants with andwithout ASD comorbidities. Fifteen (44.1%) patients had ASD comorbidity. There were no significant
differences between the groups with and without ASD comorbidity in term of sex, age at first visit,
age of onset, duration of illness, BMI at first visit, height at first visit, body weight at first visit,
hospitalization within one year of first visit, or number of visits. Participants with ASD comorbidity
had a higher percentage of continuous subthreshold ED pathology from childhood than participants
without ASD comorbidity (53.3% vs 15.8%, p＝0.030). Detailed interviews with the former patients
revealed symptoms such as picky eating, lack of interest in food and small appetite. However, these
symptoms did not interfere with daily life and did not meet DSM-5 diagnostic criteria for ARFID in- 24 -

Comorbid ASD Slows BMI Gain in Adult-onset ARFID
Table 1. C omparison of clinical characteristics and background between ARFID patients with andwithout ASD comorbidity
Total(n＝34)

With ASD(n＝15)

Without ASD(n＝19)

Clinical characteristics

Number (%)

Number (%)/
Median (IQR)

Number (%)/
Median (IQR)

p valueSex (female)Age at first visit (years)
Age of onset (years)
Duration of illness (years)
BMI (kg/m2)

32 (94.1)
30.7 (21.6-44.1)
24.6 (19.7-40.2)
1.2 (0.58-4.4)

14 (93.3)
27.3 (20.3-41.1)
23.6 (18.3-30.6)
1.2 (0.58-10.5)

18 (94.7)
35.7 (22.8-52.3)
27.9 (20.0-48.8)
1.3 (0.42-4.3)

1.000
0.160
0.099
0.876

a
b
b
b

0.052
0.372
0.256

b
b
b

a

Height (cm)Body weight (kg)14.1 (13.1-15.3)
14.8 (13.7-17.7)
13.7 (12.9-15.2)
160.0 (155.0-163.0) 160.0 (155.0-161.0) 160.0 (156.0-163.0)
36.0 (33.0-42.0)
36.0 (33.0-42.0)
35.0 (32.0-40.0)

Clinical background
Hospitalization
within 1 year after initial visit 1
Number of consultations (times)
Continued subthreshold ED
pathology from childhood 2
History of physical illness
Somatic symptoms

11 (32.4)

3 (20.0)8 (42.1)

0.271-

13 (5.5-17)

14 (10.5-19)

0.639

11 (32.4)

8 (53.3)

3 (15.8)

b
＊

a

＊

0.030

14 (41.2)
18 (53.0)

3 (20.0)
5 (33.3)

11 (57.9)
13 (68.4)

0.026
＊
0.042

c

22 (64.7)
7 (20.6)
1 (2.9)4 (11.8)9 (60)
5 (33.3)
1 (6.7)0 (0)13 (68.4)
2 (10.5)0 (0)4 (21.1)

0.724
0.199
0.441
0.113

a
a
a
a

c

Predominant ARFID subtype
1) ARFID-aversive2) ARFID-limited intake
3) ARFID-limited variety
N/AContinuous variables are reported as median and IQR and compared by Mann-Whitney U test. Categorical variables
are reported as number (percentage) and compared by χ2 test or Fisherʼs exact test. a: Fisherʼs exact test, b: MannWhitney U test, c: χ2 test. 1: No patient was hospitalized more than once during the study period. 2: Patients withabnormal eating behavior not diagnostic of an eating disorder since childhood. ARFID, avoidant/restrictive food intake
disorder; ASD, autism spectrum disorder; BMI, body mass index; ED, eating disorder; and IQR, interquartile range.
＊
p＜0.05.

childhood. Members of the group without ASD comorbidity were more likely to have a history ofphysical illness (20.0% vs 57.9%, p＝0.026), such as gastrointestinal disease, and current somatic
symptoms (33.3% vs 68.4%, p＝0.042).
We also categorized the 34 patients with AO-ARFID into three subtypes18). The “aversive subtype”
accounted for 22 (64.7%) patients, the “limited intake subtype” for 7 (20.6%), and the “limited variety
subtype” for 1 (2.9%). Five (14.7%) patients had multiple subtypes. Four (11.8%) patients had no
subtypes, all of whom were in group without ASD comorbidity. There were no significant differencesin the proportions of the three subtypes between the groups with and without ASD comorbidity.
Table 2 shows the differences in core eating disorder pathology and psychopathology related toeating disorders according to the EDI between the AO-ARFID groups with and without ASD
comorbidity. The group with ASD comorbidity had significantly higher scores than the group without
comorbidity for ineffectiveness (p ＝0.030) and interoceptive awareness (p ＝0.040). Although notsignificantly different, the group with ASD comorbidity tended to have higher levels of perfectionism
than the group without ASD comorbidity (p＝0.053). There were no significant differences between- 25 -

Matsuzuka et al
Table 2. Comparison of EDI between ARFID groups with and without ASD comorbidity
at baseline
With ASD (n＝15)

Without ASD (n＝19)

Median (IQR)

Median (IQR)

p value

Drive for thinness
Bulimia
Body dissatisfaction
Ineffectiveness
Perfectionism
Interpersonal distrust
Interoceptive awareness1.0 (0.0-2.0)
0.0 (0.0-2.0)
10.5 (6.3-15.0)
11.0 (7.0-18.0)
3.0 (2.0-7.0)
7.0 (3.0-9.0)
5.0 (3.0-8.0)

1.0 (0.0-2.5)
0.0 (0)10.0 (9.0-13.0)
6.0 (4.0-10.0)
1.0 (0-3.5)
4.0 (1.0-9.0)
2.0 (0-3.5)

0.794
0.165
0.984
＊
0.030
0.053
0.202
＊
0.040

Maturity fears

5.0 (3.0-13.0)

4.5 (3.0-8.75)

0.545

Continuous variables are reported as median and IQR and compared by Mann-Whitney U test.
EDI, Eating Disorder Inventory; ARFID, avoidant/restrictive food intake disorder; ASD, autism
spectrum disorder; and IQR, interquartile range. ＊p＜0.05.

Table 3. Results from linear mixed models in ARFID
Random effects
Fixed effects
Parameters

EstimatetSE

p value

Participant

Item

SD

SD

Intercept

14.5

0.47

30.9

＜0.001＊＊2.0-

Duration

0.19

0.021

9.1

＜0.001＊＊ASD
Duration×ASD

0.61

0.71

0.86

0.39

-

-

−0.078

0.032

＊

-

-

−2.5

0.014

The independent variable was BMI, measured at one-month intervals from the start of treatment. The model was
set up with a random intercept among participants. The covariates were Duration, ASD, and Duration×ASD. The“Duration” term represented the passage of time (months) from the start of treatment. The “ASD” term represented
the presence of ASD comorbidity (ASD＝0, without ASD comorbidity; ASD＝1, with ASD comorbidity). The “Duration×
ASD” term represented the interaction term between “Duration” and “ASD”. ARFID, avoidant/restrictive food intake
disorder; ASD, autism spectrum disorder; and BMI, body mass index. ＊p＜0.05, ＊＊p＜0.01.

the two groups in drive for thinness, bulimia, body dissatisfaction, interpersonal distrust, andmaturity fears.
Table 3 shows the results of a linear mixed model analysis that compared treatment outcomes
between the groups with and without ASD comorbidity in terms of the impact of duration and thepresence of ASD comorbidity at baseline, with BMI change as the outcome. There was a main effectof duration (Estimate＝0.19, SE＝0.021, t＝9.1, p＜0.001), no main effect of presence of ASD (Estimate＝0.61, SE＝0.71, t＝0.86, p＝0.39), and a significant interaction between duration and presence ofASD (Estimate＝−0.078, SE＝0.032, t＝−2.5, p＝0.014). Although not presented in the table, subgroup
analysis of the groups with and without ASD comorbidity revealed that the slope of BMI change permonth was 0.11 for the group with ASD comorbidity and 0.19 for the group without ASD comorbidity.

Discussion
In this study, the prevalence, clinical characteristics, and impact of ASD comorbidity on BMI gain
during the one-year treatment period were examined in participants with AO-ARFID. The results- 26 -

Comorbid ASD Slows BMI Gain in Adult-onset ARFID

showed that AO-ARFID was associated with a high incidence of ASD comorbidity. ASD comorbidity
was associated with severe psychopathology and a slower BMI gain during the course of treatment.
First, the current study found that the BMI gain in the AO-ARFID group was slowed significantly
by ASD comorbidity. No differences were found in background factors that might have inf luenced
BMI between the groups with and without ASD comorbidity. In previous studies of the prognosis ofeating disorders, autistic traits were considered a predictor of negative outcomes of treatments foreating disorders 15,16). A 30-year follow-up study reported that AN with ASD comorbidity was
associated with prolonged core symptoms such as anorexia and a deterioration in other psychiatric
symptoms and socioeconomic status22). No studies to date have focused on the prognosis of AO-ARFID
with ASD comorbidity; however, the current study suggests that ASD traits and ASD-specificpsychopathology may play a role in treatment responsiveness in AO-ARFID as well as in other eating
disorders.
Second, AO-ARFID patients with ASD comorbidity had more severe psychopathology than those
without ASD comorbidity. The EDI showed that the group with comorbid ASD exhibited significantly
worse effectiveness and interoceptive awareness compared to the group without comorbid ASD.
“Ineffectiveness” denotes feelings of general inadequacy, insecurity, worthlessness, and not being incontrol of oneʼs life19). In the AO-ARFID group with ASD comorbidity, severe ineffectiveness may
make it difficult to have a positive outlook or expectation of treatment and may also cause resistanceto weight gain. “Interoceptive awareness” ref lects oneʼs lack of confidence in recognizing andaccurately identifying the emotions and sensations of hunger or satiety19). The group with ASD
comorbidity was more confused about their sense of physical symptoms and feelings, which may havemade it more difficult for them to be proactive in treatment. Although not significantly different, thegroup with ASD comorbidity tended to have higher levels of perfectionism than the group without
comorbidity. “Perfectionism” indicates excessive personal expectations of superior achievement. The
group with ASD comorbidity may have been prone to conf licts between perfectionistic achievement
standards and their own abilities. This is an important treatment-related factor that represent thebackground psychology of eating disorders.
Third, the clinical background of AO-ARFID differed between the groups with and without ASD
comorbidity. In the group with ASD comorbidity, subthreshold eating disorder-related pathology (e.g.,
picky eating, lack of interest in food, and small appetite) was more common in early childhood andpersisted into adulthood. Subthreshold eating disorder pathology may have evolved into eating
behavior problems seen in ARFID due to the increased stress load arising from the demands of social
functioning in adulthood23,24). In contrast, the group without ASD comorbidity was significantly more
likely to have a history of physical illness, such as gastrointestinal disease and current somatic
symptoms. This was consistent with previous reports 25,26). The three subtypes did not differ
significantly in frequency between the AO-ARFID groups with and without ASD comorbidity. The
most common “aversive subtype” was found in the group with ASD comorbidity with nine patients(60.0%), whereas “limited variety subtype” accounted for only one patient (6.7%). In contrast, aprevious report suggested that “limited variety subtype” is the most common in childhood-onset
ARFID with ASD comorbidity17). These differences in symptoms were consistent with the finding thatfood avoidance or restriction based on sensory aspects improve in adulthood to a level that does notinterfere with daily life27). In addition, the group without ASD comorbidity contained atypical casesthat did not fit into any of the three subtypes18). Thus, it is possible that some AO-ARFID patients- 27 -

Matsuzuka et al

without ASD may be difficult to classify in terms of the three principal features of ARFID that areused for childhood-onset cases, and that a separate classification for adults may be necessary. The
clinical characteristics of AO-ARFID did not differ significantly between the groups, but most of thepatients were female in this study. This result was similar to a Japanese report on ARFID afteradolescence2). In contrast, reports of childhood-onset ARFID indicate that 30% of patients weremale4,5,28). This discrepancy suggests that male may be less likely to develop ARFID in adulthood.
This is in agreement with the higher incidence of female in any type of eating disorders. However, wecannot rule out the possibility of adult male refraining from seeking medical attention. It has beensuggested that the stigma that eating disorders are female diseases and prejudice against mental
illnesses may be holding male back from seeing a doctor29,30). Further research on age-specific sex
ratios of ARFID is warranted to clarify this hypothesis.
Finally, this study showed that 44.1% of patients with AO-ARFID had ASD comorbidity. The
comorbidity prevalence in AO-ARFID may be higher than in previous reports of childhood-onset
ARFID (comorbidity 3%-13%)7,14). Patients with ASD often display picky eating and prefer their own
routine (what, when, and how they consume), which is reported to correlate with poor attention
switching28). These “food obsessions” have been reported to have serious effects on physical andmental health31). In childhood, eating behavior problems may be considered only a symptom of ASD,
and thus ARFID may remain underdiagnosed. Psychological intervention tailored to the ARFID
subtype in children is the treatment for ARFID (e.g., reduction of postprandial discomfort symptoms
for the “aversive subtype”, education and training in disinterest for the “limited intake subtype”, andreduction of anxiety about sensory avoidance for the “limited variety subtype”). In children, ARFID
with ASD comorbidity is treated with consideration of ASD characteristics. Therapists need to not
only provide tailored treatment to ARFID subtypes, but also guidance to overcome social difficulties(which also affect ARFID symptoms) related to the patientʼs autism spectrum characteristics, such as
limited, repetitive, and stereotyped behaviors and interests, and impaired social communication andinterpersonal interactions. In adults, specific treatment has not yet been defined. Nevertheless, thesame treatment strategy may be effective for AO-ARFID with ASD comorbidity as for children.
Further research on the treatment of adult ARFID (as well as on special handling of cases with ASD
comorbidity) is expected.
In conclusion, approximately half of the participants with AO-ARFID in this study had ASD
comorbidity, and the comorbidity prevalence was higher than that in previous reports of childhoodonset ARFID. ASD comorbidity was found to slow BMI gain during the course of treatment. The
psychopathology of AO-ARFID with ASD comorbidity was more severe than that of the group without
ASD comorbidity, which may worsen the prognosis of the former group. Therapists should be mindfulof the possibility of ASD comorbidity and make an appropriate diagnosis. Interventions that consider
the characteristics of ASD may have therapeutic effects for patients with AO-ARFID. This study isthe first to focus on the impact of ASD on AO-ARFID, and may provide insight into effective
treatment strategies in the future. In addition, some patients in the group without ASD comorbidity
did not fit the childhood-onset ARFID classification system and may have a different pathophysiology
from that of childhood cases. Therefore, further studies are needed in this regard.
Limitations
The present study had several limitations. First, this was a single-center study and may not be
generalizable to the entire AO-ARFID population. Second, the sample size was small. Although low- 28 -

Comorbid ASD Slows BMI Gain in Adult-onset ARFID

prevalence of AO-ARFID comorbid with ASD limits the sample size, we hope that the characteristics
and course of AO-ARFID will become clearer as the number of cases increases in the future. Finally,
although the present study followed weight progress over a one-year period, further long-term
observation of weight change is necessary for accurate prognostic evaluation.
Acknowledgements
All authors have no COI to declare regarding the present study.
We are grateful to the professors of the department of Medical Statistics at Osaka Metropolitan
University Graduate School of Medicine for their guidance and encouragement.
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- 30 -

Osaka City Med. J. Vol. 69, 31-39, 2023

Effectiveness of Financial Incentives for a Pedometer-based
Walking Promotion Program
ERIKO KOMIYA1), SATSUKI TANIUCHI2), MASATSUGU SHIBA3),
AYUMI SHINTANI2), and HIROAKI NAKAMURA4)
Department of Orthopedic Surgery1), Osaka City University Graduate School of Medicine;
Department of Medical Statistics2) and Orthopedic Surgery4),
Osaka Metropolitan University Graduate School of Medicine; andHealth Science Innovation Center3), Osaka Metropolitan University

Abstract
BackgroundPhysical inactivity significantly contributes to poor health and disease onset. Physical inactivity
is also associated with severe economic burdens. Japanʼs Ministry of Health, Labour and Welfare cite
the provision of various health promotion incentives aimed at health-indifferent groups, which
comprise individuals less interested in health promotion. This study investigates the relationshipbetween medical costs and a pedometer-based walking program that provides monetary incentives
based on daily step counts.
Methods
The study sample included 11560 citizens aged 40-75 years, who lived in Takaishi City and wereenrolled in the National Health Insurance between October 2016 and March 2018.
Results
The ordinal logistic regression analysis results showed that participation in a walking promotion
program with a health-promoting financial incentive was correlated with a reduction in healthcare
costs over one year in a sample of Takaishi City residents. The difference in the yearly medical cost
between the walking and control groups was 21261324 yen/year. The reductions were more than the
total monetary incentives paid under the program, which totaled 7462000 yen/year.
Conclusions
It was suggested that a walking promotion program with incentives implemented on a municipal
scale has the effect of reducing medical expenses. As program context differs widely from region toregion and country to country, future investigations are required to select appropriate incentive
schemes for programs offered in other regions and countries.
Key Words: ‌Health care cost; Health-promotion incentive; Japan; Pedometer-based
walking program; Physical inactivity
Received November 1, 2022; accepted February 13, 2023.
Correspondence to: Eriko Komiya, MD.
Department of Orthopedic Surgery, Osaka Metropolitan University Graduate School of Medicine,
1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
Tel: +81-6-6645-3853; Fax: +81-6-6646-6260
E-mail: e_komiya@omu.ac.jp- 31 -

Komiya et al

Introduction
Physical inactivity is a significant contributor to poor health and disease development. According
to the World Health Organization (WHO), inactivity is the fourth leading risk factor for death and asignificant risk factor for mortality. Eliminating physical inactivity is expected to relieve otherdiseases such as hypertension, dyslipidemia, and obesity1). Physical inactivity is also associated withsevere economic burdens. Physical inactivity generates USD 53.8 billion internationally, most ofwhich is paid by the public sector2).
To reduce physical inactivity, it is especially important to approach and encourage the healthinsensitive population to become more interested in health. Financial incentive programs have beenreported as an effective means to encourage involvement in physical activity, and Japanʼs Ministry of
Health, Labour and Welfare (MHLW) has identified the provision of various health promotion
incentives as an effective way to reach the health-insensitive population. Previous studies indicate
the effectiveness of a health-promoting financial incentive (HPFI)3). Giles et al reviewed the literatureon the effectiveness of financial incentive interventions in promoting healthy behavior changes among
non-clinical adults living in high-income countries and revealed that compared with usual care or nointervention, interventions with financial incentives are more effective in promoting healthy
behavioral changes4). Pedometers, which are used to assess physical activity, are also consideredeffective in promoting activities of daily living in a wide range of age groups; however, most of thereported studies have been conducted among hospitalized patients, while few have been conductedamong non-clinical adult populations 5-7). Although it is important to investigate the effects of
preventive medicine policies on lowering healthcare costs for the aging population in later stages oflife, current research on the effects of municipal-level programs that distribute pedometers to citizens(including healthy people), evaluate their activity levels, and provide incentives based on the distancewalked is scant. Furthermore, research on the effectiveness of such programs at the municipal levelis also limited. Therefore, this study used real data from a city where a pedometer-based walking
program was implemented to investigate the relationship between a walking program with financial
incentives based on the number of steps taken per day and real data on medical costs.

Methods
This study used data from a program implemented in Takaishi City, Osaka Prefecture, from
October 2016 to March 2018, which provided incentives for the number of daily steps taken, as
measured using a pedometer. This program was designed by the government to promote longevity byencouraging people to start and continue exercising. The incentive based on the number of steps taken
was designed to provide the program to health-conscious individuals who voluntarily began exercising
as well as to those who were indifferent to health promotion or who did not exercise regularly.
The cityʼs program was widely publicized, with information being distributed to all residential
homes, the cityʼs website, and posters placed throughout the city. The city, which is located in anindustrial area of Osaka, has a population of 56000, of which 27% are above 65 years. Takaishi City
residents who wished to participate in the walking program were required to register at the city
office, where they received a pedometer (Activity Meter with FeliCa AM-150; Tanita Corp., Tokyo,
Japan). The participants could also use the pedometer application on their smartphones. The content
of the research was made public on the cityʼs bulletin board and website. Participants who agreed toparticipate in the study at the time of registration were included. Participants could refuse- 32 -

Effectiveness of Financial Incentives for Walking

participation by informing the contact person in charge of accepting refusals for information use.
Participants brought their pedometers to special machines installed at community centers, post
offices, and stores in the city, and registered their daily number of steps monthly. Depending on thenumber of steps taken on that day, up to 6000 points (one-point equaled 1 yen) were awarded over thecourse of a year, which could then be exchanged for money certificates. The study participants were
National Health Insurance (NHI) enrollees aged between 40 and 75 years, who resided in Takaishi
City between October 2016 and March 2018. As there is no private health insurance in Japan, all theresidents are required to join one of the two public health insurance schemes7,8). This study used aclaims dataset from one of these schemes, the NHI, which covers the self-employed (e.g., farmers andfishermen), retirees, and their dependents, and the remaining 25% of the population. This dataset
included the following information: ID number, a month for which each hospital/clinic was assigned anational diagnosis code by inpatient or outpatient, the type of claims, that is, medical claims or direct
primary care claims (medical bills), number of days per month, national diagnosis codes(corresponding to the 10th revision of the International Statistical Classification of Diseases andRelated Health Problems), and medical bills (medical expenses). The data were extracted andprepared for analysis in accordance with the rules for the use of personal information. In addition tothese data, basic resident register data maintained by the city were analyzed to obtain backgroundinformation on participants (date of birth, sex, whether or not they had moved, and date of death) andprogram step count data to collect step counts. Considering the time lag between exposure (program
participation) and the effect of medical cost reduction, we defined the month in which the number ofsteps was registered as the baseline month, treated the following day, and the days after as themonth in which program participation was effective, and conducted the analysis. The participants
were treated as the walking group throughout the analysis period if they had participated in the
program at least once even if no steps were registered after the month in which the participants had
registered for the program. This research plan was approved by the ethics committee of ouruniversity [blinded for anonymous review].
AnalysesThis study aimed to evaluate the effect of program participation on medical costs. In establishing
control and walking groups, it was necessary to pay attention to factors other than program
participation that might affect medical costs for both groups. The walking program, which was the
subject of this study, was not designed for research purposes but was designed by the city to promote
exercise among its residents. In other words, the data obtained were not prepared for research.
Moreover, only the minimum necessary items were collected; therefore, there were no data on factorsthat might affect healthcare costs (medical history, smoking history, and body mass index), whichhave been reported and generally collected in previous studies on the effect of exercise on health carecosts. Therefore, to adjust as much as possible for background on non-walking medical costs in thewalking and control groups, we used medical cost data for the past six months, excluding the baselinemonth, and to estimate the potential medical costs that could be needed in that month, we definedthat value as the baseline medical cost. Additionally, the year and month were also matched toadjust for time biases, such as seasonal variations in medical costs (point-in-time matching). We
defined non-participants as those who had never registered any steps and matched them one-on-one
with program participants on age, sex, year, and baseline medical expenses (categorized in 5000 yen
increments). Repeated data were used in this study.
- 33 -

Komiya et al

The analysis was initially conducted using a mixed-effects model. However, the analysis was
problematic because the residuals did not meet normality; therefore, an ordinal logistic regression
analysis was conducted on medical costs during the first every 12 months of participation, with thedependent variable being monthly medical costs and the independent variables being program
participation, sex, age, time since participation, and baseline medical costs (Mean monthly medical
expenses in the past 6 months from period0). A two-sided 5% significance level of R version 4.0.1(https://www.r-project.org/foundation/) was used to conduct the analysis.

Results
Table 1 summarizes the descriptive statistical analyses. During the observation period, there
were 11560 citizens aged 40-75 years who resided in Takaishi City and had registered with the NHI.
Of these, 18.3%, or 2119 persons, had registered their steps. Of the sample, which had a median ageof 64.0 years, 55% were women, with a median cost per month of 7144.2 yen.
Data with duplicate IDs, unknown dates of birth, and so on were excluded and only appropriate
data were used to select one control subject with the same background factors for each participant.
As a result of the matching, there were 1945 people in both the walking group and the control group.
Table 2 lists the number of participants used in the analysis. The walking and control groups showedinsignificant demographic differences. Regarding medical expenses, the results were lower for bothgroups compared to Table 1. This would predict that the walking program would enroll more people
between the ages of 40 and 75 with relatively low healthcare costs.
Figure 2 shows the step registration rates of the program participants. The results showed that
86.4% of the participants registered their steps for more than 80% of the days during the registration
period. Figure 3 and Table 3 show the results of the ordinal logistic analysis. It was estimated thatthe odds of an increase in medical expenses in the walking group would be lower than in the control
group throughout the entire period, and a significant difference was observed especially in the period
from 2 to 11, which indicated that participating in an HPFI program was associated with a reductionin healthcare costs over 12 months.
The yearly medical costs for each group were as shown in Table 4. The total medical costs per year
was 31402791 yen in walking group and 52664115 yen for control group. Based on these results, theproject medical cost reduction was estimated to be approximately 21261324 yen/year, which was
higher than the average of 7462000 yen/year of total financial incentives paid by Takaishi City to thewalking program participants.

Table 1. Characteristics of the study sample
Takaishi citizens aged 40-75 with NHI

11560

Age, years old (median [IQR])

64.00 [51.00, 69.00]

Sex, % (freq)

Women
Men

Baseline cost, yen/month (median [IQR])

55.1 (6375)
44.9 (5185)
7144.17 [0.0, 21699.17]

Project participants, % (freq)

No83.4 (9637)

Yes

16.6 (1923)

NHI, National Health Insurance; and IQR, Interquartile Range.

- 34 -

Effectiveness of Financial Incentives for Walking

Figure 1. Process of study selection.
Table 2. Demographic data for the walking group and matched control group

N
Sex, % (freq)

Women
Men

Age
(years old),(median [IQR])
Age categories(years old),% (freq)

40-44
45-49
50-55
55-59
60-64
65-69
70-75

Baseline Cost
(yen/month)(median [IQR])
Cost categories(yen/month), % (freq)

0-49995000-9999
10000-14999
15000-19999
20000-24999
25000-29999
30000-Walking

Control

Overall

1945

1945

3890

69.1 (1344)
30.9 (601)69.1 (1344)
30.9 (601)69.1 (2688)
30.9 (1202)

65[53.00, 70.00]

65[53.00, 70.00]

65[53.00, 70.00]

9.4 (183)
9.7 (189)
8.4 (163)
8.6 (167)
12.5 (243)25.9 (504)25.5 (496)

9.4 (183)
9.7 (189)
8.4 (163)
8.6 (167)
12.5 (243)25.9 (504)25.5 (496)

9.4 (366)
9.7 (378)
8.4 (326)
8.6 (334)
12.5 (486)25.9 (1008)
25.5 (992)1008[0.00, 9521.00]

1149[0.00, 9327.00]

1079.5[0.00, 9418.50]

59.0 (1147)
7.9 (153)
7.3 (142)
6.0 (116)
4.2 (82)
3.4 (66)
12.3 (239)59.0 (1147)
7.9 (153)
7.3 (142)
6.0 (116)
4.2 (82)
3.4 (66)
12.3 (239)59.0 (2294)
7.9 (306)
7.3 (284)
6.0 (232)
4.2 (164)
3.4 (132)
12.3 (478)

＊

p

1

0.908
1

0.62
1

Comparison between the walking and control groups. [Sex, Age categories, Cost categories: Chi-square test without
continuity correction. Age, Baseline cost: Mann-Whiteny U test].
＊- 35 -

Komiya et al

Figure 2. Step registration rates of the program participants.

Figure 3. Ordinal logistic analysis plot of the relationship between project participation and health care costs. An
ordinal logistic analysis was performed for each monthʼs medical expenses for the walking and control groups, and a ＊was noted for months when the p value was less than 0.05.

- 36 -

Effectiveness of Financial Incentives for Walking
Table 3. T he result of the correlation between participation in the walking project or
not and monthly medical expenses over 12 months, performing ordinal logistic
regression analysis for each month from the start of walking as Period 0 to Period
11 after 12 months.
PeriodOdds Ratio (95% CI)p1
20.90 (0.79-1.01)
0.86 (0.76-0.97)

0.08

3

0.82 (0.72-0.93)

＜0.01

4

0.74 (0.66-0.84)

＜0.01

5

0.76 (0.67-0.87)

＜0.01

6
7

0.68 (0.60-0.77)
0.73 (0.65-0.84)

＜0.01

8

0.68 (0.60-0.78)

＜0.01

9

0.70 (0.61-0.80)

＜0.01

10
11

0.66 (0.58-0.76)
0.71 (0.62-0.81)

＜0.01

0.02

＜0.01

＜0.01

Period; The period since the subject registered for the walking project, with the month ofregistration being 0. The method of analysis was ordinal logistic regression analysis, withmedical costs as the objective variable and explanatory variables as participating in thewalking project or not, sex, age, and baseline medical cost.

Table 4. T otal medical costs per person in the walking group and control group during the
12-month follow-up periodN
Cost (yen/year)

median[IQR]

Walking

Control

1945
1074 [0,18878]

1945
6172 [0,20423]

IQR, Interquartile Range.

Discussion
The ordinal logistic regression analysis results showed that participating in a walking promotion
program with an HPFI correlated with a reduction in healthcare costs. There was a difference of21261324 yen/year in the medical cost between the walking and control groups.
A total of 2848 participants enrolled in the walking program, of which 1945 participants were
between the ages of 40 and 75 years whose data were used for the study. This was projected tofurther reduce health care costs. Up to 6000 yen/year per person was reimbursed based on thenumber of daily steps. The average annual incentive amount paid by the city to the participants was
7462000 yen/year, which is lower than the projected medical cost reduction. This result indicates thata municipal policy that provides incentives of up to 6000 yen/year, based on the number of daily steps
recorded on a pedometer, reduces medical costs to a greater extent than the amount of incentive paidby the city.
According to WHO, regular physical activity is essential for a healthy world. Therefore, in 2018, it
published the Global Action Plan on Physical Activity to reduce physical inactivity by 15% by 20309).
Previous studies indicate that HPFI may effectively promote exercise10-15). Finkelstein et al 11)
examined the effects of HPFI in a two-arm, single-blind, randomized controlled trial in which
financial incentives were given according to the number of aerobic exercise minutes recorded weekly- 37 -

Komiya et alon a pedometer over four weeks. The results showed that the incentivized group exercised 1.7 times
more per week than the control group. Therefore, the researchers concluded that associating
moderate financial incentives with aerobic exercise minutes could be an effective as well as a costeffective approach to increasing physical activity. That report had the advantage of using arandomized comparison design, but with a small sample size of only 51 participants. In contrast, thecurrent study was conducted on a municipal level and included 1945 walkers, which were more thanthose in the previous studies.
Most previous studies that used pedometers to collect information were conducted face-to-face orby manual input, limiting the sample size from which information can be collected. Moreover, moststudies were conducted on relatively small populations. Takaishi City operated the program using asystem that utilized information and communication technology. In this study, the use of activity
meters equipped with Felica simplified the collection of step counts, making it possible to research ona municipal scale. Using this automated system, participants could bring their pedometers to upload
sites (registration terminals) located throughout the city and register their step counts, making it
possible for a small number of staff members to manage a large number of participants.
Previous literature includes reports on other instances of HPFI. The possibility of decreasing
intervention effects over time after the intervention period has been reported in meta-regressions
involving various health promotion interventions, emphasizing the importance of continuing to
exercise after the intervention period ends11,13,16-18). It has also been reported that the effect of a single
incentive, such as points awarded for a health checkup, as well as behavioral change, is unlikely to besustain19). This HPFI-based walking promotion program was implemented for two years and six
months, from April 2016 to March 2019. Citizens were given incentives based on the number of steps
they took daily but were not penalized for not registering their steps. Therefore, participants were
expected to inevitably register their steps less frequently as their interest in the program waned;
however, in the end, 86.4% of the participants registered their steps on more than 80% of the days
during the year, indicating a very high participation rate. The results also suggest that the HPFI iseffective in maintaining high retention rates for relatively long-term interventions. The total
participation period depended on the registration start date; hence, the analyses were limited to only
12 months from the registration start date.
This study has certain limitations. As the participants voluntarily chose to participate in theintervention, the results may not apply to the general population. Additionally, although we gave as
much consideration as possible to the analysis methods, we cannot rule out the possibility that thelack of data during matching may have resulted in an inadequate adjustment for confounding factors.
We could not prove a causal relationship between the number of steps taken and the reduction in
medical costs owing to the lack of data on the number of steps taken by those who did not participate
in the program. Although HPFI has been shown to be an effective approach for promoting exercise
among the health-insensitive population, this study did not collect information on the extent to which
the walking group included the health-insensitive population. However, this study was not able tocollect information on the extent to which the walking group included people who were indifferent totheir health. It has not been possible to determine whether the reduction in medical costs in thisprogram was due to the participation of the health-insensitive and disease-prevention groups, but itis hoped that future studies will shed more light on the effects of the HPFI.
This studyʼs results suggest that a walking promotion program with an HPFI could help reduce- 38 -

Effectiveness of Financial Incentives for Walking

healthcare costs. For incentive programs to be effective, the rewards must be appealing to theparticipants. If the rewards are too small, the program will not work. Conversely, the larger the
rewards, the higher the program expenses paid as rewards to the participants. This programʼs
incentives were based on the HPFI guidelines from the MHLW as well as incentives granted bysimilar policies implemented in Japan. With program context differing widely from region to region
and country to country, there is a need for future investigations to inform the selection of appropriate
incentive schemes for programs offered in other regions and countries.
Acknowledgements
All authors have no COI to declare regarding the present study.
The authors thank Manabu Funatomi, Health and Welfare Department of Takaishi city, for his
managing the project site.
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expenditure: the Ohsaki Cohort Study. BMJ Open 2011;1:e000240.
8. Nakamura K, Okamura T, Kanda H, et al. The value of combining serum alanine aminotransferase levels andbody mass index to predict mortality and medical costs: a 10-year follow-up study of National Health
Insurance in Shiga, Japan. J Epidemiol 2006;16:15-20.
9. World Health Organization. Global action plan on physical activity 2018-2030: more active people for ahealthier world: at-a-glance. 2018, No. WHO/NMH/PND/18.5. World Health Organization.
10. Adams J, Giles E L, Robalino S, et al. A systematic review of the use of financial incentives and penalties toencourage uptake of healthy behaviors: protocol. Syst Rev 2012;1:51.
11. Finkelstein EA, Brown DS, Brown DR, et al. A randomized study of financial incentives to increase physicalactivity among sedentary older adults. Prev Med 2008;47:182-187.
12. Hajat C, Hasan A, Subel S, et al. The impact of short-term incentives on physical activity in a UK behavioral
incentives programme. NPJ Digit Med 2019;2:91.
13. Kato M, Goto A, Tanaka T, et al. Effects of walking on medical cost: a quantitative evaluation by simulation
focusing on diabetes. J Diabetes Investig 2013;4:667-672.
14. Paul-Ebhohimhen V, Avenell A. Systematic review of the use of financial incentives in treatments for obesityand overweight. Obes Rev 2008;9:355-367.
15. Rosenberg DE, Kerr J, Sallis JF, et al. Promoting walking among older adults living in retirement
communities. J Aging Phys Act 2012;20:379-394.
16. Oliver A, Marteau TM, Ashcroft RE. Can financial carrots improve health? J Health Serv Res Policy 2009;14:
1-2.
17. Wall J, Mhurchu CN, Blakely T, et al. Effectiveness of monetary incentives in modifying dietary behavior: areview of randomized, controlled trials. Nutr Rev 2006;64:518-531.
18. Cahill K, Perera R. Competitions and incentives for smoking cessation. Cochrane Database Syst Rev 2011;(4):CD004307.
19. Petry NM. Contingency management treatments: controversies and challenges. Addiction 2010;105:1507-1509.

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Osaka City Med. J. Vol. 69, 41-49, 2023

Clinical Significance of the AID Classification in Diabetic Foot
Ulcers: Awareness of Arteriopathy, Bacterial Infection, andFoot Deformity
SHINOBU AYABE1), OSAMI KAWARADA2), and HISASHI MOTOMURA3)
Department of Plastic and Reconstructive Surgery1), Yao Tokushukai General Hospital;
Kawarada Cardio Foot Vascular Clinic2); andDepartment of Plastic and Reconstructive Surgery3),
Osaka Metropolitan University Graduate School of Medicine

Abstract
Background
This single-center retrospective study aimed to investigate the impact of our novel AID
classification, based on arteriopathy, bacterial infection, and foot deformity, on wound healing inpatients with diabetic foot ulcers (DFUs).
Methods
This study included 115 consecutive patients with 129 limbs who were hospitalized for DFU
management. The presence of arteriopathy, bacterial infection, and foot deformity was scored as one
point each, and wound severity was graded as 1-3. The wound healing rates at 12 months were
evaluated. Comprehensive treatment including revascularization, debridement, or off loading was
implemented based on the AID concept.
Results
Arteriopathy was observed in 49.6% of patients with DFUs. A total of 82.8% of patients witharteriopathy had DFUs complicated by both bacterial infection and foot deformity; however, 10.9%
were complicated by either one. Approximately 14% of patients underwent major amputations. The
Kaplan-Meier wound healing curves were significantly (p＝0.002) different among patients with AID
scores of 1, 2, and 3; however, the wound healing rates at 12 months were 89%, 90%, and 88%,
respectively.
Conclusions
Diabetic arteriopathy is commonly complicated by bacterial infections of of DFUs and footdeformities. The AID classification effectively stratified the wound healing speed. Furthermore, AID
concept-based comprehensive treatment achieved a wound healing rate of approximately 90%.
Key Words: ‌Peripheral artery disease; Diabetes; Wound healing

Received May 24, 2022; accepted March 3, 2023.
Correspondence to: Shinobu Ayabe, MD.
Department of Plastic and Reconstructive surgery, Yao Tokushukai General Hospital,
1-17 Wakakusa-cho, Yao, Osaka 581-0011, Japan
Tel: +81-72-993-8501; Fax: +81-72-993-8567
E-mail: ayabeshinobu@gmail.com- 41 -

Ayabe et al

Introduction
Diabetes mellitus (DM) is increasing in burden globally, particularly in Asia 1). DM poses a
significant challenge in clinical practice because it changes the nature of peripheral artery disease(PAD) and increases the susceptibility to bacterial infections and foot deformities2). Diabetic footulcers (DFUs) are an amalgamation of arteriopathy, bacterial infection, and foot deformity 3).
Therefore, a multidisciplinary approach, encompassing vascular and wound management, is
indispensable for the treatment of diabetic PAD and DFU.
To date, traditional classification systems have been used in the study of PAD and DFUs, such as
Fontaine 4), Rutherford 5), Meggitt-Wagner 6), SINBAD 7), University of Texas 8), Kobe 9), and WIfI 10).
However, the Fontaine and Rutherford systems focus only on the severity of ischemia, while theMeggitt-Wagner, SINBAD, University of Texas, Kobe, and WIfI systems lack a perspective on footdeformities. There is an increasing opportunity to treat patients with DFU in vascular practice.
Thus, a simple, easy-to-use, and clinically relevant classification system for DFUs is urgently needed.
Given the individual significance of arteriopathy, bacterial infection, and foot deformity in wound
management, we recently proposed the Arteriopathy, bacterial Infection and foot Deformity (AID)
classification, which has a maximum possible score of 3 points2). We aimed to assess the impact of the
AID classification on wound healing at 12 months in patients with DFUs.

Methods
This single-center retrospective study included 115 consecutive patients (129 limbs) who were
hospitalized for DFU treatment between January 2015 and December 2016. The study protocol wasapproved by the Medical Ethical Committee of our hospital (No: TGE01275-004; August 9, 2019) andwas conducted in accordance with the Declaration of Helsinki. DFU was defined as foot ulcers inpatients with DM. Diabetes mellitus was diagnosed based on the American Diabetes Association
criteria or the use of antidiabetic agents or insulin11). Blood glucose and subsequent fasting blood
sugar levels were evaluated at admission. Hyperglycemia was controlled with insulin or an oral
hypoglycemic agent, at the discretion of the managing endocrinologist. Wound healing was defined as
the complete epithelialization of the wound.
AID classification and scoring
The AID classification comprises three elements: arteriopathy, bacterial infection, and footdeformity. Each component was scored as 0 or 1, and the wound severity was graded with an AID
score of 0 (none of the components) to 3 (all components). Representative cases of AID 1, 2, and 3 areshown in Figure 1.
Arteriopathy scoring: To assess the severity of arteriopathy-induced microcirculation disorder, thefootʼs skin perfusion pressure (SPP) (SensiLaseTM PAD3000, Vasamed Inc., Minnesota, USA) was
measured. Multiple measurements were obtained for the dorsal and plantar aspects of the affected
foot. The lowest values were selected. According to Castronuovo et al12), an SPP of ≧40 mm Hg at theproximal wound margin is associated with ≧90% likelihood of wound healing in critically ischemic
limbs. Thus, arteriopathy was defined as SPP ≧40 mm Hg, scoring 1 point.
Bacterial infection scoring: According to the guidelines of the Infectious Disease Society of America(IDSA)13) and the International Working Group on the Diabetic Foot (IWGDF)14), bacterial infection of
the wound was defined as the presence of two or more clinical signs (local swelling or induration,
erythema ＞0.5 cm2 around the wound, local tenderness or pain, increased warmth, and purulent- 42 -

Awareness of the AID Classification

Figure 1. Representative cases with AID scores of 1, 2, and 3. The simple AID classification can aid in dividing abroad spectrum of diabetic foot ulcers (DFUs) in clinical practice.

discharge) and no other cause for the inf lammatory response of the skin (e.g., trauma, gout, acuteCharcotʼs neuro-osteoarthropathy, fracture, thrombosis, or venous stasis). The presence of bacterial
infection was scored 1 point.
Foot deformity scoring: According to the guidelines of IWGDF 15), foot deformity was defined as- 43 -

Ayabe et al

alterations or deviations from the normal shape or size of the foot, such as hammer, mallet, or claw
toes, hallux valgus, prominent metatarsal heads, pes cavus, pes planus, pes equinus, or consequencesof Charcotʼs neuro-osteoarthropathy, trauma, amputations, other foot surgery, and other causes. The
presence of foot deformities was scored 1 point.
Comprehensive treatment
A comprehensive treatment was considered based on the AID concept. Medical treatment,
including antiplatelet therapy or clinically driven antibiotics, and wound care were essential. The
need for invasive treatment was determined by a team consisting of a cardiologist, vascular surgeon,and plastic surgeon based on the risks and benefits of the procedure. Endovascular or open
revascularization was considered for arteriopathy. Debridement of the infected wound and off loading(knee-high or ankle-high off loading devices, fitting footwear, or surgical off loading interventions) for
the foot deformity were implemented based on the principles of good standard care and at anexperienced plastic surgeonʼs discretion16).
Statistical analysis
Continuous data of the three groups were compared using one-way analysis of variance (ANOVA)
followed by Bonferroniʼs post-hoc test. Categorical data of the three groups were compared using thechi-square or Fisherʼs exact tests, as appropriate. Patients with different AID scores in the left andright feet were analyzed using the AID score of the first limb. Wound healing, amputation-freesurvival, and overall survival rates were calculated using Kaplan-Meier analysis and compared using
the log-rank test. Statistical significance was set at p ＜0.05. All statistical analyses were performed
using SPSS (version 21; IBM Corp., Armonk, NY, USA).

Results
The patient characteristics are shown in Table 1. All patients scored one or higher on the AID
scale. The prevalence of coronary artery disease and hemodialysis differed significantly among thethree groups (p＝0.003). In particular, their prevalence was significantly higher in those with AID 3
than in those with AID 1 and 2.
The foot characteristics are shown in Table 2A. Arteriopathy was observed in 49.6% of DFUs,
Table 1. Patient Characteristics
Variable

Overall,n＝115

AID score 1,
n＝19

AID score 2,
n＝49

AID score 3,n＝47Age, yrs

65.9±12.5

Male, n (%)
Hypertension, n (%)
Diabetes mellitus, n (%)
Random blood glucose (mg/dL)

82 (71.3)
83 (72.2)
115 (100)194±9168.5±14.8
12 (63.2)
15 (78.9)
19 (100)62.6±13.0
32 (65.3)
33 (67.3)
49 (100)

68.3±10.3
38 (80.9)
35 (74.5)
47 (100)

HbA1c (%)

7.4±2.0

Insulin use, n (%)
Dyslipidemia, n (%)
Smoking history, n (%)
Coronary artery disease, n (%)
Cerebrovascular disease, n (%)
Hemodiaylsis, n (%)

p value
0.05
0.168
0.57

173±76213±100

183±850.145

56 (48.7)
27 (23.5)
62 (53.9)
34 (29.6)

7.0±1.5
9 (47.4)
7 (36.8)
10 (52.6)
4 (21.1)

7.9±2.4
26 (53.1)
8 (16.3)
30 (61.2)
8 (16.3)

6.9±1.5
21 (44.7)
12 (25.5)
22 (46.8)＊
22 (46.8)

0.029
0.708
0.183
0.364

17 (14.8)
42 (36.5)

3 (15.8)
2 (10.5)

4 (8.2)15 (30.6)

10 (21.3)
25 (53.2)

p＜0.05 for AID 2 vs 3.

＊- 44 -

0.003
0.193
0.003

Awareness of the AID Classification

bacterial infection in 87.6%, and foot deformities in 86.8%. A history of a minor amputation wasobserved in 15.5% (20/129 limbs) of the limbs overall and in 17.9% (20/112 limbs) of the limbs with afoot deformity. Furthermore, AID 3 and AID 2 accounted for 82.8% and 10.9% of patients witharteriopathy, respectively (Fig. 2). There were significant differences in the wound location (p＝0.003),
ankle-brachial index (p＜0.001), and SPP (p＜0.001) among the three groups. There was no significant

Table 2A. Foot characteristics
Overall,
n＝129

AID score 1,
n＝22

AID score 2,n＝54

74 (57.4)
57(44)/38 (30)/
12 (9)/9 (7)/
13 (10)14 (63.6)
5 (23)/7 (32)/0 (0)/6 (27)/
4 (18)

32 (59.3)
27 (50)/15 (28)/
5 (9)/2 (4)/＊5 (9)Arteriopathy, n (%)

64 (49.6)

4 (18.2)

7 (13.0)

Bacterial infection, n (%)

113 (87.6)

9 (40.9)51 (94.4)

Foot deformity, n (%)

112 (86.8)

9 (40.9)50 (92.6)

Ankle brachial index

0.85±0.23

0.97±0.22

Dorsal skin perfusion pressure,
mm Hg

44±23

Plantar skin perfusion pressure,
mm Hg

43±24

Variable
Right, n (%)
Wound location
toe/body/heel/crural/extensive,
n (%)

＊

AID score 3,n＝53

p value

28 (52.8)

0.644

25 (47)/16 (30)
/7 (13)/1 (2)/
4 (8) †
53 (100)†‡＜0.001＊53 (100)†＜0.001＊53 (100)†＜0.0010.96±0.21

0.74±0.20†‡＜0.001

59±29

60±17

29±13†‡＜0.001

65±39

54±17

31±15†‡＜0.0010.003

p＜0.05 for AID 1 vs 2, †p＜0.05 for AID 1 vs 3, ‡p＜0.05 for AID 2 vs 3.

Table 2B. Breakdown of comprehensive treatment
Variable
Revascularization for arteriopathy,n (%)
Debridement for bacterial infection,n (%)
Off loading for foot deformity, n (%)

Overall

AID score 1

AID score 2

AID score 3

p value

45 (70.3)

1 (25)

4 (57.1)

40 (75.5)

0.075

113 (100)

9 (100)47 (92.2)

53 (100)

0.08

101 (90.2)

9 (100)47 (94)45 (84.9)

0.177

Figure 2. AID scores in arteriopathy, bacterial infection and foot deformity.

- 45 -

Ayabe et al

Figure 3. Kaplan-Meier estimates of A) wound healing, B) amputation-free survival, and C) overall survival.

difference in the intensity of revascularization (p＝0.075), debridement (p＝0.08), or off loading (p＝0.177) among the three groups (Table 2B). Even in the AID 3 group, revascularization was performed
in 75.5% of patients with arteriopathy, debridement in 100% of patients with infected wounds, andoff loading in 84.9% of patients with foot deformities.
Seventeen patients (14.8%) died and 18 limbs (14%) were amputated during the study period.
According to Kaplan-Meier analysis, wound healing was significantly (p＝0.002) different among the
AID scores of 1, 2, and 3 (Fig. 3A). Patients with AID 3 exhibited the most delayed wound healing (p＝0.037 for AID 1 vs 3, p＜0.001 for AID 2 vs 3). With AID-based comprehensive management, wound- 46 -

Awareness of the AID Classification

healing rates of those with AID scores of 1, 2, and 3 at 12 months were 89%, 90%, and 88%,
respectively. The amputation-free survival rate of AID 3 was 66% at 12 months, which was the lowest
among the three groups, without being statistically significant (Fig. 3B). The overall survival
stratified by the AID score was analyzed using the Kaplan-Meier method. The overall survival rate
was 83% at 12 months, which was the lowest for AID 3, without being statistically significant (Fig.
3C).

Discussion
The main findings of this study were as follows: 1) arteriopathy was observed in 49% of DFUs; 2)
10.9% of arteriopathy cases were complicated by bacteria-infected wounds or foot deformities, and82.8% of arteriopathy cases were complicated by both; 3) the intensity of revascularization forarteriopathy, debridement for infected wounds, and off loading for foot deformities was comparable
among the AID score groups; and 4) Kaplan-Meier estimates stratified by AID scores demonstrated
that those with AID scores of 3 exhibited the most delayed wound healing, but the wound healing
rates at 12 months was comparable (89% in AID 1, 90% in AID 2, and 88% in AID 3) .
Considerable attention should be paid to arteriopathy, even in patients with DFUs. Traditionalclassifications for the assessment of wound severity, such as Fontaine4) and Rutherford5), are based
only on arteriopathy. However, multiple disorders including arteriopathy, bacterial infection,neuropathy, foot deformity, and decubitus are associated with DFUs. To date, Meggitt-Wagner6),
SINBAD7), University of Texas8), Kobe9), and WIfI10) are available for wound assessment, given theimportance of several factors in DFUs. The Meggitt-Wagner classification is based on the depth andarea of the wound. The University of Texas and WIFI classifications are based on the depth or area of
the wound, arteriopathy, and bacterial infection. The Kobe classification focuses on neuropathy,
bacterial infection, and arteriopathy. The SINBAD classification is based on neuropathy, woundlocation, depth or area of the wound, arteriopathy, and bacterial infection. However, theseclassifications are not frequently utilized in clinical practice because of their complexity.
Furthermore, these classifications underappreciate foot deformity, which is affected by neuropathy
and a history of amputation17). Thus, a simple, easy-to-use, and clinically relevant classification of
DFUs is urgently needed in vascular medicine practice. Here, we propose the AID classification basedon arteriopathy, bacterial infection, and foot deformity.
In the present study, approximately half of the patients with AID 3 had coronary artery disease
and end-stage renal disease requiring hemodialysis. This suggests that those who scored 3 on the
AID classification were the most challenging subset of patients in clinical practice. Arteriopathyinduced microcirculation disorder was observed in 49.6% of patients with DFUs. Furthermore, AID 3
and AID 2 accounted for 82.8% and 10.9% of patients with arteriopathy, respectively. These findings
suggest that approximately half of the patients with DFUs were affected by diabetic arteriopathy,
and ＞90% coexist with bacterial infections and/or foot deformities. There was no significant differencein the intensity of revascularization for arteriopathy, debridement for bacterial-infected wounds, oroff loading for foot deformity among patient with AID scores of 1, 2, and 3 (Table 2B). These findings
suggest that the AID concept-guided comprehensive treatment should be practiced in any severity of
DFUs.
Studies that have investigated the relationship between wound classification and wound healing
are limited. Alexandrescu et al reported a significantly lower incidence of wound healing at 12- 47 -

Ayabe et almonths using the Wagner grading; the incidence was 67% for grade 3-4 wounds, and 89% for grade
1-2 wounds18). Weaver et al reported that WIfI stage 3/4 wounds had a lower incidence of healing at
12 months than WIfI stage 1/2 wounds (57.2% vs 77.3%) did19). According to a study by Ince et al,which used the SINBAD classification, a score of ≧3 was associated with an increase in healing time7).
In the present study, Kaplan-Meier estimates demonstrated different wound healing speeds among
those with AID scores of 1, 2, and 3. Patients with AID 3 exhibited the most delayed wound healing.
However, with an AID-based multidisciplinary treatment approach (revascularization in 76%,debridement in 100%, and off loading in 85%), the wound healing rate at 12 months among the threegroups was comparable (88%, 89%, and 90%, in those with AID 3, AID 1, and AID 2, respectively).
These findings suggest that the AID score can predict wound healing speed and that an AID-based
comprehensive treatment could facilitate wound healing in approximately 90% of the mostchallenging DFUs. Focusing on each component of the AID classification facilitates treatment
planning in clinical practice. Arteriopathy requires revascularization, bacterial infection requires
debridement, and foot deformity requires off loading. AID concept-guided comprehensive treatment
can ensure optimal treatment, which may result in similar wound healing rates for AID 1 and AID 2,
and similar amputation-free survival rates for AID 2 and AID 3 at 12 months follow-up.
Our study had several limitations. First, it was retrospective observational study, which may beaffected by confounding factors. Second, a relatively small number of patients were enrolled in thisstudy. Third, the indications, timing, and intensity of revascularization, debridement, and off loadingmight have been heterogeneous. Finally, the AID classification does not consider the DFU complexity,
such as the size, depth, or location of wounds, which might be associated with wound healing. The
AID classification is intended to expeditiously appreciate the importance of the three essential
components in the management of contemporary DFUs. Larger prospective studies are needed toconfirm and externally validate the clinical significance of AID classification.
In conclusion, our study showed that diabetic arteriopathy is commonly complicated by both
infected wounds (bacterial) and foot deformities in the context of DFUs. The AID classification was
clinically relevant for wound healing stratification. Furthermore, AID concept-guided comprehensive
treatment could facilitate wound healing in approximately 90% of all patients with DFUs at 12
months.
Acknowledgements
All authors have no COI to declare regarding the present study.
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Editorial Committee

Daisuke Tsuruta, MD (Chief Editor)
Yasuhiro Fujiwara, MD

Wakaba Fukushima, MD

Takeo Goto, MD

Yoshikazu Hiura, MD

Masayuki Hosoi, MD

Kazuo Ikeda, MD

Koki Inoue, MD

Kenichi Kohashi, MD

Yukio Miki, MD

Atsushi Shioi, MD

Toshiyuki Sumi, MD

Kishiko Sunami, MD

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