JMIR FORMATIVE RESEARCH

Yamashiki et al

Original Paper

Narrowing the Patient–Physician Gap Based on Self-Reportingand Monthly Hepatologist Feedback for Patients With
Alcohol-Related Liver Disease: Interventional Pilot Study Usinga Journaling Smartphone App
Noriyo Yamashiki1,2, MD, PhD; Kyoko Kawabata3, PhD; Miki Murata1,2, MD, PhD; Shunichiro Ikeda4, MD, PhD;
Takako Fujimaki3, MS; Kanehiko Suwa1,2, MD, PhD; Toshihito Seki1, MD, PhD; Eiji Aramaki3, PhD; Makoto
Naganuma2, MD, PhD
1

Department of Gastroenterology and Hepatology, Kansai Medical University Medical Center, Osaka, Japan

2

The Third Department of Internal Medicine, Division of Gastroenterology and Hepatology, Kansai Medical University, Osaka, Japan

3

Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan

4

Department of Psychiatry, Kansai Medical University, Osaka, Japan

Corresponding Author:
Noriyo Yamashiki, MD, PhD
Department of Gastroenterology and Hepatology
Kansai Medical University Medical Center
Fumizono 10-15
Moriguchi
Osaka, 570-8507
Japan
Phone: 81 6 6992 1001
Email: yamashno@takii.kmu.ac.jp

Abstract
Background: Screening and intervention for alcohol use disorders (AUDs) are recommended to improve the prognosis ofpatients with alcohol-related liver disease (ALD). Most patients’ smartphone app diaries record drinking behavior for
self-monitoring. A smartphone app can be expected to also be helpful for physicians because it can provide rich patient information
to hepatologists, leading to suitable feedback. We conducted this prospective pilot study to assess the use of a smartphone app
as a journaling tool and as a self-report–based feedback source for patients with ALD.
Objective: The aims of this study were assessment of whether journaling (self-report) and self-report–based feedback can help
patients maintain abstinence and improve liver function data.
Methods: This pilot study used a newly developed smartphone journaling app for patients, with input data that physicians canreview. After patients with ALD were screened for harmful alcohol use, some were invited to use the smartphone journaling app
for 8 weeks. Their self-reported alcohol intake, symptoms, and laboratory data were recorded at entry, week 4, and week 8.
Biomarkers for alcohol use included gamma glutamyl transferase (GGT), percentage of carbohydrate-deficient transferrin totransferrin (%CDT), and GGT-CDT (GGT-CDT= 0.8 × ln[GGT] + 1.3 × ln[%CDT]). At each visit, their recorded data werereviewed by a hepatologist to evaluate changes in alcohol consumption and laboratory data. The relation between those outcomes
and app usage was also investigated.
Results: Of 14 patients agreeing to participate, 10 completed an 8-week follow-up, with diary input rates between 44% and100% of the expected days. Of the 14 patients, 2 withdrew from clinical follow-up, and 2 additional patients never used thesmartphone journaling app. Using the physician’s view, a treating hepatologist gave feedback via comments to patients at each
visit. Mean self-reported alcohol consumption dropped from baseline (100, SD 70 g) to week 4 (13, SD 25 g; P=.002) and remained
lower at week 8 (13, SD 23 g; P=.007). During the study, 5 patients reported complete abstinence. No significant changes were
found in mean GGT and mean %CDT alone, but the mean GGT-CDT combination dropped significantly from entry (5.2, SD
1.2) to the week 4 visit (4.8, SD 1.1; P=.02) and at week 8 (4.8, SD 1.0; P=.01). During the study period, decreases in mean total

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e44762 | p. 1
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Yamashiki et albilirubin (3.0, SD 2.4 mg/dL to 2.4, SD 1.9 mg/dL; P=.01) and increases in mean serum albumin (3.0, SD 0.9 g/dL to 3.3, SD
0.8 g/dL; P=.009) were recorded.
Conclusions: These pilot study findings revealed that a short-term intervention with a smartphone journaling app used by bothpatients and treatment-administering hepatologists was associated with reduced drinking and improved liver function.
Trial Registration: UMIN CTR UMIN000045285; http://tinyurl.com/yvvk38tj(JMIR Form Res 2023;7:e44762) doi: 10.2196/44762
KEYWORDS
alcohol-related liver disease; alcohol use disorder; alcoholism; smartphone; mobile health

Introduction
Background
Alcohol-related liver disease (ALD), including cirrhosis-related
death and hepatocellular carcinoma, is a leading cause ofliver-related death [1,2]. In the Asia-Pacific region, alcohol has
become the third most common cause of liver-related deaths
after hepatitis B and hepatitis C [3]. Consequently, liver disease
etiology has been changing from viral hepatitis to non-viralliver diseases [4-7].
As one part of treatment strategies, abstinence from alcohol isstrongly recommended for patients with alcoholic cirrhosis or
alcoholic hepatitis [8]. In primary care settings, “screening andbrief intervention” are effective to reduce drinking in the general
population [9]. Recent guidelines have also recommended thisscreening and brief intervention approach for patients with ALD[8,10]. An Alcohol Use Disorders Inventory Test (AUDIT)
score greater than 8 is predictive of harmful or hazardous alcoholuse. A score greater than 20 is suggestive of moderate-to-severe
alcohol use disorder (AUD) [8,10]. Alcohol screening
questionnaires including CAGE [11,12] and the AUDIT-C (3questions related to alcohol consumption from the fullquestionnaire) [13-15] in an outpatient setting are useful topredict subsequent hospitalization for gastrointestinal conditions[16]. Moreover, the usefulness of the AUDIT-C to identify risksof alcohol relapse after liver transplantation has also been shown[17].
For patients with moderate-to-severe AUD, a brief intervention
is not sufficient. Referral to treatment for AUD by an addiction
specialist is ideal [8,10]. However, among high-risk drinkers,
the proportion of patients receiving treatment for liver
dysfunction or alcoholism is low. A survey revealed that only
0.8% of risky drinkers wish to stop drinking and that only 5.1%
of risky drinkers are undergoing treatment for liver dysfunction[18]. These reports suggest that many patients with ALD and
AUD refuse to reduce drinking or to accept addiction
counseling. Considering this background, the key issue with
AUD treatment for patients with ALD is reduction of patients’drinking by hepatologists, even without the support of addiction
specialists.
Digital tools including smartphone apps might be used as
feedback and planning support, educational tools, or othersupport in order to improve health care [19].

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

Feedback and Planning Support
Feedback is a core service supported by most systems. Popularfeedback items provided to users are cost and calorie equivalents
to a person’s alcoholic beverage consumption, as well as
drinking-related health risks [19,20]. Scott and colleagues [21]reported a project to assess granular self-monitoring feedback
using ecological momentary assessments and immediate
recovery support through ecological momentary interventions.

Education and Coaching
Educational support provides knowledge related to diseases(alcohol addiction) and risks associated with alcohol. It enables
the encouragement of self-motivated alcohol reduction.
Drinkaware [22] proposes the following educational factors:
assessment and feedback, high-risk places for drinking, choosing
and using supportive people for change, cravings and their
management, problem-solving skills, communication, refusal
to drink skills, and fun nondrinking activities.

Emergency Support
Emergency support is useful in managing expected or
out-of-control situations. The Addiction-Comprehensive HealthEnhancement Support System (A-CHESS) [23] provides
emergency support called the panic button. If the patient presses
the panic button, then the system sends an emergency message
to 2 contact persons who have been prepared in advance.

Effectiveness of Existing Apps
Most apps have successfully demonstrated effectiveness. One
key issue is safety. For example, Glowacki et al [24] reported
that a mismatched text message sometimes exacerbates
unhealthy behavior.
Another issue is that these approaches have been examined insettings of alcohol dependence but not in settings of ALD.
Reported smartphone apps are used mainly by patients [20-26];
physicians do not use them.
A smartphone app is presumably able to provide rich patient
information to hepatologists, leading to effective feedback. To
assess patient information, a physician (hepatologist) can review
the following 2 types of information from a patient's input: (1)alcohol consumption that indicates the patient's drinking statusand (2) a patient narrative that can imply the patient's motivation.
Using that information, hepatologists can give suitable feedback
to patients.

JMIR Form Res 2023 | vol. 7 | e44762 | p. 2
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Objectives
The objectives of this study were to assess the use of asmartphone app as a journaling tool and a source of feedback
based on self-report for patients with ALD, specifically its
effects on (1) abstinence from alcohol and (2) improvement inbiomarkers of alcohol use and liver function tests.

Methods
Patients
The target hospital was a main hospital in the Osaka area ofJapan. Approximately 200 patients with liver disease are seenin the outpatient department each week, including about 10
cases referred from general practitioners. During August 2021
to May 2022, we recruited patients with ALD who were seen
either in the hospital or an outpatient clinic. When the patient’s
liver disease was regarded as caused by harmful alcohol intake,Yamashiki et alpatients were given educational booklets that included
information about health risks and harmful alcohol use.
Abstinence from alcohol was recommended by hepatologists(NY, MM, KS), especially for patients with liver cirrhosis or
hepatocellular carcinoma. This was advice with no implied nor
actual penalty. When AUD was suspected based on the AUDITscore [13], psychiatric outpatient care was provided withconsent, but no recovery program was routinely provided at our
hospital. When an ALD patient was admitted to our hospitalfor any liver-related condition, a psychiatric liaison team roundwas scheduled by agreement.
No patient who had been abstinent for more than 1 year or who
had refused to reduce or abstain from alcohol intake was invited
to participate in the study. During the period, 23 patients wereinvited, 14 of whom provided written informed consent. The
patient flow is portrayed in Figure 1.

Figure 1. Patient flow. %CDT: percentage of carbohydrate-deficient transferrin to transferrin.

Data Collection and Follow-Up

sensitivity to detect heavy drinkers and was calculated as [27]GGT-CDT = 0.8 × ln(GGT) + 1.3 × ln(%CDT).

Outpatient appointments were set at week 4, week 8, and week
24. However, changes to appointments were allowed 1 week
before and after the date that had been initially set.

Feedback From Hepatologists

Patient physical data and clinical characteristics were obtainedat the time of entry into the study. Laboratory data, including
aspartate aminotransferase, alanine aminotransferase, gamma
glutamyl transferase (GGT), total bilirubin, albumin, blood cellcount, prothrombin international normalized ratio, and serum
percentage of carbohydrate-deficient transferrin to transferrin(%CDT) level, were measured at baseline, week 4, week 8 andweek 24. The level of serum %CDT was analyzed using direct
immunonephelometric assay (N Latex CDT; Siemens Healthcare
Diagnostics). A combined index based on GGT and CDT
measurements (GGT-CDT) has been shown to improve
https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

At each follow-up visit (week 4 and week 8), the treating
hepatologist (NY, MM, and KS) provided self-report–based
feedback in addition to regular outpatient practice. Theinterventions in this study are presented in Table 1. The
hepatologist referred to the “physician’s view” to review
whether drinking status was reported, how much ethanol was
consumed on average, and what type of input was made. An
example of using a physician’s view in an outpatient setting is
the following: A patient and a clinician viewed the graph ofalcohol consumption together, thereby enabling the patient toconfirm the amount and frequency of alcohol consumption.
However, we used no specific feedback protocol. The feedback
JMIR Form Res 2023 | vol. 7 | e44762 | p. 3
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
manner differed among hepatologists because no special training

Yamashiki et al
for behavioral health was executed.

Table 1. Intervention in this study in comparison with regular outpatient practice.
Intervention

This study

Regular outpatient practiceAttending physician

Hepatologist

Hepatologist

Outpatient frequency

Monthly (entry and weeks 4 and 8 are mandatory) Every 1 month to 3 months

Self-treatment for alcohol use

Educational booklets about alcohol risks plus
journaling (smartphone app)

Educational booklets about alcohol risks

Outpatient treatment for alcoholuse (available information)

Feedback (liver function tests plus alcohol consumption plus patient report)

Feedback according to laboratory results for liver function
tests

Referral to psychiatrists

In agreement

In agreement

Hospitalization/inpatient service

Hospitalization to the gastrointestinal (GI) unit as Hospitalization to GI unit as indicated/liaison team round inindicated/liaison team round in agreementagreement

Addiction team counseling

Referral with consent

Smartphone App Development
There were 2 developed systems: (1) a client smartphone
journaling app for patients and (2) a server app for physicians’
use.

Smartphone App
A web-based smartphone journaling app was provided. Eachpatient reported their drinking status and an associated narrative.
The drinking status data were converted automatically into

Referral with consent

ethanol amounts. A screenshot of the app is presented in Figure
2. To record in the diary, text input is required (Figure 2B).
Patients can monitor the drinking amount as colored icons (ie,green denoting no alcohol consumption, and red denoting
drinking more than 60 g of ethanol). During this study, patientswere required to use this smartphone journaling app from theentry day until the clinic visit appointment at week 8. Patientcomments on the smartphone app were collected via aquestionnaire at week 8. After 8 weeks, the app was used on a
voluntary basis.

Figure 2. Smartphone app (A) month view and (B) day view.

Server App
At the outpatient clinic, the treating doctor referred to thedoctor’s view of the patient input (Figure 3). The amount ofhttps://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

daily alcohol intake is shown as a line graph of the ethanol
equivalent (Figure 3A). All diary inputs are also shown (Figure
3B).

JMIR Form Res 2023 | vol. 7 | e44762 | p. 4
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Yamashiki et al

Figure 3. Physician interface: (A) overview and (B) detailed view.

Using the server app, the percentage of diary inputs and theaverage alcohol intake were calculated as:
Diary input rate (%) = (number of days with a diaryrecording/number of days from entry through week 8) × 100
Daily ethanol consumption (g/day) = sum of daily ethanol use(g)/number of days with a diary recording during the expectedperiodLags between the date of the smartphone journaling app andthe date and time of the response were calculated using server
data. If a day’s diary data were entered on the following day,then the input lag was designated as 1 day. We designated thisgap as the input time lag.

Statistical Analyses
Normally or near-normally distributed variables are reported
as means and SDs. Equality between groups was comparedusing the Student t test. The results obtained before and after
the intervention were compared using paired t tests.
Non-normally distributed continuous data are reported as
medians and ranges. They were compared using Wilcoxon
rank-sum tests. Categorical variables were compared using
chi-square tests or Fisher exact tests when appropriate.
Correlations between self-reported average daily ethanol

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

consumption and biomarkers of alcohol use were analyzed using
Pearson correlation coefficient tests. All analyses wereconducted using software (STATA ver. 15; Stata Corp). P values
less than .05 were considered significant.

Ethical Considerations
This study was approved by the Kansai Medical University
Center for Ethical Review (No. 2020252) and was conductedin compliance with the Declaration of Helsinki.

Results
Baseline patient information and clinical characteristics of the
14 patients are presented in Table 2: 8 were male, one-half (7/14,50%) resided alone, 6 were unemployed, 5 were consulting a
psychiatrist for various reasons, and 3 had been previously
diagnosed with AUD. None of the recruited patients agreed toreceive a new referral to the addiction team during this 8-weekperiod. Alcohol was used daily by 12 (86%) of the 14 patients,
while 2 were non-daily drinkers. The average ethanol intake
was 97 (SD 61) g daily. The mean AUDIT score at entry was
20 (SD 8). Of the 14 patients, 10 (71%) were diagnosed withcirrhosis. The patients included in this study were not transplantcandidates.

JMIR Form Res 2023 | vol. 7 | e44762 | p. 5
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Yamashiki et al

Table 2. Baseline patient information and clinical characteristics (n=14).
FactorValuesAge (years), mean (SD)51.3 (10.5)

Male gender, n (%)

8 (57)

Race/ethnicity (Asian), n (%)

14 (100)

Body mass index (kg/m2), mean (SD)24.9 (4.1)

Smoking, n (%)
Current

10 (71)Past

2 (14)None

2 (14)

Living alone, n (%)

7 (50)

Unemployed, n (%)

6 (43)

Psychiatric comorbidity, n (%)
AUDa, depression, and anorexia

1 (7)AUD and depression

1 (7)

AUD

1 (7)

Anorexia

1 (7)Panic disorder

1 (7)

None

9 (64)

Medical comorbidity, n (%)
Hypertension

1 (7)Diabetes

1 (7)Posttreatment hepatitis C

1 (7)Chronic pancreatitis

1 (7)Duration of drinking (years), mean (SD)31 (10)Daily ethanol intake (g), mean (SD)97 (61)

AUDITb score at entry, mean (SD)20 (8)

AUDIT score at entry, n (%)

a≥8

13 (93)≥20

8 (57)

Aspartate aminotransferase (AST; U/L), mean (SD)71 (43)

Alanine aminotransferase (ALT; U/L), mean (SD)47 (46)

Gamma glutamyl transferase (GGT; U/L), mean (SD)359 (448)

Total bilirubin (mg/dL), mean (SD)

2.8 (2.3)

Albumin (g/dL), mean (SD)

3.0 (0.9)

Platelets (10 × 103/μL), mean (SD)15.7 (10.0)

White blood cells (/μL), mean (SD)

5500 (1687)

INRc, mean (SD)1.37 (0.42)%CDTd (%), mean (SD)2.60 (1.01)

Cirrhosis, n (%)

10 (71)Child-Pugh score, median (range)

8 (5-13)Child class B or C, n (%)

8 (57)

AUD: alcohol use disorder.

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e44762 | p. 6
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Yamashiki et alb

AUDIT: Alcohol Use Disorders Inventory Test.

c

INR: international normalized ratio.

d

CDT: carbohydrate-deficient transferrin.

Recording data are presented in Table 3. Overall, 10 of 14 (71%)
patients completed 8 weeks of follow-up and laboratory tests(cases 1-10). Of the 14 patients, 2 were lost to follow-up (cases
12, 14), 2 never filled in the data (cases 13, 14), and 1 filled inonly the last one-half of the data (case 11) in the smartphoneapp. Among the 10 patients who completed 8 weeks offollow-up, their 8-week visit actually occurred between day 56
and day 70 from entry for 7 cases, but the follow-up was delayed
up to day 119 for 3 cases due to patient request. Of the 10

patients, 5 patients were able to use the smartphone journaling
app continually (input rate of more than 70%) and were able toabstain from alcohol completely. However, 5 patients continued
to use the smartphone journaling app (input rate of 44%-100%)
but were unable to abstain from alcohol completely. Of these5 patients, 4 had an intake that exceeded 20 g of pure alcoholon 80% to 100% of the days on which they drank (data notshown in the table).

Table 3. Summary of recorded data and input time lag.
Case
numbera

Input rate

All periods

Entry to week 4

Week 4 to week 8

Expected
recordingdays, nDays with
input, n(%)

Days abstinent, n (%)

Days drinking, Days
Input
Alcohol intake
n (%)
with intime lag (g), meanput, n (%) (days), (SD)
mean
(SD)

Days
with input, n(%)

Input timelag (days),mean (SD)

Alcohol intake(g), mean
(SD)

1

67

52 (77)52 (77)0

39 (100)

0.6 (0.8) 0

13 (46)

0

0

2

68

61 (89)61 (89)034 (82)

1.1 (1.1) 0

27 (100) 2.7 (1.8)

0

3

63

57 (90)57 (90)033 (94)0.1 (0.3) 0

24 (85)0.3 (0.4)

0

4

65

63 (96)63 (96)035 (94)0.1 (0.3) 0

28 (100) 0.0 (0.1)

0

5

82

82 (100)

82 (100)0

28 (100)

1.6 (0.9) 0

35 (100) 4.2 (3.8)

0

a

6

63

28 (44)3 (4)25 (39)28 (100)3 (2)

51 (28)0

777

39 (50)31 (40)8 (10)4 (9)9.7(14.5)

7 (12)

35 (100) 12.7 (9.0)

7 (17)

870

61 (87)13 (18)48 (68)34 (97)

1.1 (1.1) 42 (23)27 (77)40 (22)9119

119 (100)

15 (12)104 (87)

42 (100)

8.2 (7.3) 76 (23)77 (100) 18.5 (11.3)

68 (30)106852 (76)29 (42)23 (33)34 (97)0.1 (0.3) 4 (6)27 (81)18 (27)1185

29 (34)29 (34)

0

1 (1)

0

28 (100) 8.5 (6.8)

0

12

28

8 (28)2 (7)

6 (21)

8 (28)

1.8 (1.2) 46 (35)

0

N/A

N/A

13

0

0

—b

—

—

—

—

—

—

—

14

0

0

—

—

—

—

—

—

—

—

0

N/A

6.1 (4.5)

0.8 (0.6)

N/A

N/A: not applicable.

b

No data.

Changes in the self-reported average daily ethanol intake
according to the diary inputs are shown in Figure 4A. For the
10 patients who filled in their diary during the study period, theaverage ethanol intake decreased significantly from baseline

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX(100, SD 70 g) to week 4 (13, SD 25 g; P=.002) and frombaseline to week 8 (13, SD 23 g; P=.007), but no change wasapparent from week 4 through week 8 (P=.94).

JMIR Form Res 2023 | vol. 7 | e44762 | p. 7
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Yamashiki et al

Figure 4. (A) Self-reported average daily alcohol use as grams of ethanol; changes in (B) gamma glutamyl transferase (GGT), (C) percentage ofcarbohydrate-deficient transferrin to transferrin (%CDT), (D) GGT-CDT, (E) total bilirubin (T-Bili), and (F) albumin. Solid lines indicate cases who
did not abstain from drinking, while dashed lines indicate cases who completely abstained.

No significant correlation was found between daily ethanol
consumption and GGT (r=0.23, P=.19), %CDT (r=0.25, P=.16),
or GGT-CDT (r=0.24, P=.18).
Changes in laboratory data are presented in Figures 4B-4F. No
significant changes were found for mean GGT and mean %CDT
alone, but the mean GGT-CDT combination dropped
significantly from entry (5.2, SD 1.2) to the week 4 visit (4.8,
SD 1.1; P=.02) and the week 8 visit (4.8, SD 1.0; P=.01; Figures
4B, 4C, and 4D). The total bilirubin level dropped significantly
from entry (3.0, SD 2.4 mg/dL) to the week 4 visit (2.4, SD 1.9
mg/dL; P=.01), but no additional change was observed fromweek 4 through week 8 (2.6, SD 2.3 mg/dL; P=.53; Figure 4E).
The mean serum albumin level increased significantly fromentry (3.0, SD 0.9 g) to week 4 (3.3, SD 0.8 g/dL; P=.009). The
same tendency was maintained until week 8 in cases of complete
abstinence (Figure 4F).

missing amounts immediately before their week 8 follow-up
visit (Multimedia Appendix 1). Participants who were notcompletely abstinent and drank heavily tended to have larger
input time lags than those who were completely abstinent. The
input time lags were not large for those who were completely
abstinent.
We also collected patient comments about the smartphone app
via a questionnaire (Multimedia Appendix 2). The results
indicated that the “app usability” and “ease of viewing the app
screen” were highly rated: 1 patient aged in his 60s commented
that the screen was difficult to read. Three patients reported thatusing the app helped them abstain from drinking more or less,2 patients said they wanted some kind of feedback function,and 3 patients expressed negative opinions about keeping adiary. Additional comments are presented in Multimedia
Appendix 2.

The relation between the input time lags and amount of alcohol
consumed is shown in Table 3. Irrespective of the amount of
alcohol consumed, the input time lags remained small until
week 4. Subsequently, the number of days that the input lagged
increased gradually. Respondents had a tendency to input
https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e44762 | p. 8
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Discussion
Principal Findings and Comparison With Earlier
Work
This pilot study evaluated the use of a smartphone journaling
app by patients with ALD. About 60% of candidate patientswere evaluated in this study. Of them, 71% completed 8 weeks
of journaling. One-half of the patients who continued diary
input achieved 8 weeks of complete abstinence.
With ALD on the rise, efforts for harm reduction and alcohol
consumption reduction are attracting attention [28]. For patientswith both ALD and AUD, screening and brief interventions andreferral to treatment are useful. Subsequent pharmacotherapy
and psychiatric intervention such as cognitive therapy andmotivation enhancement therapy might also be useful [10,28].
However, the percentage of patients actually receiving treatment
is probably small [13,29]. Patients might visit a hospital andmight receive a diagnosis of ALD only after symptoms such as
jaundice appear. Some patients are resistant to intervention by
psychiatric and multidisciplinary teams because they are notproperly diagnosed with AUD or because they do not accept adiagnosis of AUD.
Biomarkers are convenient for assessing alcohol consumption
in an outpatient setting [30,31]. Routinely available laboratory
tests such as mean corpuscular volume and GGT can be done
easily, but they do not show greater amounts of alcohol
consumption directly. Blood-urine alcohol concentration has ashort half-life. For that reason, it might not reflect the amountof alcohol consumed during outpatient evaluations. The %CDT
has been used as a biomarker to detect heavy (>50 g) alcohol
consumption within the prior several weeks [10,30-32]. Suzuki
et al [32] reported a cut-off value for excessive drinkers (men
≥60 g/day, women ≥40 g/day) of 1.9% (sensitivity=0.77,
specificity=0.77). Reportedly, the combination of GGT and%CDT (GGT-CDT) shows higher sensitivity for detection ofheavy drinking and higher prediction accuracy for detection ofexcessive alcohol consumption [27,33]. Hietala et al [27]reported the cut-off value for heavy drinkers as 4.18 for men
and as 3.81 for women using a derived formula(sensitivity=0.90, specificity=0.98). However, the use of %CDT
for patients with liver disease remains limited because of its
low sensitivity and low specificity [30]. Elevated %CDT values
have been noted frequently in liver transplant candidates with
end-stage liver disease [34]. Currently, other biomarkers suchas ethyl glucuronide and phosphatidylethanol are used widely[35].
For this study, liver function and %CDT were evaluated because
other alcohol consumption–specific markers have not beencommercially available. Although the number of patients was
too low to evaluate the diagnostic performance of %CDT, thismeasure can reflect changes related to alcohol consumption fortime series monitoring of individual cases. However, we foundno correlation between self-reported ethanol consumption and%CDT. The sensitivity of %CDT was low probably because of
the large number of patients with advanced cirrhosis.

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

Yamashiki et al
The usefulness of diaries for recording alcohol consumption
was demonstrated, but their potential as an adjunctive test for
judging treatment effects was also suggested by our results.
Nevertheless, the tendencies found in diary entries suggest that
input rates were high among abstinent patients, whereas therates were low among continuing drinkers, also indicating along lag separating the date of entry and the date of diarycompletion. Considering the resistance of patients to having
their drinking habits known by their treating physicians, somepatients might refuse direct testing for alcohol consumption as
well.
One-half of the patients who completed the study achieved 8
weeks of self-reported abstinence. By checking the physician'sview of the server app, the daily drinking status can be notedat a glance. The alcohol consumption amount was graphed,making it possible to ascertain the self-reported amount of
alcohol consumed. Most patients in this study had liver cirrhosis.
Of the 5 patients who abstained from alcohol, 4 had severe Childclass C cirrhosis, suggesting that more severe conditions might
be a factor facilitating abstinence. Other factors such as theabsence of a comorbid psychiatric disorder and having a job
were also notable.
For a short period of time, a trend toward improved nutritional
status (albumin) was apparent as a result of abstinence bypatients using the smartphone journaling app. Other indicators
were not found to be significant in this study. However, the factthat patients' proactive approaches to abstinence behavior
engender improved liver function can motivate them to seek
and benefit from future treatment.
One benefit of using a smartphone app is the possibility ofidentifying when the patient entered a record. For paper-based
diaries, it is difficult to ascertain when the patient makes anentry. In this study, the results of analyzing input record datelags in the data indicated that patients who were sober tended
to input data into the smartphone journaling app almost everyday. By contrast, nonabstaining patients tended to enter manyrecords together at one time, according to the date of their
medical visit. In such cases, the input record date lags tended
to be greater in the fourth and eighth weeks of the study. Basedon these considerations, a certain number of patients probably
used the smartphone journaling app with some awareness ofthe date of their medical visit. Therefore, providing automatic
feedback through the smartphone app is necessary to raiseawareness and motivation. Alternatively, an input time lag mightprovide a hint that a patient needs more care.

Limitations
This study has several limitations. The smartphone journaling
app used for this study was set up so that a one-line diary could
not be saved without entering a single word, irrespective ofwhether the user had been drinking. Results from a questionnairecompleted after using the app indicated that “entering daily
diary entries is difficult.” Possible future improvements to theapp described herein include allowing free-form diary entries
and allowing a stamp function that can help users simply express
their mood on a given day.

JMIR Form Res 2023 | vol. 7 | e44762 | p. 9
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
Another limitation is this current study had no control arm
participants who had received treatment (4 weeks and 8 weeks)
without the smartphone app. We assume that such control cases
would show no significant change in the short term (4 weeks
or 8 weeks). However, a larger scale and double-arm,
randomized controlled trial is highly desirable.
Nonetheless, this pilot study showed positive results among
patients with ALD who were recommended to reduce or quit
drinking. Among multiple functions provided by the smartphone

Yamashiki et alapp (journaling, physician’s view-based feedback), precisely
which function was the main trigger demands further study.

Conclusions
In conclusion, the use of an alcohol use journaling app in
combination with hepatologists’ follow-up was associated with
reduction of daily alcohol intake and improvement of liverfunction test results. The positional gap separating patients andphysicians was narrowed by sharing the journaling app. Further
studies are expected to elucidate the use of digital tools in an
ambulatory care setting.

Acknowledgments
The kind assistance of Hiroko Yoshii, RN, and Hisako Yamada, PSW, with this research is greatly appreciated. This work was
supported by JSPS KAKENHI Grant Numbers 21H03175 and 21H03543.

Data Availability
The data sets analyzed during the current study are not publicly available for reasons of patient privacy, but they are available
from the corresponding author on reasonable request.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Input-record date lag, daily alcohol use, and date of hospital visit. (a) and (b) represent cases without complete sobriety; (c) and(d) represent cases with complete sobriety.
[PNG File , 41 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Questionnaire about a journaling smartphone-app.
[PNG File , 152 KB-Multimedia Appendix 2]

References
1.

2.
3.

4.

5.

6.

7.

8.

GBD 2016 Alcohol Collaborators. Alcohol use and burden for 195 countries and territories, 1990-2016: a systematic analysis
for the Global Burden of Disease Study 2016. Lancet. 2018 Sep 22;392(10152):1015-1035 [FREE Full text] [doi:10.1016/S0140-6736(18)31310-2] [Medline: 30146330]
Rehm J, Samokhvalov AV, Shield KD. Global burden of alcoholic liver diseases. J Hepatol. 2013 Jul;59(1):160-168 [FREEFull text] [doi: 10.1016/j.jhep.2013.03.007] [Medline: 23511777]
Sarin SK, Kumar M, Eslam M, George J, Al Mahtab M, Akbar SMF, et al. Liver diseases in the Asia-Pacific region: aLancet Gastroenterology and Hepatology Commission. Lancet Gastroenterol Hepatol. 2020 Feb;5(2):167-228 [doi:10.1016/S2468-1253(19)30342-5] [Medline: 31852635]
Enomoto H, Ueno Y, Hiasa Y, Nishikawa H, Hige S, Takikawa Y, et al. Japan Etiology of Liver Cirrhosis Study Group in
the 54th Annual Meeting of JSH. The transition in the etiologies of hepatocellular carcinoma-complicated liver cirrhosis
in a nationwide survey of Japan. J Gastroenterol. 2021 Feb;56(2):158-167 [FREE Full text] [doi:10.1007/s00535-020-01748-x] [Medline: 33219410]
Enomoto H, Ueno Y, Hiasa Y, Nishikawa H, Hige S, Takikawa Y, et al. Japan Etiology of Liver Cirrhosis Study Group in
the 54th Annual Meeting of JSH. Transition in the etiology of liver cirrhosis in Japan: a nationwide survey. J Gastroenterol.
2020 Mar;55(3):353-362 [FREE Full text] [doi: 10.1007/s00535-019-01645-y] [Medline: 31768801]
Paik JM, Golabi P, Biswas R, Alqahtani S, Venkatesan C, Younossi ZM. Nonalcoholic fatty liver disease and alcoholic
liver disease are major drivers of liver mortality in the United States. Hepatol Commun. 2020 Jun;4(6):890-903 [FREEFull text] [doi: 10.1002/hep4.1510] [Medline: 32490324]
Tateishi R, Okanoue T, Fujiwara N, Okita K, Kiyosawa K, Omata M, et al. Clinical characteristics, treatment, and prognosis
of non-B, non-C hepatocellular carcinoma: a large retrospective multicenter cohort study. J Gastroenterol. 2015
Mar;50(3):350-360 [FREE Full text] [doi: 10.1007/s00535-014-0973-8] [Medline: 24929638]
European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of alcohol-related liverdisease. J Hepatol. 2018 Jul;69(1):154-181 [doi: 10.1016/j.jhep.2018.03.018] [Medline: 29628280]

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e44762 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
9.

10.

11.
12.
13.

14.

15.

16.

17.

18.

19.
20.

21.

22.

23.

24.

25.

26.

27.

28.

O'Donnell A, Anderson P, Newbury-Birch D, Schulte B, Schmidt C, Reimer J, et al. The impact of brief alcohol interventionsin primary healthcare: a systematic review of reviews. Alcohol Alcohol. 2014;49(1):66-78 [FREE Full text] [doi:10.1093/alcalc/agt170] [Medline: 24232177]
Crabb DW, Im GY, Szabo G, Mellinger JL, Lucey MR. Diagnosis and treatment of alcohol-associated liver diseases: 2019
Practice Guidance From the American Association for the Study of Liver Diseases. Hepatology. 2020 Jan;71(1):306-333[FREE Full text] [doi: 10.1002/hep.30866] [Medline: 31314133]
Ewing JA. Detecting alcoholism. The CAGE questionnaire. JAMA. 1984 Oct 12;252(14):1905-1907 [doi:10.1001/jama.252.14.1905] [Medline: 6471323]
Fiellin DA, Reid MC, O'Connor PG. Screening for alcohol problems in primary care: a systematic review. Arch Intern
Med. 2000 Jul 10;160(13):1977-1989 [doi: 10.1001/archinte.160.13.1977] [Medline: 10888972]
Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M. Development of the Alcohol Use Disorders Identification
Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption--II. Addiction.
1993 Jun;88(6):791-804 [doi: 10.1111/j.1360-0443.1993.tb02093.x] [Medline: 8329970]
Bradley KA, Bush KR, Epler AJ, Dobie DJ, Davis TM, Sporleder JL, et al. Two brief alcohol-screening tests from the
Alcohol Use Disorders Identification Test (AUDIT): validation in a female Veterans Affairs patient population. Arch Intern
Med. 2003 Apr 14;163(7):821-829 [doi: 10.1001/archinte.163.7.821] [Medline: 12695273]
Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): aneffective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol
Use Disorders Identification Test. Arch Intern Med. 1998 Sep 14;158(16):1789-1795 [doi: 10.1001/archinte.158.16.1789][Medline: 9738608]
Au DH, Kivlahan DR, Bryson CL, Blough D, Bradley KA. Alcohol screening scores and risk of hospitalizations for GI
conditions in men. Alcohol Clin Exp Res. 2007 Mar;31(3):443-451 [doi: 10.1111/j.1530-0277.2006.00325.x] [Medline:
17295729]
Donnadieu-Rigole H, Olive L, Nalpas B, Winter A, Ursic-Bedoya J, Faure S, et al. Follow-up of alcohol consumption after
liver transplantation: interest of an addiction team? Alcohol Clin Exp Res. 2017 Jan;41(1):165-170 [doi: 10.1111/acer.13276][Medline: 27936489]
Osaki Y, Kinjo A, Higuchi S, Matsumoto H, Yuzuriha T, Horie Y, et al. Prevalence and trends in alcohol dependence andalcohol use disorders in Japanese adults; results from periodical nationwide surveys. Alcohol Alcohol. 2016 Jul;51(4):465-473[doi: 10.1093/alcalc/agw002] [Medline: 26873982]
Colbert S, Thornton L, Richmond R. Smartphone apps for managing alcohol consumption: a literature review. Addict Sci
Clin Pract. 2020 May 07;15(1):17 [FREE Full text] [doi: 10.1186/s13722-020-00190-x] [Medline: 32381062]
Gonzalez VM, Dulin PL. Comparison of a smartphone app for alcohol use disorders with an Internet-based intervention
plus bibliotherapy: A pilot study. J Consult Clin Psychol. 2015 Apr;83(2):335-345 [FREE Full text] [doi: 10.1037/a0038620][Medline: 25622202]
Scott CK, Dennis ML, Gustafson DH. Using smartphones to decrease substance use via self-monitoring and recovery
support: study protocol for a randomized control trial. Trials. 2017 Aug 10;18(1):374 [FREE Full text] [doi:10.1186/s13063-017-2096-z] [Medline: 28797307]
Attwood S, Parke H, Larsen J, Morton KL. Using a mobile health application to reduce alcohol consumption: a mixed-methods
evaluation of the Drinkaware track and calculate units application. BMC Public Health. 2017 May 17;17(1):394 [FREEFull text] [doi: 10.1186/s12889-017-4358-9] [Medline: 28511698]
Glass JE, McKay JR, Gustafson DH, Kornfield R, Rathouz PJ, McTavish FM, et al. Treatment seeking as a mechanism ofchange in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders. J
Subst Abuse Treat. 2017 Jun;77:57-66 [FREE Full text] [doi: 10.1016/j.jsat.2017.03.011] [Medline: 28476273]
Glowacki EM, Bernhardt JM, McGlone MS. Tailored texts: An application of regulatory fit to text messages designed toreduce high-risk drinking. Health Informatics J. 2020 Sep;26(3):1742-1763 [FREE Full text] [doi:10.1177/1460458219889279] [Medline: 31808717]
Garnett C, Michie S, West R, Brown J. Updating the evidence on the effectiveness of the alcohol reduction app, Drink
Less: using Bayes factors to analyse trial datasets supplemented with extended recruitment. F1000Res. 2019;8:114 [FREEFull text] [doi: 10.12688/f1000research.17952.2] [Medline: 31354942]
Crane D, Garnett C, Michie S, West R, Brown J. A smartphone app to reduce excessive alcohol consumption: Identifying
the effectiveness of intervention components in a factorial randomised control trial. Sci Rep. 2018 Mar 12;8(1):4384 [FREEFull text] [doi: 10.1038/s41598-018-22420-8] [Medline: 29531280]
Hietala J, Koivisto H, Anttila P, Niemelä O. Comparison of the combined marker GGT-CDT and the conventional laboratory
markers of alcohol abuse in heavy drinkers, moderate drinkers and abstainers. Alcohol Alcohol. 2006;41(5):528-533 [doi:10.1093/alcalc/agl050] [Medline: 16799164]
Leggio L, Mellinger JL. Alcohol use disorder in community management of chronic liver diseases. Hepatology. 2023 Mar
01;77(3):1006-1021 [doi: 10.1002/hep.32531] [Medline: 35434815]

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

Yamashiki et al

JMIR Form Res 2023 | vol. 7 | e44762 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
29.

30.

31.
32.

33.

34.
35.

Yamashiki et al

Cohen E, Feinn R, Arias A, Kranzler HR. Alcohol treatment utilization: findings from the National Epidemiologic Survey
on Alcohol and Related Conditions. Drug Alcohol Depend. 2007 Jan 12;86(2-3):214-221 [doi:10.1016/j.drugalcdep.2006.06.008] [Medline: 16919401]
Arnts J, Vanlerberghe BTK, Roozen S, Crunelle CL, Masclee AAM, Olde-Damink SWM, et al. Diagnostic accuracy ofbiomarkers of alcohol use in patients with liver disease: a systematic review. Alcohol Clin Exp Res. 2021 Jan;45(1):25-37[FREE Full text] [doi: 10.1111/acer.14512] [Medline: 33190239]
Staufer K, Yegles M. Biomarkers for detection of alcohol consumption in liver transplantation. World J Gastroenterol.
2016 Apr 14;22(14):3725-3734 [FREE Full text] [doi: 10.3748/wjg.v22.i14.3725] [Medline: 27076757]
Suzuki T, Eguchi A, Shigefuku R, Nagao S, Morikawa M, Sugimoto K, et al. Accuracy of carbohydrate-deficient transferrin
as a biomarker of chronic alcohol abuse during treatment for alcoholism. Hepatol Res. 2022 Jan;52(1):120-127 [doi:10.1111/hepr.13642] [Medline: 33797850]
Shibamoto A, Namisaki T, Suzuki J, Kubo T, Iwai S, Tomooka F, et al. Clinical significance of gamma-glutamyltranspeptidase
combined with carbohydrate-deficient transferrin for the assessment of excessive alcohol consumption in patients withalcoholic cirrhosis. Medicines (Basel). 2021 Jul 19;8(7):39 [FREE Full text] [doi: 10.3390/medicines8070039] [Medline:
34357155]
DiMartini A, Day N, Lane T, Beisler AT, Dew MA, Anton R. Carbohydrate deficient transferrin in abstaining patients with
end-stage liver disease. Alcohol Clin Exp Res. 2001 Dec;25(12):1729-1733 [FREE Full text] [Medline: 11781505]
Niemelä O. Biomarker-based approaches for assessing alcohol use disorders. Int J Environ Res Public Health. 2016 Jan
27;13(2):166 [FREE Full text] [doi: 10.3390/ijerph13020166] [Medline: 26828506]

Abbreviations
A-CHESS: Addiction-Comprehensive Health Enhancement Support System
ALD: alcohol-related liver disease
AUD: alcohol use disorder
AUDIT: Alcohol Use Disorders Inventory Test
CDT: carbohydrate-deficient transferrin
GGT: gamma glutamyl transferase

Edited by A Mavragani; submitted 04.12.22; peer-reviewed by R Kornfield, A Quanbeck; comments to author 27.12.22; revised version
received 16.01.23; accepted 20.11.23; published 19.12.23
Please cite as:
Yamashiki N, Kawabata K, Murata M, Ikeda S, Fujimaki T, Suwa K, Seki T, Aramaki E, Naganuma M
Narrowing the Patient–Physician Gap Based on Self-Reporting and Monthly Hepatologist Feedback for Patients With Alcohol-Related
Liver Disease: Interventional Pilot Study Using a Journaling Smartphone App
JMIR Form Res 2023;7:e44762
URL: https://formative.jmir.org/2023/1/e44762
doi: 10.2196/44762
PMID:©Noriyo Yamashiki, Kyoko Kawabata, Miki Murata, Shunichiro Ikeda, Takako Fujimaki, Kanehiko Suwa, Toshihito Seki, Eiji
Aramaki, Makoto Naganuma. Originally published in JMIR Formative Research (https://formative.jmir.org), 19.12.2023. Thisis an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information,a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

https://formative.jmir.org/2023/1/e44762

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e44762 | p. 12(page number not for citation purposes)