This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428) Mobile Apps for Cancer Care and Cancer Prevention and Screening (210) mHealth for Wellness, Behavior Change and Prevention (2922) mHealth for Patient Education (240) Chemotherapy and Side Effects Management (60) Innovations and Technology in Cancer Care (431) Nutrition, Physical Activity, Healthy Lifestyle for Cancer Patients and Survivors (141)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21088, first published .
Usefulness of Smartphone Apps for Improving Nutritional Status of Pancreatic Cancer Patients: Randomized Controlled Trial

Usefulness of Smartphone Apps for Improving Nutritional Status of Pancreatic Cancer Patients: Randomized Controlled Trial

Usefulness of Smartphone Apps for Improving Nutritional Status of Pancreatic Cancer Patients: Randomized Controlled Trial

Authors of this article:

Jiyoung Keum1, 2 Author Orcid Image ;   Moon Jae Chung2 Author Orcid Image ;   Youngin Kim3, 4 Author Orcid Image ;   Hyunyoung Ko4 Author Orcid Image ;   Min Je Sung2, 5 Author Orcid Image ;   Jung Hyun Jo2 Author Orcid Image ;   Jeong Youp Park2 Author Orcid Image ;   Seungmin Bang2 Author Orcid Image ;   Seung Woo Park2 Author Orcid Image ;   Si Young Song2 Author Orcid Image ;   Hee Seung Lee2 Author Orcid Image
Article Authors Cited by (19) Tweetations (3) Metrics

    Original Paper

    1Division of Gastroenterology, Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Republic of Korea

    2Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea

    3Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

    4Noom Korea, Inc, Seoul, Republic of Korea

    5Department of Gastroenterology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea

    Corresponding Author:

    Hee Seung Lee, MD, PhD

    Division of Gastroenterology, Department of Internal Medicine

    Institute of Gastroenterology

    Yonsei University College of Medicine

    50 Yonsei-ro

    Seodaemun-gu

    Seoul, 03722

    Republic of Korea

    Phone: 82 1050586865

    Email: LHS6865@yuhs.ac


    Background: Approximately 80% of pancreatic ductal adenocarcinoma (PDAC) patients suffer from anorexia, weight loss, and asthenia. Most PDAC patients receive chemotherapy, which often worsens their nutritional status owing to the adverse effects of chemotherapy. Malnutrition of PDAC patients is known to be associated with poor prognosis; therefore, nutritional management during chemotherapy is a key factor influencing the outcome of the treatment. Mobile apps have the potential to provide readily accessible nutritional support for patients with PDAC.

    Objective: We aimed to evaluate the efficacy of a mobile app–based program, Noom, in patients receiving chemotherapy for PDAC.

    Methods: We prospectively enrolled 40 patients who were newly diagnosed with unresectable PDAC from a single university-affiliated hospital in South Korea, and randomly assigned them into a Noom user group (n=20) and a non-Noom user group (n=20). The 12-week in-app interventions included meal and physical activity logging as well as nutritional education feedback from dietitians. The non-Noom user group did not receive any nutrition intervention. The primary outcomes were the changes in the nutritional status and quality of life (QoL) from the baseline to 12 weeks. The secondary outcomes included the changes in the skeletal muscle index (SMI) from the baseline to 12 weeks. The European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Core Questionnaire (QLQ-C30) and the Patient-Generated Subjective Global Assessment (PG-SGA) were used as paper questionnaires to assess the QoL and nutritional status of the patients. Intention-to-treat and per-protocol analyses were conducted. Regarding the study data collection time points, we assessed the nutritional status and QoL at the baseline (T0), and at 4 (T1), 8 (T2), and 12 (T3) weeks. Abdominal computed tomography (CT) imaging was conducted at the baseline and after 8 weeks for tumor response and SMI evaluation. The skeletal muscle area (cm2) was calculated using routine CT images. The cross-sectional areas (cm2) of the L3 skeletal muscles were analyzed.

    Results: Between February 2017 and January 2018, 48 patients were assessed for eligibility. Totally 40 patients with pancreatic cancer were included by random allocation. Only 17 participants in the Noom user group and 16 in the non-Noom user group completed all follow-ups. All the study participants showed a significant improvement in the nutritional status according to the PG-SGA score regardless of Noom app usage. Noom users showed statistically significant improvements on the global health status (GHS) and QoL scales compared to non-Noom users, based on the EORTC QLQ (P=.004). The SMI decreased in both groups during chemotherapy (Noom users, 49.08±12.27 cm2/m2 to 46.08±10.55 cm2/m2; non-Noom users, 50.60±9.05 cm2/m2 to 42.97±8.12 cm2/m2). The decrement was higher in the non-Noom user group than in the Noom user group, but it was not statistically significant (-13.96% vs. -3.27%; P=.11).

    Conclusions: This pilot study demonstrates that a mobile app–based approach is beneficial for nutritional and psychological support for PDAC patients receiving chemotherapy.

    Trial Registration: ClinicalTrials.gov NCT04109495; https://clinicaltrials.gov/ct2/show/NCT04109495.

    JMIR Mhealth Uhealth 2021;9(8):e21088

    doi:10.2196/21088

    Keywords

    pancreatic ductal adenocarcinoma; mobile app; nutritional support; quality of life; chemotherapy 


    Cancer cachexia is associated with poor therapeutic response, treatment-related adverse events, and low quality of life (QoL) in pancreatic ductal adenocarcinoma (PDAC) patients [1]. Approximately 80% of PDAC patients suffer from a wasting syndrome known as “cancer anorexia-cachexia syndrome,” which is characterized by anorexia, weight loss, asthenia, and poor prognosis [2-7]. Patients with PDAC have high risk of nutritional malabsorption and metabolic problems compared to patients with other types of cancers, as the pancreas plays a crucial role in exocrine and endocrine functions [8-11].

    In addition, almost 80% of PDAC patients are not operable at diagnosis and receive palliative chemotherapy [12]. Chemotherapy aggravates anorexia, nausea, vomiting, and abdominal pain, which can also affect the patients’ QoL and nutritional status. Unfortunately, studies on nutritional evaluation and management of PDAC patients during chemotherapy are insufficient. Malnutrition of PDAC patients is associated with chemotherapy-induced toxicity, low adherence to anticancer treatment, as well as poor QoL and survival [2,4,6,13]. Therefore, research on the nutritional management for PDAC patients during active treatment should be considered.

    Digital health care systems, especially mobile apps, have the potential to provide readily accessible nutritional and psychological support for cancer patients [14-23]. To date, there has been no randomized controlled clinical trial to evaluate the effectiveness of app-based programs targeting patients with PDAC undergoing chemotherapy. The main purpose of this pilot study was to evaluate the efficacy of mobile app–based supportive care for PDAC patients in the aspects of nutritional status, skeletal muscle index (SMI) change, and QoL.


    Study Participants

    In this randomized controlled trial (RCT) (Trial number NCT 04109495), the study participants were prospectively recruited at a tertiary hospital in South Korea between February 2017 and January 2018. The inclusion criteria were as follows: (1) males or females aged between 20 and 70 years; (2) patients newly diagnosed with PDAC within the last 3 months and slated to receive chemotherapy, (patients) able to access the Internet through their mobile phones; and (4) patients able to read and write Korean. The exclusion criteria were as follows: (1) history of abdominal surgery within the past year and with plans to undergo abdominal surgery; (2) acute illness or infection status (pneumonia, sepsis, shock, etc.); (3) known chronic liver and obstructive pulmonary diseases; (4) known absorption disorder due to gastrointestinal (GI) mucosal disease (ulcerative colitis, Crohn disease, acute and chronic diarrhea, etc.); (5) severe major illness (heart failure, liver failure, kidney failure on hemodialysis, etc.); (6) pregnancy or breastfeeding; (7) use of steroids within the past month before recruitment; (8) being diagnosed with or suspected of having peritoneal seeding or GI obstruction; and (9) history of consuming nutritional supplements.

    Study Design

    This study was a 12-week prospective, single-center, nonblinded, RCT. The clinicians introduced this study to eligible patients in the clinics, and the researchers met interested patients and confirmed their eligibility. After obtaining written informed consent, all patients were randomly assigned in a 1:1 ratio to the Noom user group or non-Noom user group by the clinical research coordinator (Figure 1). Treatment allocation was performed by the randomized permuted block method using random number tables. As this is a pilot study, we set a target sample size of 40 patients considering the rules of thumb. Browne cites a general flat rule to include at least 30 subjects or more to estimate a parameter [24]. Owing to the nature of the intervention, participant details could not be blinded. This study was approved by the institutional review board of the Severance Hospital (Approval number 1-2016-0061).

    ‎
    Figure 1. Flow chart of the study recruitment process.
    View this figure

    Interventions

    Noom (Noom Inc.) is a mobile app for weight management that is commercially available on Google Playstore and Apple Appstore. Noom has a unique curriculum and human coaching intervention, which is widely used in health and fitness apps [25,26]. We adopted this program to the nutritional and behavior intervention for PDAC patients undergoing chemotherapy. The major goal of nutritional intervention was to encourage caloric intake to maintain the nutritional status and QoC of PDAC patients undergoing chemotherapy. Although the commercialized version of the Noom app was designed for weight loss and healthy dietary intake, we used functions such as food logging, step count, weight logging, and messaging for tracking balanced caloric intake and muscle gain. To achieve this goal, the Noom app offered the following interventions: (1) interactive interface with coach–participant messaging, (2) daily articles for basic health knowledge, (3) food logging with color coding, and (4) automated feedback-based food choices (see Multimedia Appendix 1). The articles provided to patients mainly included basic health knowledge, information on how to organize a diet using calorie density, and exercise, and lifestyle information. Patient-specific feedback was provided by the coach. Participants were asked to log their weight by self-report providing information on their meals and physical activity in the app more than 4 days per week.

    The coach, who is a clinical dietitian, provided nutritional intervention based on the following goals: (1) Guide participants to consume more calories than the recommended intake calculated by the Harris–Benedict equation [27,28] with additional disease-related energy requirements [29]. (2) Provide more than four feedbacks per week on nutritional intake. (3) Check the participants’ step counts and exercise logs once a week to promote light physical activity. Noom aims to provide nutritional support for PDAC patients through self-management by monitoring their meals and in-app activities. The clinical research coordinator helped the study participants download the Noom app onto their mobile phones and register themselves on the app. A unique username was generated with a personal password. The study participants did not need to pay for accessing Noom.

    The participants in both the groups answered paper questionnaires in the presence of the clinical research coordinator and underwent blood tests at the baseline, and at 4, 8, and 12 weeks. The questionnaire items included the gender, age, body weight, type of diagnosed digestive disease, treatment method, status of oral nutritional supplements, and past medical history of the patients. The European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Core Questionnaire (QLQ-C30, version 3.0) and the Patient-Generated Subjective Global Assessment (PG-SGA) were also used to assess the QoL and nutritional status of the patients.

    App activity was calculated as the summation of the recorded events, including meals, exercise, weight input, number of messages, and step counts. Participants with app activity for more than 9 weeks were defined to be “above average users” (10/40), and participants with app activity for less than 9 weeks were defined to be “below average users” (7/40). A 9-week period was determined based on the median value of the app users’ activity.

    Comparator

    As opposed to the Noom user group, the non-Noom user group did not have access to Noom. This group did not receive any nutrition intervention and attended only study assessments. They received the chemotherapeutic agent as usual. To reduce the bias related to the usually prescribed appetite stimulant, the subjects in both the groups who showed no statistical differences received the same dosage of the appetite stimulant. In this study, patients diagnosed with pancreatic cancer received chemotherapy without regular nutritional intervention.

    Variables

    The primary objective was to investigate the changes in the QoL or nutritional status, which were calculated from the EORTC QLQ-C30 and the PG-SGA score, over time according to Noom usage. The EORTC QLQ-C30 is a 30-item cancer-specific questionnaire that incorporates 5 functional scales (physical, role, cognitive, emotional, and social), 3 symptom scales (fatigue, pain, and nausea/vomiting), a global health status (GHS) and QoL scale, several single items assessing additional symptoms commonly reported by cancer patients (dyspnea, loss of appetite, insomnia, constipation, and diarrhea), and the financial impact of the disease [30]. A higher score on the GHS and QoL scales indicates a good QoL, but a higher score on the symptom scales indicates poor QoL.

    The PG-SGA is a scoring method for nutritional measurement integrating body weight, food intake, nutritional difficulties and activities, and therapeutic information provided by physicians. A score ≥9 indicates a critical need for nutritional intervention [31]. Several studies have verified that the PG-SGA is a reliable and valid assessment of the nutritional status of cancer patients; therefore, we used the PG-SGA scale as a nutritional status assessment tool [14,32-34]. A trained nurse assessed all the PG-SGA scores to maintain consistency in the test results.

    The secondary objective was to observe changes in the SMI according to Noom usage. We evaluated whether the SMI was associated with Noom usage at the baseline and during the follow-up period. The skeletal muscle area (cm2) was calculated using routine computed tomography (CT) images through the picture archiving and communication system (PACS), an image system using Image J software (US National Institutes of Health) [35,36]. Cross-sectional areas (cm2) of the L3 skeletal muscles were analyzed using Image J. At the L3 level, the field of view included the psoas, paraspinal muscles, and abdominal wall muscles. Currently, the most frequently used landmark in the body composition imaging studies for sarcopenia is the L3 level [37]. We segmented the tissues based on the Hounsfield unit of CT scanning using Image J with assistance from a well-trained and an experienced medical doctor. We had previously published several studies using this method [38,39]. The skeletal muscle area was normalized for height (m2) and calculated as the SMI (cm2/m2) [40].

    Age, sex, BMI, Eastern Cooperative Oncology Group performance status (ECOG PS), smoking history, tumor extent, tumor size, chemotherapy regimen, and laboratory characteristics (leukocyte, hemoglobin, platelet, albumin, creatinine, and carbohydrate antigen [CA] 19-9) were also examined.

    In this study, we attempted to remove the confounding factors such as steroid ingestion, including appetite stimulants, megestrol, and herbs. Clinicians used to prescribe megestrol to stimulate the appetite of cancer patients. To reduce the confounding factors with respect to appetite stimulants, we prescribed the same dose of megestrol (160 mg/day) for all the enrolled patients, except for those who showed good appetite without stimulants. The prescription was confirmed by the clinical judgment of the attending physician.

    Statistical Analysis

    Data are expressed as the median, n (%), or n, as appropriate. Variables were compared using Chi-square tests or Fisher exact tests for categorical data and Student t tests for continuous variables to evaluate the statistical significance of the differences in the baseline characteristics between Noom and non-Noom users. Variables related to the in-app actions of the above average and below average user groups were compared using the Mann-Whitney test. The primary outcomes of the nutritional status and QoL, as measured by the PG-SGA and EORTC QLQ, were analyzed using intention-to-treat analysis and linear mixed models. Intention-to-treat analysis with the last observation carried forward was applied to account for missing data. The secondary outcome of SMI was assessed in a per-protocol analysis, using the Mann-Whitney test. P<.05 was considered to indicate statistical significance. Statistical analyses were performed using the SPSS (version 23.0, IBM Corp.).


    Patient Characteristics

    Between February 2017 and January 2018, 48 patients were assessed for eligibility. A total of 40 patients were enrolled and randomized into 2 groups (Noom users, n=20; non-Noom users, n=20) (Figure 1). After 7 patients dropped out, 17 Noom users and 16 non-Noom users completed all the follow-ups. Attrition was 18% (7/40 participants), including 2 patients (one in the Noom user group and another in the non-Noom user group) who could not continue chemotherapy owing to severe sepsis or progression of disease, and 5 patients (2 in the Noom user group and 3 in the non-Noom user group) who withdrew their informed consent. The baseline variables in Table 1 did not show a significant difference between participants who were included in the intention-to-treat population (n=40) and per-protocol population (n=33).

    The median age was 61.5 years (range 34-78 years), and 25 of the 40 patients (63%) were male. All the recruited patients had unresectable PDAC at the time of diagnosis. The baseline characteristics did not show statistically significant differences between the 2 groups, except for the baseline BMI and hemoglobin. Most of the patients received palliative folinic acid, fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX) as a first-line chemotherapy (17/20 [85%] of Noom users and 18/20 [90%] of non-Noom users).

    Table 1. Baseline characteristics of the study population.
    VariablesaIntention-to-treat analysis (N=40)Per-protocol analysis (N=33)
    Noom users
    (n=20)    		Non-Noom users
    (n=20)    		P valuebNoom users
    (n=17)    		Non-Noom users
    (n=16)    		P valueb
    Age, median, years (range)62
    (45-70)    		61
    (34-78)    		.2562
    (45-70)    		61.5
    (34-78)    		.31
    Sex, male, n (%)13 (65)12 (60).9910 (58.8)10 (62.5).83
    Height (m)1.63±0.091.63±0.09.71.63 ± 0.091.63 ± 0.08.95
    Weight (kg)58.4±7.963.5 ± 11.3.1858.2 ± 8.462.8 ± 10.9.22
    BMI (kg/m2)21.91 ± 1.5723.5 ± 2.72.0321.83 ± 1.6923.46 ± 2.57.04
    ECOG PS,c n (%)






    		0-117
    (85)    		18
    (90)    		.9914
    (82.4)    		14
    (87.5)    		.99

    		2-33 (15)2 (10)
    		3 (17.6)2 (12.5)
    Smoking history






    		Never16 (80)14 (70).4714 (82.4)14 (87.5).71

    		Former or current4 (20)6 (30)
    		3 (17.6)2 (12.5)
    DM,d n (%)5 (25)6 (30).725 (29.4)5 (31.3).99
    SMI (cm2/m2)e49.62± 11.6248.43±9.91.7349.08± 12.2750.60 ± 9.05.69
    WBC,f /μL6310 (5045-7155)6,975
    (6158-8515)    		.136310 (4925-7340)6910 (6158-8045).26
    Hemoglobin, g/dL12.3 (10.6-13.6)13.0
    (11.9-14.0)    		.0412.3 (10.6-13.2)13.0 (12.0-
    13.8)    		.04
    Platelet, 103/μL204.5 (176.5-353.3)222
    (183-289)    		.95196 (178-335)237
    (196-289)    		.85
    Albumin, g/dL3.9
    (3.5-4.1)    		4.1
    (3.5-4.3)    		.873.9
    (3.5-4.1)    		3.9
    (3.3-4.1)    		.74
    Creatinine, mg/dL0.77 (0.53-0.91)0.67
    (0.56-0.82)    		.270.77 (0.55-0.91)0.69
    (0.52-0.82)    		.23
    Initial CAg 19-9, U/mL829.6 (190-2768)310.1
    (40.8-1734.8)    		.1920 (136.6-2716)310 (27.9-1516.5).07
    Elevated initial CA 19-9, U/mL19 (95)16 (80).3416 (94.1)12 (75).18
    Clinical stage, n (%)






    		Borderline resectable6 (30)6 (30).934 (23.5)4 (25).61

    		Locally advanced5 (25)6 (30)
    		4 (23.5)6 (37.5)

    		Metastatic9 (45)8 (40)
    		9 (52.9)6 (37.5)
    Tumor size, cm4.2±1.34.2±2.1.84.2±1.34.5±2.2.65
    Chemotherapy regimen, n (%)






    		FOLFIRINOXh17 (85)18 (90).9914 (82.4)14 (87.5).99

    		Gem/Nab-paclitaxeli3 (15)2 (10)　3 (17.6)2 (12.5)　

    aData are presented as n (%) for categorical variables and as median (interquartile range) or mean±SD for continuous variables.

    bP values were calculated by Student t tests for continuous data and Chi-square or Fisher exact tests for categorical data.

    cECOG PS: Eastern Cooperative Oncology Group performance status.

    dDM: diabetes mellitus.

    eThe skeletal muscle index was calculated from the muscle cross-sectional area (cm2)/height (m)2 of the lumbar muscle.

    fWBC: white blood cells.

    gCA: carbohydrate antigen.

    hFOLFIRINOX: folinic acid, fluorouracil, irinotecan, and oxaliplatin.

    iGem/Nab-paclitaxel: gemcitabine/nanoparticle albumin–bound paclitaxel.

    The baseline characteristics of the Noom participants, above average and below average users, are shown in Table 2. The values in Table 2 were calculated by per-protocol analysis, as patients who dropped out (n=7) did not complete the entire 9-week period, which is the median value of the app users’ activity. The 17 Noom users were divided as follows: 10 above average users and 7 below average users. There were no baseline differences in the sex, age, and baseline BMI between the 2 Noom user groups (Table 2, sex, 59% vs. 60%; age, 62 vs. 62.5 years; baseline BMI, 21.8 vs. 21.5).

    Table 2. Baseline characteristics of Noom users.
    VariablesaNoom users (N=17)Above average users (n=10)Below average users (n=7)
    Baseline
    		　　

    		Sex, male, n (%)10 (58.8)6 (60)4 (57.1)

    		Age, median, years (range)62 (45-70)62.5 (45-70)62 (58-65)

    		Height (m)1.63±0.091.64±0.11.61 ± 0.08

    		Weight (kg)58.2±8.458.32±8.6158.04±8.75

    		BMI (kg/m2)21.83±1.6921.54±1.4622.24±2.02

    aP values were calculated by the Mann-Whitney test between the two user groups.

    In the intention-to-treat population, 15 of the 20 Noom users (75%) and 14 of 20 non-Noom users (70%) received megestrol, but there was no significant difference in the number of prescribed patients (P=.72) and the total dose of the drug (9,440 mg vs. 12,240 mg, P=.06). In the per-protocol population, 12 of the 17 Noom users (71%) and 13 of the 16 non-Noom users (81%) received metestrol, but there was no significant difference in the number of prescribed patients (P=.69) and the total dose of the drug (11,120 mg vs. 12,320 mg, P=.09).

    Improvement of Nutritional Status Through Mobile App Usage

    In the intention-to-treat analysis, all the study participants showed a significant improvement in the nutritional status according to the PG-SGA score regardless of Noom app usage (Figure 2A, P=.001). In the per-protocol analysis, the above average users showed a significant improvement in the PG-SGA score (Figure 2B, P=.03).

    ‎
    Figure 2. Change in PG-SGA score according to Noom usage over time. (A) All study participants show an improvement in nutritional status according to the PG-SGA score (P=.001). (B) All Noom users show improvement in their nutritional status according to the PG-SGA score regardless of their app activity differences. Above average users showed significant improvement in PG-SGA score (P=.03). PG-SGA: Patient-Generated Subjective Global Assessment.
    View this figure

    There were significant differences in the total protein and energy intakes between the above average and below average users (Table 3, 1.3 vs. 1g/kg/day, P=.02; 25.2 vs. 17.7 kcal/kg/day, P=.04). In the per-protocol analysis, 7 of the 10 above average users (70%) met the individual minimum protein intake requirement, and 6 of the 10 above average users (60%) met the individual minimum energy intake requirement. However, none of the below average users met the minimum recommended daily intake of protein and calories.

    The above average users documented meal data more frequently in the Noom app (15.4 meals per week vs. 5.06 meals per week) and showed an increase in their body weight and BMI compared to the below average users (Table 3, body weight, +1.16% vs. -4.43%; BMI, +0.21 vs. -0.81).

    Table 3. Effects of Noom app intervention on dietary intake, weight, BMI, and engagement characteristics.
    VariablesNoom users (N=17)Above average users (n=10)Below average users (n=7)
    12 weeks



    		Total protein intake(g/kg/day)1 (0.6-1.4)1.3 (0.9-1.6)a1 (0.5-1)

    		Total energy intake(kcal/kg/day)19.9 (13.9-26.8)25.2 (17.5-32.7)b17.7 (12.1-20.8)

    		Weight loss (kg)-0.66±4.310.68±4.80c-2.57±2.76

    		Weight loss (%)-1.14±7.551.16±8.31-4.43±5.21

    		BMI change (kg/m2)-0.21±1.430.21±1.60d-0.81±0.93
    In-app actionse



    		Meal input frequency (meals per week)11.15±7.6915.41±6.76f5.06±3.95

    		Total exercise input frequency (every 12 weeks)3.35±8.195.6±10.28g0.14±0.38

    		Articles read (articles/week)0.98±1.841.47±2.30.27±0.33

    		Number of weight inputs (times/week)0.6±0.720.83±0.70.27±0.65

    		Messages to coach (messages/week)5.14±5.566.19±4.353.63±7.03

    		Steps recorded (steps/week)17,168.23±20,718.0223,999.58±24,595.55h7,409.17±6,951.79

    aThere was a significant difference in the total protein intake during the 12 weeks between above average and below average users; P=.02. The P value was calculated by Mann-Whitney tests.

    bThere was a significant difference in the total energy intake during the 12 weeks between above average and below average users; P=.04.

    cThere was no significant difference in the changes at 12 weeks between the above average and below average users; P=.10.

    dThere was no significant difference in the changes at 12 weeks between the above average and below average users; P=.09.

    eP values were calculated by Mann-Whitney tests between the 2 user groups.

    fAll the changes from the baseline to 12 weeks were significant in the above average and below average users; P=.007.

    gAll the changes from the baseline to 12 weeks were significant in the above average and below average users; P=.01.

    hAll the changes from the baseline to 12 weeks were significant in the above average and below average users; P=.02.

    Improvement in QoL Through Mobile App Usage

    There was no statistically significant difference in the EORTC QLQ score between the Noom users and non-Noom users (Figure 3A). However, on the GHS and QoL scale, there was a statistically significant improvement in the Noom user group compared to the non-Noom user group during the study period (Figure 3B, P=.004).

    ‎
    Figure 3. Change in EORTC QLQ-C30 scores according to Noom usage over time. (A) There was no statistically significant difference in EORTC QLQ score between the Noom users and non-Noom users. (B) Noom users showed more statistically significantly improvement on the GHS and QoL scale compared to the non-Noom users over time (P=.004). *P values were calculated by the linear mixed model. EORTC QLQ-C30: European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire; GHS: global health status; QoL: quality of life.
    View this figure

    Skeletal Muscle Change After Mobile App Usage

    When receiving chemotherapy, the SMI decreased in both groups (Noom users, 49.08±12.27 cm2/m2 to 46.08±10.55 cm2/m2; non-Noom users, 50.60±9.05 cm2/m2 to 42.97±8.12 cm2/m2). The decrement was higher in non-Noom user group than in the Noom user group, but it was not statistically significant (-13.96% vs. -3.27%; P=.11). In the per-protocol analysis, there was a statistically significant increment in the SMI of the above average user group compared to the non-Noom user group (Figure 4, +5.58% vs. -13.96%; P=.04).

    ‎
    Figure 4. Change in SMI from baseline at 8 weeks. Above average users showed a statistically significant increase in muscle mass after 2 months of Noom usage compared to non-users (P=.04). *P values were calculated by Mann-Whitney tests between the 2 groups. SMI: skeletal muscle index.
    View this figure

    Chemotherapy Response According to Mobile App Usage

    Furthermore, the overall best response to chemotherapy according to Noom usage was also analyzed in the per-protocol analysis. The overall best response was defined as the best response recorded from the start of chemotherapy until disease progression, recurrence, or the start of new chemotherapy sessions. A decrease in the tumor size was more prominent in the above average users than in the non-Noom users, although this was not statistically significant, possibly owing to the small study population (-15.6% vs. -6.3%; P=.17).

    There were no differences in terms of the progression-free survival, overall survival, and duration of chemotherapy between the Noom users and non-Noom users.


    Principal Findings

    Mobile apps have been used in the management of chronic diseases such as obesity and hypertension, as well as exercise measurement; they were rarely used directly in hospital care settings. To the best of our knowledge, this is the first study that evaluates the short-term effects of mobile app–based coaching on the change in the nutritional status, SMI, and QoL during PDAC management. The above average Noom users in this pilot study showed statistically significant improvements in their nutritional status. The SMI significantly increased in the above average users compared to the non-Noom users. Moreover, the Noom users showed statistically significant improvements in QoL compared to the non-Noom users based on the GHS and QoL scales of the EORTC QLQ. These findings showed that PDAC patients who receive chemotherapy could be supported by mobile app–based coaching for improving their nutritional and health conditions.

    Previous studies have highlighted the importance of supportive care for patients with advanced cancer, including PDAC. Early palliative supportive care led to significant improvements in nutritional statuses, QoL, depression levels, and symptom burden [41-44]. In particular, malnutrition during chemotherapy can induce various adverse effects in humans owing to dysfunction of the intestinal mucosa, decreased immune function, decreased functioning of major organs such as the liver, kidney, and heart, and changes in drug dynamics [10,45-48]. We hypothesized that early nutritional supervision as a supportive treatment could improve the nutritional and psychological conditions of patients with PDAC. In particular, we used a mobile app with human coaching as a nutritional support tool. Even though both groups showed improvements in the PG-SGA score regardless of the frequency of Noom app usage, the above average users showed significant improvements in the PG-SGA scores after using the app. Britton et al reported that the minimum significant difference for the PG-SGA score was 2 points [49]. In this study, the PG-SGA score improved by more than 2 points at 12 weeks in all groups, indicating clinical significance. Therefore, PDAC patients receiving chemotherapy could benefit from using the Noom app, in addition to the usual palliative care.

    There have been some attempts at evaluating the efficacy of mobile apps for assisting cancer patients. One randomized study on 114 women with breast cancer who were starting chemotherapy found that e-support program users had better outcomes at 12 weeks for self-efficacy, symptom interference, and QoL compared to users in the control group [15]. Our study also confirmed that QoL improved after using the mobile app. However, the app content used in the two studies differed. Apps used in the Chinese study focused on self-efficacy, social support, and symptom management for patients, whereas the app used in our study focused on nutritional management. Another systematic review also revealed the benefits of app-based programs on the physical activity level, dietary behavior, and health-related QoL in populations diagnosed with solid tumors [50]. Similarly, in our study, the above average users showed better dietary behavior such as inputting their meal data more frequently, with more of them meeting the individual minimum protein and energy intake requirements compared to the below average users. Furthermore, the above average users showed significant improvement in their nutritional status at 12 weeks based on the PG-SGA score. In the present study, the patients had an average PG-SGA score ≥9 points at the baseline, which indicates that almost all participants needed nutritional intervention [2,6,51].

    According to previous studies, malnutrition, weight loss, and sarcopenia are risk factors strongly associated with limited tolerance for chemotherapy, short survival times, and poor QoL in PDAC patients. We calculated the SMI using the sarcopenia measurement method. Sarcopenia was measured by CT-based skeletal muscle area assessment, as in many previous studies [52,53]. Loss of skeletal muscle mass is known to be associated with cancer cachexia [54,55]. In the present study, when undergoing chemotherapy, the above average Noom users showed an increment in the SMI, and there was a significant difference between them and the non-Noom users. We assumed that Noom users would be more motivated to improve nutritional intake while monitoring their food intake through the app, and human coaches who are professional nutritionists may have helped Noom users obtain proper nutrition.

    A previous retrospective study suggested that early nutritional intervention may affect the overall survival of PDAC patients undergoing chemotherapy [8]. In this study, nutritional intervention included face-to-face dietary consultation with a dietitian. Several studies reported that malnutrition and sarcopenia are factors that are strongly associated with limited tolerance for chemotherapy [3,11]. Despite the increasing evidence demonstrating an association between the nutritional status and clinical outcomes, there is no standard nutritional management tool for PDAC patients undergoing chemotherapy. Although the overall best response to chemotherapy according to Noom usage was not statistically significant, further confirmatory research is needed to achieve better results.

    We did not analyze the factors associated with high usage of the app. Although we did not perform statistical analysis, clinical dieticians who coached the patients considered sex as a relevant factor. Female patients or caregivers were considered to use the Noom app more frequently. Further, male patients under 60 years of age were considered to use the Noom app more often. We believe that further studies on mobile app usage according to the age, sex, and education level of patients and their caregivers, as well as the performance status of the patients will benefit future research on nutritional interventions for cancer patients.

    Comparison With Prior Work

    Most of the nutritional studies on pancreatic cancer patients undergoing chemotherapy have been performed with face-to-face interventions [56]. Expert dieticians provided essential dietary suggestions and prescribed oral nutritional supplements. These studies have shown positive outcomes, such as improved weight and QoL [8,57,58]. Bauer et al [57] found that cancer patients who received weekly counseling by a dietitian and were advised to consume protein- and energy-dense oral nutritional supplements showed clinically significant improvements in their nutritional status and QoL. Our findings showed a higher improvement in QoL (median change 33.3 vs. 16.7) but lower improvement in nutritional status based on the PG-SGA score (median change 4.5 vs. 9) than those observed in the study by Bauer et al [57], although a direct comparison between the studies was difficult. However, face-to-face studies have limitations in terms of time and space. Mobile health technology is an innovative way to overcome this limitation. Face-to-face interventions can give feedback only on the day of intervention, but with mobile apps, coaches can provide immediate feedback daily based on the patients’ meal records. In addition, face-to-face nutritional education is unlikely to be implemented universally owing to time and space constraints. On the other hand, it has the advantage of providing personalized education for patients through their meal records using mobile apps.

    Limitations

    Our study had several limitations. First, this study could not evaluate the long-term effect of the Noom app, as we conducted this pilot study over a period of only 12 weeks. Further research on the long-term effect of mobile apps on PDAC patients is needed. Second, the study participants were recruited from a single center, and the sample size was small. Furthermore, the requirement of access to the mobile app may have resulted in the participation of a more educated population, potentially limiting the generalization of this study. However, as the number of people familiar with using mobile apps increases over time, it is expected that supportive care using mobile apps could become a promising intervention method. Therefore, additional multicenter-mediated validation is needed to confirm the results of this study. However, one of the strengths of this study was it is the first such study to investigate the use of a mobile app in providing supportive care to PDAC patients. Third, the baseline BMI differed between the Noom users and non-Noom users. The Noom users had a lower BMI at the time of diagnosis compared to that of the non-Noom users. However, the BMI changes in the between the 2 groups did not differ significantly at 12 weeks (P=.99). Therefore, the baseline BMI differences between the 2 groups did not affect the results. Furthermore, SMI reduction was more prominent in the non-Noom users than in the Noom users, despite the higher baseline SMI in the non-Noom users. Fourth, we could not measure the nutritional intake of the non-Noom users owing to the study design; therefore, it was not possible to analyze whether the Noom users consumed more calories and specific nutrients compared to the non-Noom users.

    Conclusions

    This pilot study demonstrated that a mobile app–based approach for providing nutritional and psychological support could be beneficial for patients with PDAC undergoing chemotherapy. Mobile apps could be useful tools for providing prompt and appropriate nutritional support and monitoring of PDAC patients.

    Acknowledgments

    The authors wish to thank Noom Inc for supporting this study by providing the mobile app and Boryung Co, Ltd for supplying megestrol acetate.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Screenshots of the Noom app.

    PNG File , 399 KB
    ‎
    Multimedia Appendix 2

    CONSORT-eHEALTH checklist (V 1.6.1).

    PDF File (Adobe PDF File), 1104 KB
    1. Fearon K. Cancer cachexia: developing multimodal therapy for a multidimensional problem. Eur J Cancer 2008 May;44(8):1124-1132. [CrossRef] [Medline]
    2. Ozola Zalite I, Zykus R, Francisco Gonzalez M, Saygili F, Pukitis A, Gaujoux S, et al. Influence of cachexia and sarcopenia on survival in pancreatic ductal adenocarcinoma: a systematic review. Pancreatology 2015;15(1):19-24. [CrossRef] [Medline]
    3. Kurita Y, Kobayashi N, Tokuhisa M, Goto A, Kubota K, Endo I, et al. Sarcopenia is a reliable prognostic factor in patients with advanced pancreatic cancer receiving FOLFIRINOX chemotherapy. Pancreatology 2019 Jan;19(1):127-135. [CrossRef] [Medline]
    4. Basile D, Parnofiello A, Vitale MG, Cortiula F, Gerratana L, Fanotto V, et al. The IMPACT study: early loss of skeletal muscle mass in advanced pancreatic cancer patients. J Cachexia Sarcopenia Muscle 2019 Apr;10(2):368-377 [FREE Full text] [CrossRef] [Medline]
    5. Choi Y, Oh D, Kim T, Lee K, Han S, Im S, et al. Skeletal muscle depletion predicts the prognosis of patients with advanced pancreatic cancer undergoing palliative chemotherapy, independent of body mass index. PLoS One 2015;10(10):e0139749 [FREE Full text] [CrossRef] [Medline]
    6. Basile D, Corvaja C, Caccialanza R, Aprile G. Sarcopenia: looking to muscle mass to better manage pancreatic cancer patients. Curr Opin Support Palliat Care 2019 Dec;13(4):279-285. [CrossRef] [Medline]
    7. Gärtner S, Krüger J, Aghdassi AA, Steveling A, Simon P, Lerch MM, et al. Nutrition in pancreatic cancer: a review. Gastrointest Tumors 2016 May;2(4):195-202 [FREE Full text] [CrossRef] [Medline]
    8. Trestini I, Carbognin L, Sperduti I, Bonaiuto C, Auriemma A, Melisi D, et al. Prognostic impact of early nutritional support in patients affected by locally advanced and metastatic pancreatic ductal adenocarcinoma undergoing chemotherapy. Eur J Clin Nutr 2018 Mar;72(5):772-779. [CrossRef] [Medline]
    9. Ferrucci LM, Bell D, Thornton J, Black G, McCorkle R, Heimburger DC, et al. Nutritional status of patients with locally advanced pancreatic cancer: a pilot study. Support Care Cancer 2011 Nov;19(11):1729-1734 [FREE Full text] [CrossRef] [Medline]
    10. Gilliland T, Villafane-Ferriol N, Shah K, Shah R, Tran Cao H, Massarweh N, et al. Nutritional and metabolic derangements in pancreatic cancer and pancreatic resection. Nutrients 2017 Mar;9(3):243 [FREE Full text] [CrossRef] [Medline]
    11. Ryan AM, Prado CM, Sullivan ES, Power DG, Daly LE. Effects of weight loss and sarcopenia on response to chemotherapy, quality of life, and survival. Nutrition 2019;67-68:110539. [CrossRef] [Medline]
    12. Kamisawa T, Wood LD, Itoi T, Takaori K. Pancreatic cancer. Lancet 2016 Jul;388(10039):73-85. [CrossRef] [Medline]
    13. Tan BH, Birdsell LA, Martin L, Baracos VE, Fearon KC. Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer. Clin Cancer Res 2009 Nov;15(22):6973-6979 [FREE Full text] [CrossRef] [Medline]
    14. de Bruin JS, Schuh C, Seeling W, Luger E, Gall M, Hütterer E, et al. Assessing the feasibility of a mobile health-supported clinical decision support system for nutritional triage in oncology outpatients using Arden Syntax. Artif Intell Med 2018 Nov;92:34-42. [CrossRef] [Medline]
    15. Zhu J, Ebert L, Liu X, Wei D, Chan SW. Mobile breast cancer e-support program for Chinese Women with breast cancer undergoing chemotherapy (part 2): multicenter randomized controlled trial 21088. JMIR Mhealth Uhealth 2018 Apr;6(4):e104 [FREE Full text] [CrossRef] [Medline]
    16. van der Hout A, van Uden-Kraan CF, Holtmaat K, Jansen F, Lissenberg-Witte BI, Nieuwenhuijzen GAP, et al. Role of eHealth application Oncokompas in supporting self-management of symptoms and health-related quality of life in cancer survivors: a randomised, controlled trial. Lancet Oncol 2020 Jan;21(1):80-94. [CrossRef] [Medline]
    17. Cheng C, Ho RTH, Guo Y, Zhu M, Yang W, Li Y, et al. Development and feasibility of a mobile health-supported comprehensive intervention model (CIMmH) for improving the quality of life of patients with esophageal cancer after esophagectomy: prospective, single-arm, nonrandomized pilot study. J Med Internet Res 2020 Aug;22(8):e18946 [FREE Full text] [CrossRef] [Medline]
    18. Ahmad AR, Murad HR. The impact of social media on panic during the COVID-19 pandemic in Iraqi Kurdistan: online questionnaire study. J Med Internet Res 2020 May;22(5):e19556 [FREE Full text] [CrossRef] [Medline]
    19. Young-Afat DA, van GCH, Bruinvels DJ, van DPCC, Witkamp AJ, Sijtsema S, et al. Patients' and health care providers' opinions on a supportive health app during breast cancer treatment: a qualitative evaluation. JMIR Cancer 2016 Jun;2(1):e8 [FREE Full text] [CrossRef] [Medline]
    20. Moore TJ, Alsabeeh N, Apovian CM, Murphy MC, Coffman GA, Cullum-Dugan D, et al. Weight, blood pressure, and dietary benefits after 12 months of a Web-based nutrition education program (DASH for health): longitudinal observational study. J Med Internet Res 2008 Dec;10(4):e52 [FREE Full text] [CrossRef] [Medline]
    21. Low CA, Dey AK, Ferreira D, Kamarck T, Sun W, Bae S, et al. Estimation of symptom severity during chemotherapy from passively sensed data: exploratory study. J Med Internet Res 2017 Dec;19(12):e420 [FREE Full text] [CrossRef] [Medline]
    22. Soh JY, Cha WC, Chang DK, Hwang JH, Kim K, Rha M, et al. Development and validation of a multidisciplinary mobile care system for patients with advanced gastrointestinal cancer: interventional observation study. JMIR Mhealth Uhealth 2018 May;6(5):e115 [FREE Full text] [CrossRef] [Medline]
    23. Kim HJ, Kim SM, Shin H, Jang J, Kim YI, Han DH. A mobile game for patients with breast cancer for chemotherapy self-management and quality-of-life improvement: randomized controlled trial. J Med Internet Res 2018 Oct;20(10):e273 [FREE Full text] [CrossRef] [Medline]
    24. Browne RH. On the use of a pilot sample for sample size determination. Stat Med 1995 Sep;14(17):1933-1940. [CrossRef] [Medline]
    25. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med 2013 Nov;45(5):576-582. [CrossRef] [Medline]
    26. Patel R, Sulzberger L, Li G, Mair J, Morley H, Shing MN, et al. Smartphone apps for weight loss and smoking cessation:quality ranking of 120 apps. N Z Med J 2015 Sep;128(1421):73-76. [Medline]
    27. Souza MTP, Singer P, Ozorio GA, Rosa VM, Alves MMF, Mendoza López RV, et al. Resting energy expenditure and body composition in patients with head and neck cancer: an observational study leading to a new predictive equation. Nutrition 2018;51-52:60-65. [CrossRef] [Medline]
    28. Sasaki M, Okamoto H, Johtatsu T, Kurihara M, Iwakawa H, Tanaka T, et al. Resting energy expenditure in patients undergoing pylorus preserving pancreatoduodenectomies for bile duct cancer or pancreatic tumors. J Clin Biochem Nutr 2011 May;48(3):183-186 [FREE Full text] [CrossRef] [Medline]
    29. Elliott L, Molseed LL, McCallum PD, American Dietetic Association. The Clinical Guide to Oncology Nutrition 2nd Edition. USA: American Dietetic Association; 2006.
    30. Fayers P, Bottomley A, EORTC Quality of Life Group, Quality of Life Unit. Quality of life research within the EORTC-the EORTC QLQ-C30. European Organisation for Research and Treatment of Cancer. Eur J Cancer 2002 Mar;38 Suppl 4:S125-S133. [CrossRef] [Medline]
    31. Bauer J, Capra S, Ferguson M. Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer. Eur J Clin Nutr 2002 Aug;56(8):779-785. [CrossRef] [Medline]
    32. Solheim TS, Laird BJ, Balstad TR, Stene GB, Bye A, Johns N, et al. A randomized phase II feasibility trial of a multimodal intervention for the management of cachexia in lung and pancreatic cancer. J Cachexia Sarcopenia Muscle 2017 Oct;8(5):778-788 [FREE Full text] [CrossRef] [Medline]
    33. Argilés JM, Anguera A, Stemmler B. A new look at an old drug for the treatment of cancer cachexia: megestrol acetate. Clin Nutr 2013 Jun;32(3):319-324. [CrossRef] [Medline]
    34. Lorton CM, Griffin O, Higgins K, Roulston F, Stewart G, Gough N, et al. Late referral of cancer patients with malnutrition to dietitians: a prospective study of clinical practice. Support Care Cancer 2020 May;28(5):2351-2360. [CrossRef] [Medline]
    35. Gomez-Perez SL, Haus JM, Sheean P, Patel B, Mar W, Chaudhry V, et al. Measuring abdominal circumference and skeletal muscle from a single cross-sectional computed tomography image: a step-by-step guide for clinicians using National Institutes of Health ImageJ. JPEN J Parenter Enteral Nutr 2016 Mar;40(3):308-318 [FREE Full text] [CrossRef] [Medline]
    36. Wu C, Chang M, Lyadov VK, Liang P, Chen C, Shih TT, et al. Comparing western and eastern criteria for sarcopenia and their association with survival in patients with pancreatic cancer. Clin Nutr 2019 Apr;38(2):862-869. [CrossRef] [Medline]
    37. Lee K, Shin Y, Huh J, Sung YS, Lee IS, Yoon KH, et al. Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol 2019 Feb;20(2):205-217 [FREE Full text] [CrossRef] [Medline]
    38. Keum J, Lee HS, Kang H, Jo JH, Chung MJ, Park JY, et al. Single-center risk factor analysis for FOLFIRINOX associated febrile neutropenia in patients with pancreatic cancer. Cancer Chemother Pharmacol 2020 Apr;85(4):651-659. [CrossRef] [Medline]
    39. Lee HS, Kim SY, Chung MJ, Park JY, Bang S, Park SW, et al. Skeletal muscle mass predicts poor prognosis in patients with advanced pancreatic cancer undergoing second-line FOLFIRINOX chemotherapy. Nutr Cancer 2019;71(7):1100-1107. [CrossRef] [Medline]
    40. Martin L, Birdsell L, Macdonald N, Reiman T, Clandinin MT, McCargar LJ, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 2013 Apr;31(12):1539-1547. [CrossRef] [Medline]
    41. Temel JS, Greer JA, Muzikansky A, Gallagher ER, Admane S, Jackson VA, et al. Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med 2010 Aug;363(8):733-742. [CrossRef] [Medline]
    42. Kavalieratos D, Corbelli J, Zhang D, Dionne-Odom JN, Ernecoff NC, Hanmer J, et al. Association between palliative care and patient and caregiver outcomes: a systematic review and meta-analysis. JAMA 2016 Nov;316(20):2104-2114 [FREE Full text] [CrossRef] [Medline]
    43. Temel JS, Greer JA, El-Jawahri A, Pirl WF, Park ER, Jackson VA, et al. Effects of early integrated palliative care in patients with lung and GI cancer: a randomized clinical trial. J Clin Oncol 2017 Mar;35(8):834-841 [FREE Full text] [CrossRef] [Medline]
    44. Vanbutsele G, Pardon K, Van Belle S, Surmont V, De Laat M, Colman R, et al. Effect of early and systematic integration of palliative care in patients with advanced cancer: a randomised controlled trial. Lancet Oncol 2018 Mar;19(3):394-404. [CrossRef] [Medline]
    45. Nitenberg G, Raynard B. Nutritional support of the cancer patient: issues and dilemmas. Crit Rev Oncol Hematol 2000 Jun;34(3):137-168. [CrossRef] [Medline]
    46. Sierzega M, Niekowal B, Kulig J, Popiela T. Nutritional status affects the rate of pancreatic fistula after distal pancreatectomy: a multivariate analysis of 132 patients. J Am Coll Surg 2007 Jul;205(1):52-59. [CrossRef] [Medline]
    47. Schwegler I, von Holzen A, Gutzwiller J, Schlumpf R, Mühlebach S, Stanga Z. Nutritional risk is a clinical predictor of postoperative mortality and morbidity in surgery for colorectal cancer. Br J Surg 2010 Jan;97(1):92-97. [CrossRef] [Medline]
    48. Baracos VE. Cancer-associated malnutrition. Eur J Clin Nutr 2018 Sep;72(9):1255-1259. [CrossRef] [Medline]
    49. Britton B, McCarter K, Baker A, Wolfenden L, Wratten C, Bauer J, et al. Eating As Treatment (EAT) study protocol: a stepped-wedge, randomised controlled trial of a health behaviour change intervention provided by dietitians to improve nutrition in patients with head and neck cancer undergoing radiotherapy. BMJ Open 2015 Jul;5(7):e008921 [FREE Full text] [CrossRef] [Medline]
    50. Kiss N, Baguley BJ, Ball K, Daly RM, Fraser SF, Granger CL, et al. Technology-supported self-guided nutrition and physical activity interventions for adults with cancer: systematic review. JMIR Mhealth Uhealth 2019 Feb;7(2):e12281 [FREE Full text] [CrossRef] [Medline]
    51. Davidson W, Ash S, Capra S, Bauer J, Cancer Cachexia Study Group. Weight stabilisation is associated with improved survival duration and quality of life in unresectable pancreatic cancer. Clin Nutr 2004 Apr;23(2):239-247. [CrossRef] [Medline]
    52. Tan BH, Birdsell LA, Martin L, Baracos VE, Fearon KC. Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer. Clin Cancer Res 2009 Nov 15;15(22):6973-6979 [FREE Full text] [CrossRef] [Medline]
    53. Dalal S, Hui D, Bidaut L, Lem K, Del Fabbro E, Crane C, et al. Relationships among body mass index, longitudinal body composition alterations, and survival in patients with locally advanced pancreatic cancer receiving chemoradiation: a pilot study. J Pain Symptom Manage 2012 Aug;44(2):181-191 [FREE Full text] [CrossRef] [Medline]
    54. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 2011 May;12(5):489-495. [CrossRef] [Medline]
    55. Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. Cancer-associated cachexia. Nat Rev Dis Primers 2018 Jan;4:17105. [CrossRef] [Medline]
    56. Hendifar AE, Petzel MQB, Zimmers TA, Denlinger CS, Matrisian LM, Picozzi VJ, Precision Promise Consortium. Pancreas cancer-sssociated weight loss. Oncologist 2019 May;24(5):691-701 [FREE Full text] [CrossRef] [Medline]
    57. Bauer JD, Capra S. Nutrition intervention improves outcomes in patients with cancer cachexia receiving chemotherapy--a pilot study. Support Care Cancer 2005 Apr;13(4):270-274. [CrossRef] [Medline]
    58. Vashi P, Popiel B, Lammersfeld C, Gupta D. Outcomes of systematic nutritional assessment and medical nutrition therapy in pancreatic cancer. Pancreas 2015 Jul;44(5):750-755. [CrossRef] [Medline]

    ‎
    CA: carbohydrate antigen
    CT: computed tomography
    ECOG PS: Eastern Cooperative Oncology Group performance status
    EORTC: European Organization for Research and Treatment of Cancer
    FOLFIRINOX: folinic acid, fluorouracil, irinotecan, and oxaliplatin
    GHS: global health status
    GI: gastrointestinal
    PACS: picture archiving and communication system
    PDAC: pancreatic ductal adenocarcinoma
    PG-SGA: Patient-Generated Subjective Global Assessment
    QLQ-C30: Quality of Life Core Questionnaire
    QoL: quality of life
    RCT: randomized control trial
    SMI: skeletal muscle index

    Edited by L Buis; submitted 05.06.20; peer-reviewed by N Kiss, A Ramazan; comments to author 18.08.20; revised version received 20.11.20; accepted 17.06.21; published 31.08.21

    Copyright

    ©Jiyoung Keum, Moon Jae Chung, Youngin Kim, Hyunyoung Ko, Min Je Sung, Jung Hyun Jo, Jeong Youp Park, Seungmin Bang, Seung Woo Park, Si Young Song, Hee Seung Lee. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 31.08.2021.

    This is 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 mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.