More than 50% of patients with cancer are likely to develop undernutrition upon diagnosis [1]. At least 5% of patients with cancer who are malnourished report drastic weight loss [2]. In total, 3 out of 5 patients report a significant weight reduction ranging from 1 to 10 kg 6 months after a cancer diagnosis [3]. Surprisingly, approximately 20% of patients with cancer die of undernutrition and its complications [4]. It is necessary to implement an early screening and detection of undernutrition based on the parameters of dietary intake, biochemical indexes, and body weight and composition. The overall nutritional status can be evaluated using cancer-specific assessment tools such as the Subjective Global Assessment, Patient-Generated Subjective Global Assessment (PG-SGA), and Mini Nutritional Assessment [5].

European Society of Parenteral and Enteral Nutrition guidelines have highlighted the importance of a multidisciplinary approach in managing undernutrition among patients with cancer [4]. However, this nutritional issue is not considered as equally important as the cancer disease itself [6,7]. If undernutrition is left untreated, this can result in poor immune response, increased treatment toxicities, impaired quality of life (QoL), increased risk of infection, increased admission rates and hospital stays, and increases in health care costs [4,7,8].

Overnutrition or excessive body fatness is another nutritional disorder that should be gaining greater attention in survivorship care [9,10]. Approximately 1 in 3 survivors of cancer report having obesity and not meeting the American Cancer Society’s BMI guidelines of <30 kg/m² [10]. It is highly recommended that those living with or free of cancer eat a balanced diet to reduce the risk of recurrence and promote healthy survivorship [11].

To sustain a normal body weight, patients with cancer are advised to consume enough food to meet their daily requirement of energy and protein. In view of the differences in energy expenditure, the European Society of Parenteral and Enteral Nutrition recommends that the total energy requirement of patients with cancer be similar to that of survivors or healthy populations [4]. This elucidates that focusing on the basic principle of a balanced diet could be a nutrition guideline for patients with cancer, particularly those who are undernourished.

Studies have shown that approximately 90% of patients with cancer perceive nutrition support as an essential component in oncology care. However, less than half of patients with cancer are seen by dietitians [12]. According to the PG-SGA score, in a study by Pinho et al [1], 45% of patients with cancer required dietary intervention. In spite of that, dietetic support is not readily accessible to patients throughout their cancer journey. The high prevalence of undernutrition is commonly observed in people with upper digestive cancer, head and neck cancer, and lung cancer [1,2]. Still, in a study by Deftereos et al [7], approximately 40% of patients with upper digestive cancer did not receive any dietetic intervention before surgery.

Poor access to dietary services can be attributable to several factors, including lack of qualified dietitian staffing, lack of integration of nutrition services, lack of medical reimbursement for nutrition services, lack of awareness of cancer-related malnutrition, and inconsistent practice of nutritional risk screening in oncology [8]. Without a professional evidence-based dietary intervention, patients are likely to obtain information from the media or their peers. Conflicting information about nutrition makes them confused about what they should eat to optimize their well-being after a cancer diagnosis. Due to fear of cancer recurring, survivors can be desperate to modify their dietary habits [13,14]. This results in the adoption of unproven dietary strategies, including fad diets, juicing, and herbs and supplements, as well as avoiding certain food groups that are essential to their health [12].

The World Health Organization has called for a global initiative to leverage the use of digital health in areas of clinical medicine and public health [15]. During the COVID-19 pandemic, the application of digital technology targeting from planning and tracking, medical supplying, and screening for infection to clinical management was successful [16]. The pandemic has brought about an accelerated growth of digital health use to deliver continuous health care services while reducing virus transmission. For instance, telemedicine allowed for appointment scheduling and enhanced feasible health care delivery during the pandemic [17]. In addition, the use of digital health encourages engagement between practitioners and patients, as well as ensuring a sustainable health care system [18,19].

To date, the implications of mobile app use in cancer screening, prevention, and management have been greatly highlighted [20,21]. However, there is a lack of empirical evidence that focuses on populations with cancer [22-24] and mobile app platforms [22], particularly for healthy eating and nutritional management. This systematic review aimed to determine the effectiveness of commercial or cancer-specific nutritional apps in improving nutrition-related health outcomes for people receiving treatment for or living with cancer.

The protocol for this review was registered with PROSPERO (registration number: CRD42023330575) [25]. This review was reported based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines (Multimedia Appendix 1) [26].

Systematic searches were conducted across 4 databases—CENTRAL, Embase, PubMed, and Scopus—in May 2024. The search strategy incorporated Medical Subject Headings (MeSH), keywords, and free-text searches that related to the 3 main concepts: mobile apps, cancer, and nutrition. The search string used in the literature search was as follows: “Mobile Applications”[Mesh] OR “mobile application*”[tw] OR “mobile apps”[tw] OR “mobile app”[tw] OR “mobile technolog*”[tw] OR “mobile health”[tw] OR mHealth[tw] OR smartphone[tw] OR “smart phone”[tw] OR telemedicine[tw] AND “Neoplasms”[Mesh] OR cancer*[tw], neoplasm*[tw] OR oncology[tw] OR tumour*[tw] OR tumor*[tw] OR malignant[tw] OR malignanc*[tw] AND “Diet, Food, and Nutrition”[Mesh] OR nutrition[tw] OR diet[tw] OR eat[tw] OR food[tw] (Multimedia Appendix 2). It included original articles published between January 2013 and December 2023 and in the English language. This is a change from the registered protocol [25].

First, EndNote (version 20.3; Clarivate Analytics) was used to identify and remove duplicates from the list. The titles and abstracts of articles were screened independently by 2 reviewers (KLSN and MA) to identify potential eligible studies. The references retrieved from the search were categorized as excluded or included based on the population, intervention, comparator, outcome, and study design criteria [27]:

A full-text screening was carried out by reviewing in detail the studies that were not excluded at the first screening based on the inclusion criteria. Each full text was retrieved and assessed independently by the same authors before inclusion in the review. Non–English-language articles were excluded. Any disagreements during the selection process were resolved through consensus.

The data were extracted systematically from each article by KLSN and then checked by MA. The data included were authors, publication date (year), country, study design, sample size, participant characteristics, and details on the mobile app intervention. Next, data were extracted based on the type of population (survivors or patients receiving treatment), components of the app (eg, diet alone or diet plus physical activity), duration of the intervention and follow-up, and outcome measures (body weight, body composition, QoL, and dietary factors). A comparison of the descriptive findings was made across the studies. The outcome data between groups and before and after the intervention within groups were compared using mean differences and significance values (P value). The heterogeneity of the interventions and measures precluded a statistical combination of the quantitative findings; therefore, a meta-analysis was not conducted.

Analysis of the risk of bias was conducted using the Review Manager (version 5.4; The Cochrane Collaboration) software. The Risk of Bias 2 (RoB 2) and Risk of Bias in Nonrandomized Studies (ROBINS) tools were used for RCTs and non-RCTs, respectively. The risk of bias assessment was carried out by 2 reviewers independently (KLSN and MA). All discrepancies were resolved through consensus.

The seven areas included in the RoB 2 tool were (1) random sequence generation, (2) allocation concealment, (3) blinding of participants and personnel, (4) blinding of outcome assessment, (5) incomplete outcome data, (6) selective reporting, and (7) other bias. For the ROBINS tool, the seven areas included were (1) bias due to confounding, (2) bias in selection of participants for the study, (3) bias in classification of interventions, (4) bias due to deviations from the intended intervention, (5) bias due to missing data, (6) bias in measurement of outcomes, and (7) bias in selection of the reported results. According to the Cochrane Handbook for Systematic Reviews of Interventions, each area was assigned a classification of low, unclear, or high risk of bias [28].

A total of 1296 articles were identified from all database searches. After 31.17% (404/1296) of duplicates were removed, the abstracts and titles of 68.83% (892/1296) of relevant articles were screened. The full texts of 5.7% (51/892) of these studies were retrieved and assessed for eligibility based on the population, intervention, comparator, outcome, and study design criteria. Finally, 13 articles were eligible to be included in this review. The procedure for article selection is shown in Figure 1 [26].

Of the 13 studies, 9 (69%) reported the percentage of participants who remained in the study over the intervention or follow-up periods. A total of 44% (4/9) of these studies reported a retention rate of >90% [29,31,35,40], 44% (4/9) reported retention rates of 70% to 90% [33,36,37,39], and 11% (1/9) reported a retention rate of <70% [34]. Table 2 provides a more detailed description.

The assessment of risk of bias was conducted for each study. The RoB 2 assessment is shown in Figure 2 [29-35]. In total, 8% (1/13) of the studies had a low risk of bias in all aspects [35]. A total of 15% (2/13) of the studies were reported as double blind [34,35]. Due to uncertainty or unblinded treatment allocation, the quality of 38% (5/13) of the trials was considered low with regard to performance and detection bias [29-33]. There was an unclear risk of selection bias in these 5 trials due to limited information about allocation concealment [29-33] and generation of a randomized sequence [30-32]. Huggins et al [34] reported a low retention rate (<50%), with the use of the multiple imputation approach for handling missing data. The suboutcomes resulting from a web-based intervention were not reported in a breast cancer study investigating the effect of mobile app–based training on QoL [32]. One study did not report the P value for the difference in breast cancer well-being after the intervention [30].

The ROBINS assessment is shown in Figure 3 [36-41]. A total of 17% (1/6) of the studies mentioned the frequency of mobile app use (at least 5 days) during the intervention [37], whereas the remaining 83% (5/6) of the studies did not report the intervention status. In total, 67% (4/6) of the studies reported a low retention rate or uncertainty about missing data management [36,37,39,41]. A total of 50% (3/6) of the studies had a high risk of bias in the selection of study participants, which could affect the quality of the intervention and outcomes [36,38,39]. In total, 33% (2/6) of the studies did not provide information on whether there was a deviation from the intended intervention [37,41]. Fuemmeler et al [38] failed to show the changes in weight and height measurements after the intervention. A total of 17% (1/6) of the studies did not provide information about P values of weight loss data [37]. In total, 33% (2/6) of the studies reported no information on whether any confounding factors were present [38,41]. A total of 17% (1/6) of the studies had a high risk of bias in outcome measurements that resulted from inappropriate methods of delivering the intervention [37] and measuring outcomes [42].

A summary of outcome measures and study findings can be found in Table 2.

Nutritional mobile apps for populations with cancer have the potential to improve body weight or composition, nutritional status, dietary adherence, and QoL across the continuum of cancer care. The apps offered the basic functions of recording and tracking users’ food intake and weight in general. It was unclear whether custom-developed mobile apps were efficacious for nutrition-related health outcomes and QoL in cancer care. However, incorporating commercial mobile apps seemed to be beneficial for improving nutritional care in populations with cancer. This could be due to the implementation of self-monitoring of their progress, a necessary step in delivering quality nutrition care [5]. This review observed that the beneficial effect of stand-alone interventions was comparable with that of multicomponent interventions. However, the results may not be able to discern the magnitude of the difference due to limited data. Overall, the studies included in this review were of low to moderate quality. For RCTs, lack of blinding and biased treatment allocation were the major concerns. Failure to define the intervention status in terms of types, frequency, and timing reported by non-RCTs made the evaluation of nutritional mobile apps challenging.

Among the interventions that aimed to support weight loss, almost half (2/5, 40%) reported successful weight control among patients with cancer and overweight. In public health research, the common measures are BMI, waist circumference, waist-to-hip ratio, and body fat percentage [47]. Waist-to-height ratio has also been known to be a good surrogate in predicting the risk of noncommunicable diseases [48,49]. Implementing effective dietary strategies for successful weight loss is highly recommended to reduce the risk of cancer recurrence in long-term survivorship [4]. A review underpinned the beneficial effects of eHealth interventions on weight management in survivors of cancer, with a greater impact if combined with dietary counseling [50].

Our review found reduced intake of fast food [29] and sugary food [38] in app users. When aiming at weight loss, adhering to healthy eating guidelines should be the goal to sustain good health and well-being. A bariatric study highlighted the need to change eating behaviors for sustainable weight management [51]. Self-monitoring weight changes and dietary behavior is a common feature in app-based weight loss programs. The use of mobile app interventions for improved eating behavior and diet quality seems to be promising [52]. There are multiple factors influencing eating habits among school-aged children, particularly role modeling and parenting styles [53]. In addition to app gamification, creating a conducive learning environment in schools and at home could be a way to promote healthy eating habits among children.

The primary concern regarding undernutrition is the lack of energy that the body needs to undergo cancer treatment, which could result in treatment toxicities, longer hospital stays, or reduced QoL [4]. Among interventions that aim to support weight gain in patients with cancer who were malnourished, delivering nutrition support via mobile app platforms may help prevent drastic weight loss and improve skeletal muscle mass and overall nutritional status. However, the findings of this review do not reflect the long-term beneficial effects due to lack of data. Despite the growing development of nutrition apps, tailoring dietary interventions to individuals’ needs, nutritional status, cancer type, treatment plan, and comorbid conditions is still an unmet need [54].

Of the 6 studies that focused on QoL, only 2 (33%) showed significant changes in QoL at the treatment phase based on the EORTC QLQ-C30 and FACT-ES [32,33]. A review that focused on app-based interventions to improve nutrition or lifestyle behaviors in patients with breast cancer showed a similar finding during chemotherapy [55]. This could be due to enhanced user engagement by improving self-motivation, health information, social support, and goal setting [56]. The 2 cancer-specific tools used in our review were the EORTC QLQ-C30 [44] and FACT-G [45], which allow for a multidimensional assessments of QoL. These tools yield a comprehensive evaluation of individuals’ progress. QALYs, which account for both QoL and survival, have been increasingly used as a standard measure to evaluate disease burden at the population or regional level [57,58]. However, Huggins et al [34] reported no significant results for QALYs in groups that received the intervention via mobile app or telephone compared to controls. Failure to obtain significant results could be due to less participants who continued to use the mobile app after the intervention period.

This is the first review that has evaluated the impact of app-based dietary interventions in cancer care. The review was based on a systematic search strategy that focused on nutrition interventions delivered via mobile app platforms and on populations with cancer. However, this review has certain limitations. First, only English-language articles were included in the search for this review. Second, the heterogeneity of study designs, interventions, app features, and cancer types was substantial, requiring the results to be interpreted cautiously. Third, the inconsistent measurement and reporting of incomplete data made comparisons difficult across the studies. Finally, this review included pilot studies that comprised small sample sizes (11/13, 85% of the interventions enrolled <70 participants per group), resulting in limited generalizability of the study findings.

Mobile app–based nutrition interventions have a favorable effect on nutritional status and QoL in patients with cancer. In addition, mobile apps that incorporate nutrition interventions could also be beneficial for survivors after cancer treatment. However, it was unclear whether custom-developed apps were efficacious for improved nutrition-related outcomes and QoL. The continuity of nutritional care in patients with cancer via mobile app platforms could help in achieving a healthy weight by improving their adherence to dietary guidelines. Although most studies yielded favorable outcomes, they were rated as being of low to moderate quality.

Future studies should emphasize randomized controlled designs, larger sample sizes, diet-only mobile apps, greater homogeneity of outcome measures and population characteristics, and high participant engagement and retention within the study.