Evaluating Mobile Health Apps for Customized Dietary Recording for Young Adults and Seniors: Randomized Controlled Trial

Understanding Older Adults' Experiences With Technologies for Health Self-management: Interview Study

BACKGROUND

Many older adults now use technologies such as wearable devices and telehealth services to support their health and well-being while living independently at home. However, older adults vary in how they use these technologies, and there is a lack of knowledge regarding the motivations that influence their acceptance and use of health-related technologies in home environments.

OBJECTIVE

This study aimed to understand the types of technologies that older adults use to support their health and the factors that motivate them to use their chosen technologies to support their health. In addition, we aimed to understand the factors that enable the effective use of technologies for health self-management and to identify the barriers that can negatively affect the adoption of technologies.

METHODS

A total of 22 older adults participated in semistructured interviews regarding their experiences of using technologies for health self-management. Interview transcripts were analyzed through an in-depth thematic analysis.

RESULTS

The interviews revealed that a range of technologies, such as videoconferencing software, fitness trackers, and other devices, were being used by older adults to support their health. Interviews showed that participants were motivated to use technologies to monitor health issues, to stay active and connected, and to record and change their behavior in the light of foreseen risks related to their future health status. Enablers that facilitated the effective use of technologies include social and organizational influence, convenient access to health care and safety provided by the technology, and easy setup and low cost of the technology. Barriers include information overload and a sense of futility about future health decline; telehealth being an inadequate substitute for in-person consultation; concerns about trust related to privacy and accuracy; and technologies being stigmatizing, uncomfortable to use, expensive, and unfamiliar.

CONCLUSIONS

This study suggested that older adults were using a variety of technologies to prevent or prepare for future health decline, evidencing a resilient attitude toward health and aging. In addition, older adults were willing to continue using the technology when there was a perceived need. The enabler mentioned by most participants was the social and organizational influence that included health care staff, family, friends, and organizations. This analysis provides a better understanding of how older adults use technologies to support their health and can guide the provision of appropriate health technologies for them.

Usability evaluation of mHealth apps for elderly individuals: a scoping review

BACKGROUND

Usability is a key factor affecting the acceptance of mobile health applications (mHealth apps) for elderly individuals, but traditional usability evaluation methods may not be suitable for use in this population because of aging barriers. The objectives of this study were to identify, explore, and summarize the current state of the literature on the usability evaluation of mHealth apps for older adults and to incorporate these methods into the appropriate evaluation stage.

METHODS

Electronic searches were conducted in 10 databases. Inclusion criteria were articles focused on the usability evaluation of mHealth apps designed for older adults. The included studies were classified according to the mHealth app usability evaluation framework, and the suitability of evaluation methods for use among the elderly was analyzed.

RESULTS

Ninety-six articles met the inclusion criteria. Research activity increased steeply after 2013 (n = 92). Satisfaction (n = 74) and learnability (n = 60) were the most frequently evaluated critical measures, while memorability (n = 13) was the least evaluated. The ratios of satisfaction, learnability, operability, and understandability measures were significantly related to the different stages of evaluation (P < 0.05). The methods used for usability evaluation were questionnaire (n = 68), interview (n = 36), concurrent thinking aloud (n = 25), performance metrics (n = 25), behavioral observation log (n = 14), screen recording (n = 3), eye tracking (n = 1), retrospective thinking aloud (n = 1), and feedback log (n = 1). Thirty-two studies developed their own evaluation tool to assess unique design features for elderly individuals.

CONCLUSION

In the past five years, the number of studies in the field of usability evaluation of mHealth apps for the elderly has increased rapidly. The mHealth apps are often used as an auxiliary means of self-management to help the elderly manage their wellness and disease. According to the three stages of the mHealth app usability evaluation framework, the critical measures and evaluation methods are inconsistent. Future research should focus on selecting specific critical measures relevant to aging characteristics and adapting usability evaluation methods to elderly individuals by improving traditional tools, introducing automated evaluation tools and optimizing evaluation processes.

Mobile Health Interventions and RCTs: Structured Taxonomy and Research Framework

Mobile Health Interventions (MHIs) have addressed a range of healthcare challenges and have been evaluated using Randomized Controlled Trials (RCTs) to establish clinical effectiveness. Using PRISMA we conducted a systematic literature review of RCTs for MHIs and identified 70 studies which were analyzed and classified using Nickerson-Varshney-Muntermann (NVM) taxonomy. From the resultant iterations of the taxonomy, we extracted insights from the categorized studies. RCTs cover a wide range of health conditions including chronic diseases, general wellness, unhealthy practices, family planning, end-of-life, and post-transplant care. The MHIs that were utilized by the RCTs were varied as well, although most studies did not find significant differences between MHIs and usual care. The challenges for MHI-based RCTs include the use of technologies, delayed outcomes, patient recruitment, patient retention, and complex regulatory requirements. These variances can lead to a higher rate of Type I/Type II errors. Further considerations are the impact of infrastructure, contextual and cultural factors, and reductions in the technological relevancy of the intervention itself. Finally, due to the delayed effect of most outcomes, RCTs of insufficient duration are unable to measure significant, lasting improvements. Using the insights from seventy identified studies, we developed a classification of existing RCTs along with guidelines for MHI-based RCTs and a research framework for future RCTs. The framework offers opportunities for (a) personalization of MHIs, (b) use of richer technologies, and (c) emerging areas for RCTs.

Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting

Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is cumbersome in terms of dish accuracy and time-consuming. The recent technology in computer vision with automatic recognition of dishes has the potential to support better dietary assessment. However, due to the wide variety of available foods, especially local dishes, improvements in food recognition are needed. In this research, we proposed an AI-based multiple-dish food recognition model using the EfficientDet deep learning (DL) model. The designed model was developed taking into consideration three types of meals, namely single-dish, mixed-dish, and multiple-dish, from local Taiwanese cuisine. The results demonstrate high mean average precision (mAP) = 0.92 considering 87 types of dishes. With high recognition performance, the proposed model has the potential for a promising solution to enhancing dish reporting. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud-computing-based system to enhance the accuracy of current dietary intake reporting tasks.

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review

BACKGROUND

Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown.

OBJECTIVE

The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties.

METHODS

We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242.

RESULTS

Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating.

CONCLUSIONS

This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1186/s13643-020-01375-w.

Women's Usage Behavior and Perceived Usefulness with Using a Mobile Health Application for Gestational Diabetes Mellitus: Mixed-Methods Study

The prevalence of gestational diabetes mellitus (GDM) is increasing, and only a few mobile health (mHealth) applications are specifically designed to manage GDM. In this mixed-methods study, a follow-up study of a randomized controlled trial (RCT) analyzed a largely automated mHealth application-based lifestyle coaching program to (a) measure the application's usage behavior and (b) explore users' perceptions of its usefulness in GDM management. Quantitative data were collected from the 170 application users who had participated in the intervention arm of the RCT. Semi-structured interviews (n = 14) captured users' experiences when using the application. Data were collected from June 2019 to January 2020. Quantitative data were analyzed descriptively, and interviews were analyzed thematically. Only 57/170 users (34%) logged at least one meal, and only 35 meals on average were logged for eight weeks because of the incorrectly worded food items and limited food database. On the contrary, an average of 1.85 (SD = 1.60) weight values were logged per week since the weight tracking component was easy to use. Many users (6/14 (43%)) mentioned that the automatic coach messages created an immediate sense of self-awareness in food choices and motivated behavior. The findings suggest that for GDM management, a largely automated mHealth application has the potential to promote self-awareness of healthy lifestyle choices, reducing the need for intensive human resources. Additionally, several gaps in the application's design were identified which need to be addressed in future works.

Design and Usability Evaluation of Mobile Voice-Added Food Reporting for Elderly People: Randomized Controlled Trial

BACKGROUND

Advances in voice technology have raised new possibilities for apps related to daily health maintenance. However, the usability of such technologies for older users remains unclear and requires further investigation.

OBJECTIVE

We designed and evaluated two innovative mobile voice-added apps for food intake reporting, namely voice-only reporting (VOR) and voice-button reporting (VBR). Each app features a unique interactive procedure for reporting food intake. With VOR, users verbally report the main contents of each dish, while VBR provides both voice and existing touch screen inputs for food intake reporting. The relative usability of the two apps was assessed through the metrics of accuracy, efficiency, and user perception.

METHODS

The two mobile apps were compared in a head-to-head parallel randomized trial evaluation. A group of 57 adults aged 60-90 years (12 male and 45 female participants) was recruited from a retirement community and randomized into two experimental groups, that is, VOR (n=30) and VBR (n=27) groups. Both groups were tested using the same set of 17 food items including dishes and beverages selected and allocated to present distinct breakfast, lunch, and dinner meals. All participants used a 7-inch tablet computer for the test. The resulting data were analyzed to evaluate reporting accuracy and time efficiency, and the system usability scale (SUS) was used to measure user perception.

RESULTS

For eight error types identified in the experiment, the VBR group participants were significantly (P<.001) more error prone owing to the required use of button-tapping actions. The highest error rates in the VOR group were related to incomprehensible reporting speech (28/420, 6.7%), while the highest error rates in the VBR group were related to failure to make required button taps (39/378, 10.3%). The VOR group required significantly (P<.001) less time to complete food reporting. The overall subjective reactions of the two groups based on the SUS surpassed the benchmark and were not significantly different (P=.20).

CONCLUSIONS

Experimental results showed that VOR outperformed VBR, suggesting that voice-only food input reporting is preferable for elderly users. Voice-added apps offer a potential mechanism for the self-management of dietary intake by elderly users. Our study contributes an evidence-based evaluation of prototype design and selection under a user-centered design model. The results provide a useful reference for selecting optimal user interaction design.

TRIAL REGISTRATION

International Standard Randomized Controlled Trial Registry ISRCTN17335889; http://www.isrctn.com/ISRCTN17335889.

Enabling Older Adults' Health Self-Management through Self-Report and Visualization-A Systematic Literature Review

Aging is associated with a progressive decline in health, resulting in increased medical care and costs. Mobile technology may facilitate health self-management, thus increasing the quality of care and reducing costs. Although the development of technology offers opportunities in monitoring the health of older adults, it is not clear whether these technologies allow older adults to manage their health data themselves. This paper presents a review of the literature on mobile health technologies for older adults, focusing on whether these technologies enable the visualization of monitored data and the self-reporting of additional information by the older adults. The systematic search considered studies published between 2009 and 2019 in five online databases. We screened 609 articles and identified 95 that met our inclusion and exclusion criteria. Smartphones and tablets are the most frequently reported technology for older adults to enter additional data to the one that is monitored automatically. The recorded information is displayed on the monitoring device and screens of external devices such as computers. Future designs of mobile health technology should allow older users to enter additional information and visualize data; this could enable them to understand their own data as well as improve their experience with technology.

The Quality of Mobile Apps Used for the Identification of Pressure Ulcers in Adults: Systematic Survey and Review of Apps in App Stores

Background

The increasing global use of smartphones has contributed to the growing use of apps for various health conditions, showing promising results. Through mobile apps, it is possible to perform chronological and iconographic follow-up of wounds, such as pressure ulcers, using a simple and practical tool. However, numerous surveys have pointed out issues related to the functionality, design, safety, and veracity of app information.

Objective

The objective of this study was to perform a systematic review of published studies regarding mobile apps and a systematic survey in app stores looking for apps developed to identify, evaluate, treat, and/or prevent pressure ulcers in adults, and to evaluate those apps based on software quality characteristics.

Methods

This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main bibliographic databases were searched between January 1, 2007 and October 15, 2018, and an app survey was performed in app stores. The selected studies were evaluated according to software quality characteristics by the International Organization for Standardization/International Electrotechnical Commission (ie, ISO/IEC 25010:2011) that involve functionality, efficiency, compatibility, usability, reliability, safety, maintenance, and portability.

Results

The search in databases and web-based app stores returned a total of 2075 studies. After removal of duplicates and screening of titles and abstracts, 48 complete articles were evaluated for eligibility, and among these, six were included for qualitative synthesis.

Conclusions

In this review, it was observed that all studies involved the initial phase of app development or improvement, and therefore, the apps still need to be evaluated using different software quality characteristics, so that in the future, a gold standard can be approached. Therefore, the prescription of an app for the identification, evaluation, treatment, and/or prevention of pressure ulcers in adults is currently limited. However, the evaluated studies provided important insights for future research. It is of utmost importance that future surveys develop apps jointly with users, using collaborative and cocreative processes and assess patients in real-world situations across different service settings, and they should consider different ethnicities, so that apps are useful to end users, such as patients, family members, health professionals, and students, in the health area. In addition, it is necessary for studies to describe the methodological course of app development in a clear and objective way in order to ensure reproducibility of the study and to offer inputs to allow future research to approach the development of ideal apps that are geared to positively impact the health of end users.

Trial Registration

PROSPERO CRD42018114137; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=114137

Usability of Food Size Aids in Mobile Dietary Reporting Apps for Young Adults: Randomized Controlled Trial

Background

Young adults are more likely to use self-managed dietary reporting apps. However, there is scant research examining the user experience of different measurement approaches for mobile dietary reporting apps when dealing with a wide variety of food shapes and container sizes.

Objective

Field user experience testing was conducted under actual meal conditions to assess the accuracy, efficiency, and subjective reaction of three food portion measurement methods embedded in a developed mobile app. Key-in–based aid (KBA), commonly used in many current apps, relies on the user’s ability to key in volumes or weights. Photo-based aid (PBA) extends traditional assessment methods, allowing users to scroll, observe, and select a reduced-size image from a set of options. Gesture-based aid (GBA) is a new experimental approach in which the user makes finger movements on the screen to roughly describe food portion boundaries accompanied by a background reference.

Methods

A group of 124 young adults aged 19 to 26 years was recruited for a head-to-head randomized comparison and divided into 3 groups: a KBA (n=42) control group and PBA (n=41) and GBA (n=41) experimental groups. In total, 3 meals (ie, breakfast, lunch, and dinner) were served in a university cafeteria. Participants were provided with 25 dishes and beverages for selection, with a variety of food shapes and containers that reflect everyday life conditions. The accuracy of and time spent on realistic interaction during food portion estimation and the subjective reaction of each aid were recorded and analyzed.

Results

Participants in the KBA group provided the highest accuracy in terms of hash brown weight (P=.004) and outperformed PBA or GBA for many soft drinks in cups. PBA had the best results for a cylindrical hot dog (P<.001), irregularly shaped pork chop (P<.001), and green tea beverage (660 mL; P<.001). GBA outperformed PBA for most drinks, and GBA outperformed KBA for some vegetables. The GBA group spent significantly more time assessing food items than the KBA and PBA groups. For each aid, the overall subjective reaction based on the score of the System Usability Scale was not significantly different.

Conclusions

Experimental results show that each aid had some distinguishing advantages. In terms of user acceptance, participants considered all 3 aids to be usable. Furthermore, users’ subjective opinions regarding measurement accuracy contradicted the empirical findings. Future work will consider the use of each aid based on food or container shape and integrate the various advantages of the 3 different aids for better results. Our findings on the use of portion size aids are based on realistic and diverse food items, providing a useful reference for future app improvement of an effective, evidence-based, and acceptable feature.

Trial Registration

International Standard Randomized Controlled Trial Registry ISRCTN36710750; http://www.controlled-trials.com/ISRCTN36710750.