Introduction
Background
Advancements in early detection and treatment modalities have led to a consistent increase in the survival rates of patients diagnosed with cancer. In the United States, the number of survivors of cancer reached 18.1 million in 2022, and this figure is projected to increase to 26 million by 2040 [1]. With rising cancer survival rates, there is a notable trend of aging among survivors of cancer. In 2022, over two-thirds (67%) of US survivors of cancer were aged 65 years or older [1].
Older adults often experience multiple chronic health conditions, and survivors of cancer show an even higher prevalence of these issues compared to those without cancer [2]. Common co-occurring conditions in older patients with cancer include musculoskeletal conditions, such as osteoarthritis and sciatica; mental health disorders, such as depression and anxiety; and cardiovascular diseases, such as hypertension and coronary artery disease [2,3]. These conditions may exacerbate susceptibility to functional decline, with older survivors of cancer facing an accelerated deterioration in grip strength, gait speed, and overall physical performance compared to their peers without cancer [4]. Moreover, the presence of multiple chronic conditions and functional limitations in older survivors of cancer are associated with a reduced quality of life, increased risk of early mortality, and significant economic burden [5,6].
Given the challenges associated with both cancer and aging, innovative approaches to monitoring and managing the health of older survivors of cancer are essential for improving their health and well-being outcomes. The collection and sharing of pertinent health data can offer survivors of cancer and their health care providers an opportunity to enhance their health [7]. The rise of consumer technologies, such as smartphone apps and wearable devices, has paved the way for the creation and use of tools that enable individuals to gather their own health-related data [8]. These data are related to their physical behavior (eg, step count or sleep patterns), physiological parameters (eg, heart rate or blood pressure), biochemical markers (eg, blood glucose levels), and environmental factors (eg, air quality or temperature) in which they are present.
With the advancement of information and communication technologies, the interest in and the applications of patient-generated health data (PGHD) are growing. PGHD refer to “health-related data—including health history, symptoms, biometric data, treatment history, lifestyle choices, and other information—created, recorded, gathered, or inferred by or from patients or their designees (ie, care partners or those who assist them)” [9]. PGHD are distinct from data sourced from clinical environments or interactions with health care providers. The primary distinction is that PGHD are recorded by patients and not providers, and their sharing is directed by the patients [9]. Patients can collect these data either passively or actively through mobile health apps, wearable devices (eg, fitness bands), medical devices (eg, blood glucose monitors), or survey instruments on mobile devices [7]. Passive data collection occurs when data are gathered automatically with minimal patient input, typically using automated systems or sensors. By contrast, active data collection requires patients to be actively involved, typically by responding to surveys.
PGHD enable the consistent, long-term monitoring of diverse health-related information that can be easily exchanged between patients and health care providers [10]. These data can complement the information that health care systems typically gather through providers, offering individualized baseline indicators. With PGHD, both patients and health care providers can quickly discern changes in the health status, allowing for more appropriate and prompt interventions [11].
PGHD have found applications in monitoring and managing various long-term health conditions. For instance, PGHD can be used to identify undiagnosed obesity and diabetes, assess the risk of these conditions, and predict glycemic events in patients with diabetes [12]. In the context of HIV care, PGHD help alleviate financial and temporal pressures by reducing the need for frequent in-person consultations [11]. Continuous monitoring through PGHD enhances medication adherence and increases patients’ understanding of their health status [11]. For survivors of cancer, who face higher posttreatment health risks, PGHD enable real-time tracking of health parameters such as activity levels, symptom progression, and medication adherence, facilitating early detection of issues and more personalized interventions [7]. Beyond individual care, PGHD can also benefit larger survivors of cancer communities by contributing to population-level insights, informing public health strategies, enhancing survivorship guidelines, and supporting research on cancer survivorship.
While there is the potential for using PGHD in older adults with cancer, few review studies have focused on PGHD in these patients. A comprehensive understanding of the characteristics and use of PGHD in older adults with cancer will help improve patient engagement in health care and enable researchers to apply PGHD in geriatric cancer care. This will ultimately lead to the effective application of PGHD in enhancing the health and well-being of older adults with cancer.
Objectives
This scoping review aimed to identify the characteristics of research on PGHD as applied to older adults with cancer and to assess the current use of PGHD. The main research question for the scoping review was “What is the present landscape of research concerning PGHD in older adults with cancer?”
The specific research questions were as follows: (1) What are the characteristics of the studies conducted on PGHD in older adults with cancer? (2) What are the characteristics of older adults with cancer who have been the subject of PGHD-related research? and (3) What are the characteristics of PGHD that have been gathered from older adults with cancer?
Methods
Design
This scoping review was conducted following the guidelines of Arksey and O’Malley [13] and the JBI (Joanna Briggs Institute) methodology for scoping reviews [14]. This scoping review was also performed according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) guidelines Multimedia Appendix 1 [15]. The population, concept, and context for this scoping review were as follows: (1) population—older adults with cancer, (2) concept—PGHD, which were collected using devices; and (3) context—no limitation for setting. The protocol of the scoping review was registered in the Open Science Framework Registry.
Search Strategy
In this study, 6 electronic databases—PubMed, Embase, CINAHL, Cochrane Library, Scopus, and Web of Science—were searched for relevant literature. Considering the population, concept, and context of this scoping review, the search strategy was developed by combining keywords and index terms from each database (Multimedia Appendix 2). PGHD as a concept was introduced as a Medical Subject Headings (MeSH) term by PubMed in 2018. To retrieve relevant literature before the introduction of PGHD as a MeSH term, we further constructed PGHD-specific search terms by combining the keywords “patient-generated” and “devices.” In addition, the search was limited to the index terms included in the titles and abstracts of the studies to ensure proper identification of the literature. An experienced librarian at the researcher’s university confirmed this search strategy. A literature search was conducted using a confirmed search strategy on October 21, 2022. After the literature search, the reference lists of the selected studies were screened to identify the gray literature.
Eligibility Criteria and Study Selection
To assess the eligibility of studies on PGHD in older adults with cancer, the inclusion criteria were as follows: (1) studies that included older adults with a mean or median age of 65 years or older; (2) studies that included older adults with cancer, regardless of the type of cancer or treatment status; and (3) studies that applied PGHD, including wearable devices that measure patients’ physical activity or physiological features or mobile phones that measure patient-reported outcomes (PROs). The exclusion criteria were as follows: (1) studies in which PGHD were selectively used in some of the participants; (2) studies comparing results using PGHD for validation of other tools; (3) publications other than original studies, such as editorials, letters, or protocols; (4) publications that were not peer reviewed, such as abstracts, conference proceedings, dissertations, or theses; and (5) studies written in languages other than English or Korean.
The studies identified in the literature search were exported to the reference management software program (EndNote X9; Clarivate Analytics). After deleting duplicate results using EndNote, the results were extracted with a Microsoft Excel program (Microsoft Corp) for literature screening. Two researchers (Yesol K and GK) independently selected studies based on the inclusion and exclusion criteria. The titles and abstracts were initially reviewed; subsequently, the full texts were reviewed to select studies for final inclusion. If the two researchers did not agree on a particular study, a consensus was reached through discussion and the participation of a third researcher (MC).
Data Extraction
Data from the selected studies were extracted using Microsoft Excel in a structured data charting format, which was constructed by the researchers. It comprised the study, participant, and PGHD characteristics. First, the study characteristics included published year, country, continent, study design, and dataset. Second, the participant characteristics included the study setting, type of cancer, treatment status, and sample size. Lastly, the PGHD characteristics included the type of device that was used to measure PGHD, type of assessment by PGHD, on-body locations of the wearable devices that measure PGHD, duration of wear of the devices that measure PGHD, type of PGHD collection, and type of PGHD use. The types of PGHD collection were categorized as passive (using sensor-based devices that automatically acquire and transfer PGHD from patients), active (requiring active patient engagement to collect PGHD that are not easily collected automatically), and combined methods [16]. The types of PGHD use were categorized into the following four categories: (1) identification, monitoring, review, and analysis; (2) feedback and reporting; (3) motivation; and (4) education and coaching [16]. Two researchers (Yesol K and GK) independently extracted the data, and disagreements, if any, were resolved by consensus through discussion and the participation of a third researcher (MC).
Data Analysis
The data extracted from this scoping review are summarized and presented using descriptive statistics. The results were mapped and are presented in the form of tables, charts, or figures according to the purpose and research questions of this scoping review.
Results
Study Selection
The literature selection process is illustrated in Figure 1 as a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. A total of 1090 articles were retrieved from 6 database searches. After the elimination of duplicates, the titles and abstracts of 659 (60.5%) articles were reviewed. Subsequently, the full texts of 367 (33.7%) articles were reviewed to determine eligibility for this review. The reasons for the exclusion of articles from the full-text review are listed in Figure 1. After the screening, 71 (6.5%) articles were selected. A review of the references of the 71 selected articles led to the additional selection of 17 articles, resulting in a selection of a total of 88 articles for the review [17-104].
Enlarge this image.Figure 1. PRISMA flow diagram of this study. PGHD: patient-generated health data; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Study Characteristics
The studies included in this scoping review were published between 2006 and 2022 (Multimedia Appendix 3). There has been a discernible upward trend in publication volume, with more than 70% of the articles published since 2017 (69/88, 78%) [36-104]. Geographically, North America emerged as the leading contributor to the body of research, accounting for 43% (38/88) of the studies. The study designs were categorized into nonexperimental and experimental designs. Among studies with nonexperimental designs, a survey-based study design was most frequently adopted (39/88, 44%). Among studies with experimental designs, a pre-experimental design was most frequently adopted (28/88, 32%). Data derived from original research constituted the basis for more than 60% of the studies (59/88, 67%; Table 1).
Table 1.Characteristics of included studies (n=88).
| Study characteristics | Studies, n (%) | Reference numbers | ||
| Continents | ||||
| Asia | 8 (9) | [37,48,53,76,82,97,99,102] | ||
| Europe | 30 (34) | [17,18,20,28,32,33,35,38,40,42,43,45,50,58,61,66,69,71,73,74,78,79,83-85,89,90,96,101,104] | ||
| North America | 38 (43) | [21-23,29,30,36,41,44,46,47,49,51,54-57,60,62-65,67,68,70,75,77,80,81,86,87,91-95,98,100,103] | ||
| South America | 1 (1) | [52] | ||
| Africa | 0 (0) | N/Aa | ||
| Oceania | 11 (13) | [19,24-27,31,34,39,59,72,88] | ||
| Study design | ||||
| Nonexperimental design | ||||
| Survey | 39 (44) | [17-19,21-23,26-28,30,34,35,37-39,41,44,46-49,51,55,57,59,62,67,68,73,74,76,78,81,83,89,95,97,99,102] | ||
| Qualitative study | 3 (3) | [33,42,96] | ||
| Methodological research | 2 (2) | [91,94] | ||
| Experimental design | ||||
| True experimental design | 13 (15) | [25,31,50,56,60,70,82,84,86-88,93,98] | ||
| Quasi-experimental design | 3 (3) | [43,58,66] | ||
| Pre-experimental design | 28 (32) | [20,24,29,32,36,40,45,52-54,61,63-65,69,71,72,75,77,79,80,85,90,92,100,101,103,104] | ||
| Dataset | ||||
| Original research | 59 (67) | [17,18,20,24,26-32,37,39,41,43,44,46-50,52-54,57,60,61,63,64,66-70,73-75,77,79-87,90-93,96-103] | ||
| Other research | 24 (27) | [21,25,33-36,38,40,42,45,51,56,58,59,65,71,72,76,78,88,89,94,95,104] | ||
| National data or cohort data | 5 (6) | [19,22,23,55,62] | ||
aN/A: not applicable.
Participant Characteristics
The participant characteristics are presented in Table 2. Most studies were conducted at the participants’ homes (69/88, 78%). Among the included studies, none focused solely on older adults living alone. Only 15 (17%) of the 88 studies distinguished participant characteristics based on whether they lived alone or with others, and most participants in these studies lived with others. Of the 88 studies, 64 (73%) specified the selection criteria for study participants according to cancer type. These studies included 76 cases of various types of cancer, the most common of which was prostate cancer (22/76, 29%). Regarding gender proportions, 66 (75%) of the 88 studies had a higher proportion of men, while 20 (23%) studies had a higher proportion of women. Of these, 18 (20%) studies focused exclusively on men, all involving patients with prostate cancer. In contrast, 3 (3%) studies focused exclusively on women, all involving patients with breast cancer. Half of the studies were conducted on patients who received active treatment (29/88, 33%) or surgery (15/88, 17%); a quarter were conducted on survivors of cancer (22/88, 25%). In this review, survivors of cancer are defined as individuals who have completed their primary cancer treatments—such as surgery, chemotherapy, or radiation therapy—and are in the posttreatment phase. The median sample size was 50 (IQR 33.8-84.0), and more than 40% of the studies included fewer than 50 (39/88, 44%) participants. In the studies included in this review, the mean age of participants ranged from 65 (SD 7) to 76.9 (SD 10.5) years.
Table 2.Characteristics of participants (n=88).
| Participant characteristics | Studies, n (%) | Reference numbers | |
| Setting | |||
| Home | 69 (78) | [18-27,30-35,37-52,54-65,67,69-72,74,76,78-80,82-84,86-90,92,93,95-97,100,104] | |
| Hospital (outpatient) | 3 (3) | [29,36,53] | |
| Hospital (inpatient) | 5 (6) | [17,62,73,99,102] | |
| Home and hospital (combined) | 11 (13) | [28,68,75,77,81,85,91,94,98,101,103] | |
| Type of cancera | |||
| Prostate | 22 (29) | [21-23,25,31,32,34,38,40,42,43,50,51,54,56,60,61,70,82,85,88,92] | |
| Gastrointestinal | 21 (28) | [20,23,28,33,35,38,41,45,50,58,64-66,68,71,72,77,78,91,94,101] | |
| Lung | 18 (24) | [20,26,27,30,36,37,46,48,49,53,57,75-77,86,94,97,101] | |
| Bladder, urethra, or urothelial | 5 (7) | [63,66,83,92,98] | |
| Others | 10 (13) | [19,23,59,66,72,79,80,84,100,103] | |
| Gender proportions | |||
| Higher proportion of men | 66 (75) | [18,20-22,25-32,34-44,46-48,50,51,53-56,58-61,63-66,68-71,73,74,76-79,82-85,88-92,95-99,101,102] | |
| Higher proportion of women | 20 (23) | [17,19,23,24,33,49,52,57,62,67,72,75,80,81,86,87,93,94,100,104] | |
| Equal proportion of men and women | 1 (1) | [103] | |
| Not reported | 1 (1) | [45] | |
| Treatment status | |||
| Diagnosed with cancer with no limitation of treatment | 11 (13) | [26,27,29,44,48,53,59,62,76,79,88] | |
| Received active treatment | 29 (33) | [21,28,30,32,33,36,37,39,40,42,43,45,51,52,54,56,64,65,67,78,80,82-85,92,97,103,104] | |
| Underwent surgery | 15 (17) | [58,63,66,71,74,75,77,80,81,89-91,94,98,101] | |
| Survivor of cancer | 22 (25) | [19,20,22-25,31,34,35,38,41,49,50,55,60,61,72,87,93,95,96,100] | |
| Received end-of-life and palliative care | 4 (5) | [47,73,99,102] | |
| Not reported | 7 (8) | [17,18,46,57,69,70,86] | |
| Sample size (participants) | |||
| ≤49 | 39 (44) | [21,24,28-30,32,33,39,41,42,45,46,52-54,63,68,71-73,76,77,79,82,83,85,86,88,90,93,95-100,102-104] | |
| 50-99 | 28 (32) | [17,18,20,26,27,37,40,47,48,56-60,64,65,69,74,75,78,80,81,87,89,91,92,94,101] | |
| 100-149 | 11 (13) | [22,23,25,34,43,44,55,61,66,67,84] | |
| ≥150 | 10 (11) | [19,31,35,36,38,49-51,62,70] | |
aThere were a total number of 76 cases of various types of cancer among 64 studies.
PGHD Characteristics
The characteristics of the PGHD identified in each study are summarized in Table 3. In this review, research-grade wearable devices were used in more than half of the cases (57/113, 50.4%): these included the ActivPal Monitor (PAL Technologies Ltd), Stepwatch Activity Monitor (Modus Health), and ActiGraph GT3x Activity Monitor. Consumer-grade wearable devices were used in 28 (24.8%) cases: these included Fitbit, Apple Watch, Garmin band, Microsoft Band, Samsung Gear, and Xiaomi MiBand. Smartphones, tablet PCs, and computers were used to collect data using mobile apps or web-based surveys in 25 (22.1%) cases. Other devices, including smart mattresses and an instant camera, were used in 3 (2.7%) cases.
Table 3.Characteristics of patient-generated health data (n=88).
| Patient-generated health data characteristics | Studies, n (%) | Reference numbers | |||||
| Device typea | |||||||
| Research-grade wearable devices | 57 (50.4) | [17-28,30,31,34,35,37-39,41,47-51,54-56,59-67,72,73,76,78,82,87,88,93,95,101] | |||||
| Consumer-grade wearable devices | 28 (24.8) | [45,46,57,60,68,70,72,74,77,80,86,87,89-91,93-95,97,98,100,103] | |||||
| Smartphones, tablet PCs, or computers for mobile apps or surveys | 25 (22.1) | [29,32,33,36,40,42-44,52,53,58,69-71,75,79,81,83-85,89,91,92,96,104] | |||||
| Other devices | 3 (2.7) | [99,100,102] | |||||
| Assessment typeb | |||||||
| Physical activity | 69 (56.1) | [17-28,31,34,35,38,39,41,45,46,48-50,52,54-57,59-68,70,72,74,76,77,80-82,86-91,93-95,98,100-103] | |||||
| Sleep | 12 (9.8) | [17,18,30,37,47,48,51,57,73,80,91,103] | |||||
| Vital signs | 13 (10.6) | [28,45,63,78,89-91,97,99] | |||||
| Patient-reported outcomes | 23 (18.7) | [29,32,33,36,40,42-44,53,58,69-71,75,79,83-85,89,91,92,96,104] | |||||
| Others | 6 (4.9) | [45,63,78,90,91,100] | |||||
| On-body location of wearable devicec | |||||||
| Wrist | 35 (29.9) | [17,18,21,28,30,37,41,45,47,48,51,54,57,61-63,72-74,76-78,80,86,87,89-91,93,94,97,100,103] | |||||
| Waist | 11 (9.4) | [26,27,49,60,67,68,70,82,93,95] | |||||
| Hip | 13 (11.1) | [19,22,23,31,34,38,39,50,52,59,65,72,88] | |||||
| Lower extremity (leg, ankle, or foot) | 8 (6.8) | [20,35,66,67,87,95,101] | |||||
| Others (neck, chest, arm, finger, skin, or forehead) | 13 (11.1) | [18,28,45,55,63,66,78,90,95] | |||||
| Nonbody (cloth or mattress) | 7 (6.0) | [45,46,60,63,98,99,102] | |||||
| Not admitted (mobile apps or camera) | 30 (25.6) | [24,25,29,32,33,36,40-44,53,56,58,64,69-71,75,79,81,83-85,89,91,92,96,100,104] | |||||
| Wearing period of devicea | |||||||
| ≤1 week | 47 (41.6) | [17-23,26,29,34-39,41,44,46-49,52,53,55,57,59,62,63,66,67,73,76,78,82,87,95,97,100] | |||||
| > 1 week or ≤4 weeks (1 month) | 23 (20.4) | [27,28,30,32,50,51,54,61,64,65,68,72,75,88-90,93,94,96] | |||||
| 5-12 weeks (3 months) | 25 (22.1) | [24,31,40,42,43,45,54,60,74,77,79,85,86,89-93,98,101,103] | |||||
| > 12 weeks (3 months) | 16 (14.2) | [25,33,56,58,69-72,80,81,83,84,87,99,102,104] | |||||
| Not reported | 2 (1.8) | [18,70] | |||||
| Collectiontype | |||||||
| Passive collection | 63 (72) | [17-28,30,31,34,35,37-39,41,45-52,54-57,59-68,72-74,76-78,80-82,86-88,93-95,97-99,101-103] | |||||
| Active collection | 20 (23) | [29,32,33,36,40,42-44,53,58,69,71,75,79,83-85,92,96,104] | |||||
| Combined collection | 5 (6) | [38,70,89,91,100] | |||||
| Collection support by health care providers | 20 (23) | [32,36,40,42-44,47,58,74,83,86,87,89,90,92,97,98,101,103,104] | |||||
aThe total number is 113 due to multiple categories.
bThe total number is 123 due to multiple categories.
cThe total number is 117 due to multiple categories.
Regarding the type of assessment by PGHD, more than half of the assessments (69/123, 56.1%) focused on physical activity measured through step count, moderate-to-vigorous physical activity, and sedentary behavior. Sleep, including the sleep-wake cycle, circadian rhythm, and movement in bed, were measured in 12 (9.8%) cases. There were 23 (18.7%) cases of PROs, including symptoms and quality of life, and 6 (4.9%) cases of other assessments, such as body weight and exposure to light intensity.
Regarding the on-body locations of wearable devices that measure PGHD, approximately one-third were worn on the wrist (35/116, 30.2%). Additionally, mobile apps or cameras were used without attaching them to the body in 25.9% (30/116) of cases. Regarding the wearing period of the device, the device was worn for ≤1 week in 47 (41.6%) cases and for > 12 weeks (3 months) in 16 (14.2%) cases.
PGHD were collected in various ways, including passive, active, and combined collection (Table 3 and Figure 2). PGHD were primarily passively collected from participants (63/88, 72%), constituting most of the included studies published between 2006 and 2022. Furthermore, in 20 (22%) studies, PGHD were actively collected from participants from 2015 onward. However, few studies combined passive and active collection methods for PGHD (5/88, 6%), which have been used in research since 2020.
In detail, 25 studies actively collected PGHD, including combined collection methods. Among these, smartphones, tablet PCs, or computers were the predominant devices for active data collection through mobile apps or surveys (23/25, 92%). Except for 1 study that used computers, of the 22 studies using smartphones and tablet PCs, 6 (27%) used the Android (Google LLC) operating system, 5 (23%) used iOS (Apple Inc), 3 (14%) used both, and 8 (36%) did not specify the operating system. Regarding device ownership, 10 (45%) studies provided devices to participants, 3 (14%) studies relied on participants’ own devices, 4 (18%) studies used both approaches, and 5 (23%) studies did not report on this aspect.
Among the studies included in this review, approximately a quarter (21/88, 24%) had a sample size of over 100 participants. The PGHD monitoring period varied from 1 day to 24 weeks, with over 60% (13/21) of studies reporting a duration of 1 week or less. Additionally, more than three-quarters of these studies (16/21) collected PGHD passively, primarily using wearables, including both research-grade and consumer-grade devices. Of the 88 studies, 67 (76%) reported on participants’ adherence to PGHD collection, with adherence rates ranging from 36% to 100%. More than half of these studies (36/67, 54%) reported an adherence rate above 80%. In addition, the dropout rates reported in 57 longitudinal studies ranged from 0% to 72.4%. Approximately one-fifth of these studies (13/57, 23%) completed PGHD collection with full participant retention.
In this review, health care providers supported PGHD collection from participants in only 20 (23%) studies. Health care providers assisted with the synchronization of the devices, troubleshooting of technical issues, and so forth (10/20, 50%) [42,44,74,86,87,89,98,101,103,104]. They also provided reminders to participants, encouraging PGHD collection (9/20, 45%) [32,36,40,43,58,83,92,97,101]. In addition, health care providers directly assisted with data collection (1/20, 5%) [47] and requested additional information if participants did not respond appropriately (1/20, 5%) [90]. In the case of support methods, phone calls (11/20, 55%) [40,42,43,47,83,86,87,89,90,92,97], text messages (1/20, 5%) [86], app push notifications (4/20, 20%) [32,40,43,58], emails (1/20, 5%) [83], and in-person contact (2/20, 10%) [44,47] were identified.
In this study, the stages of PGHD use were classified as follows: (1) identification, monitoring, review, and analysis; (2) feedback and reporting; (3) motivation; and (4) education and coaching (Multimedia Appendix 4). Identification, monitoring, review, and analysis of PGHD were implemented across all studies (88/88, 100%). Other stages were implemented by interacting with the participants and health care providers. Feedback and reporting were applied in 34 (39%) studies by using PGHD to aid participants in checking their data and receiving technical advice [24,26,32,33,38,40,42,43,52-54,56,58,60,63,69-72,74,75, 79,82,83,86-90,92,93,98,101,104]. PGHD was used as a motivator for participants in approximately one-third of the studies (30/88, 34%) [24,32,33,40,42,43,53,54,56,58,60,63,65, 70,71,74,75,77,79,82,86-88,92,93,96,98,100,101,103]. It included directly encouraging participants to generate data, reminding them, setting goals, and supporting them in the process of generating PGHD. Approximately one-fifth of the studies (19/88, 22%) provided education and coaching using PGHD, enhancing the attainment of additional information, knowledge, and skills [25,32,40,53,56,58,70-72,75,77,79,82,87, 88,92,93,100,104].
Discussion
Principal Findings
This scoping review presents a broad overview and summary of the studies on PGHD use among older adults with cancer. Following are the key findings of this scoping review, which included a total of 88 studies published between 2006 and 2022. A variety of devices were used to collect PGHD, including research-grade wearable devices, consumer-grade wearable devices, and smartphones or tablet PCs. These devices assessed different PGHD in older adults with cancer, including physical activity, sleep, vital signs, and PROs. In most studies, PGHD were collected passively, requiring no additional effort from the patient, other than wearing the device. Active and mixed PGHD collection methods have recently been applied to research. In most studies, there was no support from health care providers when PGHD were collected from older adults with cancer. PGHD were used for the identification, monitoring, review, and analysis in all studies included in the review. However, fewer than half of the studies identified advanced use of PGHD, such as feedback and reporting, motivation, and education and coaching.
Study Characteristics
Since 2017, an increasing number of studies have been published on the applications of PGHD in older adults with cancer. This may be attributed to the fact that the MeSH term for PGHD was introduced in PubMed in 2018. Given the current publication trend, this can be considered an early stage of use of PGHD-related research in older adults with cancer, indicating that there is a need for further research. Approximately one-third of the included studies used secondary sources for PGHD; most were original, while 5 used cohort data. These 5 studies used the following cohort data: the Baltimore Longitudinal Study of Aging [55] and the National Health and Nutrition Examination Survey [19,22,23,62] from the United States, which measured the participants’ physical activity. The advantage of using data from a cohort is that PGHD can be collected from a relatively large number of people and can be used by many researchers simultaneously, which can have a positive impact on patient health care. Therefore, it is recommended to consider including PGHD when constructing a large cohort study.
Participants Characteristics
Approximately 80% (69/88) of the studies reviewed in this analysis focused on the application of PGHD in home settings, underscoring its well-established use outside of hospital environments. Our findings indicate that it is valuable to provide health care providers with continuously collected data on health status at home through PGHD [105]. PGHD can offer patients valuable insights into their health by allowing them to monitor their health at home between hospital visits or during self-care [106]. Moreover, the application of PGHD eliminates time and location constraints, thereby enhancing patient accessibility and participation in their health care [107].
In this review, the cancer treatment statuses of the participants differed. Notably, the cancer survivorship phase, which constituted a quarter of this review, often lacked ongoing health care beyond periodic hospital follow-up visits. Multiple studies have highlighted the unmet need of survivors of cancer for continued care postactive treatment [108,109]. However, PGHD offers several benefits for survivors of cancer. Petersen [7] indicated that it is beneficial for survivors of cancer to apply for PGHD to improve and promote their health. For instance, the collection of PGHD at home, encompassing various health aspects, such as symptoms, can assist health care providers in identifying and managing health issues effectively [7]. In other words, PGHD have the potential to improve patient engagement among survivors who do not frequently receive hospital care and allow health care providers to access patients in a clinical setting. This makes it a promising tool for enhancing the quality of life and health outcomes of survivors of cancer, thereby encouraging future studies to actively use PGHD in their management.
PGHD Characteristics
We found that wearable devices were used to collect approximately three-quarters of the PGHD. These wearable devices have expanded the scope of research beyond the confines of electronic health records by enabling integrated data acquisition from patients [110]. Previous reviews reporting on the use of wearable devices across various health domains indicated that approximately 86% data were collected using consumer-grade wearable devices, including Fitbits, whereas approximately 14% were from research-grade devices [111]. In contrast, in this study, research-grade wearable devices were used in approximately half of the cases and consumer-grade in approximately a quarter. This difference can be attributed to the perceived suitability and higher measurement accuracy of research-grade wearable devices in research settings, particularly among older adults with cancer, which is the target population of this review [112]. Despite this, recent clinical studies have shown an increase in the use of consumer-grade wearable devices, and their reliability and validity have been established [113]. Given their relative affordability and lower burden of wear, consumer-grade wearable devices, which enable continuous usage, are considered to have a high potential for widespread use [114]. Therefore, rather than distinguishing between the types of wearable devices, the focus should be on the selection of the most appropriate device for collecting PGHD, considering the specific circumstances and environments of older adults with cancer.
In this review, more than half of the collected PGHD items focused on physical activity. Previous studies have shown that older adults with cancer tend to engage in less physical activity compared to middle-aged patients [115]. Moreover, physical activity has been demonstrated to enhance physical function and overall quality of life in older adults with cancer [116,117]. This underscores the importance of prioritizing increased physical activity as a critical factor in improving the health of this population. PGHD can increase health awareness and patient engagement, leading to positive effects on physical activity [107]. Essentially, it can help older adults with cancer in making health behavior changes that enhance physical activity while supporting their autonomy [7]. Moreover, self-monitoring of physical activity using PGHD can motivate individuals to engage in these activities [118]. Therefore, future research using PGHD to monitor and encourage physical activity among older adults with cancer may improve their health outcomes.
This review identified PGHD that measure PROs, including symptoms, in older adults with cancer, which is crucial for enhancing treatment and health outcomes [119]. While visiting a hospital in person and objectively measuring their condition is challenging for patients with cancer, self-reporting conditions, including symptoms, can be more beneficial [120]. Patients were able to report the symptoms they experienced in real time from their homes through PGHD, and contrary to expectations, they did not find it burdensome to report daily [40]. PGHD offer a means for patients with cancer to communicate their condition directly to health care providers, thereby eliminating time and distance constraints and benefiting both the patients and providers [7]. Especially, older patients with cancer are at a higher risk of reporting more severe symptoms [121]. Therefore, enabling older patients with cancer to assess their condition, including symptoms, through PGHD without time and distance limitations can facilitate the early detection of deterioration and personalized care [119]. However, none of the studies included in this review actively involved older adults with cancer in selecting the PGHD to be collected. Therefore, future studies should consider selecting and collecting PGHD items based on participants’ needs to enhance engagement.
In addition, based on the data collected through the PROs, reports or alerts about symptoms were provided to patients, and health care providers offered advice or interventions for symptom management [32,42,79]. This indicates that patients, even when not in the hospital, can experience gratitude and psychological comfort, knowing that they are connected to and receiving care from health care providers and are managing their health issues [40]. PGHD enabled patients to gain confidence in self-management of their symptoms, suggesting a positive impact on their health management.
This review categorizes PGHD collection into passive, active, and combined methods, with the passive collection being predominantly used (63/88, 72%). This differs from a prior review of PGHD for disease prevention and health promotion, in which active or partially active methods were more common in healthy populations [16]. Passive methods that use wearable devices for objective data collection without an added burden have been in use since 2006. Given the focus on older adults with cancer in this review, simpler passive methods have been widely adopted. In addition, health care providers tend to view passively collected PGHD as more reliable and more challenging to manipulate, as indicated in previous studies [122].
In contrast, approximately one-fifth of the studies used the active collection method, which showed a gradual increase over time. This method, which involves direct and active participation of the patients in data collection, allows patients to report their subjective states, contributing to enhanced patient engagement [123]. However, a previous review identified conflicting characteristics between passive and active collection [16]. Passive approaches were found to be less burdensome for patients, but may reduce their participation, whereas active approaches require voluntary effort, but motivate patient involvement [16]. Considering these characteristics, a combined collection approach may be ideal for PGHD. In this review, only approximately 6% (5/88) of the studies used a combined collection method, which started to appear in 2020. For example, in the study by Low et al [91], passive data collection using wearable and smartphone sensors for PGHD, combined with active data collection of patient-reported symptoms, facilitated personalized and adaptive symptom assessments to enable early detection of adverse events. This approach may ultimately enhance patient well-being by enabling timely interventions throughout long-term cancer treatment and recovery. Thus, future research should consider using a combined data collection approach to comprehensively assess the health status of older adults with cancer.
An adequate level of digital literacy is essential for patients to generate PGHD, comprehend their significance, and receive motivation for behavioral changes to enhance adherence. However, concerns have arisen about the potential disparities among those unable to collect or use PGHD for various reasons. Age is a contributing factor, as older adults may struggle to adopt new technologies owing to their unfamiliarity [124]. Moreover, patients with cancer often experience higher physical and psychological symptom burdens than do those without cancer, potentially reducing their inclination to use digital health technologies [125]. Therefore, future studies may require additional strategies to identify and enhance digital literacy among older adults with cancer. A tailored approach that integrates demographic factors (eg, age or gender), geographic factors (eg, place of residence), socioeconomic factors (eg, education level), behavioral factors (eg, information-seeking behavior), and social factors (eg, social support) could promote more effective use of wearable devices and mobile apps for PGHD collection and use [126].
Regarding the PGHD use stages, all studies used PGHD at the first stage, primarily collecting and analyzing PGHD, to identify and monitor the health status of older adults with cancer for research purposes. This aligns with a previous review of disease prevention and health promotion, in which many studies reviewed and analyzed PGHD [16]. However, the first stage of PGHD use can be considered as merely using the gathered information for analysis. To facilitate bidirectional rather than unidirectional communication through PGHD, patients must be able to check or access their generated data. The studies included in this review initially identified and analyzed PGHD, and then PGHD were used for advanced use depending on the purpose of the study. In only approximately one-third of the studies, the patients were provided with information and feedback on PGHD to help them understand their condition; PGHD motivated patients to achieve health-related goals and was used for education or coaching interventions. Therefore, offering actionable recommendations based on PGHD is expected to increase patient engagement in health behaviors, especially among older adults. This implies that the use of PGHD should not be limited to the generation of data through wearable devices but should extend to accessing this data and receiving feedback that motivates behavior change. Similarly, when collecting PROs including symptoms via mobile apps, a bidirectional structure is necessary in which patients receive advice or guidance in addition to reporting their information.
To promote the widespread use of PGHD in research involving older adults with cancer, it is crucial to increase adherence to digital devices for PGHD measurements. This necessitates identifying the needs, barriers, and facilitators for older adults with cancer regarding PGHD, and providing education where there are gaps to encourage adherence. In particular, patients with lower digital literacy encounter greater barriers to using PGHD [16,127]; this can negatively impact the collection and use of PGHD, emphasizing the need for initiatives to improve digital literacy.
Cultural considerations are also essential for adopting PGHD among older adults with cancer. A survey conducted across four European countries revealed that over half of the participants recognized the usefulness of PGHD and expressed willingness to share it with health care providers to improve their own health [128]. In contrast, for community-dwelling older adults in urban areas of South Korea, family or peer influence was reported as both a facilitator and a barrier to adopting digital technologies [129]. Considering the unique cultural attitudes and behavioral intentions that influence technology adoption is crucial for effectively collecting and using PGHD among older adults with cancer [130].
Moreover, the quality of PGHD is a significant concern, influenced by user-related factors, device and technology, and data governance [110]. Researchers should not only focus on collecting PGHD but also on enhancing data quality for effective use. Additionally, the establishment of standards or frameworks for assessing the quality, reliability, and accuracy of PGHD from various devices is essential. Meeting these requirements will enable the sharing and integration of PGHD with health care providers for use in patient care and health management in clinical settings. Ultimately, future research should aim to integrate PGHD from nonhospital settings into health care systems, such as electronic health records or research registries, to ensure continuity of care.
Limitations
This scoping review endeavored to apply a rigorous methodology; however, it has a few limitations. First, the PubMed MeSH term for PGHD has only been included since 2018, indicating that some studies, corresponding to PGHD, may not have been searched. To compensate for this, search terms corresponding to PGHD such as wearable devices and patient report were included. Furthermore, even if PGHD were not explicitly stated, studies deemed to be PGHD through discussions among researchers were included in the review. Additionally, the references of the selected studies were reviewed to ensure that there were no omissions. Second, this review did not include gray literature in the literature selection process. By excluding theses and conference papers, and focusing only on peer-reviewed studies, it is possible that recent literature containing the latest information on PGHD was excluded. Third, this study included older patients with various types of cancer at different treatment stages, suggesting a need for caution regarding the heterogeneity related to cancer in the application of PGHD. Therefore, careful interpretation of the results of this review is warranted, and future research should focus on understanding the use of PGHD according to the specific types of cancer and treatment stages. Finally, although the inclusion criteria of this review focused on studies with a mean or median age of 65 years or older, some studies may have included participants younger than 65 years. This could affect the generalizability of the findings to the geriatric population, as the presence of younger participants may not fully reflect the health characteristics and challenges faced by older adults.
Conclusions
This scoping review investigated and summarized the overall characteristics and use stages of PGHD among older adults with various types and stages of cancer. Research involving PGHD in older adults with cancer is gradually increasing and is primarily conducted at home through wearable devices or mobile apps. PGHD encompass a collection of physiological variables, such as physical activity, sleep, and vital signs, along with PROs, using both passive and active methods, and have been used in various aspects of research. However, while PGHD collected from older adults with cancer were universally used for analysis within the studies, their use for providing feedback, motivation, and interventions that could impact patients and researchers was limited. These findings suggest that future research should emphasize the use of PGHD, which interact with patients to provide patient-centered care through patient engagement. Furthermore, health care, especially in areas such as physical activity and symptom management, is critical for older adults with cancer, and PGHD can positively influence their active participation in health care. Therefore, our findings may provide insights into PGHD that can be used by health care providers and researchers in future research in the context of geriatric cancer care.
Acknowledgments
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2020R1A6A1A03041989) and the Brain Korea 21 FOUR Project funded by the National Research Foundation (NRF) of Korea, Yonsei University College of Nursing.
Data Availability
The protocol of the scoping review was registered in the Open Science Framework Registry. All data generated in the review is available in the main text and in Multimedia Appendix 3 amd Multimedia Appendix 4.
Conflicts of Interest
None declared.
------------
Abbreviations
| JBI: Joanna Briggs Institute |
| MeSH: Medical Subject Headings |
| PGHD: patient-generated health data |
| PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
| PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review |
| PRO: patient-reported outcome |
------------
Edited by A Mavragani; submitted 14.02.24; peer-reviewed by S Mildrum Chana, A Billis; comments to author 12.06.24; revised version received 05.11.24; accepted 16.12.24; published 04.02.25.
Miller KD, Nogueira L, Devasia T, Mariotto AB, Yabroff KR, Jemal AB, et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72(5):409-436.
Kenzik KM, Kent EE, Martin MY, Bhatia S, Pisu M. Chronic condition clusters and functional impairment in older cancer survivors: a population-based study. J Cancer Surviv. 2016;10(6):1096-1103.
Soto-Perez-de-Celis E, Li D, Yuan Y, Lau YM, Hurria A. Functional versus chronological age: geriatric assessments to guide decision making in older patients with cancer. Lancet Oncol. 2018;19(6):e305-e316.
Siddique A, Simonsick EM, Gallicchio L. Functional decline among older cancer survivors in the Baltimore longitudinal study of aging. J Am Geriatr Soc. 2021;69(11):3124-3133.
Corbett T, Bridges J. Multimorbidity in older adults living with and beyond cancer. Curr Opin Support Palliat Care. 2019;13(3):220-224.
Sehl M, Lu X, Silliman R, Ganz PA. Decline in physical functioning in first 2 years after breast cancer diagnosis predicts 10-year survival in older women. J Cancer Surviv. 2013;7(1):20-31.
Petersen C. Patient-generated health data: a pathway to enhanced long-term cancer survivorship. J Am Med Inform Assoc. 2016;23(3):456-461.
Demiris G, Iribarren SJ, Sward K, Lee S, Yang R. Patient generated health data use in clinical practice: a systematic review. Nurs Outlook. 2019;67(4):311-330.
Shapiro M, Johnston D, Wald J, Mon D. Patient-generated health data. RTI International. 2012. URL: https://www.rti.org/sites/default/files/resources/patientgeneratedhealthdata.pdf [accessed 2023-09-21]
Chung AE, Basch EM. Potential and challenges of patient-generated health data for high-quality cancer care. J Oncol Pract. 2015;11(3):195-197.
Hewitt C, Lloyd KC, Tariq S, Durrant A, Claisse C, Kasadha B, et al. Patient-generated data in the management of HIV: a scoping review. BMJ Open. 2021;11(5):e046393.
Nagpal MS, Barbaric A, Sherifali D, Morita PP, Cafazzo JA. Patient-generated data analytics of health behaviors of people living with type 2 diabetes: scoping review. JMIR Diabetes. 2021;6(4):e29027.
Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32.
Peters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119-2126.
Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473.
Nittas V, Lun P, Ehrler F, Puhan MA, Mütsch M. Electronic patient-generated health data to facilitate disease prevention and health promotion: scoping review. J Med Internet Res. 2019;21(10):e13320.
Fernandes R, Stone P, Andrews P, Morgan R, Sharma S. Comparison between fatigue, sleep disturbance, and circadian rhythm in cancer inpatients and healthy volunteers: evaluation of diagnostic criteria for cancer-related fatigue. J Pain Symptom Manage. 2006;32(3):245-254.
Fouladiun M, Körner U, Gunnebo L, Sixt-Ammilon P, Bosaeus I, Lundholm K. Daily physical-rest activities in relation to nutritional state, metabolism, and quality of life in cancer patients with progressive cachexia. Clin Cancer Res. 2007;13(21):6379-6385.
Lynch BM, Dunstan DW, Healy GN, Winkler E, Eakin E, Owen N. Objectively measured physical activity and sedentary time of breast cancer survivors, and associations with adiposity: findings from NHANES (2003-2006). Cancer Causes Control. 2010;21:283-288.
Maddocks M, Byrne A, Johnson CD, Wilson RH, Fearon KCH, Wilcock A. Physical activity level as an outcome measure for use in cancer cachexia trials: a feasibility study. Support Care Cancer. 2010;18:1539-1544.
Hanisch LJ, Gehrman PR. Circadian rhythm of hot flashes and activity levels among prostate cancer patients on androgen deprivation therapy. Aging Male. 2011;14(4):243-248.
Lynch BM, Dunstan DW, Winkler E, Healy GN, Eakin E, Owen N. Objectively assessed physical activity, sedentary time and waist circumference among prostate cancer survivors: findings from the national health and nutrition examination survey (2003-2006). Eur J Cancer Care (Engl). 2011;20:514-519.
Loprinzi PD, Lee H, Cardinal BJ. Objectively measured physical activity among US cancer survivors: considerations by weight status. J Cancer Surviv. 2013;7:493-499.
Frensham LJ, Zarnowiecki DM, Parfitt G, King S, Dollman J. The experiences of participants in an innovative online resource designed to increase regular walking among rural cancer survivors: a qualitative pilot feasibility study. Support Care Cancer. 2014;22:1923-1929.
Galvão DA, Spry N, Denham J, Taaffe DR, Cormie P, Joseph D, et al. A multicentre year-long randomised controlled trial of exercise training targeting physical functioning in men with prostate cancer previously treated with androgen suppression and radiation from TROG 03.04 RADAR. Eur Urol. 2014;65(5):856-864.
Granger CL, Denehy L, McDonald CF, Irving L, Clark RA. Physical activity measured using global positioning system tracking in non-small cell lung cancer: an observational study. Integr Cancer Ther. 2014;13(6):482-492.
Granger CL, McDonald CF, Irving L, Clark RA, Gough K, Murnane A, et al. Low physical activity levels and functional decline in individuals with lung cancer. Lung Cancer. 2014;83(2):292-299.
Roche VP, Mohamad-Djafari A, Innominato PF, Karaboué A, Gorbach A, Lévi FA. Thoracic surface temperature rhythms as circadian biomarkers for cancer chronotherapy. Chronobiol Int. 2014;31(3):409-420.
Aktas A, Hullihen B, Shrotriya S, Thomas S, Walsh D, Estfan B. Connected health: cancer symptom and quality-of-life assessment using a tablet computer: a pilot study. Am J Hosp Palliat Care. 2015;32(2):189-197.
Dean GE, Abu Sabbah EA, Yingrengreung S, Ziegler P, Chen H, Steinbrenner LM, et al. Sleeping with the enemy: sleep and quality of life in patients with lung cancer. Cancer Nurs. 2015;38(1):60-70.
Livingston PM, Craike MJ, Salmon J, Courneya KS, Gaskin CJ, Fraser SF, et al. Effects of a clinician referral and exercise program for men who have completed active treatment for prostate cancer: a multicenter cluster randomized controlled trial (ENGAGE). Cancer. 2015;121(15):2646-2654.
Sundberg K, Eklöf AL, Blomberg K, Isaksson A, Wengström Y. Feasibility of an interactive ICT-platform for early assessment and management of patient-reported symptoms during radiotherapy for prostate cancer. Eur J Oncol Nurs. 2015;19(5):523-528.
Drott J, Vilhelmsson M, Kjellgren K, Berterö C. Experiences with a self-reported mobile phone-based system among patients with colorectal cancer: a qualitative study. JMIR mHealth uHealth. 2016;4(2):e66.
Gaskin CJ, Craike M, Mohebbi M, Salmon J, Courneya KS, Broadbent S, et al. Associations of objectively measured moderate-to-vigorous physical activity and sedentary behavior with quality of life and psychological well-being in prostate cancer survivors. Cancer Causes Control. 2016;27(9):1093-1103.
van Roekel EH, Winkler EA, Bours MJ, Lynch BM, Willems PJ, Meijer K, et al. Associations of sedentary time and patterns of sedentary time accumulation with health-related quality of life in colorectal cancer survivors. Prev Med Rep. 2016;4:262-269.
Basch E, Pugh SL, Dueck AC, Mitchell SA, Berk L, Fogh S, et al. Feasibility of patient reporting of symptomatic adverse events via the patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE) in a chemoradiotherapy cooperative group multicenter clinical trial. Int J Radiat Oncol Biol Phys. 2017;98(2):409-418.
Chang WP, Lin CC. Changes in the sleep-wake rhythm, sleep quality, mood, and quality of life of patients receiving treatment for lung cancer: a longitudinal study. Chronobiol Int. 2017;34(4):451-461.
Golsteijn RHJ, Bolman C, Peels DA, Volders E, de Vries H, Lechner L. A web-based and print-based computer-tailored physical activity intervention for prostate and colorectal cancer survivors: a comparison of user characteristics and intervention use. J Med Internet Res. 2017;19(8):e298.
Jeffery E, Lee YG, McVeigh J, Straker L, Wooding T, Newton RU, et al. Feasibility of objectively measured physical activity and sedentary behavior in patients with malignant pleural effusion. Support Care Cancer. 2017;25:3133-3141.
Langius-Eklöf A, Christiansen M, Lindström V, Blomberg K, Hälleberg Nyman M, Wengström Y, et al. Adherence to report and patient perception of an interactive app for managing symptoms during radiotherapy for prostate cancer: descriptive study of logged and interview data. JMIR Cancer. 2017;3(2):e18.
Lawrence LM, Stone MR, Rainham DG, Keats MR. Environments associated with moderate-to-vigorous physical activity and sedentary behavior of colorectal cancer survivors. Int J Behav Med. 2017;24:120-126.
Nyman MH, Frank C, Langius-Eklöf A, Blomberg K, Sundberg K, Wengström Y. Patients' perspective on participation in care with or without the support of a smartphone app during radiotherapy for prostate cancer: qualitative study. JMIR mHealth uHealth. 2017;5(7):e107.
Sundberg K, Wengström Y, Blomberg K, Hälleberg-Nyman M, Frank C, Langius-Eklöf A. Early detection and management of symptoms using an interactive smartphone application (Interaktor) during radiotherapy for prostate cancer. Support Care Cancer. 2017;25(7):2195-2204.
Thomas S, Walsh D, Shrotriya S, Aktas A, Hullihen B, Estfan B, et al. Symptoms, quality of life, and daily activities in people with newly diagnosed solid tumors presenting to a medical oncologist. Am J Hosp Palliat Med. 2017;34(7):611-621.
Wynter-Blyth V, MacKinnon T. Streamlining perioperative care for oesophago-gastric cancer surgery patients using home remote monitoring. Primary Health Care. 2017;27(5):27-31.
Bade BC, Brooks MC, Nietert SB, Ulmer A, Thomas DD, Thomas DD, et al. Assessing the correlation between physical activity and quality of life in advanced lung cancer. Integr Cancer Ther. 2018;17(1):73-79.
Bernatchez MS, Savard J, Savard MH, Aubin M, Ivers H. Sleep-wake difficulties in community-dwelling cancer patients receiving palliative care: subjective and objective assessment. Palliat Support Care. 2018;16(6):756-766.
Chang WP, Smith R, Lin CC. Age and rest-activity rhythm as predictors of survival in patients with newly diagnosed lung cancer. Chronobiol Int. 2018;35(2):188-197.
D'Silva A, Gardiner PA, Boyle T, Bebb DG, Johnson ST, Vallance JK. Associations of objectively assessed physical activity and sedentary time with health-related quality of life among lung cancer survivors: a quantile regression approach. Lung Cancer. 2018;119:78-84.
Golsteijn RHJ, Bolman C, Volders E, Peels DA, de Vries H, Lechner L. Short-term efficacy of a computer-tailored physical activity intervention for prostate and colorectal cancer patients and survivors: a randomized controlled trial. Int J Behav Nutr Phys Act. 2018;15:106.
Gonzalez BD, Small BJ, Cases MG, Williams NL, Fishman MN, Jacobsen PB, et al. Sleep disturbance in men receiving androgen deprivation therapy for prostate cancer: the role of hot flashes and nocturia. Cancer. 2018;124(3):499-506.
Soto-Perez-De-Celis E, Kim H, Rojo-Castillo MP, Sun CL, Chavarri-Guerra Y, Navarrete-Reyes AP, et al. A pilot study of an accelerometer-equipped smartphone to monitor older adults with cancer receiving chemotherapy in Mexico. J Geriatr Oncol. 2018;9(2):145-151.
Tang FW, Chan CW, Choy YP, Loong HH, Chow KM, So WK. A feasibility study on using tablet personal computers for self-reported symptom assessment in newly diagnosed lung cancer patients. Int J Nurs Pract. 2018;24(4):e12658.
Trinh L, Arbour-Nicitopoulos KP, Sabiston CM, Berry SR, Loblaw A, Alibhai SMH, et al. RiseTx: testing the feasibility of a web application for reducing sedentary behavior among prostate cancer survivors receiving androgen deprivation therapy. Int J Behav Nutr Phys Act. 2018;15:49.
Wanigatunga AA, Gresham GK, Kuo PL, Martinez-Amezcua P, Zipunnikov V, Dy SM, et al. Contrasting characteristics of daily physical activity in older adults by cancer history. Cancer. 2018;124(24):4692-4699.
Alibhai SMH, Santa Mina D, Ritvo P, Tomlinson G, Sabiston C, Krahn M, et al. A phase II randomized controlled trial of three exercise delivery methods in men with prostate cancer on androgen deprivation therapy. BMC Cancer. 2019;19:2.
Fujisawa D, Temel JS, Greer JA, El-Jawahri A, Traeger L, Jacobs JM, et al. Actigraphy as an assessment of performance status in patients with advanced lung cancer. Palliat Support Care. 2019;17(5):574-578.
Gustavell T, Sundberg K, Segersvärd R, Wengström Y, Langius-Eklöf A. Decreased symptom burden following surgery due to support from an interactive app for symptom management for patients with pancreatic and periampullary cancer. Acta Oncol. 2019;58(9):1307-1314.
Jeffery E, Lee YCG, Newton RU, Lyons-Wall P, McVeigh J, Nowak AK, et al. Body composition and nutritional status in malignant pleural mesothelioma: implications for activity levels and quality of life. Eur J Clin Nutr. 2019;73(10):1412-1421.
Kenfield SA, van Blarigan EL, Ameli N, Lavaki E, Cedars B, Paciorek AT, et al. Feasibility, acceptability, and behavioral outcomes from a technology-enhanced behavioral change intervention (prostate 8): a pilot randomized controlled trial in men with prostate cancer. Eur Urol. 2019;75(6):950-958.
Lemanska A, Poole K, Griffin BA, Manders R, Saxton JM, Turner L, et al. Community pharmacy lifestyle intervention to increase physical activity and improve cardiovascular health of men with prostate cancer: a phase II feasibility study. BMJ Open. 2019;9(6):e025114.
Loprinzi PD, Nooe A. Objectively measured physical activity and all-cause mortality among cancer survivors: national prospective cohort study. South Med J. 2019;112(4):234-237.
Metcalf M, Glazyrine V, Glavin K, Dahlgren A, Michael C, Bechtel M, et al. The feasibility of a health care application in the treatment of patients undergoing radical cystectomy. J Urol. 2019;201(5):902-908.
Ngo-Huang A, Parker NH, Bruera E, Lee RE, Simpson R, O'Connor DP, et al. Home-based exercise prehabilitation during preoperative treatment for pancreatic cancer is associated with improvement in physical function and quality of life. Integr Cancer Ther. 2019;18:1534735419894061.
Parker NH, Ngo-Huang A, Lee RE, O'Connor DP, Basen-Engquist KM, Petzel MQ, et al. Physical activity and exercise during preoperative pancreatic cancer treatment. Support Care Cancer. 2019;27:2275-2284.
Porserud A, Aly M, Nygren-Bonnier M, Hagströmer M. Objectively measured mobilisation is enhanced by a new behaviour support tool in patients undergoing abdominal cancer surgery. Eur J Surg Oncol. 2019;45(10):1847-1853.
Zahiri M, Chen KM, Zhou H, Nguyen H, Workeneh BT, Yellapragada SV, et al. Using wearables to screen motor performance deterioration because of cancer and chemotherapy-induced peripheral neuropathy (CIPN) in adults - toward an early diagnosis of CIPN. J Geriatr Oncol. 2019;10(6):960-967.
Barkley R, Khalil M, Shen P, Levine EA, Votanopoulos K, Clark CJ. Feasibility of low-cost accelerometers in measuring functional recovery after major oncologic surgery. J Surg Oncol. 2020;121:279-285.
Buergy D, Siefert V, Neumaier C, Ganslandt T, Sperk E, Blessing M, et al. Prospective trial on telemonitoring of geriatric cancer patients using handheld devices. Strahlenther Onkol. 2020;196(3):205-212.
Chan JM, van Blarigan EL, Langlais CS, Zhao S, Ramsdill JW, Daniel K, et al. Feasibility and acceptability of a remotely delivered, web-based behavioral intervention for men with prostate cancer: four-arm randomized controlled pilot trial. J Med Internet Res. 2020;22(12):e19238.
Gustavell T, Sundberg K, Langius-Eklöf A. Using an interactive app for symptom reporting and management following pancreatic cancer surgery to facilitate person-centered care: descriptive study. JMIR mHealth uHealth. 2020;8(6):e17855.
Hardcastle SJ, Jiménez-Castuera R, Maxwell-Smith C, Bulsara MK, Hince D. Fitbit wear-time and patterns of activity in cancer survivors throughout a physical activity intervention and follow-up: exploratory analysis from a randomised controlled trial. PLoS One. 2020;15(10):e0240967.
Jakobsen G, Engstrøm M, Thronæs M, Løhre ET, Kaasa S, Fayers P, et al. Sleep quality in hospitalized patients with advanced cancer: an observational study using self-reports of sleep and actigraphy. Support Care Cancer. 2020;28:2015-2023.
Jonker LT, Hendriks S, Lahr MM, van Munster BC, de Bock GH, van Leeuwen BL. Postoperative recovery of accelerometer-based physical activity in older cancer patients. Eur J Surg Oncol. 2020;46(11):2083-2090.
Kneuertz PJ, Jagadesh N, Perkins A, Fitzgerald M, Moffatt-Bruce SD, Merritt RE, et al. Improving patient engagement, adherence, and satisfaction in lung cancer surgery with implementation of a mobile device platform for patient reported outcomes. J Thorac Dis. 2020;12(11):6883-6891.
Kuo LC, Chang WP, Huang HC, Lin CC. Association of time-varying rest-activity rhythm with survival in older adults with lung cancer. Cancer Nurs. 2020;43(1):45-51.
Lafaro KJ, Raz DJ, Kim JY, Hite S, Ruel N, Varatkar G, et al. Pilot study of a telehealth perioperative physical activity intervention for older adults with cancer and their caregivers. Support Care Cancer. 2020;28(8):3867-3876.
Lévi F, Komarzynski S, Huang Q, Young T, Ang Y, Fuller C, et al. Tele-monitoring of cancer patients' rhythms during daily life identifies actionable determinants of circadian and sleep disruption. Cancers (Basel). 2020;12(7):1938.
Maguire R, Connaghan J, Arber A, Klepacz N, Blyth KG, McPhelim J, et al. Advanced symptom management system for patients with malignant pleural mesothelioma (ASyMSmeso): mixed methods study. J Med Internet Res. 2020;22(11):e19180.
Manda S, Yimer HA, Noga SJ, Girnius S, Yasenchak CA, Charu V, et al. Feasibility of long-term proteasome inhibition in multiple myeloma by in-class transition from bortezomib to ixazomib. Clin Lymphoma Myeloma Leuk. 2020;20(11):e910-e925.
Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, et al. Using smartphones to capture novel recovery metrics after cancer surgery. JAMA Surg. 2020;155(2):123-129.
Park S, Kim K, Ahn HK, Kim JW, Min G, Chung BH, et al. Impact of lifestyle intervention for patients with prostate cancer. Am J Health Behav. 2020;44(1):90-99.
Taarnhøj GA, Lindberg H, Dohn LH, Omland LH, Hjøllund NH, Johansen C, et al. Electronic reporting of patient-reported outcomes in a fragile and comorbid population during cancer therapy - a feasibility study. Health Qual Life Outcomes. 2020;18:225.
Tolstrup LK, Bastholt L, Dieperink KB, Möller S, Zwisler A, Pappot H. The use of patient-reported outcomes to detect adverse events in metastatic melanoma patients receiving immunotherapy: a randomized controlled pilot trial. J Patient Rep Outcomes. 2020;4:88.
Appleyard S, Larkin M, Stewart E, Minton O, Gilbert D. Digital medicine in men with advanced prostate cancer - a feasibility study of electronic patient-reported outcomes in patients on systemic treatment. Clin Oncol (R Coll Radiol). 2021;33(12):751-760.
Bade BC, Gan G, Li F, Lu L, Tanoue L, Silvestri GA, et al. "Randomized trial of physical activity on quality of life and lung cancer biomarkers in patients with advanced stage lung cancer: a pilot study". BMC Cancer. 2021;21:352.
Blair CK, Harding E, Wiggins C, Kang H, Schwartz M, Tarnower A, et al. A home-based mobile health intervention to replace sedentary time with light physical activity in older cancer survivors: randomized controlled pilot trial. JMIR Cancer. 2021;7(2):e18819.
Evans HEL, Galvão DA, Forbes CC, Girard D, Vandelanotte C, Newton RU, et al. Acceptability and preliminary efficacy of a web- and telephone-based personalised exercise intervention for individuals with metastatic prostate cancer: the ExerciseGuide pilot randomised controlled trial. Cancers (Basel). 2021;13(23):5925.
Jonker LT, Lahr MMH, Oonk MHM, de Bock GH, van Leeuwen BL. Post-discharge telemonitoring of physical activity, vital signs, and patient-reported symptoms in older patients undergoing cancer surgery. Ann Surg Oncol. 2021;28(11):6512-6522.
Jonker LT, Plas M, de Bock GH, Buskens E, van Leeuwen BL, Lahr MMH. Remote home monitoring of older surgical cancer patients: perspective on study implementation and feasibility. Ann Surg Oncol. 2021;28(1):67-78.
Low CA, Li M, Vega J, Durica KC, Ferreira D, Tam V, et al. Digital biomarkers of symptom burden self-reported by perioperative patients undergoing pancreatic surgery: prospective longitudinal study. JMIR Cancer. 2021;7(2):e27975.
Msaouel P, Oromendia C, Siefker-Radtke AO, Tannir NM, Subudhi SK, Gao J, et al. Evaluation of technology-enabled monitoring of patient-reported outcomes to detect and treat toxic effects linked to immune checkpoint inhibitors. JAMA Netw Open. 2021;4(8):e2122998.
Pinto BM, Kindred M, Franco R, Simmons V, Hardin J. A 'novel' multi-component approach to promote physical activity among older cancer survivors: a pilot randomized controlled trial. Acta Oncol. 2021;60(8):968-975.
Rossi LA, Melstrom LG, Fong Y, Sun V. Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data. J Surg Oncol. 2021;123(5):1345-1352.
Sada YH, Poursina O, Zhou H, Workeneh BT, Maddali SV, Najafi B. Harnessing digital health to objectively assess cancer-related fatigue: the impact of fatigue on mobility performance. PLoS One. 2021;16(2):e0246101.
Schuit AS, van Zwieten V, Holtmaat K, Cuijpers P, Eerenstein SEJ, Leemans CR, et al. Symptom monitoring in cancer and fully automated advice on supportive care: Patients' perspectives on self-management strategies and the eHealth self-management application Oncokompas. Eur J Cancer Care (Engl). 2021;30(6):e13497.
Shih CH, Chou PC, Chou TL, Huang TW. Measurement of cancer-related fatigue based on heart rate variability: observational study. J Med Internet Res. 2021;23(7):e25791.
Strother M, Koepsell K, Song L, Faerber J, Bernard J, Malkowicz S, et al. Financial incentives and wearable activity monitors to increase ambulation after cystectomy: a randomized controlled trial. Urol Oncol. 2021;39(7):434.e31-434.e38.
Tanaka H, Fukui S, Maeda I, Hatano Y, Higuchi A, Higami Y, et al. The change over time of vital signs with consideration for opioid use in the last 2 weeks of life among cancer patients in a palliative care unit: continuous measurement of vital signs using a non-wearable monitor. Cancer Med. 2021;10(24):8799-8807.
Bentley JR, Yu X, Karmarkar AM, Downer B, Prochaska J, Lyons EJ. Feasibility and thematic analysis of narrative visualization materials with physical activity monitoring among breast cancer survivors. BMC Cancer. 2022;22(1):553.
de Leeuwerk ME, Botjes M, van Vliet V, Geleijn E, de Groot V, van Wegen E, et al. Self-monitoring of physical activity after hospital discharge in patients who have undergone gastrointestinal or lung cancer surgery: mixed methods feasibility study. JMIR Cancer. 2022;8(2):e35694.
Higami Y, Higuchi A, Tanaka H, Moriki Y, Utsumi M, Yamakawa M, et al. Nonwearable actigraphy to assess changes in motor activity before and after rescue analgesia in terminally ill patients with cancer: a pilot study. Int J Nurs Pract. 2022;28(4):e13019.
Oswald LB, Li X, Carvajal R, Hoogland AI, Gudenkauf LM, Hansen DK, et al. Longitudinal collection of patient-reported outcomes and activity data during CAR-T therapy: feasibility, acceptability, and data visualization. Cancers (Basel). 2022;14(11):2742.
Passardi A, Foca F, Caffo O, Tondini CA, Zambelli A, Vespignani R, et al. A remote monitoring system to optimize the home management of oral anticancer therapies (ONCO-TreC): prospective training-validation trial. J Med Internet Res. 2022;24(1):e27349.
Purswani JM, Dicker AP, Champ CE, Cantor M, Ohri N. Big data from small devices: the future of smartphones in oncology. Semin Radiat Oncol. 2019;29(4):338-347.
Cohen DJ, Keller SR, Hayes GR, Dorr DA, Ash JS, Sittig DF. Integrating patient-generated health data into clinical care settings or clinical decision-making: lessons learned from project HealthDesign. JMIR Hum Factors. 2016;3(2):e26.
Omoloja A, Vundavalli S. Patient generated health data: benefits and challenges. Curr Probl Pediatr Adolesc Health Care. 2021;51(11):101103.
Burg MA, Adorno G, Lopez EDS, Loerzel V, Stein K, Wallace C, et al. Current unmet needs of cancer survivors: analysis of open-ended responses to the American Cancer Society Study of Cancer Survivors II. Cancer. 2015;121(4):623-630.
Mayer DK, Nasso SF, Earp JA. Defining cancer survivors, their needs, and perspectives on survivorship health care in the USA. Lancet Oncol. 2017;18(1):e11-e18.
Cho S, Ensari I, Weng C, Kahn MG, Natarajan K. Factors affecting the quality of person-generated wearable device data and associated challenges: rapid systematic review. JMIR mHealth uHealth. 2021;9(3):e20738.
Huhn S, Axt M, Gunga HC, Maggioni MA, Munga S, Obor D, et al. The impact of wearable technologies in health research: scoping review. JMIR mHealth uHealth. 2022;10(1):e34384.
Yavelberg L, Zaharieva D, Cinar A, Riddell MC, Jamnik V. A pilot study validating select research-grade and consumer-based wearables throughout a range of dynamic exercise intensities in persons with and without type 1 diabetes: a novel approach. J Diabetes Sci Technol. 2018;12(3):569-576.
Bai Y, Welk GY, Nam YH, Lee JA, Lee JM, Kim Y, et al. Comparison of consumer and research monitors under semistructured settings. Med Sci Sports Exerc. 2016;48(1):151-158.
Strain T, Wijndaele K, Pearce M, Brage S. Considerations for the use of consumer-grade wearables and smartphones in population surveillance of physical activity. J Meas Phys Behav. 2022;5(1):8-14.
Tarasenko Y, Chen C, Schoenberg N. Self-reported physical activity levels of older cancer survivors: results from the 2014 National Health Interview Survey. J Am Geriatr Soc. 2017;65(2):e39-e44.
Blair CK, Morey MC, Desmond RA, Cohen HJ, Sloane R, Snyder DC, et al. Light-intensity activity attenuates functional decline in older cancer survivors. Med Sci Sports Exerc. 2014;46(7):1375-1383.
Courneya KS, Karvinen KH. Exercise, aging, and cancer. Appl Physiol Nutr Metab. 2007;32(6):1001-1007.
Jim HSL, Hoogland AI, Brownstein NC, Barata A, Dicker AP, Knoop H, et al. Innovations in research and clinical care using patient-generated health data. CA Cancer J Clin. 2020;70(3):182-199.
Pritchett JC, Patt D, Thanarajasingam G, Schuster A, Snyder C. Patient-reported outcomes, digital health, and the quest to improve health equity. Am Soc Clin Oncol Educ Book. 2023;43:e390678.
Morgan R, Sail K, Witte L. Patient-reported outcome data. In: Richesson RL, Andrews JE, Fultz Hollis K, editors. Clinical Research Informatics. Cham, Switzerland. Springer International Publishing; 2023:291-308.
Bubis LD, Davis LE, Canaj H, Gupta V, Jeong Y, Barbera L, et al. Patient-reported symptom severity among 22,650 cancer outpatients in the last six months of life. J Pain Symptom Manage. 2020;59(1):58-66.e4.
Kim B, Ghasemi P, Stolee P, Lee J. Clinicians and older adults' perceptions of the utility of patient-generated health data in caring for older adults: exploratory mixed methods study. JMIR Aging. 2021;4(4):e29788.
Nghiem J, Adler DA, Estrin D, Livesey C, Choudhury T. Understanding mental health clinicians' perceptions and concerns regarding using passive patient-generated health data for clinical decision-making: qualitative semistructured interview study. JMIR Form Res. 2023;7:e47380.
Jacob C, Sezgin E, Sanchez-Vazquez A, Ivory C. Sociotechnical factors affecting patients' adoption of mobile health tools: systematic literature review and narrative synthesis. JMIR mHealth uHealth. 2022;10(5):e36284.
Hasnan S, Aggarwal S, Mohammadi L, Koczwara B. Barriers and enablers of uptake and adherence to digital health interventions in older patients with cancer: a systematic review. J Geriatr Oncol. 2022;13(8):1084-1091.
Zhang Y, Xu P, Sun Q, Baral S, Xi L, Wang D. Factors influencing the e-health literacy in cancer patients: a systematic review. J Cancer Surviv. 2023;17(2):425-440.
Zakerabasali S, Ayyoubzadeh SM, Baniasadi T, Yazdani A, Abhari S. Mobile health technology and healthcare providers: systemic barriers to adoption. Healthc Inform Res. 2021;27(4):267-278.
Karampela M, Ouhbi S, Isomursu M. Connected health user willingness to share personal health data: questionnaire study. J Med Internet Res. 2019;21(11):e14537.
Baek J, Kim H, Choi S, Hong S, Kim Y, Kim E, et al. Digital literacy and associated factors in older adults living in urban South Korea: a qualitative study. Comput Inform Nurs. 2024;42(3):226-239.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-340.
© 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Kim, Geonah
; Cho, Hyeonmi
; Kim, Yeonju
; Choi, Mona