Effectiveness of a tele-nursing system for palliative cancer pain in patients with advanced cancer: A pilot randomized controlled trial

Abstract

Objective

This study evaluated the effectiveness of the Cancer Pain Monitoring System (CAPAMOS), a tele-nursing system for cancer pain relief.

Methods

A pilot randomized controlled trial was conducted from October 2019 to February 2023 among outpatients at Tohoku University Hospital. Participants were randomly assigned in a 1:1 ratio to either the intervention or the control group. Eligible patients reported an average pain score of ≥ 3 on the numerical rating scale over 24 hours. Pain, self-management, and quality of life (QoL) were assessed through questionnaires at enrolment (T0), and at two (T1) and four weeks (T2).

Results

A total of 48 participants were enrolled. No significant group × time interactions were observed for any outcome measure. In the CAPAMOS group, the average pain scores on the Japanese Brief Pain Inventory decreased over time: 4.0, 3.0, and 3.0 (P = 0.02) at T0, T1, and T2 respectively, with significant improvement from T0 to T2 (P = 0.02). Furthermore, the pain interference with enjoyment of life decreased over time: 5.0, 2.0, and 3.50 at T0, T1, and T2 respectively, with significant improvement from T0 to T1 (P = 0.02). No improvements were observed in the control group. Secondary outcomes, including self-management and QoL over four weeks, were not significantly different between the two groups. In the CAPAMOS group, “recording pain and opioid use,” score on the Opioid Self-Management Scale for Advanced Cancer Patients increased from a score of 20.5 at T0 to 21.5 at T2 (P = 0.03).

Conclusions

This study found no significant difference between the CAPAMOS and control groups after four weeks. However, the CAPAMOS group showed improvements in cancer pain and its impact on daily life. This suggests that CAPAMOS tele-nursing intervention may require more than four weeks for effective pain relief.

Trial registration

The study was registered with the University Hospital Medical Information Network (UMIN Clinical Trial Registry ID: 000038334).

Introduction

Cancer-related pain is highly prevalent, affecting 55.0% of patients undergoing chemotherapy and 66.4% of those with advanced, metastatic, or terminal illnesses; moderate-to-severe pain is particularly common, reported by 32.4% of patients during chemotherapy and by 51.9% of those in advanced stages.¹ In Japan, the average length of hospital stays for cancer patients has been decreasing annually, leading to an increasing number of patients receiving outpatient treatment. Consequently, the responsibility for pain management has shifted from clinical settings to home care. Approximately 60% of outpatients with metastatic or recurrent cancer report pain, and approximately 20% experience moderate-to-severe pain in clinical practice.² Therefore, regardless of the stage of cancer, palliative care should be integrated with oncological treatment.

Opioid medications are widely acknowledged as effective for cancer pain relief.³ Evidence suggests that patients with high opioid self-management skills achieve better pain control.⁴ Therefore, enabling patients to independently manage their pain with opioid analgesics is essential. However, outpatients with advanced cancer who fail to adhere to prescribed opioid regimens experience higher levels of pain-related interference in their daily lives.⁵ This underscores the need for enhanced support systems to empower patients in effectively self-managing their pain at home.

To address this issue, various interventions have been implemented, including home visits and telephone consultations aimed at developing patients’ self-care skills.6, 7, 8, 9 Additionally, educational interventions during hospitalization and follow-up phone calls after discharge have been shown to alleviate cancer pain and concerns about opioid analgesics use.¹⁰ As 74% of the global population in 2025 use the internet,¹¹ digital tools offer promising opportunities to support health management.¹²

Patient-reported outcomes (PROs)— patients’ self-assessments without interpretation by health care professionals—are recommended for pain evaluation.¹³ The European Society for Medical Oncology (ESMO) specifically recommends the use of electronic PROs (e-PROs) through digital devices, incorporating automated alert functions to flag severe symptoms.¹⁴ When an alert is triggered, an oncology nurse can triage the patient and provide appropriate management, including symptom intervention, coaching, and referrals for medical consultation. Web-based symptom reporting, combined with nurse-received electronic alert interventions for patients undergoing regular chemotherapy for solid tumors, improves quality of life (QoL), reduces emergency room visits and hospitalizations, extends chemotherapy duration,¹⁵ and prolongs survival time.¹⁶

Research on pain management in cancer patients has explored various tele-monitoring interventions, including nurse-led daily calls,¹⁷ pharmacist-led interventions such as medication management apps,¹⁸ physician interventions such as automated voice systems for symptom reporting,¹⁹ and digital platforms enabling physician and pharmacist interventions for pain management.²⁰ Considering the findings of existing research, remote follow-up is expected to enhance pain self-management through appropriate opioid use and ultimately, improve QoL, contributing to more effective pain relief. However, these studies lacked assessments of patients’ self-management skills. Enhancing patient coping mechanisms is essential for enabling them to manage worsening symptoms at home and to maintain their QoL.

This study aimed to evaluate the effectiveness of the Cancer Pain Monitoring System (CAPAMOS), a tele-nursing system developed to alleviate cancer-related pain. The primary outcome was the change in pain levels, while the secondary outcomes included assessments of opioid self-management and health-related QoL.

Methods

Study design

We conducted an unblinded, parallel-group, randomized clinical trial to evaluate the effectiveness of tele-monitoring care through the CAPAMOS in outpatients with advanced cancer pain at Tohoku University Hospital in Japan. Trial reporting adhered to the Consolidated Standards of Reporting Trials (CONSORT) guidelines (Supplemental Consort 2025 checklist item description).

Participants

The eligibility criteria for participating in the study were: (1) a diagnosis of advanced cancer; (2) outpatient status; (3) regular opioid administration for cancer pain relief; (4) an average pain score of ≥ 3 over 24 hours, as measured by the numerical rating scale (NRS); (5) a prognosis of more than one month; (6) age ≥ 20 years; (7) awareness of their diagnosis; and (8) consent to participate. The exclusion criteria were: (1) regular opioid use for conditions unrelated to cancer pain, such as dyspnea, and (2) physical, mental, or cognitive impairments that could interfere with participating in the study.

Potential participants were identified through physician referral. The research staff explained the purpose of the study, and the participants provided written informed consent (by signing a consent form) during in-person interactions before enrolment in the study. This study monitored health conditions, such as pain, and was not expected to cause any direct harm. If a participant’s doctor determined that they were not in an adequate physical condition to continue with the study, they were asked to withdraw.

Setting

This study was conducted between October 2019 and February 2023. Participants were recruited from the Department of Medical Oncology, Palliative Medicine, Breast Surgery, and Respiratory Medicine at Tohoku University Hospital in Japan. These departments were selected based on their frequent use of opioids in the hospital.

Intervention and control

CAPAMOS care

Participants assigned to the intervention group received the CAPAMOS in addition to standard oncological care. CAPAMOS was developed as a tele-nursing platform to support cancer pain management in the home setting, with particular attention to usability and accessibility.²¹

The system comprised five main functions: (1) educational resources on cancer pain and pain management precautions; (2) medication monitoring, including documentation of analgesic use; (3) assessment of physical condition, including vital signs and pain intensity; (4) graphical tracking of changes in physical condition over time; and (5) direct communication with health care providers through video calls, telephone calls, or email contact with a tele-nurse.

The system was programmed to automatically notify the tele-nurse when predefined alert thresholds were exceeded. Alerts were generated when patients reported a Numerical Rating Scale (NRS) pain score of ≥ 7 or used rescue opioid medication five or more times within a 24-h period.

Participants used CAPAMOS at home and completed a daily electronic diary. Tele-nurses monitored patient-reported data and contacted participants during weekday daytime hours as needed. If no data had been entered on the previous day, the tele-nurse first attempted to contact the patient by telephone, followed by email if necessary. When an alert was triggered, tele-nurses provided support according to a predefined intervention algorithm. Additional details regarding the intervention protocol have been reported elsewhere.²²

Control group

At enrolment, participants of both groups received education on managing cancer pain. Participants in the control group were provided with a pain diary and a pain relief booklet. Nursing advice was available for the control group participants by telephone at the outpatient clinic.

Data collection procedures and outcomes

Data were collected at baseline upon enrolment (T0), and at 2 weeks (T1) and 4 weeks (T2) after enrolment. At each assessment point, participants in both groups completed study questionnaires during scheduled hospital visits.

The primary outcome was pain severity and pain-related interference, assessed using the Japanese version of the Brief Pain Inventory–Short Form (BPI-J).²³ The BPI-J measures pain severity during the preceding 24 hours, including worst pain and average pain intensity, as well as the extent to which pain interferes with daily functioning. Pain interference is evaluated across multiple domains, including general activity, mood, walking ability, work, interpersonal relationships, sleep, and enjoyment of life. Each item is scored on an 11-point numeric rating scale ranging from 0 to 10, with higher scores indicating greater pain severity and functional interference.

Secondary outcomes included opioid self-management and QoL. Opioid self-management was evaluated using the Opioid Self-Management Scale for Advanced Cancer Patients (OSSA).⁴ The OSSA comprises 33 items across six subscales assessing patients’ abilities related to opioid use and cancer pain management. Responses are rated on a five-point Likert scale ranging from 0 (“no”) to 4 (“yes”), with higher scores indicating better opioid self-management. QoL was assessed using the Japanese version of the EQ-5D-5L.²⁴

Sample size

The sample size for this study was estimated with reference to a prior study on telemonitoring of cancer-related pain in outpatients over a six-week period. Assuming a mean difference to be detected of d = 1.8 and a standard deviation of σ = 2, the required sample size was calculated using the formula:²⁵

$$n=\frac{{2\sigma }^2{\left\{Z\left(\alpha /2\right)+Z\left(\beta \right)\right\}}^2}{d^2}$$

By substituting the parameter values (Z(α/2) = 1.960, Z(β) = 0.842, d = 1.8 and σ = 2) into this equation, the calculated minimum sample size was 19.38 participants per group. After accounting for an anticipated approximately 20% attrition rate, the final target sample size was increased to 24 participants per group (total n = 48).

Randomization and blinding

Participants were randomized in a 1:1 ratio using a random number table. A random number table was generated by a researcher independent of the intervention and assigned by the interventionist. Participants were assigned to either the CAPAMOS group or the control group. Owing to the nature of the intervention, neither the participants nor the research staff were blinded to the group allocation. Data collection and analysis were performed by the interventionist; therefore, the data collectors were not blinded.

Data analysis

Statistical analyses were performed using SPSS 30.0 J for Windows (IBM, NY, USA). Participant characteristics and outcomes were summarized as frequencies and percentages for categorical variables, and as median (IQR) for continuous variables. Baseline demographic data as well as BPI-J, OSSA and EQ-5D-5L scores were compared between the CAPAMOS and control groups using Mann–Whitney U test for continuous variables and Chi-square tests for categorical variables.

The primary endpoint was the comparison of BPI-J scores between the two groups at four weeks. Changes in BPI-J scores within and between groups from T0 to T1 and T2 were analyzed using Generalized Linear Mixed Models (GLMM) with a gamma distribution and a log link function. To accommodate the gamma distribution, a constant of + 0.001 was added to all BPI-J scores prior to analysis. All subjects were included in the analysis. Missing data were addressed using complete case analysis.

Within-group changes in pain were assessed using the Friedman test. If there were significant differences, the Bonferroni test was performed to identify the time point of change. Excluding dropouts, 22 patients in the CAPAMOS group and 20 patients in the control group were included for the analysis.

Secondary endpoints (OSSA and EQ-5D-5L from T0 to T2) were analyzed using the signed-rank sum test for differences between T0 and T2 within groups and the Mann–Whitney U test for differences in change between both groups from T0 to T2. This analysis excluded patients who dropped out. All P-values were two-tailed, with a significance level set at 0.05.

Results

Study flow and baseline characteristics

A flowchart illustrating participant distribution during the study period is presented in Fig. 1. A total of 48 participants were randomly allocated to either the CAPAMOS or control group. Some participants in both groups dropped out prior to the two-week follow-up period for reasons such as worsening symptoms. Some dropped out before the four-week follow-up period due to hospital transfer or worsening symptoms. No intervention-related adverse events were observed in either group. No follow-up observations were conducted after the intervention period.

Fig. 1.

Participant flow diagram.

The demographic characteristics of the participants are summarized in Table 1. The overall median age was 53.50 years, and the majority were female (n = 34, 70.8%). The most common cancer type was breast cancer (n = 14, 29.2%), followed by colorectal cancer (n = 11, 22.9%), and pancreatic cancer (n = 8, 16.7%). Most participants (n = 35, 72.9%) were prescribed oxycodone as an opioid analgesic. No significant differences were found between the two groups in demographic characteristics.

Table 1.

The demographic characteristics of participants.

		Over all (n = 48)	CAPAMOS group (n = 24)	CONTROL group (n = 24)	P value
Sex, n (%)	Male	14 (29.2%)	8 (33.3%)	6 (25.0%)	0.75a
	Female	34 (70.8%)	16 (66.7%)	18 (75.0%)
Age (years), median (IQR)		53.50 (45.25–60.50)	53.50 (47.00–59.00)	53.50 (44.25–62.75)	1.00b
Type of cancer, n (%)	Breast	14 (29.2%)	8 (33.3%)	6 (25.0%)	0.95a
	Colorectal	11 (22.9%)	5 (20.8%)	6 (25.0%)
	Pancreas	8 (16.7%)	3 (12.5%)	5 (20.8%)
	Esophageal	3 (6.3%)	2 (8.3%)	1 (4.2%)
	Lung	3 (6.3%)	2 (8.3%)	1 (4.2%)
	Liver	2 (4.2%)	1 (4.2%)	1 (4.2%)
	Others	7 (14.6%)	3 (12.5%)	4 (16.7%)
PS, n (%)	0	3 (6.2%)	2 (8.3%)	1 (4.2%)	0.90a
	1	27 (56.3%)	13 (54.2%)	14 (58.3%)
	2	11 (22.9%)	6 (25.0%)	5 (20.8%)
	3	7 (14.6%)	3 (12.5%)	4 (16.7%)
Caregiver, n (%)	Yes	45 (93.8%)	23 (95.8%)	22 (91.7%)	1.00a
	No	3 (6.3%)	1 (4.2%)	2 (8.3%)
Job, n (%)	Full-time	13 (27.1%)	7 (29.2%)	6 (25.0%)	0.63a
	Part-time	8 (16.7%)	5 (20.8%)	3 (12.5%)
	Unemployed	27 (56.3%)	12 (50.0%)	15 (62.5%)
Around-the-clock opioid analgesics, n (%)	Oxycodone	35 (72.9%)	15 (62.5%)	20 (83.3%)	0.26a
	Morphine	1 (2.1%)	0	1 (4.2%)
	Fantanyl	1 (2.1%)	1 (4.2%)	0
	Hydromorphone	10 (20.8%)	7 (29.2%)	3 (12.5%)
	Narupane	1 (2.1%)	1 (4.2%)	0
As-needed opioid analgesics, n (%)	Oxycodone	36 (75.0%)	16 (66.7%)	20 (83.3%)	0.26a
	Morphine	1 (2.1%)	0	1 (4.2%)
	Fantanyl	1 (2.1%)	1 (4.2%)	0
	Hydromorphone	10 (20.8%)	7 (29.2%)	3 (12.5%)
Daily morphine equivalent dose (mg/d), median (IQR)		40.00 (30.00–60.00)	30.00 (16.88–82.50)	45.00 (30.00–60.00)	0.43b
Duration of opioid analgesics use median (IQR)		4.50 (2.00–12.00)	4.50 (2.00–12.00)	5.00 (2.00–12.00)	0.87b

CAPAMOS, Cancer Pain Monitoring System; PS, Performance Status; IQR, interquartile range.

^aChi-square test;

^bMann-Whitney U test.

Characteristics of the pain, opioids self-management and QoL at baseline

Baseline characteristics of the outcome measures are presented in Table 2. The worst and average pain scores were 6.00 and 4.00, respectively, on a 0–10 scale, indicating moderate to severe pain. The median total OSSA and QoL scores were 134.50 and 0.68, respectively. No significant differences were observed between the CAPAMOS and the control groups in any of the baseline outcome measures, supporting the validity of randomization (P > 0.05).

Table 2.

Characteristics of the pain, opioids self-management and QoL at baseline.

			Over all (n = 48)	CAPAMOS group (n = 24)	CONTROL group (n = 24)	P value
BPI-J	Severity items	Worst pain	6.00 (4.00–8.00)	5.00 (4.00–7.00)	6.50 (4.00–8.00)	0.35
		Average pain	4.00 (3.00–5.00)	4.00 (3.00–5.00)	4.00 (3.00–5.00)	0.60
	Interference items	General activity	4.00 (3.00–7.00)	3.00 (2.25–6.75)	5.00 (3.00–7.00)	0.33
		Mood	3.00 (2.00–7.00)	3.00 (2.00–6.00)	3.50 (1.25–7.00)	0.89
		Walking ability	3.00 (2.00–7.00)	3.00 (1.25–5.00)	3.00 (2.00–5.00)	0.88
		Normal work	4.00 (2.00–7.00)	3.00 (2.00–5.00)	4.00 (1.25–7.00)	0.76
		Relations with other people	2.00 (0.00–5.00)	2.00 (0.00–4.75)	1.50 (0.00–5.00)	0.70
		Sleep	3.00 (1.00–6.00)	2.50 (1.00–4.75)	4.50 (2.00–7.00)	0.13
		Enjoyment of life	5.00 (2.00–7.00)	5.00 (2.25–7.25)	5.00 (2.00–7.00)	0.81
OSSA	Total score		134.50 (125.00–142.50)	131.50 (124.25–138.75)	138.00 (126.75–146.00)	0.11
		Managing opioids and coping with pain (range: 9–45)	39.00 (36.00–42.75)	39.50 (37.00–42.00)	38.50 (35.25–43.00)	0.77
		Talking to health care professionals (range: 9–35)	30.50 (28.00–34.00)	31.50 (29.00–35.00)	30.00 (28.00–33.75)	0.17
		Talking to friends and family (range: 5–25)	20.00 (19.00–23.00)	20.00 (18.25–22.00)	20.00 (18.25–22.00)	0.23
		Recording pain and opioid use (range: 5–25)	19.00 (17.00–24.00)	20.50 (17.00–23.75)	19.00 (17.25–24.00)	0.98
		Living with the disease (range: 4–20)	15.00 (13.00–16.00)	16.00 (12.50–18.00)	14.00 (13.00–16.00)	0.07
		Understanding the characteristics of pain (range: 3–15)	9.00 (7.00–10.00)	9.00 (8.25–10.75)	8.50 (6.00–10.00)	0.12
EQ-5D-5L			0.68 (0.56–0.87)	0.74 (0.58–0.85)	0.65 (0.52–0.87)	0.63

QoL, quality of life; CAPAMOS, Cancer Pain Monitoring System; BPI-J, Japanese Brief Pain Inventory; OSSA, Opioid Self-Management Scale for Advanced Cancer Patients; EQ-5D-5L, Japanese version of the EuroQol 5-Dimension 5-Level questionnaire.

Mann–Whitney U test.

Endpoints

BPI-J changes for the two groups in the interaction of time

The group × time changes for the BPI-J are presented in Table 3. No significant group × time interaction effects were observed for any BPI-J items, indicating that the trajectories of change over time did not significantly differ between groups. The results for sleep interference showed a significant main effect of group [F(1,128) = 8.92, P= 0.001], but no significant main effect of time and group × time interaction. This suggests that although there were consistent differences between the groups, no significant improvement was observed over time, indicating that the intervention did not demonstrate any specific therapeutic effect.

Table 3.

Between-group differences in pain.

				T0 (Estimated mean ± SE)	95% Cl	T1 (Estimated mean ± SE)	95% Cl	T2 (Estimated mean ± SE)	95% Cl		F	df1	df2	P value
BPI-J	Severity items	Worst pain	CAPAMOS group	6.67 ± 0.64	5.51–8.06	6.09 ± 0.61	4.99–7.42	5.92 ± 0.59	4.86–7.21	Group	1.66	1	128	0.20
			CONTROL group	7.39 ± 0.71	6.12–8.94	6.37 ± 0.64	5.22–7.77	6.59 ± 0.68	5.37–8.09	Time	1.46	2	128	0.24
										Group × time	0.09	2	128	0.91
		Average pain	CAPAMOS group	4.95 ± 0.50	4.05–6.04	4.31 ± 0.45	3.50–5.30	3.94 ± 0.42	3.20–4.85	Group	1.25	1	128	0.27
			CONTROL group	5.01 ± 0.51	4.11–6.13	4.71 ± 0.50	3.82–5.80	4.49 ± 0.49	3.62–5.57	Time	2.00	2	128	0.14
										Group × time	0.25	2	128	0.78
	Interference items	General activity	CAPAMOS group	5.12 ± 0.76	3.81–6.87	4.38 ± 0.66	3.15–5.82	4.96 ± 0.77	3.65–6.74	Group	1.35	1	128	0.25
			CONTROL group	6.32 ± 0.94	4.71–8.49	4.52 ± 0.70	3.32–6.15	5.46 ± 0.88	3.97–7.50	Time	2.17	2	128	0.12
										Group × time	0.21	2	128	0.81
		Mood	CAPAMOS group	4.82 ± 0.78	3.50–6.64	3.50 ± 0.59	2.50–4.88	4.02 ± 0.68	2.88–5.62	Group	2.47	1	128	0.12
			CONTROL group	5.10 ± 0.83	3.70–7.03	4.53 ± 0.77	3.24–6.34	5.06 ± 0.89	3.58–7.15	Time	1.25	2	128	0.29
										Group × time	0.31	2	128	0.73
		Walking ability	CAPAMOS group	4.96 ± 1.03	3.29–7.47	3.64 ± 0.79	2.38–5.58	4.13 ± 0.89	2.69–6.33	Group	0.10	1	128	0.75
			CONTROL group	4.48 ± 0.93	2.97–6.74	3.69 ± 0.80	2.41–5.67	3.93 ± 0.88	2.53–6.12	Time	1.06	2	128	0.35
										Group × time	0.06	2	128	0.95
		Normal work	CAPAMOS group	4.08 ± 0.64	2.99–5.58	3.32 ± 0.51	2.45–4.49	3.46 ± 0.56	2.50–4.77	Group	0.50	1	128	0.48
			CONTROL group	4.46 ± 0.70	3.26–6.09	3.69 ± 0.55	2.66–4.86	3.85 ± 0.66	2.74–5.40	Time	0.99	2	128	0.38
										Group × time	0.00	2	128	1.00
		Relationships with other people	CAPAMOS group	3.00 ± 0.61	2.01–4.48	1.82 ± 0.39	1.20–2.77	1.91 ± 0.41	1.26–2.91	Group	1.95	1	128	0.17
			CONTROL group	2.54 ± 0.52	1.70–3.80	2.41 ± 0.51	1.58–3.67	3.50 ± 0.78	2.26–5.44	Time	0.96	2	128	0.39
										Group × time	1.71	2	128	0.18
		Sleep	CAPAMOS group	4.03 ± 0.80	2.73–5.96	2.72 ± 0.56	1.81–4.08	3.48 ± 0.72	2.32–5.24	Group	8.92	1	128	0.01
			CONTROL group	5.71 ± 1.13	3.86–8.44	4.65 ± 0.96	3.09–6.99	4.99 ± 1.07	3.27–7.62	Time	1.61	2	128	0.20
		Enjoyment of life	CAPAMOS group	5.01 ± 0.79	3.67–6.83	3.17 ± 0.52	2.29–4.38	4.15 ± 0.68	3.00–5.73	Group × time	0.19	2	128	0.83
										Group	2.14	1	128	0.15
			CONTROL group	5.16 ± 0.81	3.78–7.04	4.25 ± 0.70	3.07–5.89	5.08 ± 0.87	3.62 ± 7.11	Time	2.62	2	128	0.08
										Group × time	0.44	2	128	0.64

BPI-J, Japanese Brief Pain Inventory; CAPAMOS, Cancer Pain Monitoring System; SE, standard error.

General Linear Mixed Model.

Within-group changes in BPI-J are shown in Table 4. There was no significant change in the control group, but there was a significant change in the CAPAMOS group. Average BPI-J pain scores across the three time points were significantly different: T0 4.0, T1 3.0, and T2 3.0 (P= 0.02). Additional Bonferroni tests showed a significant improvement in pain from T0 to T2 (P= 0.02). The interference with enjoyment of life across the three time points was significantly different: T0 5.0, T1 2.0, and T2 3.5. Additional Bonferroni tests showed a significant improvement in pain interference from T0 to T1 (P= 0.02), indicating an improvement in pain interference over two weeks.

Table 4.

Within-group differences in pain.

				n	T0	T1	T2	P value
BPI-J	Severity items	Worst pain	CAPAMOS group	22	5.00 (4.00–7.00)	5.00 (3.00–7.00)	5.00 (3.00–7.25)	0.21
			CONTROL group	20	5.50 (4.00–7.75)	6.00 (3.25–7.75)	5.50 (3.00–8.00)	0.82
		Average pain	CAPAMOS group	22	4.00 (3.00–5.00)	3.00 (2.00–4.00)	3.00 (2.00–4.25)	0.02
			CONTROL group	20	4.00 (3.00–5.00)	4.00 (2.25–5.75)	4.00 (2.00–5.75)	0.53
	Interference items	General activity	CAPAMOS group	22	3.00 (1.75–5.25)	3.00 (2.00–4.25)	3.00 (2.00–5.25)	0.33
			CONTROL group	20	4.50 (3.00–6.75)	3.50 (1.25–5.75)	5.00 (2.00–6.75)	0.25
		Mood	CAPAMOS group	22	3.00 (2.00–6.00)	3.00 (1.75–4.25)	3.00 (2.00–5.00)	0.45
			CONTROL group	20	3.50 (2.25–7.75)	4.50 (1.00–6.00)	4.50 (2.25–7.00)	0.55
		Walking ability	CAPAMOS group	22	3.00 (1.00–5.00)	2.50 (1.00–4.00)	3.00 (1.00–5.00)	0.15
			CONTROL group	20	3.00 (2.00–5.00)	1.50 (0.00–5.00)	2.00 (0.00–6.00)	0.29
		Normal work	CAPAMOS group	22	3.00 (1.75–5.00)	3.00 (2.00–5.00)	3.00 (1.00–6.00)	0.96
			CONTROL group	20	4.50 (2.00–7.00)	3.00 (2.00–5.00)	4.00 (1.00–6.75)	0.27
		Relationships with others	CAPAMOS group	22	2.00 (0.00–4.25)	1.00 (0.00–3.00)	2.00 (0.75–3.00)	0.44
			CONTROL group	20	1.50 (0.00–5.00)	2.00 (0.00–5.00)	3.50 (1.00–6.00)	0.50
		Sleep	CAPAMOS group	22	2.00 (1.00–4.25)	1.50 (1.00–3.25)	2.00 (1.00–5.00)	0.50
			CONTROL group	20	3.00 (1.25–6.75)	3.50 (1.00–6.00)	4.00 (1.25–6.50)	0.48
		Enjoyment of life	CAPAMOS group	22	5.00 (2.00–5.75)	2.00 (1.00–4.25)	3.50 (2.00–6.25)	0.02
			CONTROL group	20	4.50 (1.25–7.00)	4.00 (0.25–6.00)	5.00 (3.00–7.00)	0.88

BPI-J, Japanese Brief Pain Inventory; CAPAMOS, Cancer Pain Monitoring System.

Friedman test.

Secondary endpoints

The results of changes over time in secondary outcomes are shown in Table 5.

Table 5.

Between-group and within-oup changes in secondary outcomes.

		CAPAMOS group (n = 22)			CONTROL group (n = 20)			T0-T2 P valueb
		T0	T2	P valuea	T0	T2	P valuea
OSSA	Total score	138.0 (128.3–146.0)	136.5 (127.5–148.0)	0.78	131.5 (123.3–138.5)	123.5 (118.5–143.3)	0.86	0.85
	Managing opioids and coping with pain (range: 9–45)	39.5 (37.0–42.0)	41.0 (36.0–44.0)	0.35	38.5 (35.3–43.0)	39.0 (33.5–42.0)	0.33	0.46
	Talking to health care professionals (range: 9–35)	31.5 (29.0–35.0)	31.5 (29.0–35.0)	0.15	29.5 (27.3–32.0)	28.5 (26.0–34.8)	0.53	0.16
	Talking to friends and family (range: 5–25)	21.0 (19.0–25.0)	22.0 (19.0–25.0)	0.39	20.0 (17.3–21.8)	20.0 (17.0–20.0)	0.73	0.97
	Recording pain and opioid use (range: 5–25)	20.5 (16.8–23.5)	21.5 (19.0–23.3)	0.03	19.0 (17.3–24.0)	21.5 (19.0–24.0)	0.39	0.44
	Living with the disease (range: 4–20)	16.0 (13.5–18.0)	15.0 (12.8–16.0)	0.25	14.5 (13.3–16.0)	14.0 (12.0–16.0)	0.25	0.49
	Understanding the characteristics of pain (range: 3–15)	9.5 (8.0–11.0)	10.0 (9.0–11.3)	0.54	8.0 (6.0–10.0)	8.0 (7.0–10.8)	0.57	0.87
EQ-5D-5L		0.77 (0.63–0.87)	0.70 (0.58–0.92)	0.68	0.70 (0.53–0.89)	0.77 (0.58–0.95)	0.35	0.26

OSSA, Opioid Self-Management Scale for Advanced Cancer Patients; EQ-5D-5L, Japanese version of the EuroQol 5-Dimension 5-Level questionnaire.

^aWilcoxon signed-rank sum test;

^bMann-Whitney U test.

Opioid self- management

There was no significant change in the OSSA scores between the two groups during the study period. For the within-group comparison, there was no significant change in the OSSA scores within the control group. However, there was a significant improvement in the CAPAMOS group, with “recording pain and opioid use,” showing an increase from a score of T0 20.5 to T2 21.5 (P = 0.03).

QOL

The EQ-5D-5L was not significantly different between the control group and the CAPAMOS groups from T0 to T2 (P = 0.26). There were also no significant differences within groups.

Compliance with CAPAMOS use

The CAPAMOS group used the system for an average of 25.55 out of the 28 days (SD: ± 3.60). The overall compliance rate was 91%.

Discussion

Main findings

This study evaluated the effectiveness of a tele-nursing intervention in which patients with advanced cancer recorded their daily pain levels and opioid use through the CAPAMOS system, with follow-up support provided by a tele-nurse. The results showed that the CAPAMOS intervention for the relief of cancer pain did not result in any statistically significant changes after considering the interaction between time and group. However, within the CAPAMOS group, there was a significant improvement in average pain at four weeks and in interference with enjoyment of life at two weeks (Table 4).

A key finding of this study is the potential need for a longer intervention period—beyond four weeks—for meaningful pain relief. Although CAPAMOS alone reduced mean pain and pain-related disability, the results were insufficient to significantly improve pain relief between the two groups. This lack of significance may be attributed to an insufficient sample size; the prior finding²⁶ used for the sample size calculation was not derived from a randomized controlled trial, which may have led to an underpowered study. Randomized controlled trials in future should re-evaluate sample size requirements by incorporating findings that account for time-by-group interaction effects to more accurately capture the intervention’s impact. Prior randomized controlled trials employing tele-monitoring through applications or Information and Communication Technology systems have reported lower pain levels compared to controls at one month.^18,20 However, few studies have incorporated time interaction effects in their analyses.^19,27 Among studies that did consider time interactions, interventions lasting four to six weeks,¹⁹ six weeks,⁶ and eight weeks²⁷ were found to be effective in reducing pain. According to a recent systematic review²⁸ of tele-communication technologies for pain management in patients with advanced cancer, evaluating the time-series effects of tele-health interventions and establishing a direct relationship between duration and pain relief remains challenging. However, the review suggests that daily reporting may lead to more effective outcomes than reporting twice weekly. The results of this study suggest that tele-nursing may reduce patient pain; however, in an intervention designed with daily reporting, signs of effectiveness only began to emerge after four weeks. These findings further suggest that a CAPAMOS intervention period longer than four weeks may be necessary to achieve significant pain relief compared to the usual care control group.

A novel finding of this study is that CAPAMOS can help alleviate the emotional distress associated with living in pain, as evidenced by reduced scores for pain-related interference with enjoyment of life following a two-week intervention. Reports indicate that advanced cancer patients receiving home care utilize pain relief to cope with the existential distress arising from their autonomy being threatened by pain.²⁹ These patients continue to live by fulfilling their roles in life and finding joy and reasons for living, which is why their self-management of pain is crucial for maintaining their autonomy.³⁰ Follow-up using m-health for cancer patients has been reported to improve their self-efficacy.³¹ Given these findings, although the precise mechanism remains unclear, findings from the CAPAMOS usability study suggest that the system may enhance pain management by fostering a greater sense of control.²¹ Patients reported benefits such as, “Looking at the data helps me get used to self-management,” and “There is comfort of being monitored by medical professionals and being able to consult without going to the hospital.” By providing support tailored to the patients’ daily lives, CAPAMOS may have facilitated self-management and contributed to an improved ability to maintain enjoyment in everyday routines.

While no significant between-group effects on measures of self-management ability were observed in this study, self-management of pain and opioid records improved within the CAPAMOS group. As no prior studies have specifically assessed self-management abilities in this context, direct comparisons are limited. In this study, participant compliance with the CAPAMOS system was 91%, exceeding the approximately 70% compliance rate observed in a prior study,¹⁹ in which participants were prompted to complete a daily pain diary. The latest systematic reviews report that providing real-time feedback to patients effectively contributes to the relief of cancer pain.²⁸ As compliance with the pain diary entries was not assessed in the control group, the effect of self-management could not be determined. It is possible that adherence improved in the CAPAMOS group due to the tele-nurse’s feedback on the data entered. Nevertheless, since there was no baseline difference in self-management abilities and both groups demonstrated high overall compliance, it is likely that participants in both groups were similarly engaged. Additionally, all participants in both groups received pain education and educational tools, including algorithms for managing unrelieved pain. This may have contributed to the lack of differences in self-management abilities between the groups.

Implications for nursing practice and research

This study identified the effective duration of telenursing interventions for pain relief and demonstrated that improving self-management through such interventions can reduce pain levels. Specifically, the observation that more than four weeks of intervention are required could serve as a foundation for scaling this pilot study into a full-scale clinical trial. Furthermore, the results suggest a new hypothesis: telenursing improves cancer pain self-management, thereby reducing interference with daily life and enhancing patient well-being. Future research is needed to further elucidate these underlying mechanisms.

In the practice of telenursing for symptom relief in cancer patients, a safe and versatile telenursing system and an organizational framework to support telenursing are essential. The care process emphasizes the importance of assessing patients' telenursing eligibility, building trust, efforts to obtain accurate symptom data, and evidence-based practice.³² Regarding the clinical context in Japan, although a medical fee system for remote consultations in cancer nursing was introduced in 2022, several challenges remain. For instance, 5.7% of patients were ineligible for telenursing, and 12.0% reported no perceived need;³³ however, this indicates a significantly large potential target population. Despite this potential, only 4.4% of nurses currently conduct teleconsultations. A significant systemic barrier remains, as remote monitoring is not yet included in the medical fee reimbursement system in Japan. Additionally, 31.0% of nurses reported being unsure how to implement teleconsultations, further limiting their clinical application. To address these gaps, future research needs to provide a deeper understanding of specific telenursing techniques.

Limitations

This study has several limitations. First, there is a potential bias in the use of CAPAMOS. Global internet usage was projected to increase from 37% in 2014% to 71% in 2024.¹¹ Japan has maintained relatively high internet penetration, rising from 82.9% in 2014% to 84.9% in 2022.³⁴ Notably, 95.4% of individuals in their 50s would have had Internet experience in 2022, suggesting they may not be reluctant to use it. However, the region in which this study was conducted had a lower smartphone usage, at 72.1%, and even lower tablet usage, at 28.6%. A prior study indicated a preference for smartphone applications because tablets are less convenient to carry.²³ However, in this study, electronic devices were selected based on their large screens and intuitive user-friendly interfaces, considering the target older adult population. As a result, the system may not have supported timely follow-up during episodes of pain, depending on whether the patients were predominantly at home or away.

Second, blinding the participants to group allocation was not possible in our study. Many systematic reviews of eHealth interventions report that blinding is difficult due to the nature of the intervention.^35,36 The CONSORT-EHEALTH checklist (v.1.6.1) notes that in web-based trials, blinding participants is typically not feasible, and this necessity must be explicitly recognized.³⁷

Thus, the lack of blinding in this study was an unavoidable consequence of its design, and these methodological limitations should be considered when interpreting the results. Moreover, although blinding data collectors and analysts is generally recommended,³⁵ it was not possible in this study due to budgetary constraints that precluded the involvement of an independent researcher, which constitutes a further limitation.

Third, participants reported relatively low baseline pain intensity (NRS ≥ 3), which may have made it more challenging to detect changes in self-management ability. Additionally, factors such as history of opioid use and dosage are factors that influence self-management, which may in turn influence changes in pain severity. Therefore, future research should consider refining the inclusion criteria to focus on individuals who experience persistent or inadequately managed pain, or who have not yet achieved personalized pain goals. As pain is a subjective and highly individual experience, targeting patients with more complex pain profiles and accounting for confounding factors that influence self-management, such as opioid history and dosage, may provide clearer insight into the effectiveness of telenursing.

Fourth, as this study was designed as a pilot study, the sample size was small. Furthermore, missing data were handled using a complete-case analysis, which reduced the number of observations available for analysis and thereby decreased statistical power. This may have resulted in a Type II error and an underestimation of the effect, thereby limiting the generalizability of the findings. To strengthen these findings, increasing the sample size and improving statistical power will be necessary.

Fifth, there is a potential for gender-related sampling bias. In selecting clinical departments for this study, we prioritized those with high frequencies of opioid use. Although urology was initially considered to ensure a more balanced gender distribution, it was excluded due to clinical constraints, including staffing shortages. The high proportion of breast cancer patients in this study population may limit the generalizability of the findings, given reported sex differences in pain perception.³⁸ Nevertheless, no statistically significant difference was observed in the male-to-female ratio of the final study population, suggesting that gender-related bias was minimized. To enhance generalizability, future research should involve multicenter studies encompassing a broader range of clinical specialties.

Conclusions

This study found no significant difference in pain levels between the CAPAMOS group and the control group. However, cancer pain and interference in the CAPAMOS group improved. This suggests that CAPAMOS tele-nursing intervention may require more than four weeks for effective pain relief.

CRediT authorship contribution statement

Shiori Yoshida: Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Shin Takahashi: Writing – review & editing, Project administration, Methodology, Formal analysis, Conceptualization. Keita Tagami: Writing – review & editing, Project administration, Conceptualization. Narumi Harada-Shoji: Writing – review & editing, Project administration. Kota Ouchi: Writing – review & editing, Project administration. Fumiko Sato: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. All authors have read and approved the final manuscript.

Ethics statement

This study was approved by the Ethics Committee of Tohoku University Graduate School of Medicine (Approval No. 2019-1-499, Updated Approval No. 2020-1-194) and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All the participants provided written informed consent.

Data availability statement

The data that support the findings of this study are available from the corresponding author, SY, upon reasonable request. The data are not publicly available because of privacy and ethical restrictions.

Declaration of generative AI and AI-assisted technologies in the writing process

No AI tools/services were used during the preparation of this study.

Funding

This study was funded by the JSPS KAKENHI (Grant No. 18H06386). The funders had no role in the study design; data collection, analysis, or interpretation; manuscript preparation; or the decision to submit the article for publication.

Declaration of competing interest

Kota Ouchi has received honoraria for lectures, presentations, speakers’ bureaus, manuscript writing, and educational events from Ono Pharmaceutical, Bristol-Myers Squibb, Eli Lilly Japan, Merck, and Taiho Pharmaceutical. These activities were conducted outside the scope of the submitted work and are unrelated to the research presented in this study. These companies had no involvement in the study design, data collection, analysis, or the preparation of this manuscript. All other authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.

Appendix A. Supplementary data

The following is the Supporting Information to this article: