Can Remote Patient Monitoring Be the New Standard in Primary Care of Chronic Diseases, Post-COVID-19?

Introduction

The COVID-19 pandemic has transformed remote care provision, in what Wilhite et al. have declared “the telemedicine takeover.”¹ Provision has been rapidly implemented and scaled up in many places, as health care settings have needed to reduce face-to-face contact to ensure social distancing, and triage care provision to accommodate pandemic-specific responsibilities. Other settings are remotely monitoring suspected COVID-19 patients,² while several countries collect anonymized symptom data on a large scale through apps. As we build an evidence base about the chronicity of “long covid” or “post-covid” (see Nabavi³ for a brief discussion), remotely monitoring COVID-19 patients post-discharge, or those with risk factors for persistent symptoms, could be a way for health care providers to respond quickly with tailored care.

Previously, before the current pandemic, health care systems faced the challenge of managing, rather than curing, the chronic diseases with which people are living longer. Management includes preventing the impairment of functioning and helping people maintain as good a quality of life for as many years as possible.⁴ This requires frequent assessments of the patient’s health status to monitor treatment regime, prevent deterioration, and prevent the development of additional diseases, injuries, and complications. Yet, neither providers nor patients have the capacity to meet face-to-face to conduct such frequent assessments.

One solution is for patients to be able to transmit health data without seeing providers and for these data to be sent and evaluated often enough to initiate interventions or treatment adjustments before the patient’s health status becomes acute. Strategies that allow patients to remain at home, while they transmit data and receive follow-up services can be collectively referred to as remote patient monitoring (RPM). Watson et al.⁵ have recently argued that more than traditional rural access and video communication, RPM may be uniquely situated to address care needs in the context of COVID-19.

No generally accepted definition of the term RPM exists. The terms telemedicine, telehealth, and e-health are often used interchangeably. For example, three Cochrane reviews use different terms for similar interventions: in Kew and Cates,⁶remote monitoring refers to interventions that allow patients to share data using information and communication technologies and health care providers to respond, and is situated as “a form of ‘telehealth,’ otherwise referred to as ‘telecare,’ ‘digital health,’ ‘mHealth,’ or ‘telemedicine’” (page 7). McLean et al.⁷ writes that “telemedicine” implies health care is being delivered, and instead uses telehealthcare to mean the electronic transfer of patient data and the receipt of provider feedback. In Flodgren et al.,⁸interactive telemedicine specifically means providers respond to patient data transmission in real time, while remote monitoring services also include provider feedback, but not in real time (page 7). Two 2021 overviews of systematic reviews used telehealth to cover all patient-provider interaction, including RPM, except when conducted over the phone or using noninteractive websites.^9,10 Mordaunt¹¹ pointed out in a recent commentary that a lack of precise definition, particularly in a systematic review, seriously hampers meta-analyses as well as qualitative summaries of effect.

The Norwegian Directorate of Health (DoH) is interested in implementing a specific type of RPM that occurs within the primary health care services. We worked closely with the DoH to describe this type of RPM, as described in the review protocol.¹² First, the patient answers questions about their own health condition using a digital device, and/or takes measurements of metabolic data related to their diagnosis using digital devices. These health data are then transmitted to a provider. In step 2, data evaluation, the patient’s data are received and evaluated by the provider. Alternatively, the assessment may be automated (i.e., evaluated by a program, as in a “traffic light” system), and the program forwards data it evaluates as high-risk to health care providers for further follow-up. In the third and final step, follow-up, a provider follows up with the patient if the patient’s health data indicate a concern.

Research Question

The relevance to Norway and countries with similar health care system needs of the types of RPM implemented in previous studies is unclear. The DoH needs knowledge of the effectiveness and cost utility of RPM, and particularly which patient group may benefit most. This review therefore sought to answer the following question: what is the effect of a specific type of RPM on clinical and health care utilization outcomes of certain groups of chronically ill patients?

Methods

We conducted an overview of reviews. A review protocol was developed by the research team, peer reviewed, approved by the DoH, and published in Norwegian and in English.^12,13

Inclusion and Exclusion Criteria

We searched for overviews of reviews and systematic reviews, with the following inclusion criteria:

• Population: persons who are 18 years of age or older, have a chronic disease (cancer, cardiovascular disease, chronic lung diseases, chronic musculoskeletal disorders, diabetes, hypertension, impaired vision/hearing, mental disorders, or osteoporosis), and are neither in the very early nor very acute phase of these conditions.

• Intervention: RPM according to the definition above; RPM provided in the primary health care services; RPM involving phones, mobile phones, videos, and portable/implantable devices; and data sent regularly (at least twice per year).

• Comparison: standard care not involving RPM, or another type of monitoring that does not meet our definition.

• Outcomes: mental health (symptoms or diagnoses); diagnosis-specific physical health; physical functioning level; quality of life; consumption of health services (hospital admissions, emergency care, number of bed-days, outpatient consultations, nursing home stays, home care [both home nursing and practical assistance], and general practitioner consultations); and health service costs.

• Year: search for literature conducted 2015 or later.

We excluded reviews if participants were reported to have reduced cognitive function, as they may not be able to report their own health outcomes. Purely internet-based RPM or RPM executed through mobile applications on phones or tablets was not of interest to the DoH, and therefore reviews of these types of RPM were also excluded. Finally, reviews that explicitly excluded Norway or the part of the world in which Norway is located, for example, reviews of low- and middle-income countries, were also excluded, to focus on countries with more immediate economic similarities to Norway. We had no language exclusion a priori.

Literature Search

An information specialist developed and conducted systematic searches for literature in MEDLINE, Embase, PsycINFO, Epistemonikos, Cochrane Database of Systematic Reviews, and Web of Science. We employed both subject headings (e.g., MeSH terms in MEDLINE) and free text terms characterizing the intervention and population. The complete search strategies and results are in Appendix A1.

Study Selection

Two review authors independently assessed all titles and abstracts from the systematic literature search for eligibility using Rayyan.¹⁴ Full-text publications were retrieved when one or both author(s) judged the review to likely meet the inclusion criteria. Full-text publications were then read by two authors independently using Covidence,¹⁵ with final inclusion based on consensus by the two authors. As anticipated in the protocol, few of the overviews and systematic reviews read in full text described interventions thoroughly enough for us to determine eligibility. Therefore, when assessing a systematic review, we retrieved each review’s included primary studies and assessed eligibility after reading in full text. If a systematic review included at least one primary study that met all our inclusion criteria, the entire systematic review was included in our review, along with only the primary study that met our inclusion criteria. (When reading an overview of systematic reviews, we did not proceed to primary studies, but read the full text of each included systematic review. If any single systematic review met our criteria, we included the entire overview.) Appendix A2 contains a list of publications excluded after full-text review.

Assessment of Included Systematic Reviews

After a review was read in full text and determined to meet our definition of RPM and the other inclusion criteria, we assessed its methodological quality using the NIPHs checklist for systematic reviews (Appendix A3). Two authors independently assessed methodological quality and met to discuss conflicts. Any disagreement was resolved through discussion. Only reviews rated as having high methodological quality were included in our review; in practice, this required that a review met all items on the checklist.

Assessing Risk of Bias in Included Primary Studies

We extracted and presented systematic review of authors’ own risk of bias assessments of included primary studies. All the reviews with relevant primary studies included randomized controlled studies (RCTs), and used the Cochrane Risk of Bias Tool. The authors of one review¹⁶ modified this tool slightly by not reporting blinding of participants and personnel and other biases and instead reporting funding as a separate (risk of bias) criterion. We chose to report systematic review authors’ judgment on funding as part of the assessment of other biases.

Data Extraction and Synthesis

One author extracted data from the included systematic reviews, and another author double checked data extraction for accuracy and completeness. If the systematic review(s) did not sufficiently report findings or characteristics, we proceeded to the RCT itself to extract the necessary information. As neither entire overviews of reviews nor systematic reviews met our inclusion criteria, we summarized only data from relevant RCTs the reviews contained.

Interventions lasted 6 months (four RCTs), 9 months (three RCTs), or 12 months (four RCTs). When an RCT measured an outcome at multiple time points, the most recent measurement was used. In one RCT, data were collected 3 months after the completion of the intervention; the remainder of the RCTs collected outcome data at intervention completion. We presented normally distributed results for each primary outcome in a forest plot and reported raw mean differences, standardized mean differences, odds ratios, or rate ratios; non-normally distributed outcomes were reported as medians. However, to avoid misleading readers into thinking that our overview included a meta-analysis (inappropriate because this review is an overview of systematic reviews, and not a systematic review that exhaustively searched for and identified RCTs), we did not produce the summary statistic within forest plots or report these summary statistics in the summary of findings table. Detailed results and forest plots for 23 secondary outcomes are available upon request.

Data for one outcome, HbA1c, were only available as adjusted for baseline values in both Dario et al.¹⁷ and Egede et al.,¹⁸ and were presented alongside the remaining RCTs’ unadjusted outcomes. One RCT¹⁹ contained more than two arms: usual care, high-intensity RPM, and low-intensity RPM. The high-intensity RPM differed only from the low-intensity arm in that the former included automated messages that were more tailored to each patient, compared to the latter. We analyzed only data from the high-intensity arm, as dividing this RPM into two comparisons would have duplicated the usual care group’s data.

Missing Data

Several RCTs failed to report standard deviations. For the purposes of visualizing outcomes in forest plots, we borrowed standard deviations from RCTs with the most similar patient population. Wakefield et al.’s¹⁹ missing systolic blood pressure standard deviations were borrowed from Magid et al.,²⁰ due to both patient populations coming from the United States and having co-occurring hypertensive and diabetic patients. Schillinger et al.’s²¹ standard deviations were used for Carter et al.’s²² blood pressure and body mass index, outcomes due to both patient groups being American, obese, and urban, and with racial minorities overrepresented.

Assessment of Certainty of the Evidence

We assessed the certainty of the evidence for each of the primary outcomes using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology²³ and GRADEpro software.²⁴ GRADE helps systematic reviewers assess how valid and reliable the findings of their review are, and can be used even when meta-analytic pooled effect estimates are not available.²⁵ An assessment for each primary outcome is conducted using five criteria: risk of bias, the precision of effect estimates, the consistency of individual study results, how direct the research question is answered by the evidence, and the risk of publication bias. The GRADE assessments were conducted using the software GRADEpro.

Protocol Deviations

During our full-text review of RCTs, we decided to include patients with hypertension, although they were not one of the eight original chronic condition groups. This was because of the overlap between diabetes and hypertension among many of the included RCTs’ patients, and because hypertension is a common comorbidity with many of the other chronic conditions of interest. The only practical consequence of this protocol deviation was to allow the inclusion of one RCT, Magid et al.,²⁰ which recruited hypertensive patients and reported that nearly half also had either diabetes or renal disease.

Results

Results of the Literature Search

The literature search for reviews resulted in 3,373 unique records, as exhibited in Figure 1. We excluded 151 publications after full-text review, most commonly for not reporting on RPM as defined by the DoH, not being a systematic review, or not being of high methodological quality. We included four systematic reviews.^16,26–28 Appendix A2 lists all publications excluded after full-text review, with reasons for exclusion and chronic disease category.

Description of the Included Systematic Reviews

The four included systematic reviews searched for RCTs, cluster RCTs, quasi-RCTs, controlled before-and-after studies, and interrupted time series studies of different types of remote communication or health care delivery. Every review defined their intervention of interest differently and with a slightly different name, as displayed in Table 1. Bittner et al.²⁶ searched for telerehabilitation services, explicitly including remote monitoring within this definition, for patients with impaired vision. Faruque et al.¹⁶ searched broadly for telemedicine interventions, defined as all electronic forms of communication, among diabetes patients. Kebede et al.²⁸ focused on diabetes type 2 patients using digital interventions, meaning any technology-based intervention. Posadzki et al.²⁷ searched for e-health interventions among patients with long-term conditions. Faruque et al.¹⁶ excluded studies that involved patients with gestational diabetes, and Kebede et al.²⁸ excluded studies with diabetes type 1 patients. Aside from this, there were no other disease-related exclusion criteria specified by the systematic reviews.

Table 1. Description of Included Systematic Reviews

SYSTEMATIC REVIEW	SEARCH DATE	RCTsa	DESCRIPTION OF RPM, IN THE AUTHORS’ WORDS/INTERVENTIONS OF INTEREST	CHRONIC DISEASE
Bittner et al.26	June 2015	0 included in this review; 0 analyzed by the authors.	Telerehabilitation: rehabilitation services delivered through information and communication technologies, and including monitoring and clinical evaluation services.	Low vision
Faruque et al.16	November 2015	7 included in this review, out of 111 analyzed by the authors: Wakefield et al.,19 Schillinger et al.,21 Carter et al.,22 Rodriguez-Idigoras et al.,29 Nicolucci et al.,30 Steventon et al.,31 Stone et al.32	Telemedicine: all electronic forms of communication between provider and patient (telephone, smartphone application, e-mail, text messaging, web portal, “smart” device, or glucometer).	Diabetes type 1 or 2
Kebede et al.28	June 2017	3 included in this review, out of 21 analyzed by the authors: Dario et al.,17 Egede et al.,18 Wild et al.33	Digital interventions: technology based, such as m-health interventions, web-based interventions, interventions delivered through the use of a personal digital assistant, a tablet, a computer, the internet, telemedicine, videoconferencing, telehealth, or other forms of e-health.	Diabetes type 2
Posadzki et al.27	June 2015	1 included in this review, out of 132 analyzed by the authors: Magid et al.20	e-health interventions: interventions that use devices featuring interactive wireless communication capability, operating web-based applications and with high portability (such as smartphone, computer, and personal digital assistance tools), or interventions comprising self-care, self-management, self-care, behavioral change, or education dissemination.	Any long-term condition

Descriptions of the Included RCTS from the Reviews

With the exception of Bittner, a review that found no relevant studies, these reviews’ interventions of interest were defined broadly, and therefore, the RCTs they analyzed also reported on a wide array of interventions. Altogether, only 11 RCTs implemented an intervention that met our definition of RPM. These RCTs were conducted in Italy, Spain, United Kingdom, and the United States. None of these RCTs occurred in more than one of the included reviews. Seven RCTs involved diabetes type 2 patients, one RCT included both type 1 and 2 diabetes patients, two RCTs included patients with both hypertension and diabetes, and one RCT included only hypertensive patients.

Patients’ Health Status

The RCTs included patients with average ages ranging from 51 to 68. Six RCTs reported on participants’ comorbidities /multimorbidities. About 4 of 10 patients in Rodriguez-Idigoras et al.²⁹ and Nicolucci et al.³⁰ reported dyslipidemia. Chronic obstructive pulmonary disease was reported by 6.3–14.2% in Dario et al.,¹⁷ Steventon et al.,³¹ and Stone et al.,³² and heart failure by 3.6–16.1% in Steventon et al.,³¹ Stone et al.,³² and Nicolucci et al.³⁰ Stroke was reported by 2.4–4.7% of the patients in Dario et al.¹⁷ and Nicolucci et al.³⁰

Given these similarities in multimorbidities, all 11 RCTs could be grouped together as involving “diabetes and/or hypertension” patients.

It is worth noting that psychiatric morbidities were not reported by any of the RCTs. They were exclusion factors of five RCTs in some manner, such as “reliance on psychotropic medication,”²² “mental conditions, depression, or high anxiety;… abuse of drugs or alcohol,”³⁰ “alcohol or drug abuse/dependency, active psychosis or acute mental disorder,”¹⁸ “psychotic illness,”²¹ and “psychosis.”¹⁹

Descriptions of the Various Types of RPM used in the Included RCTS

In accordance with our definition, RPM referred to the three steps of digital data transmission, evaluation, and follow-up. Still, there were a variety of devices used to transmit data, three methods of data evaluation, and some variation in the method of follow-up response given to patients, as summarized in Table 2. In all RCTs, patients collected and transmitted up to three biometric measurements: blood glucose, blood pressure, and weight. Frequency of patient data transmission varied from three times a day to twice a month, with two RCTs individualizing frequency according to clinical histories. Patient data were evaluated manually, automatically by a monitoring center, or automatically by the RPM device itself. Follow-up was individualized medical care such as medication adjustment, discussion of adherence, counseling on behavioral changes such as diet, smoking, weight management, and physical activity, and support for other conditions. Care was often described as focusing on helping patients self-manage their conditions. In about half of the RCTs, patients were only followed up with by providers if their data had been evaluated (manually or automatically) as concerning. In the remaining RCTs, patients received scheduled follow-up regardless of data values; both scheduled follow-up and follow-up indicated by concerning data; or automated responses if data were not of concern, and personal follow-up if data were concerning.

Table 2. Description of Remote Patient Monitoring Implemented in the Included Randomized Controlled Studies

AUTHOR	CHRONIC DISEASE	DATA TRANSMISSION	DATA EVALUATION	FOLLOW-UP RESPONSE
Carter et al.22	Diabetes type 2	Weight and blood pressure sent 1/week and blood glucose sent 3/day; using a laptop that was equipped with a wireless scale, a blood pressure cuff, and a glucometer	Manual review by telehealth nurse.	Nurse discussed data with patient over videoconference during biweekly calls. Additional support/contact: health education videos and online resources, and a social networking module that allowed patients to contact each other.
Dario et al.17	Diabetes type 2	Blood glucose measured (frequency not reported) with a glucometer connected to a telecare device that sent data to an e-health center	Alerts automatically generated by e-health center if data values crossed pre-specified thresholds.	If automatic alert was generated, e-health staff contacted clinician. Clinician took subsequent action according to normal protocols and contacted patients by telephone or other unspecified methods. If an emergency, eHealth center contacted next of kin and emergency department.
Egede et al.18	Diabetes type 2	Blood glucose and blood pressure sent 1/day, using a commercial telehealth device that uploaded blood glucose and blood pressure to a central server	Manual review by nurse case manager	If necessary, nurse contacted patients by telephone to make medication adjustments. Additional contact: nurse case managers made weekly reminder calls to upload data.
Magid et al.20	Hypertension	Blood pressure sent 1/week over the patient’s usual telephone, using an interactive voice response phone system	Manual review by pharmacist	If data values exceeded guideline-recommended treatment goals, pharmacists contacted patients to review medication adherence, adjust medications, and provide counseling on healthy therapeutic lifestyle changes, using the interactive voice response system or telephone. Pharmacists contacted GP in the case of medication adjustments.
Nicolucci et al.30	Diabetes type 2 and hypertensive (>130/80 mmHg)	Blood glucose, blood pressure, and weight sent 2/month, using a weight scale, glucometer, and a sphygmomanometer, respectively, connected through Bluetooth to a device that transmitted data in real time to a telehealth center.	Alerts automatically generated by telehealth center if data values concerning	Telehealth center nurses forwarded alerts to GPs, who contacted patients. Additional contact: telehealth nurses also contacted patients monthly to discuss results and barriers to compliance, using text messages, e-mail, or telephone.
Rodriguez-Idigoras et al.29	Diabetes type 2	Blood glucose measured using a glucometer and sent through patient’s usual telephone to a call center; no required frequency reported, but actual frequency was an average of 7/months	Alarms automatically generated by call center if data values outside normal range.	Call center staff contacted GP and patient by telephone. Unspecified “standard protocols” were followed.
Schillinger et al.21	Diabetes type 2	Blood glucose and blood pressure sent 1/week, using telephone touchpads during an automated telephone call	Evaluated by an automated telephone support system	The telephone system either immediately responded with a narrated health education message or the system alerted a nurse, and the nurse contacted the patient.
Steventon et al.31	Diabetes type 2	Blood glucose and blood pressure sent up to 5/week, with the frequency adjusted according to participants’ individual clinical histories, using a freestanding telehealth unit or a television set top box that connected to a blood pressure monitor and glucometer or to weighing scales/pulse oximeters	Traffic light system: automatic evaluation at monitoring center	If “red,” monitoring center staff reviewed data 1/day and contacted the patient for further evaluation, to offer disease management advice, or to give referrals. Contact was made using the telehealth unit or other unspecified methods.
Stone et al.32	Diabetes type 1 or 2	Blood glucose, blood pressure, and weight sent 1/day, using a commercial home telemonitoring device that transmitted measurements to a central server	Traffic light system: automatic evaluation by the device	If “red,” nurse contacted patients and adjusted medication, over the telephone or using the home monitoring system. Additional support: monthly calls to provide individualized self-management counseling tailored to specific issues, based on data values.
Wakefield et al.19	Diabetes type 2 and hypertension	Blood glucose and blood pressure sent 1/day, using a commercial home telehealth device that sent and received data through the patient’s landline	Manual review by nurse, 1/day	Tailored, automated responses sent based on data. Nurses reviewed data daily and contacted the patients if necessary. Additional support: daily prompts to enter data and educational content, sent through the telehealth device.
Wild et al.33	Diabetes type 2	Blood glucose sent at least 2/week, and blood pressure, and weight sent at least 1/week; using Bluetooth-enabled blood pressure, blood glucose, and weight monitors that transmitted data through a supplied modem	Manual review by primary care nurse or family practice clinician, recommended 1/week.	Provider changed treatment if necessary to comport with national guidelines for diabetes and hypertension management if necessary, and provided advice on lifestyle modification, information about medication effects; the method of communicating back to the patient was not specified.

Usual Care as a Comparator

In all RCTs, participants were recruited from existing general practitioner lists, health care networks, or other patient pools, indicating that they had already some minimum amount of contact or usual care with the primary health services. The usual care described in seven RCTs appeared to be quite minimal, such as an educational pamphlet, encouragement to contact providers, or yearly review of health status.^{18–20,22,29,30,33} This was also the case for the two RCTs that specified they compared RPM adjunct to usual care, with usual care alone.^21,31

In the remaining two RCTs, usual care contained the same self-monitoring that the RPM group conducted, but without the benefit of digital transmission of these data or guaranteed provider knowledge of health status.^17,20 In Dario et al. patients in usual care were supposed to measure HbA1c and bring paper logs to providers.¹⁷ In Stone et al., usual care patients were supposed to measure HbA1c, blood pressure, and weight daily, and discuss these with diabetes nurse educators over the phone once per month.³² Neither of these RCTs reported the actual frequency of self-monitoring, making it difficult to conclude the extent to which usual care in these RCTs differs from usual care in the remaining RCTs.

Self-monitoring may have been practiced by patients in usual care in Rodriguez-Idigoras et al. and Schillinger et al.^21,29 Six-month self-monitoring was an inclusion criterion for Rodriguez-Idigoras et al., to recruit among patients already capable of complying with a monitoring regime, although there was no mention of the usual care group being expected to continue. Schillinger et al. specified that if usual care patients were already self-monitoring when enrolled in the RCT, they were encouraged to continue doing so; no estimate was provided of how common this was. In both cases, as in Dario et al. and Stone et al., any self-monitoring conducted by usual care patients would not have been digitally transmitted or evaluated by providers.

Risk of Bias in the RCTS

The review authors’ judgments of each risk of bias domain are presented as percentages across all 11 included RCTs in Figure 2. Majority of RCTs were assessed as having low risk of selection bias, detection bias, attrition bias, reporting bias, and other bias. They were assessed as having unclear risk of performance bias. In many instances, this was due to nonreporting of blinding of participants and personnel.

The review authors’ judgments about each risk of bias domain for each included RCT separately are available upon request.

Effects of RPM on Patients with Diabetes and/or Hypertension

In this section, each of the subsections includes a narrative summary of the findings for a primary outcome, as well as a presentation of the results by means of forest plots. In addition, we give results of the GRADE assessment (our evaluation of the certainty of the evidence) for each of the eight primary outcomes. Table 3 provides an overview of the conducted GRADE assessments.

Table 3. Summary of Findings Table: Remote Patient Monitoring Compared to Usual Care for Chronic Diseases

OUTCOMES	EFFECT	NO. OF PARTICIPANTS (STUDIES)	CERTAINTY OF THE EVIDENCE (GRADE)
HbA1c, %	Most studies showed a reduction, from 0.23% lower to 1.08% lower. However, only four exceeded the suggested minimum clinically important difference of ≥0.5%.a RPM probably slightly reduces HbA1c.	2,235 (10 RCTs)	⨁⨁⨁◯ Moderateb
Systolic blood pressure, mmHg	No study showed a statistically significant effect, but tended to benefit RPM. RPM probably leads to a slight reduction.	1,407 (7 RCTs)	⨁⨁⨁◯ Moderateb
Diastolic blood pressure, mmHg	No study showed an effect. RPM may make no difference.	1,207 (6 RCTs)	⨁⨁◯◯ Lowc,d
Total cholesterol	No study showed an effect. RPM may make no difference	664 (3 RCTs)	⨁⨁◯◯ Lowd,e
Patients with hospitalizations or ER visits, all cause	No effect. RPM may make no difference.	249 (1 RCT)	⨁⨁◯◯ Lowf,g
Quality of life (SF-12/SF-36), mental health component	Two studies showed no effect, and one showed a small benefit to usual care patients. RPM may make no difference.	698 (3 RCTs)	⨁⨁◯◯ LOWe,h
Quality of life (SF-12/SF-36), physical health component	Usual care reported higher scores, with a small effect size. RPM probably harms this outcome.	698 (3 RCTs)	⨁⨁⨁◯ Moderateh
Hospital Anxiety and Depression Scale total score	No effect. RPM may make no difference.	257 (1 RCT)	⨁⨁◯◯ Lowg,i
GRADE Working Group grades of evidence  High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.  Moderate certainty: we are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different  Low certainty: our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect  Very low certainty: we have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect

Figure 3 displays forest plots for each primary outcome. Forest plots omit group sizes and raw data values, such as mean values for RPM and usual care groups, for ease of reading. These numbers are available from the authors upon request.

Among the 10 RCTs that reported HbA1c, there appeared to be a small pooled reduction among the RPM group by the end of interventions (Fig. 3). With three exceptions that reported either no difference or a slight benefit to usual care patients,^17,19,29 the RPM patients in the RCTs reported an average of 0.23–1.08% lower HbA1c scores than the usual care groups. However, only in four studies was this reduction above the suggested threshold for clinical meaningfulness, ≥0.5%.^34–36 Moreover, due to considerable heterogeneity among the studies and uncertainty about the applicability of the findings, we have low confidence in this finding.

Systolic blood pressure was measured by seven studies, with no differences between the RPM and usual care patients overall. Studies reported mean values both above and below NICEs recommended target of 135/85.³⁷ Due to the consistency of each RCT’s estimates, it is likely that the true effect of RPM on systolic blood pressure is close to the estimates we see here, namely, a small reduction.

Diastolic blood pressure was measured by six studies, again with no mean differences between the RPM and usual care patients. In all studies, mean diastolic values were below NICEs recommended target of 135/85.³⁷ We are not confident that the true effect of RPM on diastolic blood pressure is close to the effect estimates we see in this study, both because of moderate heterogeneity and effect estimates spread widely and in directions that favor both RPM and usual care.

Total cholesterol was measured by three studies. None of the studies reported a mean difference between the RPM and usual care patients. We have low confidence in this finding, given large heterogeneity and individual effect estimates that show not only opposite findings but also findings with almost no overlap with one another.

One study reported patients who had a hospitalization or an emergency room visit during the course of the intervention, with no difference between the two groups. As this finding drew from only one RCT, which was also judged to have a high risk of bias due to commercial funding, we are not confident that the true effect of RPM on this outcome is close to the estimate presented in this study.

Health-related quality of life (HRQOL) was one of the few patient-reported outcomes. The standardized mean difference of HRQOL in the mental component summary of two HRQOL instruments was no different for the RPM or usual care patients in three studies. We have low certainty in this finding, and RPM may in fact have an effect on this component, potentially negatively. In the physical component summary of the same instruments, the usual care group reported slightly higher mean HRQOL than the RPM group. In this component, the three studies’ effect studies agreed more with one another, but our certainty was still downgraded due to potential biases. We are moderately certain that RPM has a small negative impact on physical HRQOL.

One study reported the total score from the Hospital Anxiety and Depression Scale (HADS), with no difference between the RPM and usual care patients.

Only 1 of 23 secondary outcomes showed an effect of RPM, and this effect was negative: the usual care group weighed 5.40 kg less at the end of the intervention in Wild et al.,³³ with a range of 0.42 kg less to 10.38 kg less. Twenty-two of 23 secondary outcomes, reported by 1 to 2 RCT each, showed no effect of RPM. These included achievement of normal blood pressure, achievement of target HbA1c, daytime ambulatory systolic blood pressure, daytime ambulatory diastolic blood pressure, body mass index, overall HRQOL, diabetes knowledge, HADS anxiety subscale, HADS depression subscale, contact with general practitioner, practice nurse visits, primary care physician encounters with procedures, amount of emergency department visits, all-cause hospitalization, all-cause emergency hospitalization, bed days for all-cause hospitalization, bed days for diabetes-related hospitalized patients, patients who visited a specialist, amount of outpatient visits, patients with home visits, start-up, ongoing, and total costs, and costs per quality-adjusted life year gained. Forest plots of secondary outcomes are available from the authors upon request.

Discussion

This overview of systematic reviews sought to assess the effectiveness of a specific type of RPM on chronically ill patients’ clinical outcomes and health care utilization. Only 11 RCTs captured by 4 high-quality reviews were included. We summarized results from 31 outcomes (4 primary and 23 secondary), and RPM had 2 positive effects (slight reductions in HbA1c and systolic blood pressure) compared to standard treatment, and 2 negative effects (reduction in the physical health component of HRQOL and increase in weight). RPM appeared to have no effect on the remaining five primary outcomes (diastolic blood pressure, cholesterol, the mental health component of HRQOL, the number of patients with a hospitalization or emergency room visit, and HADS scores), and no effect on 22 of 23 secondary outcomes.

We assessed 155 systematic reviews and their ∼3,500 RCTs, and only 4 high-quality reviews met our criteria. Of these 4 reviews, 176 included RCTs, only 11 reported on interventions that met our definition of RPM, and only with patients with diabetes and/or hypertension. This indicates that the type of RPM of interest to the Norwegian DoH is not commonly implemented. However, our findings overall comport with previous reviews that have used broader definitions of RPM, such as those utilizing internet-based technologies, fully automated programs without provider input, and interventions organized in specialist health services.

Reviews with the broadest definitions of RPM have reported clinically meaningful reductions in HbA1c,¹⁶ including in primary care settings,³⁸ although both of these meta-analyses contained substantial amounts of unexplained heterogeneity. Reviews that have parsed RPM further into component types have found no significant effect on HbA1c (statistically, clinically, or both) of automated telephone messages,²⁷ text messages,²⁸ website only,³⁹ and mobile phone only.³⁹ Combined website and mobile strategies were found to be effective, −0.77%,³⁹ as was web-based RPM when including personal digital assistants, tablet, computer, and smartphone interventions.²⁸ The delivery method or components of RPM may moderate the effect it has on HbA1c. RPM using combination mobile-and-website components and RPM using online components were more effective than other single-component technologies, such as text messaging, automated telephone, website, or mobile technologies. Our included RCTs might have been collectively less effective because they tended to utilize single-component technologies. Our finding of little or no effect of RPM on blood pressure of patients with diabetes and/or hypertension therefore contributes to a mixed body of evidence.^8,27,40–43

Given that RPM defined more broadly has been reported to have positive effects on HbA1c and, in some cases, blood pressure among people with diabetes and/or hypertension, our definition of RPM appears to have captured the types that are least effective. If RPM is to be implemented among post-COVID-19 patients or other chronic disease patients, more trials are needed to be certain of its benefits over standard care, and to establish cost effectiveness.

Monitoring itself, even including frequent feedback on health status, may not be sufficient to enact the behavior change often needed in these two disease groups, such as medication, diet, and physical activity modification. The type of RPM we searched for is perhaps better conceptualized as a data transmission and feedback system, and additional, targeted support integrated within feedback—such as behavioral change counseling, review of barriers to medication compliance, dis/satisfaction with the intervention itself—may be needed to actually ensure behavior modifications. For conditions in which adhering to provider feedback is acceptable and easy for patients, for example, instructions to adjust the dose of a well-tolerated medication, or a new appointment with a specialist on a day that was already planned to be spent at a hospital, feedback alone may be effective. For diabetes and/or hypertension, it appears that patients may need to be supported in adhering to provider feedback.

Patient groups should be involved in RPM implementation and evaluation, to maximize potential for modification and ultimately efficacy. Patients may have preferences as to the frequency of feedback from providers, the content of such feedback, and even the method of contact. Pekmezaris et al. provide one example of a participatory approach to designing an RPM program for heart failure patients,⁴⁴ while Ware et al. describe suggestions made by patients for program modification after conclusion.⁴⁵ Further impetus to involve patients in the development of effective RPM strategies is the consistent report of racial, ethnic, and socioeconomic equalities in other types of monitoring and telemedicine programs.^46,47 RPM strategies must be designed, or re-designed, not only with cost effectiveness, scalability, and health outcomes in mind but also with health equity as an equally important requirement.

This review was conducted pre-pandemic. How relevant are our findings now, in the context of COVID-19? We are aware of one recent rapid review of “remote home monitoring” organized through primary or specialist care, and provided mainly to patients with suspected COVID-19 infection before being admitted.² The majority of the included 27 studies may have met our definition of RPM, as they used self-monitoring on paper/online forms or wearable sensors. However, methodological quality of these studies was poor, and outcomes could not be summarized; we therefore lack knowledge of the impact of monitoring strategies. In another recent rapid review of barriers to using “remote monitoring technologies” with COVID-19 patients, equity-related barriers were the most common.⁴⁸ These included financial barriers and lack of access to technology, membership in a patient group with particular needs (including chronic diseases), and low health literacy. Wilhite et al.’s recent survey of health care providers reports much of the same barriers to remote monitoring during COVID-19, particularly patients’ lack of access based on socioeconomic status, and patients’ and providers’ low technology skills.¹ To summarize, we do not yet know the most effective way to remotely manage COVID-19 patients, and current strategies have not become magically more equitable than pre-pandemic remote monitoring. Pre-pandemic lessons about health technology inequality must inform current pandemic responses.

Strengths and Weaknesses

A strength of this overview is the definition of RPM developed in collaboration with the commissioner/policy maker, and an exhaustive search strategy that allowed us to capture interventions that were not called “remote patient monitoring,” but nevertheless met our definition. While the specificity of the definition required screening of ∼3,500 RCTs included in 155 systematic reviews, a time-consuming practice atypical of an overview of systematic reviews, it has also ensured that the interventions summarized in this overview are relevant to the DoH. Even working within this specific definition, the interventions involved a variety of actors, data transmission methods, data evaluation methods, and response options. Each of these can be used as a possible design option in moving forward with national RPM recommendations. We were analytically limited by the methodological choices of both the included systematic reviews and RCTs, such as two studies’ failure to report standard deviations and two studies reporting outcomes adjusted for baseline values, while the rest reported unadjusted values. As we did not calculate summary effect estimates, these situations are not particularly problematic, but are worth keeping in mind when viewing forest plots.

Knowledge Gaps

It is unsurprising that the impaired vision/hearing systematic review was an empty review, as this was an exclusion criteria for many of our identified RCTs—despite the fact that impaired vision and hearing are conditions that will only increase with age, along with other chronic diseases; see, for example, Fisher et al.⁴⁹ While physical multimorbidities were reported by many RCTs, most excluded based on psychiatric morbidity. Excluding patients on the basis of psychiatric morbidities is, unfortunately, standard practice in clinical trials; investigators often assume that these potential patients will struggle more with treatment adherence than other patients. Assuming that patients with mental health problems will not comply may be selling them short.^50,51 Excluding patients who use any type of psychotropic medicine, or those with anxiety, depression, alcohol, and/or substance problems, belies the prevalence of these conditions among people with diabetes and/or hypertension.^52–55 Please see Figure 4 for a concrete agenda for developing RPM clinical research. There is both potential and need to tailor RPM to people with impaired vision/hearing and with comorbid mental health problems, particularly if they have other conditions that limit mobility and utilization of in-person health services. RPM technologies should be developed following basic universal design principles to be suitable for people with disabilities, and we encourage the inclusion of people with disabilities and comorbid mental health problems in future trials of RPM for chronic physical conditions.

Conclusion

In this comprehensive overview of four systematic reviews, we aimed to assess the effectiveness of a specific type of RPM on clinical and health care utilization outcomes for chronic disease patients. We found a slight benefit of RPM on HbA1c, and a small negative effect of RPM on one type of HRQOL, which have both been reported in previous reviews utilizing different or broader definitions of RPM. These somewhat disappointing results may be because RPM facilitates data transmission, analysis, and feedback, but does not necessarily assist patients in making or sustaining the medication, diet, or physical activity change that are often necessary for these conditions. RPM could be seen as a bridge to necessary further support, but not superior by itself to usual care. More complex RPM interventions may be required to support such complicated behavioral change, such as interventions combining multiple components, or perhaps involving specialists from the beginning. There is a clear need to capitalize on RPM innovative capacity to serve people with hearing, vision, psychiatric, and cognitive difficulties.

Extensive experience has been gained during the COVID-19 pandemic about RPM and other types of remote care. While COVID-19-specific RPM outcomes have not yet been synthesized,² we hope that best practices and lessons learned during the pandemic will be carried forward to provide high-quality RPM for post-COVID patients as well as other chronic disease groups. More complex interventions are on the horizon,⁵⁶ and the COVID-19 pandemic has catapulted forward the use of machine learning and artificial intelligence.^57,58 Enhanced RPM strategies can collect and analyze massive amounts of real-time data, genomic information, and other risk factors, and they have the potential to increase accuracy and speed of clinical decision-making and follow-up. Policymakers must keep in mind that equity is as important as efficacy. The pandemic has revealed nothing, if not the unacceptable global and national disparities in health care access and health outcomes. RPM has the potential to extend care to people who fall through the cracks of traditional services—as well as the potential to further privilege those with financial and technological resources and literacy. Involving target groups and underserved subgroups in RPM design and implementation may be the key to seeing significant benefits for people with chronic disease.

Authors’ Contributions

A.E.M. was the project leader and screened abstracts, assessed full-texts, conducted quality assessment, extracted data and analyzed, participated in GRADE assessments, and drafted the article. S.S.O. assessed full texts, checked data extraction, conducted quality assessment, and participated in GRADE assessments. T.B.J. and P.S.J.J. assessed full texts, conducted quality assessments, and participated in GRADE assessments. R.C.B. was the principal investigator, drafted the study protocol, screened abstracts, and resolved conflicts relating to study inclusion. All authors contributed to the article and read and approved the final version.

Data Availability

Data are available by the authors upon request.

Disclosure Statement

No competing financial interests exist.

Funding Information

The white paper published on our institution’s website was commissioned by the Norwegian Directorate of Health.

References

• 1. Wilhite JA, Altshuler L, Fisher H, et al. The telemedicine takeover: Lessons learned during an emerging pandemic. Telemed J E Health 2021. [Epub ahead of print; DOI: 10.1089/tmj.2021.0035. Link, Google Scholar
• 2. Vindrola-Padros C, Singh KE, Sidhu MS, et al. Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: A rapid systematic review. EClinicalMedicine 2021;37:100965. Crossref, Medline, Google Scholar
• 3. Nabavi N. Long covid: How to define it and how to manage it. BMJ 2020;370:m3489. Crossref, Medline, Google Scholar
• 4. WHO. Multimorbidity, in Technical Series on Safer Primary Care. Geneva: World Health Organization, 2016. Google Scholar
• 5. Watson AR, Wah R, Thamman R, The value of remote monitoring for the COVID-19 pandemic. Telemed J E Health 2020;26:1110–1112. Google Scholar
• 6. Kew KM, Cates CJ. Home telemonitoring and remote feedback between clinic visits for asthma. Cochrane Database Syst Rev 2016;2016:CD011714. Google Scholar
• 7. McLean S, Chandler D, Nurmatov U, et al. Telehealthcare for asthma. Cochrane Database Syst Rev 2010;2010:CD007717. Google Scholar
• 8. Flodgren G, Rachas A, Farmer AJ, Inzitari M, Shepperd S. Interactive telemedicine: Effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2015;2015:CD002098. Google Scholar
• 9. Snoswell CL, Chelberg G, De Guzman KR, et al. The clinical effectiveness of telehealth: A systematic review of meta-analyses from 2010 to 2019. J Telemed Telecare 2021. [Epub ahead of print]; DOI: 10.1177/1357633X211022907. Crossref, Google Scholar
• 10. Snoswell CL, Stringer H, Taylor ML, Caffery LJ, Smith AC. An overview of the effect of telehealth on mortality: A systematic review of meta-analyses. J Telemed Telecare 2021. [Epub ahead of print]; DOI: 10.1177/1357633X211023700. Crossref, Google Scholar
• 11. Mordaunt DA. Re: “Remote Patient Monitoring: A Systematic Review” by Farias et al. Telemed J E Health 2021;27:374–377. Link, Google Scholar
• 12. Muller AE, Berg RC. A flexible protocol for a systematic review of remote patient monitoring. Prim Health Care Res Dev 2020;21:e45. Crossref, Medline, Google Scholar
• 13. NIPH. Remote Patient Monitoring for Chronic Illnesses. Oslo: Norwegian Institute of Public Health, 2019. Google Scholar
• 14. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—A web and mobile app for systematic reviews. Syst Rev 2016;5:210. Crossref, Medline, Google Scholar
• 15. Veritas Health Innovation. Covidence Systematic Review Software [Computer Program]. Melbourne, Australia: Veritas Health Innovation. Available at https://support.covidence.org/help/how-can-i-cite-covidence (last accessed January 1, 2021). Google Scholar
• 16. Faruque LI, Wiebe N. Ehteshami-Afshar A, et al. Effect of telemedicine on glycated hemoglobin in diabetes: A systematic review and meta-analysis of randomized trials. CMAJ 2017;189:E341–E364. Google Scholar
• 17. Dario C, Toffanin R, Calcaterra F, et al. Telemonitoring of type 2 diabetes mellitus in Italy. Telemed J E Health 2017;23:143–152. Link, Google Scholar
• 18. Egede LE, Williams JS, Voronca DC, Knapp RG, Fernandes JK. Randomized controlled trial of technology-assisted case management in low income adults with type 2 diabetes. Diabetes Technol Ther 2017;19:476–482. Link, Google Scholar
• 19. Wakefield BJ, Holman JE, Ray A, et al. Effectiveness of home telehealth in comorbid diabetes and hypertension: A randomized, controlled trial. Telemed J E Health 2011;17:254–261. Link, Google Scholar
• 20. Magid D, Ho MP, Olson KL, et al. A multimodal blood pressure control intervention in 3 healthcare systems. Am J Manag Care 2011;17:e96–e103. Medline, Google Scholar
• 21. Schillinger D, Handley M, Wang F, Hammer H. Effects of self-management support on structure, process, and outcomes among vulnerable patients with diabetes: A three-arm practical clinical trial. Diabetes Care 2009;32:559–566. Crossref, Medline, Google Scholar
• 22. Carter EL, Nunlee-Bland G, Callender C. A patient-centric, provider-assisted diabetes telehealth self-management intervention for urban minorities. Perspect Health Inf Manag 2011;8:1b. Medline, Google Scholar
• 23. Guyatt G, Oxman AD, Akl EA, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol 2011;64:383–394. Crossref, Medline, Google Scholar
• 24. McMaster University (developed by Evidence Prime). I., GRADEpro GDT: GRADEpro Guideline Development Tool [Software]. Hamilton, Canada: McMaster University, 2015. Google Scholar
• 25. Murad MH, Mustafa RA, Schunemann HJ, Sultan S, Santesso N. Rating the certainty in evidence in the absence of a single estimate of effect. Evid Based Med 2017;22:85–87. Crossref, Medline, Google Scholar
• 26. Bittner AK, Wykstra SL, Yoshinaga PD, Li TJ. Telerehabilitation for people with low vision (Review). Cochrane Database Syst Rev 2015;2:CD011019. Google Scholar
• 27. Posadzki P, Mastellos N, Ryan R, et al. Automated telephone communication systems for preventive healthcare and management of long-term conditions. Cochrane Database Syst Rev 2016;12:CD009921. Medline, Google Scholar
• 28. Kebede MM, Zeeb H, Peters M, Heise TL, Pischke CR. Effectiveness of digital interventions for improving glycemic control in persons with poorly controlled type 2 diabetes: A systematic review, meta-analysis, and meta-regression analysis. Diabetes Technol Ther 2018;20:767–782. Link, Google Scholar
• 29. Rodriguez-Idigoras MI, Sepulveda-Munoz J, Sanchez-Garrido-Escudero R, et al. Telemedicine influence on the follow-up of type 2 diabetes patients. Diabetes Technol Ther 2009;11:431–437. Link, Google Scholar
• 30. Nicolucci A, Cercone S, Chiriatti A, Muscas F, Gensini G. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther 2015;17:563–570. Link, Google Scholar
• 31. Steventon A, Bardsley M, Doll H, Tuckey E, Newman SP. Effect of telehealth on glycaemic control: Analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial. BMC Health Serv Res 2014;14:334. Crossref, Medline, Google Scholar
• 32. Stone RA, Rao RH, Sevick MA, et al. Active care management supported by home telemonitoring in veterans with type 2 diabetes: The DiaTel randomized controlled trial. Diabetes Care 2010;33:478–484. Crossref, Medline, Google Scholar
• 33. Wild SH, Hanley J, Lewis SC, et al. Supported telemonitoring and glycemic control in people with type 2 diabetes: The Telescot Diabetes Pragmatic multicenter randomized controlled trial. PLoS Med 2016;13:e1002098. Crossref, Medline, Google Scholar
• 34. National Institute for Health and Care Excellence. Type 2 diabetes: Alogliptin, in Evidence summary [ESNM20]. London: National Institute for Health and Care Excellence, 2013. Google Scholar
• 35. Little RR, Rohlfing CL. The long and winding road to optimal HbA1c measurement. Clin Chim Acta 2013;418:63–71. Crossref, Medline, Google Scholar
• 36. Lenters-Westra E, Schindhelm RK, Bilo HJ, Groenier KH, Slingerland RJ. Differences in interpretation of haemoglobin A1c values among diabetes care professionals. Neth J Med 2014;72:462–466. Medline, Google Scholar
• 37. National Institute for Health and Care Excellence. Hypertension in adults: diagnosis and management, in NICE guideline (NG136). London: National Institute for Health and Care Excellence, 2019. Google Scholar
• 38. So CF, Chung J. Telehealth for diabetes self-management in primary healthcare—A systematic review and meta-analysis. J Telemed Telecare 2018;24:356–364. Crossref, Medline, Google Scholar
• 39. Shen Y, Wang FB, Zhang X, et al. Effectiveness of internet-based interventions on glycemic control in patients with type 2 diabetes: Meta-analysis of randomized controlled trials. J Med Internet Res 2018;20:e172. Crossref, Medline, Google Scholar
• 40. Lee SWH, Ooi L, Lai YK. Telemedicine for the management of glycemic control and clinical outcomes of type 1 diabetes mellitus: A systematic review and meta-analysis of randomized controlled studies. Front Pharmacol 2017;8:330. Crossref, Medline, Google Scholar
• 41. Duan Y, Xie Z, Dong F, et al. Effectiveness of home blood pressure telemonitoring: A systematic review and meta-analysis of randomised controlled studies. J Hum Hypertens 2017;31:427–437. Crossref, Medline, Google Scholar
• 42. Ma Y, Cheng HY, Cheng L, Sit JWH. The effectiveness of electronic health interventions on blood pressure control, self-care behavioural outcomes and psychosocial well-being in patients with hypertension: A systematic review and meta-analysis. Int J Nurs Stud 2019;92:27–46. Crossref, Medline, Google Scholar
• 43. Shahaj O, Denneny D, Schwappach A, et al. Supporting self-management for people with hypertension: A meta-review of quantitative and qualitative systematic reviews. J Hypertens 2019;37:264–279. Crossref, Medline, Google Scholar
• 44. Pekmezaris R, Schwartz RM, Taylor TN, et al. A qualitative analysis to optimize a telemonitoring intervention for heart failure patients from disparity communities. BMC Med Inform Decis Mak 2016;16:75. Crossref, Medline, Google Scholar
• 45. Ware P, Ross HJ, Cafazzo JA, et al. User-centered adaptation of an existing heart failure telemonitoring program to ensure sustainability and scalability: Qualitative study. JMIR Cardio 2018;2:e11466. Crossref, Medline, Google Scholar
• 46. Andersen JA, Scoggins D, Michaud T, et al. Racial disparities in diabetes management outcomes: Evidence from a remote patient monitoring program for type 2 diabetic patients. Telemed J E Health 2021;27:55–61. Link, Google Scholar
• 47. Kirkland E, Schumann SO, Schreiner A, et al. Patient demographics and clinic type are associated with patient engagement within a remote monitoring program. Telemed J E Health 2021;27:843–850. Link, Google Scholar
• 48. Houlding E, Mate KKV, Engler K, et al. Barriers to use of remote monitoring technologies used to support patients with COVID-19: Rapid review. JMIR Mhealth Uhealth 2021;9:e24743. Crossref, Medline, Google Scholar
• 49. Fisher D, Li CM, Chiu MS, et al. Impairments in hearing and vision impact on mortality in older people: The AGES-Reykjavik Study. Age Ageing 2014;43:69–76. Crossref, Medline, Google Scholar
• 50. Lawes-Wickwar S, McBain H, Mulligan K. Application and effectiveness of telehealth to support severe mental illness management: Systematic review. JMIR Ment Health 2018;5:e62. Crossref, Medline, Google Scholar
• 51. Wood AM, White IR, Thompson SG. Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin Trials 2004;1:368–376. Crossref, Medline, Google Scholar
• 52. de Groot M, Golden SH, Wagner J. Psychological conditions in adults with diabetes. Am Psychol 2016;71:552–562. Crossref, Medline, Google Scholar
• 53. Grigsby AB, Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. Prevalence of anxiety in adults with diabetes: A systematic review. J Psychosom Res 2002;53:1053–1060. Crossref, Medline, Google Scholar
• 54. Ojike N, Sowers JR, Seixas A, et al. Psychological distress and hypertension: results from the National Health Interview Survey for 2004–2013. Cardiorenal Med 2016;6:198–208. Crossref, Medline, Google Scholar
• 55. SAMHSA. Detoxification and Substance Abuse Treatment. Rockville, MD: Center for Substance Abuse Treatment, 2006. Google Scholar
• 56. Jeddi Z, Bohr A. Chapter 9—Remote patient monitoring using artificial intelligence. In: Bohr AMemarzadeh K, eds. Artificial Intelligence in Healthcare. London: Academic Press, 2020:203–234. Crossref, Google Scholar
• 57. Azam I, Usman M. Application of modern technologies on fighting COVID-19: A systematic and bibliometric analysis. Emerg Technol During Era COVID-19 Pandemic 2021;348:167–181. Crossref, Google Scholar
• 58. Golinelli D, Boetto E, Carullo G, et al. Adoption of digital technologies in health care during the COVID-19 pandemic: Systematic review of early scientific literature. J Med Internet Res 2020;22:e22280. Crossref, Medline, Google Scholar
• 59. Wilding J, Godec T, Khunti K, Pocock S, Fox R, Smeeth L, Clauson P, Fenici P, Hammar N, Medina J. Changes in HbA1c and weight, and treatment persistence, over the 18 months following initiation of second-line therapy in patients with type 2 diabetes: Results from the United Kingdom Clinical Practice Research Datalink. BMC Med 2018;16:116. Crossref, Medline, Google Scholar

Appendices

Appendix A1. Search Strategy

Database: Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily 1946 to May 21, 2019

Dato: 22.05.2019

Treff: 1,352

# Searches Results

1 Telemedicine/ 19,411

2 Telerehabilitation/ 245

3 Telenursing/ 199

4 Remote Consultation/ 4,542

5 or/1-4 23,509

6 exp Telecommunications/ 84,877

7 (care or healthcare).hw. 1,179,219

8 6 and 7 14,549

9 (telecare or telecheck* or teleconsult* or telefollow* or telehealth* or telehome* or telemanag* or telemedicine or telemental* or telemonitor* or telenursing* or telepatient* or telepsych* or telerehab* or telereport* or telesupport*).ti,ab,kf. 16,721

10 ((tele or telemedical* or tele medical*) adj (care or checkup* or check up* or consult* or followup* or follow up* or health* or home* or manag* or medicine* or mental or monitor* or nursing* or patient* or psych* or rehab* or report* or support*)).ti,ab,kf. 837

11 (ecare or econsult* or ehealth or emedicine or emental* or enursing* or erehab* or mcare or mconsult* or mhealth or mmedicine or mmental* or mnurs*).ti,ab,kf. 6,002

12 ((e or m or mobile or digital) adj (care or consult* or health* or medicine* or mental* or nursing* or rehab*)).ti,ab,kf. 7,806

13 (remote adj2 (care or checkup* or check up* or consult* or followup* or follow up* or health* or home* or manag* or medicine* or mental or monitor* or nursing or patient* or psych* or rehab* or self)).ti,ab,kf. 5,456

14 5 or 8 or 9 or 10 or 11 or 12 or 13 46,223

15 Meta-Analysis/ 101,020

16 Meta-Analysis as Topic/ 16,935

17 Systematic Reviews as Topic/ 2,257

18 Systematic Review.pt. 106,708

19 ((systematic* adj2 (overview or review* or search*)) or meta anal* or metaanal* or meta regression* or meta review* or umbrella review* or “overview of reviews” or “review of reviews” or (evidence* adj2 synth*) or synthesis review*).ti,ab,kf. 256,235

20 Review.pt. and (pubmed or medline).ab. 120,264

21 or/15-20 325,341

22 14 and 21 2,050

23 exp animals/ not humans.sh. 4,581,973

24 (news or editorial or comment).pt. 1,300,176

25 22 not (23 or 24) 2,033

26 limit 25 to yr = “2015-Current” 1,354

27 remove duplicates from 26 1,352

Database: (OVID) Embase 1974 to May 21, 2019

Dato: May 22, 2019

Treff: 713

# Searches Results

1 *telemedicine/ 11,894

2 *telehealth/ 2,600

3 *teleconsultation/ 2,826

4 *telepsychiatry/ 340

5 *telerehabilitation/ 316

6 *telenursing/ 151

7 or/1-6 17,846

8 exp *telecommunication/ 29,287

9 exp *health care delivery/ 624,637

10 8 and 9 21,385

11 (telecare or telecheck* or teleconsult* or telefollow* or telehealth* or telehome* or telemanag* or telemedicine or telemental* or telemonitor* or telenurs* or telepatient* or telepsych* or telerehab* or telereport* or telesupport*).ti,ab,kw. 22,356

12 ((tele or telemedical* or tele medical*) adj (care* or checkup* or check up* or consult* or followup* or follow up* or health* or home* or manag* or medicine* or mental or monitor* or nurs* or patient* or psych* or rehab* or report* or support*)).ti,ab,kw. 1,545

13 (ecare or econsult* or ehealth or emedicine or emental* or enurs* or erehab* or mcare or mconsult* or mhealth or mnurse or mcare or mnursing or mconsult* or mmedicine or mmental* or mnurs*).ti,ab,kw. 5,998

14 ((e or m or mobile or digital) adj (care or consult* or health* or medicine* or mental* or nurs* or rehab*)).ti,ab,kw. 9,438

15 (remote adj2 (care* or checkup* or check up* or consult* or followup* or follow up* or health* or home* or manag* or medicine* or mental or monitor* or nursing or patient* or psych* or rehab* or self)).ti,ab,kw. 8,446

16 7 or 10 or 11 or 12 or 13 or 14 or 15 47,982

17 Meta Analysis/ 162,846

18 Systematic Review/ 204,735

19 ((systematic* adj2 (overview or review* or search*)) or meta anal* or metaanal* or meta regression* or meta review* or umbrella review* or “overview of reviews” or “review of reviews” or (evidence* adj2 synth*) or synthesis review*).ti,ab,kw. 328,748

20 (review and (pubmed or medline)).ti,ab. 158,206

21 or/17-20 443,237

22 16 and 21 2,228

23 (exp animals/ or exp invertebrate/ or animal experiment/ or animal model/ or animal tissue/ or animal cell/ or nonhuman/) not (human/ or normal human/ or human cell/) 6,195,378

24 (news or editorial or comment).pt. 601,134

25 22 not (23 or 24) 2,223

26 limit 25 to yr = “2015-current” 1,479

27 limit 26 to embase 719

28 remove duplicates from 27 713

Database: (OVID) PsycINFO 1806 to May Week 2 2019

Dato: May 22, 2019

Treff: 344

# Searches Results

1 telemedicine/ 4,751

2 exp internet/ 28,372

3 exp TELECOMMUNICATIONS MEDIA/ 12,461

4 2 or 3 40,381

5 exp health care delivery/ 36,915

6 4 and 5 1,032

7 (telecare or telecheck* or teleconsult* or telefollow* or telehealth* or telehome* or telemanag* or telemedicine or telemental* or telemonitor* or telenurs* or telepatient* or telepsych* or telerehab* or telereport* or telesupport*).ti,ab,id. 3,874

8 ((tele or telemedical* or tele medical*) adj (care* or checkup* or check up* or consult* or followup* or follow up* or health* or home* or manag* or medicine* or mental or monitor* or nurs* or patient* or psych* or rehab* or report* or support*)).ti,ab,id. 192

9 (ecare or econsult* or ehealth or emedicine or emental* or enurs* or erehab* or mcare or mconsult* or mhealth or mnurse or mcare or mnursing or mconsult* or mmedicine or mmental* or mnurs*).ti,ab,id. 1,417

10 ((e or m or mobile or digital) adj (care or consult* or health* or medicine* or mental* or nurs* or rehab*)).ti,ab,id. 2,020

11 (remote adj2 (care* or checkup* or check up* or consult* or followup* or follow up* or health* or home* or manag* or medicine* or mental or monitor* or nursing or patient* or psych* or rehab* or self)).ti,ab,id. 761

12 1 or 6 or 7 or 8 or 9 or 10 or 11 8,541

13 Meta Analysis/ 4,417

14 Systematic Review.md. 21,348

15 ((systematic* adj2 (overview or review* or search*)) or meta anal* or metaanal* or meta regression* or meta review* or umbrella review* or “overview of reviews” or “review of reviews” or (evidence* adj2 synth*) or synthesis review*).ti,ab,id. 57,176

16 (review and (pubmed or medline)).ti,ab. 15,784

17 or/13-16 65,050

18 12 and 17 541

19 limit 18 to yr = “2015-current” 344

20 remove duplicates from 19 344

Database: Epistemonikos

Dato: May 22, 2019

Treff: 1,418 (Broad synthesis 33, Structured summary 5, Systematic review 1,380)

[Title/Abstract:] (telecare or telecheck* or teleconsult* or telefollow* or telehealth* or telehome* or telemanag* or telemedic* or telemental* or telemonitor* or telenursing* or telepatient* or telepsych* or telerehab* or telereport* or telesupport* or tele-care or tele-check* or tele-consult* or tele-follow* or tele-health* or tele-home* or tele-manag* or tele-medic* or tele-mental* or tele-monitor* or tele-nursing* or tele-patient* or tele-psych* or tele-rehab* or tele-report* or tele-support* or ecare or econsult* or ehealth or emedicine or emental* or enurs* or erehab* or mcare or mconsult* or mhealth* or mmedicine or mmental* or mnursing* or e-care or e-consult* or e-health or e-medicine or e-mental* or e-nurs* or e-rehab* or m-care or m-consult* or m-health* or m-medicine or m-mental* or m-nursing* or “mobile care” or “mobile consultation” or “mobile consultations” or “mobile health” or “mobile healthcare” or “mobile medicine” or “mobile mental” or “mobile nursing” or “mobile rehabilitation” or “mobile rehabilitations” or “digital care” or “digital consultation” or “digital consultations” or “digital health” or “digital healthcare” or “digital medicine” or “digital mental” or “digital nursing” or “digital rehabilitation” or “digital rehabilitations”)

OR

[Title/Abstract:] (remote) AND (care* OR checkup* OR check-up* OR “check up” OR “check ups” OR consult* OR followup* OR follow-up* OR “follow up” OR “follow ups” OR health* OR home* OR manag* OR medicine* OR mental OR monitor* OR nursing OR patient* OR psych* OR rehab* OR self-*)

Database: Cochrane Library (Wiley)

Dato: May 22, 2019

Treff: 681

ID Search Hits

#1 MeSH descriptor: [Telemedicine] this term only 1,666

#2 MeSH descriptor: [Telerehabilitation] this term only 76

#3 MeSH descriptor: [Telenursing] this term only 28

#4 MeSH descriptor: [Remote Consultation] this term only 350

#5 {or #1-#4} 2,083

#6 MeSH descriptor: [Telecommunications] explode all trees 5,541

#7 MeSH descriptor: [Delivery of Health Care] explode all trees 42,729

#8 #6 and #7 3,336

#9 (telecare or telecheck* or teleconsult* or telefollow* or telehealth* or telehome* or telemanag* or telemedicine or telemental* or telemonitor* or telenursing* or telepatient* or telepsych* or telerehab* or telereport* or telesupport*):ti,ab 3,163

#10 ((tele or telemedical* or tele-medical*) NEXT (care or checkup* or check-up* or consult* or followup* or follow-up* or health* or home* or manag* or medicine* or mental or monitor* or nursing* or patient* or psych* or rehab* or report* or support*)):ti,ab 386

#11 (ecare or econsult* or ehealth or emedicine or emental* or enursing* or erehab* or mcare or mconsult* or mhealth or mmedicine or mmental* or mnurs*):ti,ab 1,445

#12 (e-care or e-consult* or e-health or e-medicine or e-mental* or e-nurs* or e-rehab* or m-care or m-consult* or m-health* or m-medicine or m-mental* or m-nursing*):ti,ab 2,678

#13 (remote NEAR/2 (care* or checkup* or check-up* or consult* or followup* or follow-up* or health* or home* or manag* or medicine* or mental or monitor* or nursing or patient* or psych* or rehab* or self)):ti,ab 920

#14 {or #5, #8-#13} with Cochrane Library publication date Between Jan 2015 and May 2019, in Cochrane Reviews

Database: Web Of Science Core Collection [SCI-EXPANDED & SSCI] (Clarivate)

Dato: May 22, 2019

Treff: 681

# 1 TOPIC: (telecare or telecheck* or teleconsult* or telefollow* or telehealth* or telehome* or telemanag* or telemedicine or telemental* or telemonitor* or telenursing* or telepatient* or telepsych* or telerehab* or telereport* or telesupport*) 19,466

# 2 TOPIC: (((tele or telemedical* or tele-medical*) NEAR/0 (care or checkup* or check-up* or consult* or followup* or follow-up* or health* or home* or manag* or medicine* or mental or monitor* or nursing* or patient* or psych* or rehab* or report* or support*))) 1,069

# 3 TOPIC: ((ecare or econsult* or ehealth or emedicine or emental* or enursing* or erehab* or mcare or mconsult* or mhealth or mmedicine or mmental* or mnurs*)) 5,956

# 4 TOPIC: (e-care or e-consult* or e-health or e-medicine or e-mental* or e-nurs* or e-rehab* or m-care or m-consult* or m-health* or m-medicine or m-mental* or m-nursing*) 4,987

# 5 TOPIC: ((remote NEAR/1 (care* or checkup* or check-up* or consult* or followup* or follow-up* or health* or home* or manag* or medicine* or mental or monitor* or nursing or patient* or psych* or rehab* or self))) 8,044

# 6 TOPIC: (((systematic* NEAR/2 (overview or review* or search*)) or meta-anal* or metaanal* or meta-regression* or meta-review* or umbrella-review* or “overview of reviews” or “review of reviews” or (evidence* NEAR/1 synth*) or synthesis-review*)) 449,422

# 7 #5 OR #4 OR #3 OR #2 OR #1 35,408

# 8 #7 AND #6 1,639 [Indexes = SCI-EXPANDED, SSCI, A&HCI, ESCI Timespan = 2015–2019]

Appendix A2. Studies Excluded After Full-Text Review

Systematic reviews excluded after full-text assessment are listed alphabetically, along with the reason for their exclusion and the chronic disease group of interest.

SYSTEMATIC REVIEW	REASON FOR EXCLUSION	CHRONIC DISEASE
Adamson PB, Ginn G, Anker SD, Bourge RC, Abraham WT. Remote haemodynamic-guided care for patients with chronic heart failure: A meta-analysis of completed trials. Eur J Heart Fail2017;19(3):426–433.	Not RPM (specialist health services)	Heart failure
Agboola SO, Ju W, Elfiky A, Kvedar JC, Jethwani K. The effect of technology-based interventions on pain, depression, and quality of life in patients with cancer: A systematic review of randomized controlled trials. J Med Internet Res2015;17(3):e65.	Not RPM (interventions; or in specialist health services)	Cancer
Agostini M, Moja L, Banzi R, Pistotti V, Tonin P, Venneri A, et al. Telerehabilitation and recovery of motor function: A systematic review and meta-analysis. J Telemed Telecare2015;21(4):202–213.	Not RPM (exercise monitoring; internet based)	Multiple
Aminov A, Rogers JM, Middleton S, Caeyenberghs K, Wilson PH. What do randomized controlled trials say about virtual rehabilitation in stroke? A systematic literature review and meta-analysis of upper-limb and cognitive outcomes. J Neuroeng Rehabil2018;15(1):29.	Not RPM (exercise monitoring)	Stroke
Arambepola C, Ricci-Cabello I, Manikavasagam P, Roberts N, French DP, Farmer A. The impact of automated brief messages promoting lifestyle changes delivered via mobile devices to people with type 2 diabetes: A systematic literature review and meta-analysis of controlled trials. J Med Internet Res2016;18(4):e86.	Not RPM (no provider input in monitoring)	Diabetes
Aronow WS, Shamliyan TA. Comparative effectiveness of disease management with information communication technology for preventing hospitalization and readmission in adults with chronic congestive heart failure. J Am Med Dir Assoc2018;19(6):472–479.	Low/moderate methodological quality	Chronic heart failure
Bashi N, Karunanithi M, Fatehi F, Ding H, Walters D. Remote monitoring of patients with heart failure: An overview of systematic reviews. J Med Internet Res2017;19(1):e18.	No included systematic review meets our definition	Heart failure
Batsis JA, DiMilia PR, Seo LM, Fortuna KL, Kennedy MA, Blunt HB, et al. Effectiveness of ambulatory telemedicine care in older adults: A systematic review. J Am Geriatr Soc2019;67(8):1737–1749.	Not RPM (interventions)	Multiple
Bauce K, Fahs DB, Batten J, Whittemore R. Videoconferencing for management of heart failure an integrative review. J Gerontol Nurs2018;44(4):45–52.	Not systematic review	Heart failure
Bhavnani S, Waalen J, Srivastava A, Heywood JT. Which patients? Which devices? Mhealth monitoring with wearable and implantable devices in heart failure: Meta analyses of randomized trails. J Am Coll Cardiol2015;65(Suppl):A1030.	Not systematic review	Heart failure
Bush ML, Thompson R, Irungu C, Ayugi J. The role of telemedicine in auditory rehabilitation: A systematic review. Otol Neurotol2016;37(10):1466–1474.	Not RPM (specialist health services)	Impaired hearing
Cajita MI, Gleason KT, Han HR. A systematic review of mHealth-based heart failure interventions. J Cardiovasc Nurs2016;31(3):E10–E22.	Low/moderate methodological quality	Heart failure
Carbo A, Gupta M, Tamariz L, Palacio A, Levis S, Nemeth Z, et al. Mobile technologies for managing heart failure: A systematic review and meta-analysis. Telemed J E Health2018;24(2):958–968.	Low/moderate methodological quality	Heart failure
Chan C, Yamabayashi C, Syed N, Kirkham A, Camp PG. Exercise telemonitoring and telerehabilitation compared with traditional cardiac and pulmonary rehabilitation: A systematic review and meta-analysis. Physiother Can2016;68(3):242–251.	Not RPM (exercise monitoring)	Cardiovascular disease, chronic obstructive pulmonary disease
Chandak A, Joshi A. Self-management of hypertension using technology enabled interventions in primary care settings. Technol Health Care2015;23(2):119–128.	Not systematic review	Hypertension
Chen J, Jin W, Zhang XX, Xu W, Liu XN, Ren CC. Telerehabilitation approaches for stroke patients: Systematic review and meta-analysis of randomized controlled trials. J Stroke Cerebrovasc Dis2015;24(12):2660–2668.	Not RPM (exercise monitoring)	Stroke
Chen YY, Guan BS, Li ZK, Li XY. Effect of telehealth intervention on breast cancer patients’ quality of life and psychological outcomes: A meta-analysis. J Telemed Telecare2018;24(3):157–167.	Not RPM (interventions)	Cancer
Chongmelaxme B, Lee S, Dhippayom T, Saokaew S, Chaiyakunapruk N, Dilokthornsakul P. The effects of telemedicine on asthma control and patients’ quality of life in adults: A systematic review and meta-analysis. J Allergy Clin Immunol Pract2019;7(1):199–216.	Low/moderate methodological quality	Asthma
Clark RA. Telehealth in the elderly with chronic heart failure: What is the evidence? Stud Health Technol Inform2018;246(Jan):18–23.	Not systematic review	Chronic heart failure
Conway N, Webster C, Smith B, Wake D. eHealth and the use of individually tailored information: A systematic review. Health Inform J2017;23(3):218–233.	Not RPM (No provider input in monitoring)	Multiple
Cottrell MA, Galea OA, O’Leary SP, Hill AJ, Russell TG. Real-time telerehabilitation for the treatment of musculoskeletal conditions is effective and comparable to standard practice: A systematic review and meta-analysis. Clin Rehabil2017;31(5):625–638.	Not RPM (exercise monitoring)	Musculoskeletal
Cristo DD, Nascimento NPD, Dias AS, Sachetti A. Telerehabilitation for cardiac patients: Systematic review. Int J Cardiovasc Sci2018;31(4):443–450.	Not RPM (exercise monitoring)	Cardiovascular disease
Cruz JPdS. Self-management in the rehabilitation of patients with chronic obstructive pulmonary disease: The role of telemonitoring and physical activity. Dissertation Abstracts International Section C: Worldwide 2018;75(1-C):No Pagination Specified.	Low/moderate methodological quality	Chronic obstructive pulmonary disease
Da Silva RH, Moore SA, Price CI. Self-directed therapy programmes for arm rehabilitation after stroke: A systematic review. Clin Rehabil2018;32(10):1022–1036.	Not RPM (exercise monitoring)	Stroke
de la Torre-Diez I, Lopez-Coronado M, Vaca C, Aguado JS, de Castro C. Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: A systematic review. Telemed J E Health2015;21(2):81–85.	Not systematic review	Multiple
Delgoshaei B, Mobinizadeh M, Mojdekar R, Afzal E, Arabloo J, Mohamadi E. Telemedicine: A systematic review of economic evaluations. Med J Islam Repub Iran2017;31:113.	Not systematic review	Multiple
Devi R, Singh SJ, Powell J, Fulton EA, Igbinedion E, Rees K. Internet-based interventions for the secondary prevention of coronary heart disease. Cochrane Database Syst Rev2015;CD009386. DOI: 10.1002/14651858.CD009386.pub2.	Not RPM (internet based)	Coronary heart disease
Diedrich L, Dockweiler C, Kupitz A, Hornberg C. Telemonitoring in heart failure: Update on health-related and economic implications. Herz2018;43(4):298–309.	Low/moderate methodological quality	Heart failure
Dikoudi A, Sourtzi P. Εκπαιδɛυτικές παρɛμβάσɛις μɛ τη χρήση Τηλɛϊατρικής στο Σακχαρώδη Διαβήτη τύ-που ΙΙ και οι ɛπιπτώσɛις τους στο γλυκαιμι-κό έλɛγχο. Nurs Care Res2017;9(48):144–168 [in Greek].	Language	Diabetes
Dobson R, Whittaker R, Pfaeffli Dale L, Maddison R. The effectiveness of text message-based self-management interventions for poorly-controlled diabetes: A systematic review. Digit Health2017;3(Nov):1–12.	Not RPM (No provider input in monitoring)	Diabetes
Duan Y, Xie Z, Dong F, Wu Z, Lin Z, Sun N, et al. Effectiveness of home blood pressure telemonitoring: A systematic review and meta-analysis of randomised controlled studies. J Hum Hypertens2017;31(7):427–437.	Low/moderate methodological quality	Hypertension
Duan YX, Xie ZQ. Effectiveness of home blood pressure telemonitoring: A systematic review and meta-analysis of randomized controlled studies. J Hypertens2018;31(7):427–437.	Not systematic review	Hypertension
Duke DC, Barry S, Wagner DV, Speight J, Choudhary P, Harris MA. Distal technologies and type 1 diabetes management. Lancet Diabetes Endocrinol2018;6(2):143–156.	Not systematic review	Diabetes
Emtekaer Haesum LK, Ehlers L, Hejlesen O K. Influence of health literacy on outcomes using telehomecare technology: A systematic review. Health Educ J2016;75(1):72–83.	Wrong outcome	Multiple
Escriva Boulley G, Leroy T, Bernetiere C, Paquienseguy F, Desfriches-Doria O, Preau M. Digital health interventions to help living with cancer: A systematic review of participants’ engagement and psychosocial effects. Psychooncology2018;27(12):2677–2686.	Not RPM (no provider input in monitoring; specialist health services)	Cancer
Flodgren G, Rachas A, Farmer AJ, Inzitari M, Shepperd S. Interactive telemedicine: Effects on professional practice and health care outcomes. Cochrane Database Syst Rev2015;2015:CD002098.	Search before 2015	Multiple
Fridriksdottir N, Gunnarsdottir S, Zoega S, Ingadottir B, Hafsteinsdottir EJG. Effects of web-based interventions on cancer patients’ symptoms: Review of randomized trials. Support Care Cancer2018;26(2):337–351.	Not RPM (specialist health services; or internet based)	Cancer
Gandhi S, Chen S, Hong L, Sun K, Gong E, Li C, et al. Effect of mobile health interventions on the secondary prevention of cardiovascular disease: Systematic review and meta-analysis. Can J Cardiol2017;33(2):219–231.	Not RPM (specialist health services; no provider input; mobile applications; or internet based)	Cardiovascular disease
Garabedian LF, Ross-Degnan D, Wharam JF. Mobile phone and smartphone technologies for diabetes care and self-management. Curr Diabetes Rep2015;15(12):109.	Not systematic review	Diabetes
Gordon LAN. Assessment of smart watches for management of non-communicable diseases in the ageing population: A systematic review. Geriatrics (Basel)2018;3(3):56.	Not systematic review	Multiple
Gregersen TL, Green A, Frausing E, Ringbaek T, Brondum E, Suppli UC. Do telemedical interventions improve quality of life in patients with COPD? A systematic review. Int J Chron Obstruct Pulmon Dis2016;11(Apr):809–822.	Not systematic review	Chronic obstructive pulmonary disease
Grona SL, Bath B, Busch A, Rotter T, Trask C, Harrison E. Use of videoconferencing for physical therapy in people with musculoskeletal conditions: A systematic review. J Telemed Telecare2018;24(5):341–355.	Not RPM (exercise monitoring)	Musculoskeletal
Gu X, Zhu Y, Zhang Y, Sun L, Bao ZY, Shen JH, et al. Effect of telehealth interventions on major cardiovascular outcomes: A metaanalysis of randomized controlled trials. J Geriatr Cardiol2017;14(8):501–508.	Low/moderate methodological quality	Cardiovascular disease
Hall AK, Cole-Lewis H, Bernhardt JM. Mobile text messaging for health: A systematic review of reviews. Annu Rev Public Health2015;18(36):393–415.	Not RPM (interventions)	Multiple
Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: A systematic review. J Med Internet Res2015;17(2):e52.	Not systematic review	Multiple
Hanlon P, Daines L, Campbell C, McKinstry B, Weller D, Pinnock H. Telehealth interventions to support self-management of long-term conditions: A systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. J Med Internet Res2017;19(5):e172.	Included systematic reviews not described enough to assess eligibility	Multiple
Harerimana B, Forchuk C, O’Regan T. The use of technology for mental healthcare delivery among older adults with depressive symptoms: A systematic literature review. Int J Ment Health Nurs2019;28(3):657–670.	Not RPM (interventions)	Psychiatric
Health Quality Ontario. Remote monitoring of implantable cardioverter-defibrillators, cardiac resynchronization therapy and permanent pacemakers: A health technology assessment. Ont Health Technol Assess Ser2018;18(7):1–199.	Not RPM (specialist health services)	Heart failure, abnormal heart rate or rhythm
Heitkemper EM, Mamykina L, Travers J, Smaldone A. Do health information technology self-management interventions improve glycemic control in medically underserved adults with diabetes? A systematic review and meta-analysis. J Am Med Inform Assoc2017;24(5):1024–1035.	Low/moderate methodological quality	Diabetes
Honarvar B, Salehi F, Shaygani F, Hajebrahimi M, Homayounfar R, Dehghan S, et al. Opportunities and threats of electronic health in management of diabetes mellitus: An umbrella review of systematic review and meta-analysis studies. Shiraz E Med J2019;20(1):e81794.	Included systematic reviews not described enough to assess eligibility	Diabetes
Hong Y, Lee SH. Effectiveness of tele-monitoring by patient severity and intervention type in chronic obstructive pulmonary disease patients: A systematic review and meta-analysis. Int J Nurs Stud2019;92(Apr):1–15.	Low/moderate methodological quality	Chronic obstructive pulmonary disease
Hu Y, Wen X, Wang F, Yang D, Liu S, Li P, et al. Effect of telemedicine intervention on hypoglycaemia in diabetes patients: A systematic review and meta-analysis of randomised controlled trials. J Telemed Telecare2019;25(7):402–413.	Low/moderate methodological quality	Diabetes
Huang JW, Lin YY, Wu NY. The effectiveness of telemedicine on body mass index: A systematic review and meta-analysis. J Telemed Telecare2019;25(7):389–401.	Low/moderate methodological quality	Multiple
Huang K, Liu W, He D, Huang B, Xiao D, Peng Y, et al. Telehealth interventions versus center-based cardiac rehabilitation of coronary artery disease: A systematic review and meta-analysis. Eur J Prev Cardiol2015;22(8):959–971.	Not RPM (exercise monitoring)	Coronary artery disease
Huang Z, Tao H, Meng Q, Jing L. Effects of telecare intervention on glycemic control in type 2 diabetes: A systematic review and meta-analysis of randomized controlled trials. Eur J Endocrinol2015;172(3):93–101.	Search before 2015	Diabetes
Inglis SC, Clark RA, Dierckx R, Prieto-Merino D, Cleland JG. Structured telephone support or non-invasive telemonitoring for patients with heart failure. Cochrane Database Syst Rev2015;2015:CD007228.	Low/moderate methodological quality	Heart failure
Inglis SC, Conway A, Cleland JG, Clark RA. Is age a factor in the success or failure of remote monitoring in heart failure? Telemonitoring and structured telephone support in elderly heart failure patients. Eur J Cardiovasc Nurs2015;14(3):248–255.	Not systematic review	Heart failure
Jayakody A, Bryant J, Carey M, Hobden B, Dodd N, Sanson-Fisher R. Effectiveness of interventions utilising telephone follow up in reducing hospital readmission within 30 days for individuals with chronic disease: A systematic review. BMC Health Serv Res2016;16(1):403.	Not RPM (interventions; or in specialist health services)	Multiple
Jin K, Khonsari S, Gallagher R, Gallagher P, Clark AM, Freedman B, et al. Telehealth interventions for the secondary prevention of coronary heart disease: A systematic review and meta-analysis. Eur J Cardiovasc Nurs2019;18(4):260–271.	Not RPM (interventions; in specialist health services; no provider input; or internet based)	Coronary heart disease
Kalankesh LR, Pourasghar F, Nicholson L, Ahmadi S, Hosseini M. Effect of telehealth interventions on hospitalization indicators: A systematic review. Perspect Health Inf Manag2016;13:1h.	Not systematic review	Multiple
Kew KM, Cates CJ. Home telemonitoring and remote feedback between clinic visits for asthma. Cochrane Database Syst Rev2016;(8)2016:CD011714.	Not RPM (specialist health services)	Asthma
Kew KM, Cates CJ. Remote versus face-to-face check-ups for asthma. Cochrane Database Syst Rev2016;4:CD011715.	Not RPM (specialist health services; children; internet based)	Asthma
Kidholm K, Dahl Kristensen MB. Review of high quality economic evaluations of telemedicine. Int J Integr Care2016;16(5 Suppl):1–2.	Not systematic review	Multiple
Kim Y, Park JE, Lee BW, Jung CH, Park DA. Comparative effectiveness of telemonitoring versus usual care for type 2 diabetes: A systematic review and meta-analysis. J Telemed Telecare2018;25(10):587–591.	Low/moderate methodological quality	Diabetes
Kitsiou S, Pare G, Jaana M, Gerber B. Effectiveness of mHealth interventions for patients with diabetes: An overview of systematic reviews. PLoS One2017;12(3):e0173160.	Included systematic reviews not described enough to assess eligibility	Diabetes
Kitsiou S, Pare G, Jaana M. Effects of home telemonitoring interventions on patients with chronic heart failure: An overview of systematic reviews. J Med Internet Res2015;17(3):e63.	No included systematic review meets our definition	Diabetes
Klersy C, Boriani G, De Silvestri A, Mairesse GH, Braunschweig F, Scotti V, et al. Effect of telemonitoring of cardiac implantable electronic devices on healthcare utilization: A meta-analysis of randomized controlled trials in patients with heart failure. Eur J Heart Fail2016;18(2):195–204.	Not RPM (specialist health services)	Heart failure
Knox L, Rahman RJ, Beedie C. Quality of life in patients receiving telemedicine enhanced chronic heart failure disease management: A meta-analysis. J Telemed Telecare2017;23(7):639–649.	Low/moderate methodological quality	Chronic heart failure
Koblauch H, Reinhardt SM, Lissau W, Jensen PL. The effect of telepsychiatric modalities on reduction of readmissions in psychiatric settings: A systematic review. J Telemed Telecare2018;24(1):31–36.	Not systematic review	Psychiatric
Kotb A, Cameron C, Hsieh S, Wells G. Comparative effectiveness of different forms of telemedicine for individuals with heart failure (HF): A systematic review and network meta-analysis. PLoS One2015;10(2):e0118681.	No included systematic review meets our definition	Heart failure
Kraft P, Hillmann S, Rucker V, Heuschmann PU. Telemedical strategies for the improvement of secondary prevention in patients with cerebrovascular events-A systematic review and meta-analysis. Int J Stroke2017;12(6):597–605.	Not RPM (interventions; specialist health services; or internet based)	Stroke
Kruse C, Pesek B, Anderson M, Brennan K, Comfort H. Telemonitoring to manage chronic obstructive pulmonary disease: Systematic literature review. JMIR Med Inform2019;7(1):e11496.	Not systematic review	Chronic obstructive pulmonary disease
Kruse CS, Soma M, Pulluri D, Nemali NT, Brooks M. The effectiveness of telemedicine in the management of chronic heart disease—A systematic review. JRSM Open2017;8(3):e2054270416681747.	Not systematic review	Chronic heart disease
Larson JL, Rosen AB, Wilson FA. The effect of telehealth interventions on quality of life of cancer patients: A systematic review and meta-analysis. Telemed J E Health2018;24(6):397–405.	Not RPM (specialist health services)	Cancer
Lawes-Wickwar S, McBain H, Mulligan K. Application and effectiveness of telehealth to support severe mental illness management: Systematic review. JMIR Ment Health2018;5(4):e62.	Not RPM (interventions)	Psychiatric
Lee JY, Lee SWH. Telemedicine cost-effectiveness for diabetes management: A systematic review. Diabetes Technol Ther2018;20(7):492–500.	Low/moderate methodological quality	Diabetes
Lee PA, Greenfield G, Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: A systematic review and meta-analysis of systematic reviews of randomised controlled trials. BMC Health Serv Res2018;18(1):495.	Low/moderate methodological quality	Diabetes
Lee SWH, Chan CKY, Chua SS, Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: A systematic review and network meta-analysis. Sci Rep2017;7(1):12680.	Not systematic review	Diabetes
Lee SWH, Ooi L, Lai YK. Telemedicine for the management of glycemic control and clinical outcomes of type 1 diabetes mellitus: A systematic review and meta-analysis of randomized controlled studies. Front Pharmacol2017;30(8):330.	Not RPM (specialist health services)	Diabetes
Lieber BA, Taylor B, Appelboom G, Prasad K, Bruce S, Yang A, et al. Meta-analysis of telemonitoring to improve HbA1c levels: Promise for stroke survivors. J Clin Neurosci2015;22(5):807–811.	Not systematic review	Stroke
Lin MH, Yuan WL, Huang TC, Zhang HF, Mai JT, Wang JF. Clinical effectiveness of telemedicine for chronic heart failure: A systematic review and meta-analysis. J Investig Med2017;65(5):899–911.	Low/moderate methodological quality	Chronic heart failure
Liu L, Stroulia E, Nikolaidis I, Miguel-Cruz A, Rios Rincon A. Smart homes and home health monitoring technologies for older adults: A systematic review. Int J Med Inf2016;91(Jul):44–59.	Wrong outcome	Multiple
Liu P, Li G, Jiang S, Liu Y, Leng M, Zhao J, et al. The effect of smart homes on older adults with chronic conditions: A systematic review and meta-analysis. Geriatr Nurs2019;40(5):522–530.	Low/moderate methodological quality	Multiple
Long G. Impact of home telemonitoring on 30-day hospital readmission rates for patients with heart failure: A systematic review. MEDSURG Nurs2017;26(5):337–343.	Not systematic review	Heart failure
Loo Gee B, Griffiths KM, Gulliver A. Effectiveness of mobile technologies delivering Ecological Momentary Interventions for stress and anxiety: A systematic review. J Am Med Inform Assoc2016;23(1):221–229.	Not RPM (interventions)	Psychiatric
Lopez-Villegas A, Catalan-Matamoros D, Martin-Saborido C, Villegas-Tripiana I, Robles-Musso E. A systematic review of economic evaluations of pacemaker telemonitoring systems. Rev Esp Cardiol (Engl)2016;69(2):125–133.	Not RPM (specialist health services)	Cardiovascular disease
Lundell S, Holmner A, Rehn B, Nyberg A, Wadell K. Telehealthcare in COPD: A systematic review and meta-analysis on physical outcomes and dyspnea. Respir Med2015;109(1):11–26.	Not RPM (interventions)	Chronic obstructive pulmonary disease
Ma Y, Cheng HY, Cheng L, Sit JWH. The effectiveness of electronic health interventions on blood pressure control, self-care behavioural outcomes and psychosocial well-being in patients with hypertension: A systematic review and meta-analysis. Int J Nurs Stud2019;92(Apr):27–46.	Low/moderate methodological quality	Hypertension
Mani S, Sharma S, Omar B, Paungmali A, Joseph L. Validity and reliability of internet-based physiotherapy assessment for musculoskeletal disorders: A systematic review. J Telemed Telecare2017;23(3):379–391.	Not RPM (exercise monitoring)	Musculoskeletal
Marcolino MS, Oliveira JAQ, D’Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The impact of mHealth interventions: Systematic review of systematic reviews. JMIR Mhealth Uhealth2018;6(1):e23.	No included systematic review meets our definition	Multiple
McCabe C, McCann M, Brady AM. Computer and mobile technology interventions for self-management in chronic obstructive pulmonary disease. Cochrane Database Syst Rev2017;5:CD011425.	Not RPM (no provider input in monitoring)	Chronic obstructive pulmonary disease
McLean G, Murray E, Band R, Moffat KR, Hanlon P, Bruton A, et al. Interactive digital interventions to promote self-management in adults with asthma: Systematic review and meta-analysis. BMC Pulm Med2016;16(1):83.	Not RPM (no provider input)	Asthma
Michaud TL, Zhou J, McCarthy MA, Siahpush M, Su D. Costs of home-based telemedicine programs: A systematic review. Int J Technol Assess Health Care2018;34(4):410–418.	Not systematic review	Multiple
Miller L, Schuz B, Walters J, Walters EH. Mobile Technology interventions for asthma self-management: Systematic review and meta-analysis. JMIR Mhealth Uhealth2017;5(5):e57.	Not RPM (no provider input in monitoring)	Asthma
Murphy LA, Harrington P, Taylor SJ, Teljeur C, Smith SM, Pinnock H, et al. Clinical-effectiveness of self-management interventions in chronic obstructive pulmonary disease: An overview of reviews. Chron Respir Dis2017;14(3):276–288.	Included systematic reviews not described enough to assess eligibility	Chronic obstructive pulmonary disease
Mushcab H, Kernohan WG, Wallace J, Martin S. Web-based remote monitoring systems for self-managing type 2 diabetes: A systematic review. Diabetes Technol Ther2015;17(7):498–509.	Not systematic review	Diabetes
O’Brien KM, Hodder RK, Wiggers J, Williams A, Campbell E, Wolfenden L, et al. Effectiveness of telephone-based interventions for managing osteoarthritis and spinal pain: A systematic review and meta-analysis. PeerJ2018;6:e5846.	Not RPM (interventions)	Osteoporosis
O’Connor M, Munnelly A, Whelan R, McHugh L. The efficacy and acceptability of third-wave behavioral and cognitive eHealth treatments: A systematic review and meta-analysis of randomized controlled trials. Behav Ther2018;49(3):459–475.	Not RPM (interventions)	Multiple
Palm U, Kumpf U, Behler N, Wulf L, Kirsch B, Worsching J, et al. Home use, remotely supervised, and remotely controlled transcranial direct current stimulation: A systematic review of the available evidence. Neuromodulation2018;21(4):323–333.	Not systematic review	Psychiatric
Pandor A, Gomersall T, Stevens JW, Wong R. Remote monitoring strategies for patients with stable heart failure: A systematic review and network meta-analysis. Value Health2015;18(3)1717–1726.	Not systematic review	Heart failure
Park DA, Yun JE, Park JE. Comparative safety and effectiveness of telemonitoring intervention versus usual care for heart failure: A systematic review and meta-analysis. Value Health2016;19(7):19–28.	Not systematic review	Heart failure
Parthiban N, Esterman A, Mahajan R, Twomey DJ, Pathak RK, Lau DH, et al. Remote monitoring of implantable cardioverter-defibrillators: A systematic review and meta-analysis of clinical outcomes. J Am Coll Cardiol2015;65(24):2591–2600.	Not systematic review	Diabetes
Pedone C, Lelli D. Systematic review of telemonitoring in COPD: An update. Pneumonol Alergol Pol2015;83(6):476–484.	Not systematic review	Chronic obstructive pulmonary disease
Pekmezaris R, Tortez L, Williams M, Patel V, Makaryus A, Zeltser R, et al. Home telemonitoring in heart failure: A systematic review and meta-analysis. Health Aff (Millwood)2018;37(12):1983–1989.	Not systematic review	Chronic obstructive pulmonary disease
Peretz D, Arnaert A, Ponzoni NN. Determining the cost of implementing and operating a remote patient monitoring programme for the elderly with chronic conditions: A systematic review of economic evaluations. J Telemed Telecare2018;24(1):13–21.	Not systematic review	Multiple
Pfaeffli Dale L, Dobson R, Whittaker R, Maddison R. The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: A systematic review. Eur J Prev Cardiol2016;23(8):801–817.	Not RPM (specialist health services; no provider input; or internet based)	Multiple
Piga M, Cangemi I, Mathieu A, Cauli A. Telemedicine for patients with rheumatic diseases: Systematic review and proposal for research agenda. Semin Arthritis Rheum2017;47(1):121–128.	Not RPM (specialist health services)	Rheumatic diseases
Porter J, Huggins CE, Truby H, Collins J. The Effect of using mobile technology-based methods that record food or nutrient intake on diabetes control and nutrition outcomes: A systematic review. Nutrients2016;8(12):815.	Not RPM (no provider input)	Musculoskeletal
Queiros A, Alvarelhao J, Cerqueira M, Silva AG, Santos M, Rocha NP. Remote care technology: A systematic overview. Stud Health Technol Inform2017;242:111–118.	Not systematic review	Diabetes
Queiros A, Alvarelhao J, Cerqueira M, Silva AG, Santos M, Rocha NP. Remote care technology: A systematic overview. Stud Health Technol Inform2017;242:111–118.	Not systematic review	Multiple
A110. Rawstorn JC, Gant N, Direito A, Beckmann C, Maddison R. Telehealth exercise-based cardiac rehabilitation: A systematic review and meta-analysis. Heart2016;102(15):1183–1192.	Not RPM (exercise monitoring)	Multiple
Rintala A, Paivarinne V, Hakala S, Paltamaa J, Heinonen A, Karvanen J, et al. Effectiveness of technology-based distance physical rehabilitation interventions for improving physical functioning in stroke: A systematic review and meta-analysis of randomized controlled trials. Arch Phys Med Rehabil2019;100(7):1339–1358.	Not RPM (exercise monitoring)	Multiple
Rush KL, Howlett L, Munro A, Burton L. Videoconference compared to telephone in healthcare delivery: A systematic review. Int J Med Inf2018;118(Oct):44–53.	Wrong outcome	Stroke
Saeed N, Manzoor M, Khosravi P. An exploration of usability issues in telecare monitoring systems and possible solutions: A systematic literature review. Disabil Rehabil Assist Technol2019;15:271–281.	Wrong outcome	Multiple
Sandran N, Hillier S, Hordacre B. Strategies to implement and monitor in-home transcranial electrical stimulation in neurological and psychiatric patient populations: A systematic review. J Neuroeng Rehabil2019;16(1):58.	Not RPM (device fitting/monitoring)	Multiple
Santiago de Araújo Pio C, Chaves GS, Davies P, Taylor RS, Grace SL. Interventions to promote patient utilisation of cardiac rehabilitation. Cochrane Database Syst Rev2019;2:CD007131.	Wrong outcome	Psychiatric
Sarfo FS, Ulasavets U, Opare-Sem OK, Ovbiagele B. Tele-rehabilitation after stroke: An updated systematic review of the literature. J Stroke Cerebrovasc Dis2018;27(9):2306–2318.	Not systematic review	Stroke
Schroder J, van Criekinge T, Embrechts E, Celis X, Van Schuppen J, Truijen S, et al. Combining the benefits of tele-rehabilitation and virtual reality-based balance training: A systematic review on feasibility and effectiveness. Disabil Rehabil AssistTechnol 2019;14(1):2–11.	Not RPM (exercise monitoring)	Stroke
Seiler A, Klaas V, Troster G, Fagundes CP. eHealth and mHealth interventions in the treatment of fatigued cancer survivors: A systematic review and meta-analysis. Psychooncology2017;26(9):1239–1253.	Not RPM (interventions)	Stroke
Seppala J, De Vita I, Jamsa T, Miettunen J, Isohanni M, Rubinstein K, et al. Mobile phone and wearable sensor-based mHealth approaches for psychiatric disorders and symptoms: Systematic review. JMIR Ment Health2019;6(2):e9819.	Not systematic review	Cancer
Shahaj O, Denneny D, Schwappach A, Pearce G, Epiphaniou E, Parke HL, et al. Supporting self-management for people with hypertension: A meta-review of quantitative and qualitative systematic reviews. J Hypertens2019;37(2):264–279.	No included systematic review meets our definition	Psychiatric
Shen Y, Wang FB, Zhang X, Zhu XR, Sun QD, Fisher E, et al. Effectiveness of internet-based interventions on glycemic control in patients with type 2 diabetes: Meta-analysis of randomized controlled trials. J Med Internet Res2018;20(5):e172.	Low/moderate methodological quality	Diabetes
Shigekawa E, Fix M, Corbett G, Roby DH, Coffman J. The current state of telehealth evidence: A rapid review. Health Aff (Millwood)2018;37(12):1975–1982.	Not systematic review	Hypertension
Singh TP, Vangaveti VN, Kennedy RL, Malabu UH. Role of telehealth in diabetic foot ulcer management—A systematic review. Aust J Rural Health2016;24(4):224–229.	Not systematic review	Multiple
Slev VN, Mistiaen P, Pasman HR, Verdonck-de Leeuw IM, van Uden-Kraan CF, Francke AL. Effects of eHealth for patients and informal caregivers confronted with cancer: A meta-review. Int J Med Inf2016;87:54–67.	Not RPM (interventions; not remote; or in specialist health services)	Diabetes
So CF, Chung J. Telehealth for diabetes self-management in primary healthcare—A systematic review and meta-analysis. J Telemed Telecare2018; 24(5):356–364.	Low/moderate methodological quality	Diabetes
Su D, McBride C, Zhou J, Kelley MS. Does nutritional counseling in telemedicine improve treatment outcomes for diabetes? A systematic review and meta-analysis of results from 92 studies. J Telemed Telecare2016;22(6):333–347.	Not systematic review	Cancer
Su D, Zhou J, Kelley MS, Michaud TL, Siahpush M, Kim J, et al. Does telemedicine improve treatment outcomes for diabetes? A meta-analysis of results from 55 randomized controlled trials. Diabetes Res Clin Pract2016;116(Jun):136–148.	Low/moderate methodological quality	Diabetes
Suh SR, Lee MK. Effects of nurse-led telephone-based supportive interventions for patients with cancer: A meta-analysis. Oncol Nurs Forum2017;44(4):168–184.	Not RPM (specialist health services)	Diabetes
Sul AR, Lyu DH, Park DA. Effectiveness of telemonitoring versus usual care for chronic obstructive pulmonary disease: A systematic review and meta-analysis. J Telemed Telecare2018;26:189–199.	Low/moderate methodological quality	Chronic obstructive pulmonary disease
Tao KFM, Brennan-Jones CG, Capobianco-Fava DM, Jayakody DMP, Friedland PL, Swanepoel D, et al. Teleaudiology services for rehabilitation with hearing aids in adults: A systematic review. J Speech Lang Hear Res2018;61(7):1831–1849.	Not RPM (device fitting/monitoring)	Cancer
Tchero H, Kangambega P, Briatte C, Brunet-Houdard S, Retali GR, Rusch E. Clinical effectiveness of telemedicine in diabetes mellitus: A meta-analysis of 42 randomized controlled trials. Telemed J E Health2019;25(7):569–583.	Low/moderate methodological quality	Diabetes
Tchero H, Noubou L, Becsangele B, Mukisi-Mukaza M, Retali GR, Rusch E. Telemedicine in diabetic foot care: A systematic literature review of interventions and meta-analysis of controlled trials. Int J Low Extrem Wounds2017;16(4):274–283.	Not RPM (specialist health services)	Impaired hearing
Tchero H, Tabue Teguo M, Lannuzel A, Rusch E. Telerehabilitation for stroke survivors: Systematic review and meta-analysis. J Med Internet Res2018;20(10):e10867.	Not RPM (exercise monitoring; interventions)	Stroke
Triberti S, Savioni L, Sebri V, Pravettoni G. eHealth for improving quality of life in breast cancer patients: A systematic review. Cancer Treat Rev2019;74(Mar):1–14.	Not RPM (interventions; or in specialist health services)	Diabetes
Tse G, Chan C, Gong M, Meng L, Zhang J, Su XL, et al. Telemonitoring and hemodynamic monitoring to reduce hospitalization rates in heart failure: A systematic review and meta-analysis of randomized controlled trials and real-world studies. J Geriatr Cardiol2018;15(4):298–309.	Low/moderate methodological quality	Heart failure
Unni E, Gabriel S, Ariely R. A review of the use and effectiveness of digital health technologies in patients with asthma. Ann Allergy Asthma Immunol2018;121(6):680–691.	Not systematic review	Cancer
Valenzuela Espinoza A, Steurbaut S, Dupont A, Cornu P, van Hooff RJ, Brouns R, et al. Health economic evaluations of digital health interventions for secondary prevention in stroke patients: A systematic review. Cerebrovasc Dis Extra2019;9(1):1–8.	Not systematic review	Asthma
Vargas G, Cajita MI, Whitehouse E, Han HR. Use of short messaging service for hypertension management a systematic review. J Cardiovasc Nurs2017;32(3):260–270.	Low/moderate methodological quality	Hypertension
Vegesna A, Tran M, Angelaccio M, Arcona S. Remote patient monitoring via non-invasive digital technologies: A systematic review. Telemed J E Health2017;23(1):3–17.	No effect sizes	Stroke
Wang X, Shu W, Du J, Du M, Wang P, Xue M, et al. Mobile health in the management of type 1 diabetes: A systematic review and meta-analysis. BMC Endocr Disord2019;19(1):21.	Not RPM (specialist health services)	Diabetes
Wang Y, Xue H, Huang Y, Huang L, Zhang D. A systematic review of application and effectiveness of mHealth interventions for obesity and diabetes treatment and self-management. Adv Nutr (Bethesda)2017;8(3):449–462.	Not systematic review	Multiple
Wei J, Zheng H, Wang L, Wang Q, Wei F, Bai L. Effects of telephone call intervention on cardiovascular risk factors in type 2 diabetes mellitus: A meta-analysis. J Telemed Telecare2017;25(2):93–105.	Not RPM (interventions)	Diabetes
Yang Q, Van Stee SK. The comparative effectiveness of mobile phone interventions in improving health outcomes: Meta-analytic review. JMIR Mhealth Uhealth2019;7(4):e11244.	Not systematic review	Diabetes
Yang S, Jiang Q, Li H. The role of telenursing in the management of diabetes: A systematic review and meta-analysis. Public Health Nurs2019;36(4):575–586.	Not RPM (specialist health services)	Diabetes
Yasmin F, Banu B, Zakir SM, Sauerborn R, Ali L, Souares A. Positive influence of short message service and voice call interventions on adherence and health outcomes in case of chronic disease care: A systematic review. BMC Med Inf Decis Mak2016;16(Apr):46.	Not systematic review	Multiple
Yoshida Y, Boren SA, Soares J, Popescu M, Nielson SD, Simoes EJ. Effect of health information technologies on glycemic control among patients with type 2 diabetes. Curr Diabetes Rep2018;18(12):11.	Low/moderate methodological quality	Diabetes
Yun JE, Park JE, Park HY, Lee HY, Park DA. Comparative effectiveness of telemonitoring versus usual care for heart failure: A systematic review and meta-analysis. J Card Fail2018;24(1):19–28.	Low/moderate methodological quality	Heart failure
Zhang Q, Zhang L, Yin R, Fu T, Chen H, Shen B. Effectiveness of telephone-based interventions on health-related quality of life and prognostic outcomes in breast cancer patients and survivors: A meta-analysis. Eur J Cancer Care (Engl)2018;27(1):1–10.	Not RPM (interventions; not remote; or in specialist health services)	Multiple
Zhao J, Zhai YK, Zhu WJ, Sun DX. Effectiveness of telemedicine for controlling asthma symptoms: A systematic review and meta-analysis. Telemed J E Health2015;21(6):484–492.	Not RPM (interventions; specialist health services; no provider input; or internet based)	Cancer
Ντικούδη Α, Σουρτζή Π. Εκπαιδɛυτικές Παρɛμβάσɛις στον Σακχαρώδη Διαβήτη Τύπου 2 μɛ τη Χρήση Τηλɛϊατρικής: Οι Επιδράσɛις τους στην Αυτοδιαχɛίριση της Νόσου και στην Ποιότητα Ζωής των Ασθɛνών. Nosileftiki2017;56(3):187–200 [in Greek].	No effect sizes	Asthma

Appendix A3. Methodological Quality Assessment

We used NIPH’s checklist of systematic reviews to evaluate the included review’s methodological quality.

1.	Do the authors clearly describe the methods they used to identify primary studies?
2.	Was the literature search conducted satisfactorily/thoroughly?
3.	Do the authors describe which criteria they used to determine which studies to include (study design, participants, intervention, and outcome)?
4.	Were attempts made to reduce systematic bias during the study selection phase (explicit inclusion/exclusion criteria, selection made by multiple independent persons)?
5.	Were criteria to assess the internal validity of studies clearly described?
6.	Was the validity of the studies evaluated (either during inclusion or analysis of primary studies) using relevant criteria?
7.	Are the methods used when the results were summarized clearly described?
8.	Were the results of the studies properly summarized?

Evaluation of the Included Reviews’ Methodological Quality

REVIEW	1	2	3	4	5	6	7	8	SUMMARY ASSESSMENT
Bittner et al. (2015)	Yes	Yes	Yes	Yes	Yes	N/A	Yes	N/A	High
Faruque et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Kebede et al. (2018)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Posadzki et al. (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High