Category: 3. Business

Introduction

Idiopathic normal pressure hydrocephalus (iNPH) is a neurological disease characterized by cognitive impairment, gait disturbance, and urinary incontinence [], with ventricular enlargement and the DESH sign (disproportionately enlarged subarachnoid hydrocephalus) shown on brain scans []. It is a common cause of dementia in older people [] and can be treated effectively with cerebrospinal fluid (CSF) shunts [,].

However, the clinical manifestations of iNPH lack specificity, with only 50% of patients exhibiting the full triad []. Therefore, patients with iNPH are usually misdiagnosed as other neurodegenerative diseases, such as Alzheimer disease or Parkinson disease []. Unlike these conditions, the clinical symptoms of iNPH can be reversed through CSF shunting []. However, only 39% to 81% of patients with shunts show improvement 3 to 6 months after the shunt insertion []. The CSF drainage test is an important tool for evaluating patients with possible iNPH before shunt surgery [,,]. The tap test and continuous external lumbar drainage (ELD) are 2 commonly used methods []. Previous studies have shown that the tap test has a sensitivity of only 26%, whereas the continuous ELD has a sensitivity of 50% and a specificity of 80% [,]. As a result, some medical centers conduct continuous ELD directly to avoid repeated lumbar punctures and reduce the risk of misdiagnosis [-]. On the other hand, current evaluation methods for CSF drainage tests also have relatively low sensitivities. Previous studies using the Mini-Mental State Examination (MMSE), 10-m walking test (TMWT), and timed up and go (TUG) test indicate a sensitivity of 26% for tap test [,]. This implies that current assessment methods are inadequate in providing reliable guidance for clinical practice because traditional cognitive tests can cause large learning effects because of the short time intervals between tests [] and video-based gait analysis may be influenced by the subjective opinions of raters [] and lacks quantitative measures, such as stride length, step height, and gait velocity [,]. These shortcomings of traditional tests highlight the need for a more objective and quantitative evaluation method.

In recent years, digital neuropsychological evaluation equipment has emerged, which was designed to overcome the major limitations of paper and pencil tests, such as low sensitivity, subjectivity, and test-retest reliability []. They also provide randomized test paradigms and automated recording of variables such as reaction time, thereby improving evaluation efficacy []. A recent study indicated that computerized neuropsychological tests could detect cognitive impairment and postshunt improvements in patients with iNPH []. Additionally, 3-dimensional gait analysis aids in the diagnosis of iNPH by offering multidimensional gait parameters [,]. There is growing evidence demonstrating that motor and cognitive impairment in iNPH may result from interconnected neural network impairments [,], and recent research based on traditional cognitive and gait tests found that the improvement of different symptoms after ELD may follow different temporal trajectories []. Therefore, combining cognitive and gait assessments may provide a more comprehensive and complementary picture of functional response after ELD. Within the expanding field of digital medicine, the integration of digital cognitive and gait tests into diagnostic workflow has been widely investigated and implemented in other cognitive disorders [,]. However, an evidence gap remains regarding the predictive value of these digital tests in the context of ELD in patients with iNPH.

Therefore, this study aimed to (1) evaluate the improvement of cognitive and gait parameters after ELD through the application of digital tests and (2) investigate the predictive value of digital cognitive and motor assessments for shunt outcomes in patients with iNPH.

Methods

Study Design

Patients with iNPH were enrolled from an ongoing prospective cohort study at West China Hospital of Sichuan University between May 2022 and November 2023. The inclusion criteria included (1) at least one symptom of cognitive decline, gait disturbance, and urinary incontinence; (2) ventricular enlargement (Evans index >0.3), focally dilated sulci, or the DESH sign shown on brain magnetic resonance imaging; (3) CSF opening pressure ≤200 mm H₂O; and (4) informed consent. We excluded patients with (1) gait disturbance, cognitive impairment, or urinary incontinence due to other neurological diseases (cerebral hemorrhage, brain trauma, brain tumor, and intracranial infection); (2) inability to complete quantitative motor or neuropsychological tests; and (3) refusal to undergo or a negative response to continuous ELD. After enrollment, all participants underwent comprehensive baseline clinical assessments and continuous ELD. Detailed methodologies were described in the following sections. The participant flow throughout the study is summarized in . This cohort study was conducted in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines (Checklist 1).

Ethical Considerations

This study was approved by the Ethics Committee of the West China Hospital, Sichuan University (No. 2022‐538). To ensure confidentiality, all data were anonymized before analysis and stored securely, with access restricted to the research team. Participants were not compensated financially but volunteered after being informed of the study’s potential benefits.

Demographic Characteristics

We systematically collected demographic information of the enrolled patients, including age, sex, years of education, medical history, and conducted a comprehensive neurological examination. The Japanese iNPH grading scale (iNPHGS) was used to quantify the severity of symptoms of iNPH [,]. Each symptom was scored from 0 to 4, with a total score of 12; higher scores indicating more severe clinical symptoms. The modified Rankin Scale (mRS) was used to assess the patients’ daily living function [], scored from 0 to 5, with higher scores indicating a more severe inability to perform daily living.

Neuropsychological Assessments

All neuropsychological tests were conducted by 2 trained neuropsychologists. Traditional neuropsychological assessment uses the MMSE to evaluate global cognitive function []. The BrainFit computerized neuropsychological assessment device (Beijing CAS-Ruiyi Information Technology Co, Ltd, China) was used to provide computerized neuropsychological tests, including the grammatical reasoning test, the one-back test, the trail-making test (TMT), and the Stroop color-word test (SCWT). The reliability of the BrainFit system was validated among Chinese populations in previous studies [,]. The grammatical reasoning test is designed to evaluate the language comprehension and reasoning ability of the participants []. In 60 seconds, they had to determine if the way the shapes were arranged on the screen matched the textual description. The shapes were a circle and a square with a particular positional relationship, and in the middle of the screen was a sentence that described this relationship. If the sentence and shape combination matched, participants clicked “correct”; if not, they clicked “incorrect.” A new question would display within a second of each click []. The score for this test was calculated as the number of correct responses minus the number of errors. The one-back test is based on the “N-back” working memory paradigm []. Within 60 seconds, participants were required to click the “same” or “different” button to indicate whether they thought the current displayed number matched the previous one. A new number appeared within 1 second after each click. The score was calculated as “the number of correct responses minus the number of incorrect responses” []. The Trail Making Test primarily assessed the participant’s attention and reaction speed, requiring the participant to sequentially connect 25 randomly arranged numbers on a page, with the test time recorded []. The Stroop Color-Word Test was used to evaluate executive function, psychomotor speed, and cognitive flexibility. During this study, participants were asked to quickly name the color of words that describe different colors, with test time and the number of correct responses recorded [].

Gait Analysis

Traditional gait analysis includes the TMWT and the 5-m TUG test [,]. The TMWT requires the patient to walk on a 10-m-long path while recording a video. The total time and the number of steps were recorded. The TUG test requires the patient to walk on a 5-m path from one end to the other, turn 180 degrees, and then walk back to the beginning. Each test recorded the overall time, number of steps, and number of turns necessary. Each of these tests was repeated 3 times, and the average of the 3 results was recorded.

Quantitative gait analysis was evaluated using a 3-dimensional gait analysis system ReadyGo (Beijing CAS-Ruiyi Information Technology Co, Ltd), the accuracy and sensitivity of the system have been validated in previous studies [,,]. The ReadyGo system uses a single-camera setup to record 3-dimensional motion by using deep learning for precise positioning of skeletal points. For the single-gait test, participants were instructed to walk at their habitual pace on a 3 m walkway. Step width, stride length, step height, gait velocity, and turning time were the analyzed parameters. Step width was defined as the average width between the left and right feet in each image frame. A single-foot stride length was the distance between 2 landings from the same foot; the overall stride length was the average of the left and right sides. The step height was the height at which a foot could swing without touching the ground. The average step heights of the left and right feet were used for the final analysis. Gait velocity was defined as the distance between the start and end points divided by test time [].

Continuous ELD Test

Patients with possible iNPH underwent lumbar puncture and continuous ELD after obtaining informed consent. For 3 to 5 days, the daily CSF drainage volume was controlled between 100 and 150 mL [,,,]. The patients underwent traditional cognitive and gait assessments at baseline and every day after ELD, with extra digital neuropsychological and gait assessments conducted at baseline and on the third day following ELD. The criteria for a positive response to the traditional evaluation method of ELD included (1) improvement of ≥20% in either time or number of steps in the 10-m walking test or ≥10% improvement in both time and number of steps, (2) improvement of ≥10% in the TUG test time, or (3) improvement of ≥3 points in the MMSE score [,,].

Postoperative Follow-Up

Patients diagnosed with probable iNPH after ELD underwent lumboperitoneal shunt placement after informed consent was obtained. These patients were followed up regularly at 3 months, 6 months, and 1 year postoperatively. Follow-up was conducted through in-person visits or telephone interviews with the patients and their long-term caregivers. During the in-person visits, comprehensive neurological examinations and brain imaging were performed. Objective assessments of cognitive function, gait, urinary function, daily living abilities, subjective symptom improvements reported by the patients and caregivers, and postoperative complications were documented. Telephone interviews primarily assessed whether the patients and caregivers reported symptom improvement and identified any possible complications. Patients who showed an improvement of ≥1 point in mRS score compared to baseline or an improvement of ≥1 point in any iNPHGS score were defined as definite iNPH (shunt responders) [,]. Those who did not report symptom improvements during the follow-up period were classified as nonresponders and underwent pressure readjustment and long-term follow-up.

Statistical Analyses

The raw scores of grammatical reasoning, the one-back test, TMT, SCWT correct number, and reaction time were converted into standardized Z-scores based on previously reported norms for the Chinese population, facilitating comparisons across tests [,]. The z-scores for the Stroop C correct number, grammatical reasoning test, and one-back test were calculated using the formula (raw score−mean)/SD. Z-scores for TMT and Stroop C reaction time were derived by subtracting the raw score from the mean and dividing by the SD. Lower z-scores indicated more significant impairments in the corresponding cognitive domain. The cognitive Z-score for every patient was calculated by taking the mean of the Z-scores from the tests mentioned earlier [,]. The improvement rate of the cognitive Z-score was calculated as follows: (post-ELD parameter−pre-ELD parameter)/pre-ELD parameter×100%. For quantitative gait analysis, the improvement rates in gait velocity, stride length, and step height post-ELD were calculated as (post-ELD parameter − pre-ELD parameter)/pre-ELD parameter×100%. The improvement rates of the step width and turning time post-ELD were calculated as follows: (pre-ELD parameter−post-ELD parameter)/pre-ELD parameter×100%. The gait improvement rate was calculated as averages of all 5 gait parameters, and the combined improvement rate was averaged based on the improvement rate of the cognitive Z-score and gait improvement rate []. In this pilot study, gait and cognitive improvement were assigned equal weight in the combined improvement rate to reflect global functional improvement after ELD, as the gait and cognitive impairment are core symptoms in iNPH and are considered equally important in current clinical assessments such as iNPHGS [,].

For continuous variables, normally distributed data are shown as mean (SD), whereas nonnormally distributed data are presented as median (IQR). Group comparisons were performed using the Student t test for normally distributed data and the Mann-Whitney U test for skewed data, whereas comparisons of proportions were performed using the χ² test. Group comparisons before and after the ELD were performed using paired t tests for normally distributed data and Wilcoxon signed-rank tests for nonnormally distributed data. To address the limited sample size, we used Firth penalized logistic regression to evaluate the predictive value of improvement rates from each evaluation method for shunt response, and variables demonstrating significant difference in group comparison were included in subsequent multivariate models. This method effectively reduced the small-sample bias and provided more reliable coefficient estimates []. The results of Firth logistic regression models were reported by odds ratios (ORs) and receiver operating characteristic curves. Optimal cutoff values were determined based on the Youden index, and diagnostic metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value, were calculated. The DeLong test was used for AUC comparison between traditional tests and digital tests. Permutation tests with 5000 iterations on all Firth regression models and internal validation of the diagnostic metrics with 2000 bootstrap resamples were used to assess the model performance and further enhance the robustness of the statistical inference []. A P value of .05 was considered statistically significant. All statistical analyses were performed using STATA SE 16.0 (StataCorp, TX, USA) and R version 4.3.0 (R Foundation).

Results

Demographic and Clinical Characteristics of Patients With Probable iNPH

A total of 127 patients diagnosed with possible NPH were consecutively enrolled in this study (). After excluding 7 patients who refused lumbar puncture, 13 patients who developed secondary NPH, and 30 patients who were unable to cooperate with the quantitative motor and cognitive tests, 77 patients with possible iNPH have finally received continuous ELD. Of them, 70 patients were ELD responders and were diagnosed as probable iNPH by clinical neurologists, whereas 7 were negative for ELD tests. Among the 70 patients with probable iNPH, 31 refused shunt surgery and were ultimately diagnosed with probable iNPH, and 39 (55.7%) underwent lumboperitoneal shunt surgery.

presents the baseline characteristics of the 70 patients with probable iNPH. The median (IQR) age was 74 (69-80) years, and 70% (49/70) of the patients were male. The prevalence of cognitive impairment, gait disturbances, and urinary incontinence was 92.86% (65/70), 95.71% (67/70), and 54.29% (38/70), respectively, with 51.43% presenting with the full Hakim triad (). We compared the baseline characteristics between patients with iNPH who received shunt surgery and those who did not, and the shunted group exhibited a significantly more severe DESH score on brain magnetic resonance imaging (P=.007), whereas the 2 groups did not differ in demographics, symptom severity, or improvement rates after ELD. This suggests that the 2 groups were largely comparable at baseline.

As shown in and in , among the 70 patients with probable iNPH, traditional tests, including MMSE (P<.001), TUG (P=.004), TMWT time (P=.016), and steps (P<.001), showed significant improvement 3 days after ELD. For digital tests, one-back test (P=.01), SCWT correct numbers (P=.009), and composite cognitive z-scores (P=.025) improved significantly 3 days after ELD. Quantitative gait analyses also showed significant improvement in stride length (P=.01), step height (P=.048), gait velocity (P=.019), and reduced turning time (P<.001) after 3-day ELD compared to baseline.

‎

Predictive Value of Digital Neuropsychological and Gait Tests for Shunt Outcome in Patients With iNPH

A total of 39 patients with probable iNPH underwent lumboperitoneal shunt placement; after a median follow-up interval of 116 days, 34 (89.7%) showed at least a 1-point improvement in mRS or iNPHGS, leading to a clinical diagnosis of definite iNPH or being classified as “shunt responders,” 4 patients only reported subjective symptomatic improvement but not measurable through mRS or iNPHGS, and 1 patient did not report any improvement at the time of final follow-up and was managed with pressure readjustment and continued follow-up.

First, we compared the clinical characteristics between shunt responders and nonresponders (). The results demonstrated a higher proportion of male sex (P=.036), lower Evan index (P=.035), and significantly higher improvement rate of gait after ELD (P=.04) for shunt responders, whereas they did not differ significantly in age, educational level, or vascular risk factors. Multivariate Firth logistic regression model adjusted for sex and Evan index showed a significant association between a higher improvement rate of digital gait analysis and a lower risk of unfavorable shunt outcome (adjusted OR 0.90, 95% CI 0.78‐0.99; P=.03). In addition, an association between a higher combined improvement rate of digital neuropsychological and gait analysis and a lower risk of unfavorable shunt outcome was also observed (adjusted OR 0.98, 95% CI 0.95‐1.00; P=.03; ).

As shown in and , traditional tests in ELD yielded poor diagnostic performance, with an AUC of 0.55 (95% CI 0.30‐0.81), sensitivity of 40% (95% CI 12%‐77%), specificity of 71% (95% CI 54%‐83%), PPV of 17% (95% CI 5%‐45%), and NPV of 89% (95% CI 72%‐96%). In contrast, the combined digital cognitive and gait approach yielded an AUC of 0.92 (95% CI 0.83‐1.00), sensitivity of 100% (95% CI 57%‐100%), specificity of 79% (95% CI 63%‐90%), PPV of 42% (95% CI 19%‐68%), and NPV of 100% (95% CI 88%‐100%). The bootstrap-derived cutoffs showed moderate dispersion, whereas the predictive performances of all digital tests remain relatively stable (lower limits of all AUCs >0.81). The DeLong test demonstrated that combining digital cognitive and gait tests showed better predictive performance of shunt outcome compared to traditional tests (Z=2.43; P=.015).

Furthermore, the calibration curve demonstrated that the combined improvement model showed good overall calibration quality (, Spiegelhalter P=.62), with a low average calibration error of 3.0%, despite slight overconfidence (calibration slope=1.299).

‎

Discussion

Principal Findings

In this study, we investigated the predictive value of digital neuropsychological and gait analyses during ELD for shunt outcome in patients with iNPH. Our findings revealed that while both traditional and digital cognitive and gait assessments improved significantly after 3 days of ELD, the digital tests outperformed traditional testing in terms of predicting shunt outcomes.

Our study found that patients with probable iNPH showed gait improvement after ELD, which was primarily manifested as increased gait speed, longer stride length, higher step height, and shorter turning time. This is consistent with the findings of prior investigations using quantitative gait analysis []. Electronic walkways, wearable sensors, and accelerometers are all commonly used for quantitative gait analysis [,]. A prospective study using electronic walkways discovered that patients with iNPH had increased stride length and gait speed following a tap test []. Another research study has revealed that 72 hours following the tap test, patients with probable iNPH had longer stride lengths, shorter double support times, and faster cadences []. A recent study based on 3-dimensional gait analysis indicated that patients with probable iNPH may show improvements in spatiotemporal parameters, such as step length, gait speed, and cadence 24 hours after the tap test, with improvement rates ranging from 4.9% to 10.5% []. Besides, a preliminary study using a video-based method qualitatively assessed gait changes after the tap test and found significant improvements in step height [], whereas earlier studies based on inertial sensors and electronic walkways may not provide precise quantitative assessments of step height. Our vision-based system provided richer spatiotemporal gait parameters than previous methods, without the need for wearable sensors. Therefore, it can provide improvements in step height following ELD, which partially complements the findings of previous studies.

Cognitive impairment is the second most common symptom in patients with iNPH, which may be partly due to mechanical stress to the brain and the existence of Alzheimer disease co-pathologies [,]. Frontal lobe dysfunction is thought to be the classic cognitive profile in patients with iNPH []. In our study, we observed significant improvements in one-back tests and Stroop color-word tests post-ELD, indicating improvements in executive function, attention, and working memory, all of which are associated with improved frontal lobe functions. Patients with iNPH often show significant improvements in executive subfunction after CSF drainage [], with prior research showing improvements in verbal fluency, frontal assessment battery, trail-making test, and Stroop color-word tests [-]. Therefore, executive subfunction assessment is emphasized in evaluations during CSF drainage to distinguish iNPH from its mimics [,]. Previous studies have developed an executive function battery for patients with iNPH based on tests such as TMT-A, Stroop color-word tests, and digit symbol substitution tests [], and this battery demonstrates a sensitivity of 80% in predicting shunt responders with a specificity of 100%, indicating that evaluating executive function, attention, and reaction time before and after CSF drainage may be crucial in identifying shunt responders [].

Despite the widespread use of the CSF drainage test for diagnosing and predicting shunt prognosis in patients with iNPH, earlier research indicated that the sensitivity of a single-tap test might be as low as 26%, whereas continuous ELD had a sensitivity of approximately 50% [,]. Although the tap test response would be influenced by morphological features and CSF dynamic changes [], the method used to evaluate the CSF drainage test itself may be crucial for determining its overall sensitivity [,]. In the latest Japanese iNPH management guidelines, MMSE, TMWT, and TUG tests were recommended to assess changes in patients’ gait and cognitive function []. However, using these methods for assessment may result in very low sensitivity and negative predictive value []. A previous large-scale European multicenter study indicated that the negative predictive value of a single lumbar puncture drainage could be as low as 18% [], with other literature reports generally ranging from 18% to 50% []. In our cohort, traditional cognitive and gait tests exhibited a sensitivity of only 40% and a specificity of 70%, which demonstrated that traditional tests may potentially lead to misdiagnosis of shunt responders. Therefore, traditional assessment methods may be insufficient to guide the selection of shunt candidates [].

In recent years, digital neuropsychological evaluation equipment has emerged that provides randomized test paradigms and automated recording of variables such as reaction time, thereby improving test efficacy []. Preliminary research has used digital cognitive and gait evaluation tools for patients with iNPH, establishing computerized neuropsychological tests as reliable techniques for diagnosing cognitive impairments and postoperative cognitive improvements in patients with iNPH []. In contrast, a recent meta-analysis suggested that quantitative gait analysis can detect gait improvements in patients with iNPH at baseline, after the tap test, and postshunt []. These sensor-based technologies and digital systems possess high scalability, offering continuous and high-precision monitoring of cognitive and motor functions []. Our results further emphasized the predictive value of these digital assessment tools during ELD for patients with iNPH, which also showed better performance in predicting shunt responses. Therefore, the use of digital evaluation tools may aid in the selection of potential shunt candidates. Integrating these digital data into telehealth platforms would enable remote and continuous health care for patients with geographic barriers, reduce hospital visits, and enhance patient outcomes []. However, the results of our study are still preliminary; research gaps for digital assessments still exist in modest sample size, clinical standardization, and multicenter validation. Future research should explore more data-driven weighting schemes and conduct external validation as more data become available.

Strengths and Limitations

The main advantages of our study are the prospective cohort study design, which collected detailed clinical data, and comprehensive neuropsychological and gait assessment data from the patients. Additionally, we are the first to combine computerized neuropsychological assessment and 3-dimensional gait analysis to evaluate symptom changes during ELD in patients with iNPH. The limitations of the study are as follows: (1) Nearly half of the patients with positive ELD responses refused shunt surgery, and the relatively small sample size of shunted patients may constrain the statistical power, despite our use of Firth penalized regression models. Therefore, the relatively high AUC and sensitivity values should be interpreted with caution. Future multi-center studies with larger samples are needed to validate our preliminary findings. (2) Cut-off values were calculated statistically based on the Youden index. Given the relatively small sample size, these data-derived thresholds should be interpreted as preliminary estimations that require external validation in future studies with larger cohorts. (3) The high rate of shunt refusal may inevitably introduce selection bias, although the baseline characteristics between patients who accepted and refused shunts were largely comparable, except for DESH score (). (4) Several patients were excluded because of severe illness; therefore, these results may be primarily applicable to patients who have iNPH with mild-to-moderate symptoms. (5) In this study, quantitative assessments were only completed on the third day after ELD. Although previous studies indicate that a 3-day continuous ELD is sufficient to improve cognitive and gait function in patients with iNPH [,], a recent longitudinal study suggests that patients with iNPH would exhibit delayed response up to 2 weeks after ELD []. Therefore, further research is needed to determine the best evaluation time point. (6) Due to the relatively short follow-up interval, the long-term predictive value of these digital tests for shunt response was unclear, which may be affected by various prognostic factors []. (7) Although our findings on digital assessments are promising, the generalizability across different health systems and the cost-effectiveness of these digital assessments are underexplored.

Conclusions

Digital neuropsychological and motor assessments could identify gait and cognitive improvements after continuous ELD. Moreover, the digital tests showed better predictive performance for shunt outcome compared to traditional tests in our pilot study. Further validation of our findings in a multicenter setting is required to establish the optimal cutoff values for these digital evaluation tests.

Introduction

There is a critical need for scalable, cost-effective interventions to address high rates of physical inactivity and related chronic diseases, especially in underserved populations considered to be at risk. In fact, the World Health Organization estimates that further inaction could lead to 500 million new global cases of preventable noncommunicable diseases, with associated direct health care costs of US $520 billion, by 2030 []. Internet-based physical activity (PA) interventions have great potential for widespread dissemination and have already been shown to improve PA levels [-]. However, most of this work was conducted in the general population and not extended to those most at risk, including underrepresented minority populations.

Hispanic adults report the highest rates of inactivity outside of work (32.1% vs 23% for non-Hispanic White individuals) and experience disproportionate rates of associated conditions (eg, obesity and diabetes) [,]. In fact, 79% of Latinas are overweight or obese, compared to 64% of non-Hispanic White women []. Thus, in the Pasos Hacia La Salud study, we tested a web-based, PA intervention in Spanish with 205 Latinas and found significantly greater increase in weekly minutes of moderate-to-vigorous PA (MVPA) compared to a control condition [,]. Moreover, the costs associated with providing the intervention were low at 6 months (US $17 per person per month) and further decreased (US $12 per person per month) at 12 months []. While the Pasos program was shown to be effective and low cost, most intervention participants (69%) did not meet national PA guidelines at 6 months [,,], and longer-term maintenance was not evaluated.

To help sedentary Latinas achieve and maintain health-enhancing levels of PA, the intervention was refined by adding SMS text messages and further targeting key social cognitive theory [] variables (eg, self-efficacy, enjoyment, and social support). Subsequently, in the Pasos Hacia La Salud II study, the new technology- and theory-enhanced version was tested and compared with the original Pasos Hacia La Salud intervention in a randomized controlled trial (N=205) []. There were no significant between-group differences in PA at 6 or 12 months; however, the enhanced intervention arm had higher PA levels than the original intervention arm at 18 and 24 months [].

While both programs have been shown to be beneficial, the enhanced intervention appears to have an advantage in terms of long-term behavior change. However, these enhancements (eg, SMS text messages, discussion board, and additional staff contacts) likely come at additional expense (compared to the original arm). An evaluation of intervention costs is needed to determine which program is feasible and the best fit for the clinical and community setting and resources. Thus, to inform future dissemination and implementation efforts, this study examined additional costs associated with intervention enhancements and how they influence cost-effectiveness.

Methods

Overview

Pasos Hacia La Salud II was a fully powered randomized trial of 2 PA interventions: the original web-based PA intervention (original) and an enhanced arm that included additional elements to support increasing activity (enhanced). Participants were adult Latinas aged 18 to 65 years who were underactive (engaging in <60 min of MVPA per week), could read and write in Spanish, had access to technology to use the study website and receive SMS text messages, and were healthy enough for unsupervised exercise. A full description of the study protocol, measures, and participants has been published previously [,].

Intervention Description

In the original arm of the web-based intervention, participants attended a baseline session led by trained bilingual interventionists who explained MVPA and helped participants set incremental goals. Participants also completed goal-setting sessions over the phone at 1 and 9 months and at follow-up visits (6 and 12 mo). Participants were encouraged to accumulate MVPA on most days of the week and encouraged to achieve the goal of 150 minutes of MVPA by 6 months. All participants were given a pedometer (Accusplit Inc) and were encouraged to use it to self-monitor daily activity.

The interventionist also oriented participants to the study website, which participants could access throughout the intervention period (24 months). The website included tools to help participants increase their MVPA, such as self-monitoring, goal setting, social support, instruction about how to be physically active, problem solving, motivational stage-matched PA manuals, and individually tailored feedback. Further details about the tailored intervention content are described elsewhere []. Participants were encouraged to regularly access the website, log their PA on the website, and receive a tip of the week. Participants were also prompted to complete online surveys to customize their intervention content. Most web-based content was automated and did not require staff time. However, staff regularly checked the message boards to answer questions as necessary and were available to help participants with any technology issues.

In addition to the intervention components described earlier, the enhanced intervention included (1) SMS text messages; (2) additional phone calls at 2, 3, 15, and 21 months; (3) additional in-person visits at 18 and 24 months; and (4) additional data-driven content and website features that encouraged user participation. Participants were also provided with nonmonetary incentives for their website use. Participants earned points by logging into the website and using certain features, such as the goal-setting calendar. Once participants earned a given number of points, they were sent prizes, such as water bottles, phone cases, and T-shirts.

To further enhance social support for PA, a discussion board was used to facilitate meetups among participants. Research staff posted details about free and low-cost PA events in the area so that participants could meet, discuss coordinating attendance with others, overcome barriers to PA, and motivate each other to attend these events.

Ethical Considerations

The study was reviewed and approved by the Brown University Human Research Protections Program (1708001868), and all participants provided written informed consent in the language of their choosing (English or Spanish). To protect confidentiality, all data were deidentified for storage and analysis. To thank them for their time, participants received compensation for participating in study activities. Participants received US $25 for completing each of their assessment visits (at baseline and 6, 12, 18, and 24 months) with an additional US $50 bonus at 24 months if they completed all visits. In addition, all participants received incentives for filling out monthly questionnaires (US $10 per month) to generate the tailored website content. Participants also received reimbursement costs related to travel and childcare costs. Participants in the enhanced arm also received prizes for engaging with website content (as mentioned earlier). This trial has been registered at ClinicalTrials.gov (NCT03491592),

PA Measures

The main outcomes used to determine cost-effectiveness were the change in minutes of MVPA from baseline to 12 and 24 months, as measured by ActiGraph accelerometers and self-report, and the percentage of participants meeting national guidelines at 12 and 24 months. Self-reported activity was measured using the 7-day PA recall (PAR) interview, an interviewer-administered measure that has shown good reliability and validity in adult populations, including in Spanish populations [-]. Adherence to national guidelines was defined as engaging in ≥150 minutes per week of MVPA, as measured by the 7-day PAR [].

Costs

Overview

Cost calculations followed the model used in our previous publications []. Costs were calculated from a payer perspective to estimate the amount it would cost to deliver the intervention in a clinical or community setting. This included all costs associated with personnel, materials, and use and maintenance of technology to deliver the intervention, sourced from actual costs incurred during the trial.

Costs associated with research, such as recruitment, baseline and follow-up measurement visits, obtaining consent, or compensation for research participation, were not included. We also did not include costs associated with intervention development. Thus, both research and development costs are not relevant to the future implementation of the existing intervention in community or clinical settings. Excluding these costs is consistent with published guidelines on cost-effectiveness analyses [].

Personnel Time

Staff time logs were used to determine the time needed to complete each activity associated with intervention delivery. This included training time (for both the trainer and the trainee); time to compile study materials; time for study visits and phone calls (depending on study condition); and time for study maintenance activities, such as checking the online message board, resending pedometers to participants who lost them, calling the website developer with technical questions, or (in the enhanced group) offering SMS text message support. Study maintenance activities were conducted during a set time each week, rather than calculated as a per-participant activity.

Costs for personnel were calculated by determining the total time needed to deliver the intervention per participant in each condition and multiplying by actual staff salary rates, including benefits. Staff delivering the intervention were entry-level research staff with an undergraduate degree (earning a yearly salary of US $54,080 plus 31% in benefits, for a total hourly cost of US $34.05). Training staff were master’s-level behavioral scientists (earning a yearly salary of US $68,640, plus 31% in benefits, resulting in a total hourly cost of US $43.20). Overhead was estimated as an additional 10% of all personnel costs to account for the use of shared space.

Technology

Web hosting was US $75 per month for both study arms. The automated SMS text messaging was supported through the study website and thus did not incur any additional costs.

Materials

Costs for materials were based on actual costs incurred during intervention delivery. All participants in both study arms received an Acusplit pedometer to record their daily steps for US $20 each. Participants in both arms also received a study binder at their baseline visit with information about exercise, tip sheets, and logs for using their pedometers. The total cost for the binders and printed materials was US $4.95 for each participant.

Total cost for prizes in the enhanced group was calculated based on the actual cost of the items and the number of each item that was distributed. As not all participants earned all prizes, we calculated an average cost per participant for prizes to allow for an estimate of the actual cost of delivering prizes at varying rates based on participant engagement. Prize costs ranged from US $1.11 each for water bottles to US $12.50 for a yoga mat. Total cost for prizes in the enhanced arm was US $2038.

A computer was also purchased for the study staff to conduct baseline study visits, check the study website message boards, etc. The cost of the computer was US $1200.

Analysis

To allow for comparison, we used the same analysis approach as in the parent study []. Costs were calculated from a payer perspective to estimate the cost of implementing the intervention in a community or clinical setting. Total costs were calculated separately for each study arm and included the total of personnel time (including benefits and use of shared space), materials, and web hosting. Total costs for each arm were divided by the number of participants in each study arm to calculate the cost per person to deliver each intervention. Costs were calculated in US dollars for 2024.

Cost-effectiveness was calculated as the cost per additional minute of MVPA per person over the course of the study. Total increase in minutes was calculated using linear interpolation of minutes across measurement time points (baseline and 6, 12, 18, and 24 months) and subtracting baseline minutes. Main outcomes for costs and cost-effectiveness were calculated at 12 months (main study outcome) and cumulatively at 24 months. The cost of intervention delivery per person was divided by the cumulative increased minutes per person over the course of the study to estimate the cost per additional minute per person. This was done both for self-reported activity (7-day PAR) and objectively measured activity (ActiGraph accelerometer). Similarly, the cost of each intervention was divided by the number of participants in that group who met national guidelines at each time point to estimate the cost of moving 1 person from being inactive to successfully meeting guidelines for activity at 12 and 24 months.

Incremental cost-effectiveness ratios (ICERs) were calculated to determine the additional cost per minute of MVPA in the enhanced group beyond that achieved by the original group, as well as the cost per additional individual meeting guidelines in the enhanced group compared with the original group. ICERs were calculated by dividing the difference in the costs between the 2 study arms by the difference in cumulative minutes between the 2 arms and, separately, by the difference in the number of people meeting guidelines. For ICERs for guidelines, CIs were computed using the nonparametric bootstrap with 1000 replications.

Results

Overview

Participants (N=195) had a mean age of 43.3 (SD 10.29) years and were primarily of Dominican (80/195, 41%) and Colombian (33/195, 16.9%) descent. Approximately half of the participants (92/195, 47.2%) had a high school education or less. A full description of baseline characteristics and a CONSORT (Consolidated Standards of Reporting Trials) diagram have been published previously [].

PA Changes

As published previously [], participants in the enhanced group (103/195, 52.8%) increased self-reported weekly MVPA from 57.9 minutes at baseline to 115 minutes at 6 months, 106.8 minutes at 12 months, and 135.3 minutes at 24 months, while the original group (92/195, 47.2%) increased self-reported weekly MVPA from 55.2 minutes at baseline to 88 minutes at 6 months, 111.7 minutes at 12 months, and 93.9 minutes at 24 months []. ActiGraph-measured weekly MVPA in the enhanced group changed from 19.7 minutes at baseline to 47 minutes at 6 months, 44.5 minutes at 12 months, and 47.4 minutes at 24 months. In the original group, ActiGraph-measured weekly MVPA changed from 20.6 minutes at baseline to 43 minutes at 6 months, 55.9 minutes at 12 months, and 31.2 minutes at 24 months.

None of the participants met national MVPA guidelines at baseline. At 12 months, 28.2% (29/103) and 29% (27/92) of participants met guidelines according to self-report in the enhanced and original conditions, respectively. At 24 months, this increased to 36.9% (38/103) and 31% (29/92) in the enhanced and original groups, respectively.

Costs

Total costs associated with delivering the interventions are shown in . Total cost of delivering the original intervention at 12 months was US $15,741, or US $171 per person (US $14 per person per month). The enhanced intervention cost US $20,435 to deliver over the first 12 months, or US $198 per person (US $16 per person per month).

At 24 months, cumulative costs for the original intervention increased slightly to US $17,428, or US $189 per person (US $8 per person per month). However, the cumulative costs for the enhanced intervention increased to US $30,862 at 24 months, or US $300 per person (US $12 per person per month). The largest source of the difference was personnel cost for intervention delivery between 12 and 24 months ( and ).

Cost-Effectiveness

Cost per increased minute of activity is shown in . For self-reported activity, each additional minute gained over 12 months in the enhanced group cost US $0.09, compared with US $0.11 per minute in the original group. Incremental costs of increased minutes in the enhanced group were US $0.05 per minute beyond those reported by the original group.

As increases in MVPA measured by the ActiGraph were smaller, these also cost more, with each increased minute costing US $0.19 in the enhanced group and US $0.16 in the original group. As increases in the original group were larger than those in the enhanced group, ICERs could not be calculated.

Costs for cumulative increases over the 24-month period were lower. Each self-reported increased minute cost US $0.06 in the enhanced group and US $0.05 in the original group. Incremental increases in the enhanced group beyond the original group were US $0.08 per additional minute of self-reported MVPA. ActiGraph-recorded minutes over the 24-month period were US $0.12 in the enhanced group compared to US $0.10 in the original group, with incremental minutes costing US $0.20 each in the enhanced group beyond that in the original group.

Cost per person meeting guidelines was normalized per 100 people in each arm. This was higher in the enhanced arm at 12 months (US $705) than in the original arm (US $503). These rose to US $812 and US $601 at 24 months, respectively. ICERs at 24 months were US $1837 (95% CI US $730.89-US $2673.89) per additional person meeting guidelines in the enhanced arm beyond those in the original arm.

Discussion

Principal Findings

Analyses showed that the technology- and theory-enhanced PA intervention for Latinas was more costly than the original intervention but still markedly less costly than most medical interventions. The enhanced intervention cost US $300 per person for a 24-month program, or approximately US $12 per person per month. The original intervention cost US $189 per person, or approximately US $8 per person per month. The largest expense by far was personnel time, which accounted for approximately 72% and 64% of costs in the enhanced and original groups, respectively. Most of the increased cost in the enhanced intervention was attributable to additional personnel time for making additional monthly calls and providing SMS text message support. The prizes also contributed to higher costs in the enhanced group.

Costs and activity gains were similar throughout the first year, thus cost-effectiveness was also similar, with minutes gained in the enhanced group costing US $0.09 for each participant compared to US $0.11 for each participant in the original group (US $0.19 and US $0.16 by ActiGraph, respectively). During the second year in the program, the enhanced group continued to increase their MVPA, while gains in the original group declined. However, the original group had few costs incurred in the second year, while the enhanced group continued to deliver maintenance doses of intervention. Cost-effectiveness thus remained similar over the full 24 months, with each additional minute costing just US $0.05 in the original group and US $0.06 in the enhanced group (US $0.10 and US $0.12 by ActiGraph, respectively). ICERs showed that, beyond the cost of the original intervention, each additional minute in the enhanced intervention cost just US $0.08 (US $0.20 for ActiGraph-measured minutes). While more individuals in the enhanced group met guidelines at 24 months, the cost of meeting guidelines was also higher, costing US $812 per person compared to US $601 in the original arm.

While absolute minutes of PA varied between self-report and objective measures, as is commonly seen between these 2 measures [,], the overall pattern of results between the 2 were similar. While accelerometry is considered the gold standard, there is a large body of research showing the benefits associated with self-reported activity, which largely informed the development of national guidelines [,]. Self-reported MVPA allows for subjective interpretation of intensity, which may not align with universally applied cut points for accelerometers, particularly for participants who are overweight, obese, or inactive. Both the 7-day PAR and ActiGraph showed sustained increases in activity in the enhanced group over the 24-month study period, corroborating better maintenance of activity gains.

Comparison With Prior Work

These results suggest that paying for more intervention yields commensurate increases in activity. Given the enormous benefits of PA [,-], particularly if it is maintained over time [,], the additional cost of more intensive interventions that yield greater increase in PA over time is likely preferable for implementation sites when feasible. Although we could not find other studies that framed the incremental cost of an intervention in terms of additional people meeting PA guidelines, meeting PA guidelines is recognized as an important research metric []. One health economics study concluded that PA interventions that cost less than US $2900 over 2 years to help persons meet PA guidelines can be considered cost-effective []. Our finding that the enhanced group spent US $1837 per additional person meeting guidelines compared to the original group at 24 months is well below this threshold, and therefore, the intervention can be considered cost-effective.

This amount also seems a relatively small price to pay compared to the cost of managing chronic diseases associated with inactivity. The cost of managing diabetes, for example, was US $237 billion in the United States in 2017, with insulin alone costing approximately US $5000 per user annually [,]. An analysis of health care use in Australia found that individuals meeting activity guidelines were about one-third less likely to visit the emergency room or be hospitalized, half as likely to use outpatient services, and incurred approximately Aus $1400 (US $920) less in annual health care costs []. Multiple studies have shown that PA programs are not only low cost but ultimately cost saving, saving considerably more in health care costs than the programs cost [,]. One evaluation of a PA intervention for older community-dwelling adults found that, for those not initially meeting activity guidelines, the program saved US $143 to US $164 per participant over 6 months beyond the cost of the intervention []. Paying for PA programs can therefore be seen as an investment not only in the health and well-being of the participants but also in health care, particularly for populations at high risk. Given the higher costs and higher yields of the enhanced intervention, implementation of the enhanced version may be most appropriate with clinical populations managing chronic disease. As the original intervention still yielded substantial increases in activity, it may be more appropriate for community settings focusing on prevention and overall well-being.

Compared to the parent study, the original intervention in this study was slightly more expensive, costing US $14 per person per month at 12 months versus US $12; this was due to increased personnel costs due to wage increases []. Cost to implement the interventions will thus be largely dependent on staff salaries, which could vary broadly. Clinical sites could deliver the intervention via medical assistants or nursing staff, which would likely increase costs; conversely, using volunteers or automating components to reduce staff time could substantially lower costs.

Limitations

This study has several limitations. Cost and cost-effectiveness analyses were based on aggregated costs data, not individual cost data, thus results should be interpreted as overall estimates for delivering the intervention rather than individual effectiveness. The costs were also limited to a payer perspective and did not include health care or societal costs or quality-adjusted life years. However, these approaches allow for direct comparability with the previous study, which used the same analytic approach. Moreover, some costs, such as overhead for shared space, could only be estimated and would vary considerably based on the implementation site. We were unable to determine the effectiveness of each intervention component; therefore, it was not possible to determine the added cost-effectiveness of individual intervention components. Key differences between the conditions were the SMS text messaging and the additional calls and in-person visits. The cost of these components varied greatly, with SMS text messaging being almost free and highly scalable and calls and visits being costly and having limited scalability. Future trials using multiphase optimization strategy (MOST) designs should be used to identify the most effective components to optimize the cost-effectiveness of the intervention.

Finally, the study findings may not generalize to other populations or settings. The study population was primarily of Caribbean descent and l resided in a small geographic region of the United States. The parent study was carried out with Latinas in California who were predominantly of Mexican descent and reported being markedly less active at baseline but who showed similar increases in activity. Future research with other populations and settings would elucidate how generalizable things findings are.

The study has several strengths. Data were taken from a randomized trial with rigorous methodology, including multiple validated measures of PA. The trial focused on an underserved population considered to be at high risk and included long-term follow-up. Costs were based on current market prices, including published pay scales and benefits for staff. We were also able to directly compare costs and cost-effectiveness to those in the previous parent randomized controlled trial.

Conclusions

Findings highlight that additional intervention components, particularly those necessitating more staff time, yield higher intervention costs; however, they may also lead to greater increases in PA. In this study, these differences were most apparent after long-term follow-up. This could suggest that greater investment may be most appropriate in individuals who would benefit the most from long-term adherence to activity guidelines, such as those at high risk for, or managing, chronic diseases that respond to lifestyle changes. As staff time was by far the most costly component, PA interventions may become more cost-effective and more widely disseminated, as they rely more on broad reach technology. Future research should investigate whether relying fully on automated technology without face-to-face intervention components yields similar long-term effectiveness.

Introduction

The number of older adults is growing and expected to reach 2.1 billion worldwide by 2050 []. In Canada, approximately 18.5% of the population are ≥65 years old, representing more than 7 million Canadians [], and older Canadians constitute the fastest growing age group []. As a result, challenges have emerged with addressing the increasing needs of older adults who utilize health care services more frequently than other age groups []. eHealth, which refers to the use of information and communication technologies (ICTs) for health and encompasses a variety of mobile health (mHealth) apps [,] and internet use for health services and information delivery [,], presents an opportunity to enhance care quality, equity, efficiency, and management of health conditions among older adults [-].

In recent years, there has been a growing interest worldwide in studying technology in the context of older adults’ care [-]. With the global challenges related to the shortage of providers and the shift of care delivery outside of medical settings, older adults are expected to play a more active role in the management of their health []. In addition, as baby boomers move into the “third age” of retirement characterized by searching for new experiences and learning new things, there is an increasing need to assess their knowledge and use of information technologies [], which can support their health and well-being. This is particularly important in the context of global health care systems estimating an increase in the proportion of older adults [] and the limited resources available to support them.

The multidisciplinary life course theory (LCT) [,] emphasizes the integrated relation between a person’s choices and their socioeconomic context, alongside their capacity to make decisions within existing opportunities and constraints []. It is a multidisciplinary framework that integrates factors from various disciplines (eg, sociology, psychology) to understand human behavior. An individual’s family constitutes a “social group” that is embedded in a larger social context [], and questions related to this factor are at the core of the LCT []. The components characterizing the LCT include geographical location, social ties (eg, presence and attributes of family members and societal experiences), stages in life (eg, generational group differences), variability (eg, in gender, social class, education, wealth, family support), and personal control (eg, environmental opportunities and constraints), all of which can present circumstances that shape individuals’ perceptions and decisions []. Along the same line, the World Health Organization (WHO) emphasizes the importance of social determinants (ie, environment in which people live, work, age, and can access money and resources), as these can create avoidable differences in health across communities []. In addition, the Andersen model for health care utilization emphasizes the importance of enabling factors (eg, access to care) and need factors (eg, health problems) in shaping the utilization of health services []. Therefore, interactions with the health system, including factors related to access to resources, services utilization, health status, and perceived needs, represent contextual factors that can influence eHealth use.

Prior research on older adults’ use of technology for web-based socialization discussed the relevance of financial and knowledge barriers, as well as social factors related to family, social motivation, and appropriate environments [], and the need to investigate social and economic factors that persist as challenges to technology uptake []. Aside from a few studies that investigated social determinants in relation to telehealth use by athletic trainers [], eHealth engagement among people living with HIV [], eHealth literacy levels in older adults [], older adults’ perceptions and views on eHealth services [], and access to and preferences for patient portal use among older adults [], limited research exists in this area. In Canada, an earlier study with older adults focusing on health tracking behaviors showed that greater than 60% tracked their health manually, thus emphasizing the limited uptake of digital self-tracking in this group compared with the general population [] and the need to better understand their eHealth use patterns. Hong and Cho [], who reviewed instruments assessing eHealth behaviors, also called for national surveys adapted to technology development that may be leveraged to analyze eHealth behaviors for informing evidence-based policies.

Despite increasing scholarly interest in older adults’ use of technology, few studies have provided a comprehensive, nationally representative, and theory-driven portrait of eHealth engagement in this population. Most prior work has been limited to localized samples, focused on specific technologies (eg, patient portals, telemonitoring pilots), or relied on instruments not adapted to current technological realities. This lack of evidence impairs the ability of policymakers and health system leaders to design equitable digital health strategies that address the needs of the fastest-growing group of health care users.

Grounded in the LCT and Andersen health care utilization model, this study makes 3 important contributions. First, it provided the first national baseline of eHealth use among older Canadian adults across a broad range of applications, enabling pre- and postpandemic comparisons and international benchmarking. Second, it revealed how social determinants and health care system interactions shape digital health behaviors, offering conceptual insights into the factors that enable or constrain digital engagement in later life. Third, it highlighted equity-relevant gaps, identifying groups at heightened risk of digital exclusion and pointing to where targeted interventions are most urgently needed.

Methods

Study Design

A cross-sectional survey of Canadian older adults assessing their use of specific technologies or ICTs, eHealth, and health IT solutions was conducted online using a self-administered computer-assisted web interface (web surveys: 1500/2000, 75%) and by phone using computer-assisted telephone interviews (phone surveys: 500/2000, 25%).

Settings and Participants

A random national sample of 2000 Canadian residents from all provinces, aged 65 years and older, and who spoke English or French was selected from a proprietary online panel of more than 400,000 households owned by Léger, Canada’s largest and leading research and analytics firm. To ensure a random sample, no quotas were set initially, and the data were weighted after sampling according to gender, age, and region to maximize the representativeness of the Canadian population of older adults.

Assessments and Data Sources

In line with the conceptual framework shown in , the survey instrument included 3 main sections: (1) sociodemographic characteristics, living environment, and health system interaction factors; (2) eHealth and ICT use; and (3) fall detection technology (FDT) and telemonitoring technology (TM) use.

Grounded in the principles of the LCT, sociodemographic variables were measured with standardized indicators including gender, age, marital status, income, education, language, and employment. Questions assessing the living environment included the region (representing provinces), community (rural/suburb/urban/metropolitan), place of residence (home/retirement home/long-term care), and living arrangement (alone/with family/with spouse or partner) to give a comprehensive overview of the older adult social context.

To measure interactions with the health care system, we used the following indicators: private insurance (yes/no), family physician (yes/no), home care (yes/no), willingness to pay for quicker access to services (yes/no), hospitalizations and emergency room visits in the past 6 months (yes/no), and perception of own health (categorical). Satisfaction with the health care system, with access to health care services, and with the health care services received were also included in the survey (categorical).

The outcomes of interest in this study were divided into 3 categories that represent eHealth use: (1) digital health engagement, including the extent of internet use for actively searching and accessing health-related information and services online (eg, searching for online information about a health condition, accessing laboratory test results); (2) digital health communication, including the extent of willingness or interest in using digital means to communicate about health services (eg, use of email to discuss a health condition with a physician, obtain information about trusted websites); and (3) mobile health technology use, including the extent of use of mobile apps, FDTs, and TMs. The questions assessing FDT and TM use were binary (yes/no). The frequencies of use of mobile apps for health and internet for accessing information, resources, and communicating with providers were measured on a 5-point scale (1=Never to 5=Always). Interest or willingness to exchange online information was also assessed on a 5-point scale (1=Not at all to 5=Totally).

The survey instrument was pretested with 47 respondents by phone and on the web. A change was subsequently made to the skip pattern for one of the questions. Data collection was completed over a 3-week period in 2018. Although the data are not very recent, they provide a robust baseline for pre- and post–COVID-19 pandemic comparisons, especially in the absence of data on eHealth use among older adults.

Data Analysis

Descriptive data analysis was conducted to gain an understanding of the profile of older adults and their technology-related behaviors. Bivariate nonparametric tests were used for analyses of associations between continuous dependent variables (eg, search online for information) and categorical independent variables (eg, age, education). Specifically, nonparametric 2-independent samples analyses (Mann-Whitney) were conducted for independent variables with only 2 categories, and nonparametric K-independent samples analyses (Kruskal-Wallis) were conducted for independent variables with more than 2 categories. χ² and Fischer’s exact tests for categorical variables were used to examine the associations between the binary dependent variables related to mobile technology use and the determinants (eg, sociodemographic characteristics, living environment) and interactions with the health system variables. Multivariate regression analysis for eHealth scale questions and binary logistic regression analysis for binary mHealth technology questions (TM and FDT) were performed to examine the significant relationships between eHealth use and the social and health system interaction variables that showed significant associations at the bivariate analysis level. To assess potential multicollinearity among the independent variables for the multivariate analysis, we calculated the variance inflation factor (VIF) for each predictor. All independent variables, with the exception of marital status (ie, “married”; VIF~10), had a VIF lower than the general threshold of 10 []. Therefore, we retained all theoretically relevant variables to maintain the breadth of the models, which was the main aim with our modeling approach.

Ethical Considerations

Ethics approval was granted by the University of Ottawa Ethics Research Board, and all data were anonymized. Eligible respondents were provided with an information letter that explained the scope and purpose of the project. It specified that their participation was voluntary and completing the questionnaire indicated their consent for participating in this study. Léger panelists are rewarded for their participation over time using a series of financial incentives that can be accumulated and cashed out or donated for a charitable cause. Participants in this survey who completed this survey were rewarded 1200 Léger Opinion points (CAD $20=20,000 points; a currency exchange rate of CAD $1=US $0.71 is applicable) that can be redeemed through their preferred reward method.

Results

Sample Characteristics

reveals the variations in social determinants and living conditions. The majority of respondents were 65 years to 69 years old (663/2000, 33.2%), were women (1092/2000, 54.6%), were married (1201/2000, 60.1%), had a college or university degree (1243/2000, 62.2%), spoke English at home (1496/2000, 74.8%), earned less than CAD $75,000 (1184/2000, 59.2%), and lived in Ontario (760/2000, 38%) or Quebec (505/2000, 25%), which are the most populous and largest health care jurisdictions in the country. In addition, 68.5% (1369/2000) lived in metropolitan cities or suburbs, and 96.1% (1922/2000) lived in their own homes or apartments; one-third (665/2000, 33.3%) lived alone.

Health Care System Interaction

Of the repondents, who normally have access to a national health insurance, 54.9% (1098/2000) reported having private insurance (). The majority had a regular family physician (1896/2000, 94.8%), did not receive home care services (1917/2000, 95.9%), and reported good to excellent health (1661/2000, 83.1%). In addition, 8.2% (164/2000) and 13.9% (278/2000) had hospitalizations and emergency visits, respectively, in the past 6 months, and one-third (647/2000, 32.4%) were willing to pay out of pocket for quicker access to services. Overall, 61.8% (1237/2000) reported being diagnosed with one or more chronic conditions, and 13.7% (275/2000) indicated having fallen in the past 6 months (falls: median 1, IQR).

Overall, the satisfaction of respondents with the health care system was high (very satisfied: 366/2000, 18.3%; satisfied: 1086/2000, 54.3%). In addition, 79.1% (1583/2000) indicated good to high satisfaction with access to health care services, and 84.4% (1689/2000) were satisfied to very satisfied with the health care services received over the past 2 years.

ICT and eHealth

The vast majority of surveyed older adults owned a computer (1703/2000, 85.2%), 57.7% (1153/2000) reported having a tablet or iPad, and 53.9% (1077/2000) reported having a smartphone (). Fewer owned wearables or mobile devices (238/2000, 11.9%). In addition, 90.3% (1654/2000) reported using email, 50.1% (917/2000) used phone text messaging (eg, WhatsApp, Messenger), and 53.6% (983/2000) used Facebook. Except for a small percent of respondents (238/2000, 11.9%), participants in our study confirmed having used the internet over the past 6 months, mostly daily (1472/2000, 83.6%) or a few times a week (180/2000, 10.2%).

Despite frequent internet and email use and high prevalence of ICTs, the use of internet to connect with a health care professional, access test results or patient portal, or book a medical appointment was limited, although moderate use was reported for using the internet to search for online information about health conditions (median 3, IQR 2-3 on the 5-point Likert scale; ). The respondents expressed low willingness to use email to exchange information about their health with their health care professionals (median 2, IQR 1-3 on the 5-point Likert scale) and a moderate interest in obtaining information about trusted websites relevant to their health condition and accessing their medical records (median 3, IQR 1-5 on the 5-point Likert scale).

The prevalence of use of TMs and FDTs was low (189/2000, 9.4% and 84/2000, 4.2%, respectively), despite familiarity with these technologies. Among the respondents who had previously or were currently using TMs and FDTs, 81% (153/189) and 86% (72/84), respectively, indicated their willingness to use these technologies again in the future, which indicates satisfaction with these technologies.

Social Determinants, Health Care System Interaction, and eHealth Use

Bivariate analyses showed significant associations (P<.05) between sociodemographic and living environment characteristics and the majority of questions assessing eHealth use (). For example, being a man, in the younger age group (ie, 65‐69 years), and holding a graduate university degree were significantly associated with a higher willingness or interest in using the internet to access medical records, exchange medical information with family physicians or health care providers, and obtain information on trusted websites to consult about one’s condition (all P<.001). On the other hand, being a woman, being separated or divorced, and having a lower income were significantly associated with higher frequency of internet use to search for online information about health problems (all P<.001) and the tendency to self-diagnose (P<.001, P=.02, and P=.03, respectively). Living in metropolitan areas and in one’s own home were significantly associated with higher frequency of using mobile apps for health (P=.03) and TM use (P=.04). Being married or having a partner was also significantly associated with using mobile apps for health (P=.01) and FDT use (P=.01).

When examining the relationship between interactions with the health care system and eHealth use (), having private insurance and a willingness to pay out of pocket for quicker access to health care services were significantly associated with most outcome variables (eg, most P≤.001). Respondents who indicated not receiving home care services and no hospitalizations nor emergency visits reported more FDT use (P<.001, P=.002, and P=.002, respectively). Those having an excellent perception of health reported more use of mobile apps for health, higher frequency of internet use to participate in discussion forums about their health, and more willingness or interest in using email to exchange medical information with their physician about their condition.

Multivariate Analysis

Multivariate analyses examined the relationship between eHealth use and the sociodemographic characteristics and interactions with the health care system variables that were significant at the bivariate analysis level while controlling for other variables. presents which relationships were significant (P<.05) in the multivariate analyses; the detailed results (standardized coefficients, odds ratios [ORs], confidence intervals, and P values) are presented in .

eHealth use (ie, digital health engagement and digital health communication) was significantly associated with several sociodemographic variables (). Women reported more internet use to search for online information about health problems (β=0.148, P<.001) and self-diagnosis (β=0.079, P=.001), whereas older age (≥85 years) was consistently associated with lower frequency of eHealth use (internet use for self-diagnosing, interest in obtaining information about trusted websites for their health condition, accessing online medical records, and use of mobile apps for health). Interestingly, respondents with a lower income indicated a higher frequency of searching for online information about health conditions or problems (β=–0.122 for those earning between CAD $50,000 and CAD $75,000 compared with those earning less than CAD $25,000; P=.007), and English-speaking respondents (compared with their French-speaking counterparts) had a higher frequency of self-diagnosing (β=0.098, P<.001) and participating in online forums to discuss aspects related to their health (β=0.051, P=.04).

Variation in eHealth use was significantly associated with the region and community of residence. Compared with rural areas, living in the suburbs or metropolitan areas was consistently associated with a higher frequency of using mobile apps for health (β=0.135, P<.001 for metropolitan areas) and internet use for looking for information about health conditions (β=0.130, P<.001 for suburban areas), self-diagnosing (β=0.114, P<.001 for suburban areas), accessing laboratory results (suburban areas: β=0.092, P=.006; metropolitan areas: β=0.077, P=.03), accessing patient portals (suburban areas: β=0.088, P=.01; metropolitan areas: β=0.077, P=.35), and booking appointments online (β=0.105, P=.005 for metropolitan areas). Residing in retirement homes as opposed to one’s own home was significantly associated with more FDT use (OR=0.366, 95% CI 0.145‐0.923).

There were considerable differences across provinces in Canada, which may be attributed to systemic variation in availability of digital health services leading to variable access and eHealth use. When compared with persons from central Canada (ie, Prairies), Canadians residing in British Columbia reported a higher frequency of using the internet to ask health care professionals about their health (β=0.062, P=.04) and mobile apps for health (β=0.064, P=.04) and more willingness or interest in using email to exchange information about their health condition with a physician (β=0.087, P=.002) and access their online medical records (β=0.083, P=.002). Residents of Ontario also reported a higher frequency of using the internet to search for health information about their conditions (β=0.075, P=.03) and access laboratory results (β=0.297, P<.001) and patient portals (β=0.132, P<.001), whereas respondents from the Maritimes provinces were more interested or willing to use the internet to email their physicians about their health condition (β=0.093, P<.001), obtain information on trusted websites to consult about their conditions (β=0.062, P=.01), and access their online medical records (β=0.089, P<.001). Current use of the internet to access patient portals (β=0.082, P=.01) and interest in accessing online medical records (β=0.120, P=.005) were also higher for older adults in Quebec compared with residents of central Canada.

The enabling and need factors related to the interactions with the health care system investigated in this study revealed a pattern of association with eHealth use. Among the enabling factors, willingness to pay out of pocket for quicker access to health care services and having private insurance were consistently and significantly related to a higher frequency of eHealth use. Specifically, willingness to pay out of pocket was significantly associated with higher frequency of eHealth use for all the measures, with the exception of mobile app and FDT use (). Respondents who did not have private insurance reported lower frequency for searching for information about their health problem or condition (β=–0.054, P=.03) and online access to laboratory results (β=–0.055, P=.02) and patient portals (β=–0.063, P=.009). With regard to the need variables, older adults who did not have emergency visits in the past 6 months (ie, less needs) reported a significantly lower frequency of searching for information about their health problem or condition (β=–0.086, P<.001) and accessing patient portals (β=–0.058, P=.02) but more FDT use (OR=2.16, 95% CI 1.228‐3.800). However, those who did not receive home care services indicated a higher frequency of searching for information about their health problem or condition (β=0.052, P=.03) and more FDT use (OR=3.427, 95% CI 1.550‐7.596) compared with those receiving home services. Last, a high perceived health status (as excellent) was significantly associated with more frequent mobile app use as opposed to a fair (β=–0.073, P=.02) or good (β=–0.074, P=.049) perception of health.

Discussion

Principal Findings

This national survey provides the first comprehensive portrait of eHealth use among older Canadians, offering a valuable baseline for ongoing monitoring and international comparisons. Although the analyses incorporated a broad range of social and health system determinants, our goal was to capture the multidimensional nature of digital engagement and application use in later life, which sets the stage for future specific and targeted investigations. To enhance clarity, the Discussion section highlights the most important findings and their implications.

First, although the vast majority of older adults owned digital devices and reported frequent internet use, their actual engagement with eHealth tools remained limited. Use of TMs, FDTs, patient portals, and online appointment systems was low across the sample. This disconnect suggests that access alone is insufficient; awareness, perceived usefulness, and provider support are critical enablers. Importantly, those who had used TMs and FDTs expressed high willingness to use them again, underscoring the importance of initial exposure and positive experiences.

Second, consistent with prior literature, eHealth use was stratified by sociodemographic advantage. Younger age, higher income, and residence in metropolitan areas were associated with greater engagement, while older age, lower income, rural residence, and institutional living environments were linked to reduced use. These patterns reveal persistent inequities within the older adult population, even in a context of high technological readiness. Targeted efforts are needed to ensure that those with the greatest health needs and fewest resources are not left behind.

Third, interactions with the health care system emerged as powerful predictors of eHealth use. Having private insurance, willingness to pay for quicker access, and better perceived health were all associated with higher digital engagement. Conversely, those who had received home care services or experienced recent emergency visits were less likely to use digital tools. This pattern indicates that eHealth may currently serve those with fewer immediate health needs and greater resources, rather than those most in need of coordinated care. Integrating digital health into routine pathways, rather than leaving it as an “optional extra,” is essential for equity.

Fourth, women reported greater use of the internet for health information and self-diagnosis. Although our data do not allow us to assess outcomes such as health anxiety, prior research has suggested that extensive online searching can sometimes heighten distress (ie, “cyberchondria”) [,]. This represents a possible area for future investigation.

Several additional associations were observed, including the roles of education, language, and provincial differences, that further illuminate the diversity of older adults’ digital health engagement, which was quite variable across the country. For example, residents of Ontario and British Columbia reported higher use of patient portals and access to laboratory results, while those in the Maritimes expressed greater willingness to communicate with providers online. These contextual nuances reinforce the importance of tailoring digital health strategies to local and cultural environments.

Comparison With Studies in Other Jurisdictions

Our findings contribute to a growing body of international literature on digital health use among older adults and offer several contrasts with studies conducted in the United States and Europe.

First, older Canadian adults in our sample reported higher levels of interest and perceived usefulness of digital health tools across age groups, including those older than 75 years. This contrasts with US and European studies that consistently report lower adoption rates among the oldest and most socioeconomically disadvantaged groups [-]. Second, although previous studies often focus on access and skill gaps, the so-called first- and second-level digital divides, our findings highlight a third dimension: motivational readiness. Many respondents expressed a willingness to use digital tools despite limited experience, particularly when they felt supported by the health care system. This dimension was less frequently emphasized in prior large-scale survey studies []. Third, the Canadian context appears to moderate some of the demographic divides found elsewhere. For example, racial and ethnic disparities in digital health use reported in the United States [] were not observed in our sample, potentially reflecting Canada’s publicly funded and more equity-oriented health care system. Finally, our data indicate relatively high willingness to reuse certain digital health technologies, particularly TMs and various mobile apps. This is an encouraging finding given the infrastructural and digital literacy barriers reported in many European contexts [,] and suggests that, when appropriately introduced and supported, older adults can develop positive experiences with digital health tools, even in rural areas.

Taken together, our findings suggest that older Canadian adults are not uniformly resistant to digital health but rather face a complex set of motivational, attitudinal, and structural barriers. Understanding these factors in light of international comparisons can inform more targeted, inclusive, and equity-driven eHealth policies.

Study Implications and Avenues for Future Research

The findings of this national survey of older Canadians have several implications for policy, practice, and future research on digital health equity. First, the results underscore the need to move beyond binary notions of access (eg, having internet or not) to understand the multidimensional nature of digital engagement. Although structural factors such as age, education, and income remain important, our findings reveal that attitudinal variables such as perceived usefulness, trust in digital tools, and self-efficacy are equally, if not more, influential in predicting eHealth adoption. This suggests that interventions should not only address material barriers but also focus on building digital confidence and relevance in health contexts.

Second, the relatively high levels of expressed interest in eHealth technologies among Canadian older adults, even those with limited prior experience, challenge persistent narratives of older adults as digitally disengaged or resistant. This observation contrasts with trends reported in European contexts [,], where digital disengagement remains widespread, particularly among the oldest-old, women, and rural residents. Similarly, in the United States, studies by James et al [] and Schuster et al [] identified persistent digital divides by race and mental health status. In contrast, our findings point to a more complex and optimistic outlook in Canada, where universal health care, relatively equitable access to health services, and targeted digital health initiatives may be shaping more inclusive digital health trajectories.

Third, the Canadian context provides a unique lens to understand how publicly funded health care systems may buffer against some of the exclusionary forces observed in more market-based systems. For instance, unlike in the United States, where the use of digital health tools is often mediated by private insurance coverage or provider-specific platforms, Canadian respondents interact with a publicly funded system that, in principle, offers more universal access to tools such as e-prescriptions and teleconsultation portals. However, our findings indicate that actual use of digital services like these remains limited, suggesting that accessibility alone does not ensure engagement and that awareness, support, and perceived usefulness remain critical enablers.

Based on these findings, several avenues for future research emerge. Longitudinal studies are needed to assess how digital engagement evolves over time among older adults, especially as younger cohorts with greater baseline digital skills age into retirement. Moreover, future research should examine how relational dimensions, such as the support of health care professionals, caregivers, and peers, mediate eHealth use among older adults. Comparative studies across health systems and sociopolitical contexts would further elucidate the interplay of systemic and individual-level factors in shaping digital engagement. Building on these findings, targeted investigation of specific eHealth applications using mixed methods and qualitative approaches would provide a more in-depth understanding of the interaction of factors influencing their use. Finally, there is a need to develop and evaluate intervention strategies that go beyond digital literacy training. These should include motivational and psychosocial components, co-designed with older adults, to enhance perceived value and usability of digital tools in real-life care scenarios. Integrating such approaches into existing health care pathways can ensure that digital transformation in health systems is truly inclusive, responsive, and sustainable for an aging society.

Study Limitations

It is important to note some limitations associated with this research. The cross-sectional nature of this study precludes a thorough assessment of causal relationships between the social determinants and variables related to the interactions with the health care system in relation to eHealth use. For example, the odds of using FDTs were considerably higher among older adults who did not receive home care services nor were hospitalized in the past 6 months. However, it was not possible to determine whether FDT use precluded the need for home care and prevented hospitalizations or whether FDT was used due to the absence of home services and better health.

The respondents’ profile points to a relatively high level of education (62% had a college degree or higher) with the majority residing in their own homes and not alone, living in suburbs or metropolitan areas, having a regular family physician, and having private health insurance (ie, generally good access to a broad range of health care services despite limited use of home care services). Thus, we may expect a lower level of eHealth penetration among the broader older adult population, which further underscores the current suboptimal benefits that older adults are gaining from these technologies.

Since the data were collected from a single country, the generalizability of the results is limited unless the survey is replicated in other contexts. The online nature of the survey, although complemented with phone surveys, may still have excluded potential respondents who did not have access to the internet or phone calls. In addition, the closed-ended questions included in the survey did not allow us to fully uncover the reasons behind some of the association patterns that were observed.

When assessing multicollinearity, marital status (ie, “married”) had a VIF close to 10, indicating potential multicollinearity with other predictors in the models. This is expected, as marital status is closely associated with other sociodemographic variables like cohabitation. Although this multicollinearity may affect the precision of the estimated effect of marital status (ie, compresses the ß coefficient and makes it more likely to find false negatives or more conservative results), it does not bias the model overall.

Last, we must stress that there are persistent challenges with collecting comprehensive data from older adult populations [,,]. Although the data from this study are not very recent, they provide a baseline for future studies assessing changes in eHealth use among older adults post-COVID-19 pandemic. In addition, since the eHealth construct’s evolution is gradual and eHealth use among Canadian citizens in general is reported to be changing slowly [], the findings continue to be relevant and present a foundation for longitudinal comparisons.

Conclusion

This study offers the first comprehensive national assessment of eHealth use among older Canadian adults and provides a valuable baseline for ongoing monitoring and international benchmarking, including pre- and postpandemic comparisons. Our findings highlight the importance of accounting for social determinants and interactions with the health care system when investigating eHealth use in this population.

Our findings are also relevant beyond Canada. They demonstrate how universal health care systems mitigate, but do not eliminate, digital divides, offering lessons for other jurisdictions seeking to advance inclusive digital health strategies. The study reveals a new dimension of the digital divide, namely motivational readiness, which complements traditional access- and skill-based divides. Recognizing this attitudinal and relational component shifts the focus of interventions from infrastructure alone to trust-building, perceived usefulness, and provider support, thereby broadening the policy tool kit for promoting digital equity in aging societies.