The details regarding study design and outcomes for this trial are described in the published study protocol [20], thus more briefly described in this paper. The trial was designed as a cross-jurisdictional multi-site effectiveness-implementation type II hybrid RCT, which assigned equal emphasis to the implementation and effectiveness components [27][28]. A hybrid design was chosen because we were evaluating a complex intervention where complexity arises not only from the intervention, but also the context within which it is implemented [28]. The effectiveness component was designed to achieve the aims of comparative effectiveness research (e.g., informing practice/policy decisions, conducting the trial in real-world practice settings) [29]. To this end, we used the Pragmatic Explanatory Continuum Indicator Summary Version 2 (PRECIS-2) [30] to maximize pragmatism across the tool’s nine domains; pragmatic features of the trial included: recruitment of participants similar to those seen in practice, delivery of the trial in the practice setting by providers employed in the setting, flexibility in the delivery of the intervention similar to that seen in practice, no extraordinary follow-up measures to encourage participant engagement, and intention-to-treat principles applied in the analysis.

An important element in the current study (and all prior work on the ACHRU-CPP) was the involvement of patients and caregivers in the research itself. The study included older adult patients and caregivers who were actively involved as research partners, providing strategic guidance through their participation on the study’s Steering Committee and Community Advisory Boards at each of the trial sites to support local adaptation and implementation. They advised the research team on study design, patient-relevant outcomes, existing community assets, recruitment approaches, implementation strategies, interpretation of findings, and developing knowledge translation products to share the trial results.

The research questions for the effectiveness evaluation were:

1) What is the effect of the ACHRU-CPP in addition to usual care compared to usual care alone on mental health functioning (primary outcome), physical health functioning, diabetes self-management, depressive symptoms, anxiety, social support, physical activity, basic and instrumental activities of daily living, nutrition risk (poor diet), food security, and costs of use of health services (secondary outcomes) in older adults aged ≥ 65 years with diabetes and one or more chronic condition?

2) If the intervention demonstrates a treatment effect for the primary outcome, what subgroups of older adults (e.g., sex/gender groups, those with more vs less multimorbidity) benefit most from the intervention?

3) What is the effect of the intervention compared to usual care on outcomes (health-related quality of life, depressive symptoms, anxiety, caregiver strain, costs of use of health services) of family and friend caregivers aged ≥18 years?

We hypothesized that older adult participants and caregivers in the intervention group would experience greater improvements in mental and physical functioning and other health benefits and that the intervention would be cost neutral for participants compared to usual care (consistent with results from our previous studies) [22][23]. We note that the resulting small caregiver sample precluded making firm inferences regarding caregiver health benefits, thus the results of the caregiver analyses below should be regarded as exploratory.

In Canada, provincial governments have the primary responsibility for the delivery of healthcare services. Consequently, healthcare systems and services differ across the 10 provinces, and assessing Canada-wide scalability of the intervention requires testing in more than one province. The trial was conducted in two sites in each of three Canadian provinces (i.e., Ontario, Quebec and Prince Edward Island). These provinces were chosen because policy initiatives aligned with the intervention (e.g., prioritizing care for older adults and those with multimorbidity, focused on diabetes care) and the sites within them were chosen as they service a significant older adult population, reflect diversity in geography and socio-economic/cultural features, and employ providers with strong support for the intervention and availability to deliver it. Each site involved a primary care setting or diabetes (outpatient) education program, and a community partner (e.g., YMCA, community center) to collaborate with the other providers and participate in delivery of the intervention.

For the effectiveness evaluation we recruited two study populations: 1) older adults who received the intervention in addition to usual care versus usual care alone, and 2) caregivers of the older adults. Eligibility criteria pertaining to the older adults included: aged ≥ 65 years, diagnosed with type 1 or 2 diabetes, diagnosed with at least one other chronic condition (in addition to diabetes), enrolled in a primary care setting or diabetes education program, residing in the area (with no plans to leave during the study) served by the primary care setting or diabetes education program, capable of providing informed consent (or has a substitute decision-maker), and competent in English or French (or has an interpreter). Eligible caregivers were those who, at the time of enrollment of the older adult, were identified by that older adult as an adult (at least 18 years of age) family/friend caregiver.

Recruitment staff (existing personnel) from each of the primary care or diabetes education program sites were trained to identify potential older adult participants using medical/electronic records. They contacted potential clients by telephone to determine their interest; all eligible and interested clients were then contacted by the Research Assistants (RAs) who shared more information about the study and arranged a baseline assessment interview. During the scheduled interview, RAs obtained consent from all participants before they completed the initial (baseline) assessment. Before COVID-19, this was done in person during a home-based interview, so the participant provided signed informed consent. During COVID-19, RAs obtained verbal informed consent by phone and the consent process was audio-recorded. All data were collected by phone. Reasons for eligible participants declining to participate in the study were recorded and clients enrolled in the study were asked to invite their caregivers at the time of the baseline assessment.

Participants who were eligible and agreed to participate in the study were randomly assigned to the intervention or control group using a 1:1 allocation ratio, using stratified permuted block randomization with the sequence generated by a biostatistician not involved in the recruitment process. The sites were the strata, and sequences for each site were entered into a centralized web-based randomization service (REDCap Version 11.1.9).

The intervention was delivered at each site by an interprofessional team that included a registered nurse (RN) and a registered dietitian (RD)/nutritionist from the primary care site or diabetes education program, and a community program coordinator (PC) that was a kinesiologist or exercise specialist from the community partner organization. Managers at the primary care site or diabetes education program site recruited and selected the RN and RD/nutritionist. To avoid contamination in the control arm, all providers on the intervention team delivered the ACHRU-CPP and usual care to the intervention arm but different providers delivered usual care to participants in the control arm.

The core components of the intervention included: 1) up to three home/telephone/zoom visits by the RN and/or the RD/Nutritionist; 2) up to six monthly group sessions that include health education, exercise (gentle progressive physical activity) and informal peer support; 3) nurse-led care coordination and system navigation support provided by either the RN or RD to link participants to other health care professionals and community services as needed, ensure continuity of care across different providers and settings, and prevent gaps in care; 4) ongoing caregiver engagement and support; 5) monthly case conferences of the intervention team where the team developed and engaged in ongoing evaluation of the participant’s plan of care; and 6) collaboration with the primary care team and other specialists as needed. A care plan was developed at the initial home/telephone visit in collaboration with the participant, with documentation of the goals and actions to be undertaken, and progress was monitored in subsequent home visits. Key areas of focus for the care plan included adopting a holistic view that incorporated the social determinants of health, employing motivational interviewing strategies to foster self-efficacy, and providing self-management education and support for diabetes and their other chronic conditions. Ultimately, the core intervention components operationalized constructs in the social cognitive theory underpinning the intervention (8, 9, 19), such as building self-efficacy and self-control through peer-to-peer learning (e.g., in group sessions), receiving reinforcement from providers for behaviour change and goal attainment (e.g., in home visits where progress is regularly reviewed), and learning about and accessing additional resources (e.g., connecting participants with community resources to enable them to take control of their health and behaviours). Although concerns were shared with primary care and other specialists (with the participant’s consent), the care plan was not shared with them. Participants in the intervention arm were free to continue receiving their usual diabetes care services.

Control group participants continued to be offered usual care services through their primary care setting and/or local diabetes education program. The specific services that comprised usual diabetes care differed within and across provinces in terms of the length and focus of educational sessions, whether classes/services were required versus optional, access to on-site professionals (e.g., endocrinologist, dietitian, physiotherapist, exercise specialist, pharmacist), connections with community resources, and type/extent of follow-up services available.

A prior protocol paper provides a detailed description of the multiple strategies used to implement and monitor delivery of the intervention [31]. Briefly these included provider training, regular outreach meetings between researchers and the intervention team to discuss progress and address challenges, and routine completion of forms documenting intervention components delivered. Compared to previous trials, implementation strategies used in this study employed virtual methods extensively to deliver intervention components. This was done in response to the pandemic restrictions but also to facilitate communication/collaboration across multiple sites and provinces.

Research Assistants, trained on the data collection procedures, conducted two assessments (home or telephone interview) with each study participant, one at baseline and the other immediately after the completion of the 6-month intervention period, 6 months after baseline. Baseline data were collected on socio-demographic, clinical, and primary and secondary outcome variables.

Participants that met the eligibility criteria and provided their informed consent were enrolled in the study and entered into the REDCap system, and were then allocated to the intervention or control group (in accordance with the randomization sequence entered into REDCap). Participants were not informed of their group allocation, although it is unlikely that they remained blinded once the intervention began. RAs conducting the baseline and 6-month interviews were blinded to group allocation, and the statistician was blinded to the group allocation. The intervention was known to the providers delivering it, however they were unaware of the study outcomes.

Details regarding all trial outcomes (e.g., measure, timing, analytical methods) are published in Table 1 of the study protocol [20], thus we briefly summarize them here. The primary outcome was mental functioning measured by the Mental Component Summary (MCS) score from the SF-12 (30). We chose the MCS because it is widely recognized as a quality-of-life measure that is an important patient-reported outcome (PROM). We found this measure to be responsive to the intervention in our previous RCT and consider mental functioning to be important to self-efficacy, self-management, and the behaviour change constructs embedded in the theoretical foundation of the intervention [10][23]. We also collected data on a range of secondary outcome measures for participants, selected due to validation in older adults and anticipated responsiveness to the ACHRU-CPP:

Physical function measured by the Physical Component Summary (PCS) score of the SF-12 [32]; Diabetes self-management measured by the Summary of Diabetes Self-Care Activities (SDSCA) tool [33];Depressive symptoms measured by the Center for Epidemiological Studies on Depression 10-item scale (CESD-10) [34]; Anxiety measured using the Generalized Anxiety Disorder 7-item scale (GAD-7) [35];Social support measured by the Duke Social Support Index (DSSI) [36];Eating and nutrition risk measured by the SCREEN II [37];Physical activity measured by the Physical Activity in Seniors (PASE) [38]; Instrumental/basic activities of daily living limitations measured by the Older American Resources and Services instrument (OARS) [39];Shared patient-clinician decision making measured by the CollaboRATE tool [40];Use of health and social services measured by the Health and Social Services Use Index and research unit costing manual [41][42].

Quality of life, self-management, mental health and activities of daily living were identified by patient/caregiver partners as important to those living with diabetes and multimorbidity. We also attempted to collect participant data on selected clinical measures (HbA1C, e-GFR, LDL-cholesterol), but COVID-19 service interruptions caused many study participants to skip or delay screening appointments. The clinical data collected from RCT participants had many missing values, uncertain timing, and/or poor alignment with study timepoints, preventing meaningful analyses/interpretation (thus no analyses of these data were performed).

For caregivers, we collected and analyzed data on caregiver strain using the Modified Caregiver Strain Index (MSI) [43], quality of life using the SF-12, depressive symptoms using the CESD-10, and anxiety using the GAD-7. These items were identified as important caregiver-reported outcome measures by patient/caregiver partners.

The study was designed with 80% power, two-sided alpha = 0.05, and 20% attrition. It aimed to detect an effect size of 0.38 for mental functioning (primary outcome) as measured by the Mental Component Summary (MCS) score from the 12-item Medical Outcomes Study Short-Form Health Survey (SF-12), which was observed in the previous Ontario RCT [23]. The target total sample size was 264, which resulted in 88 participants per province and 44 per site [20].

The baseline demographic and clinical characteristics of participants who were randomized at baseline were summarized using descriptive statistics. Characteristics of those who dropped out compared to those that completed the study were compared descriptively. Analysis of covariance (ANCOVA) was conducted to test for group differences in the 6-month change in outcomes. Separate ANCOVA models were run for each outcome, with the 6-month outcome as the response and the baseline value of the same outcome as a covariate. The primary analyses were unadjusted for baseline differences and applied intention-to-treat (ITT) principles with participants analyzed in the groups to which they were randomized and with multiple imputation to address missing data (if substantial, e.g., >5%). Multiple imputation used multivariate imputation by chained equations with predictive-mean matching, and the imputation model included all available baseline and 6-month outcome data and covariates that were predictive of missingness. Five imputed data sets were created, the ANCOVA model was fitted to each data set, and Rubin’s rules were applied to pool model coefficients from each run [44]-[46]. Costs obtained from provincial databases were applied to the health and social service use reported by clients and caregivers [20]. These databases provide the amounts reimbursed by public and private insurance plans for insured/qualifying services, but exclude indirect and out-of-pocket patient, caregiver or productivity costs. Group differences in the 6-month change in service use costs were examined using non-parametric methods due to the skewed distributional properties of the cost data.

Pre-planned sensitivity analyses were conducted to test the robustness of the results for the primary outcome to assumptions and analytical approaches used in the primary analyses. All sensitivity analyses were conducted using complete cases. The following pre-planned sensitivity analyses were performed: a complete case analysis for comparison with the ITT results, adjusted models for significant baseline imbalances (if present), use of non-parametric methods where the parametric assumptions of the ANCOVA model were not met, and an analysis that excluded sites where participants either did not receive the intervention or did not receive it per protocol. Additionally, the COVID-19 pandemic resulted in a shift to virtual delivery of the intervention at some sites, thus a subgroup analysis exploring the treatment effect across different delivery formats (in-person, virtual, hybrid) was conducted [20].

Pre-selected factors for subgroup analyses (proposed if the treatment effect is significant for the primary outcome) were sex, number of chronic conditions, and province.

An additional unplanned sensitivity analysis was conducted, focused on “high-fidelity” only sites, in response to unplanned events that occurred during the delivery of the intervention. These events were triggered by staff shortages relating to COVID-19 responses, and the “high-fidelity” sensitivity analysis excluded sites that had to terminate the trial early due to staff redeployment, and sites that did not have a full provider team to deliver home visits or group sessions (thus may not have been able to fully address all lifestyle components of the intervention).

All statistical analyses were performed using R Version 4.4.1 (2024-06-14) and assumed a two-tailed p-value (α = 0.05). The following robust ANCOVA methods available in R were explored for outcomes where parametric assumptions were not met: WRS2 (Version 1.1 - 6, 2024-03-14), sm.ancova (Version 2.2 - 6.0, 2024-02-17) and fANCOVA (Version 0.6 - 1, 2020-11-13). The results of the robust ANCOVA models were similar for all outcomes, thus results were reported for one package (fANCOVA, which offers two statistical tests of significance: ANOVA-like statistic, variance estimator statistic).

Table 1(a) provides the participant baseline characteristics. Randomization resulted in no significant group differences. Slightly more than half of participants were female (54%), and the average age was 76 years (standard deviation = 6 years). Over half of the participants (54%) were married or living with a partner and had at least a college diploma. Approximately 25% had annual household incomes above $50,000 CAD, 90% were retired, and 36% were living alone. The mean number of chronic conditions (excluding diabetes) was 5 (standard deviation = 2 conditions). Mean depressive symptom scores were approximately 5 for both groups, which is below the at-risk cut-off for the CESD-10 (32). Mean anxiety scores were approximately 2 for both groups, which is below the at-risk threshold of 8 established for the GAD-7 [35].

Table 1. (a) Baseline characteristics of RCT participants; (b) Baseline characteristics of RCT caregivers.

Table 1(b) provides the caregiver baseline characteristics and shows that the groups were relatively similar across a range of factors. For both groups, over 75% of caregivers were female, married or living with a partner, born in Canada, and white/Caucasian; the average age (groups combined) was 64 years (standard deviation = 12.7 years). Over 60% had completed at least college or some university and were retired; and 25% (groups combined) had annual household incomes above $100,000.

Figure 1.Study flow diagram.

Figure 1 provides the study flow for participants and caregivers. Of the 839 participants assessed for eligibility, 619 (74%) met the inclusion criteria and 295 (48%) of the eligible participants accepted an invitation to join the study. Participants were randomized on a 1:1 ratio to the groups, resulting in 147 intervention and 148 control participants. Of the 295 participants randomized at baseline, 246 (83%) successfully completed the 6-month follow-up. Forty-nine (49) participants were lost to follow-up - a dropout rate of approximately 16% in each group. Reasons for refusing to participate in the study and losses to follow-up are shown in Figure 1, with the pandemic being a key reason (e.g., study stoppage, disinterest in virtual delivery). Note that complete outcome data for participants that completed the 6-month follow-up were obtained for the primary outcome (MCS) and many of the secondary outcomes (see complete case sample sizes for each group in Additional File 3), thus the dropout rate (~16%) represents the missing data rate for most outcomes.

Additional File 1 provides a comparison of those who completed the study (n = 246) compared to those who dropped out (n = 49). It shows that the two groups were similar across a range of socio-demographic and health-related characteristics; the exception was income where there was a considerably higher proportion of dropouts in the lowest annual household income category compared to completers (33% for dropouts compared to 19% for completers).

Caregivers of the 249 participants having a caregiver (84.4%) were invited to join the study, with 32 (12.9%) accepting the invitation. Caregivers were allocated to the group in which the participant was randomized, resulting in 15 caregivers in the intervention and 17 caregivers in the control group. Three (9%) caregivers were lost to follow-up, 1 in the intervention and 2 in the control group, resulting in 29 caregivers completing the 6-month data collection.

The RCT ran from July 2019 through May of 2022, with sites starting at different times and COVID-19 impacting the sites differently depending on when the site began the trial. Additional File 2 provides the timeline for the trial by province and site. In Province 1, one site switched from in-person to virtual delivery midway through the intervention, while the other site started after the lockdown thus the entire intervention was delivered virtually. In Province 2, the intervention was running in-person but stopped at both sites shortly after the lockdown began, due to interventionists being redeployed to provide pandemic assistance. The intervention was run twice in Province 3, the first time delivered entirely in-person and the second entirely virtual.

Table 2summarizes the intervention dose for home visits and group sessions by province and site. Home Visits: The engagement rate (% of participants receiving at least one home visit) was high, ranging from 83% to 95% across the sites. Mean home visits were approximately 2 (out of a maximum of 3). However, home visits were below the mean at Province 2 sites due to trial stoppage during the COVID-19 pandemic. Home visits were attended by either the RN or RD, except in Province 1/Site 2 where both providers attended all home visits (due to provider safety concerns). The proportion of participants receiving in-person home visits varied considerably depending on when the trial began relative to the COVID-19 lockdown, from 0% at Province 3 (both sites, as began after lockdown) to 95% at Province 2/Site 1 (prior to lockdown). Group Sessions: Mean group sessions attended by participants was 2 and 3 for Sites 1 and 2 (respectively) in Province 1, just under 2 for both sites in Province 3, and below 1 for the sites in Province 2. The proportion of participants receiving in-person group sessions varied in a pattern similar to that of home visits, from 0% for the sites in Province 3 (virtual cohort) to 73% at Province 1/Site 1.

Table 2. Intervention dose (home visits, group sessions) by province and site.

^aTrial stopped due to redeployment of interventionists to assist with COVID-19 pandemic; ^bAt most sites, one provider (Registered Nurse or Registered Dietitian) attended home visits, and the providers alternated visits. The exceptions were Province 1/Site 2 and 1 visit in Province1/Site 1, where both providers attended all home visits. Participants were offered a maximum of 3 visits, with the number of visits counted as 2 visits if attended by both providers. For this reason, the home visit count could exceed the maximum of 3 - e.g., in the case where a participant had 3 home visits and 2 providers attended all 3, this would count as 6 home visits.

Research Question 1: Intervention Effectiveness, Sensitivity Analyses (Participants)

Table 3 provides the ANCOVA model results, using multiple imputation (n = 295, n = 147 intervention & n = 148 control). No significant group differences were observed for the primary or secondary outcomes. Adjusted ANCOVA models were not run due to the absence of significant differences at baseline (see Table 1).

Table 3.Effectiveness analysis (ANCOVA model results, multiple imputation).

Various pre-planned sensitivity analyses were conducted, including:

1) Complete Case Analysis: Additional File 3 provides the results from the complete case analysis (n = 246), which is consistent with the results shown in Table 2 (no group differences for the primary or secondary outcomes).

2) Nonparametric ANCOVA: The ANCOVA model assumptions were checked; the most frequent violation was the absence of normalcy in the distribution of most outcomes, with only minor violations for a few outcomes for the assumptions of constant variance (SF-12 PCS) and equality of regression slopes (GAD, COLLABORATE). While ANCOVA is known to be robust to the main violation we observed across the outcomes (non-normality) (42), we performed nonparametric analyses for all outcomes where a violation of one or more parametric assumptions was observed. These results are included in Additional File 4 and are consistent with the parametric results.

3) “High-fidelity” sites: We separately examined the treatment effect in “high fidelity” sites, where the intervention was primarily delivered per protocol (which included virtual delivery). Two Quebec sites and the second delivery at the PEI site were excluded from this analysis because a) the two Quebec sites stopped the study (see Additional File 2) due to re-deployment of the interventionists to assist with the pandemic, and b) only one of the interventionists (dietitian) attended most home visits at the PEI site. Additional File 5 provides the results, which are consistent with the results in Table 1.

4) Intervention delivery format: We explored whether the treatment effect varied across delivery formats (all in-person, all virtual, a mix of in-person and virtual). Additional File 6 provides the results, which show that the absence of a treatment effect for all outcomes is seen across all three delivery formats.

5) Sex-differences: We performed a subgroup analysis, based on feedback from our interventionists during the trial indicating that females seemed to be more engaged in the intervention compared to males. Additional Files 7 & 8 provide the results, which show that the absence of a treatment effect for all outcomes as seen in the primary analysis was also the case for both females and males.

Table 4. Effectiveness evaluation (caregiver outcomes) (ANCOVA model results).

^aleast mean square (LSM) difference, ANCOVA model, Control - Intervention; ^bnormalcy assumption of ANCOVA model not met, fANCOVA (non-parametric) method shows mixed results: ANOVA-like statistic shows p = 0.39, variance estimator statistic shows p = 0.03; ^cnormalcy assumption of ANCOVA model not met, fANCOVA (non-parametric) method agrees with parametric results (p > 0.05 for both methods): Role Physical: ANOVA-like statistic p = 0.61, variance estimator statistic p = 0.33; Role Emotional: ANOVA-like statistic p = 0.55, variance estimator statistic p = 0.37; Mental Health: ANOVA-like statistics p = 0.41, variance estimator statistics p = 0.14; ^dall ANCOVA (parametric) model assumptions met; ^eequality of regression slopes assumption of ANCOVA not met fANCOVA (non-parametric) method agrees with parametric results (p > 0.05 for both methods): ANOVA-like statistics p = 0.32, variance estimator statistics p = 0.33; ^fnormalcy and homogeneity of variance assumptions of ANCOVA model not met, fANCOVA (non-parametric) method agree with parametric results (p > 0.05 for both methods): ANOVA-like statistic p = 0.53, variance estimator statistic p = 0.47.

Table 4 provides the results of the cost analysis, which shows that the two groups differed regarding the cost of the use of health and social services, with the result favouring the control group. The cost difference is attributed to the cost of the intervention, with the change in the costs of use of all other services being similar and negligible in both groups. The mean (sd) cost per person of the intervention was $CAD594.4 ($CAD437.6) and the median (IQR) cost was $CAD559.20 ($CAD417.4, $CAD594.30).

Research Question 2: Intervention Effectiveness, Subgroup Analyses (Participants)

No subgroup analyses were performed as no treatment effect was observed for the primary outcome.

Research Questions 3: Intervention Effectiveness (Caregivers)

As per Figure 1, 29 caregivers completed the baseline and 6-month data collection (n = 14 intervention, n = 15 control). Table 5 provides the results from the ANCOVA analysis, which showed a significant group difference for the caregiver strain index (MSI), favouring the control group (mean difference: −3.28, 95% CI: −5.63 to −0.91). As noted in the Methods above, due to the small caregiver sample, these results should be regarded as exploratory.

Cost differences in service use were not explored, as the data included only acute care service use (emergency department, hospital) and the groups were similar in reporting very few acute care events (e.g., at baseline and 6 months both groups reported one visit to the emergency department for health issues pertaining to the caregiver, and both groups reported one hospitalization at baseline and none at 6 months for the caregiver).

Table 5.Cost of use of health and social services analysis by group.

^aWilcoxon-Mann-Whitney test is a non-parametric analog to the independent samples t-test. The hypothesis being tested is whether the median differences are equal for the two groups; ^bA positive median cost difference indicates that median costs were higher at T2. A negative median cost difference indicates that median costs were higher at T1 (baseline); ^cIncludes the costs of the intervention for the intervention group, which includes the following costs: home (or virtual) visits, group wellness sessions, monthly case conferences, interventionist training. The median (IQR) cost of the intervention per patient was $559.20 ($417.4, $793.40) and the mean cost per patient was $594.40; ^eServices included in the categories: other healthcare provider services (e.g., nurse practitioner, physiotherapist, occupational therapist, speech and language pathologist, dietician, social worker, mental health counsellor, optometrist, chiropractor, dentist, pharmacist, personal support worker), alternative therapy services (e.g., naturopath), and social services (e.g., meals, homemaker, transportation).